diff --git a/.env.template b/.env.template index a884f3fc..5d8de984 100644 --- a/.env.template +++ b/.env.template @@ -9,6 +9,8 @@ # For LiteLLM usage GEMINI_API_KEY = "GEMINI_API_KEY" + + # For Ollama OLLAMA_API_BASE = "OLLAMA_API_BASE" # "http://localhost:11434" AZURE_API_KEY = "AZURE_API_KEY" # "my-azure-api-key" diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 00000000..949a88a1 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,20 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file +# +# Note: This project uses UV for package management, but Dependabot uses the "pip" +# ecosystem to read pyproject.toml (PEP 621 format) which is compatible with UV. + + +version: 2 +updates: + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "daily" + + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" diff --git a/.github/workflows/pypi-publish.yaml b/.github/workflows/pypi-publish.yaml index 5d24505a..f870765a 100644 --- a/.github/workflows/pypi-publish.yaml +++ b/.github/workflows/pypi-publish.yaml @@ -8,10 +8,20 @@ jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - name: Build and publish to pypi - uses: JRubics/poetry-publish@v2.0 + - uses: actions/checkout@v6 + + - name: Set up Python + uses: actions/setup-python@v6 with: - pypi_token: ${{ secrets.PYPI_API_TOKEN }} - build_format: "sdist" - ignore_dev_requirements: "yes" + python-version: '3.11' + + - name: Install uv + uses: astral-sh/setup-uv@v7 + + - name: Build package + run: uv build + + - name: Publish to PyPI + env: + UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + run: uv publish diff --git a/.github/workflows/spellcheck.yml b/.github/workflows/spellcheck.yml index 5dc182d3..87c36144 100644 --- a/.github/workflows/spellcheck.yml +++ b/.github/workflows/spellcheck.yml @@ -9,9 +9,9 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v6 - name: Check Spelling - uses: rojopolis/spellcheck-github-actions@0.33.1 + uses: rojopolis/spellcheck-github-actions@0.57.0 with: config_path: .spellcheck.yml task_name: Markdown diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 12674f8d..fc2ab708 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -24,41 +24,37 @@ jobs: - 11434:11434 volumes: - ollama:/root/.ollama + + strategy: + matrix: + model: [openai/gpt-4.1] # List of models steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: - python-version: 3.9.15 # Update with desired Python version + python-version: 3.11.0 # Update with desired Python version - - name: Cache Poetry virtualenv - id: cache - uses: actions/cache@v4 + - name: Install uv + uses: astral-sh/setup-uv@v7 with: - path: ~/.poetry/virtualenvs - key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} - restore-keys: | - ${{ runner.os }}-poetry- + enable-cache: true + cache-dependency-glob: "uv.lock" - id: "auth" - uses: "google-github-actions/auth@v2" + uses: "google-github-actions/auth@v3" with: credentials_json: ${{ secrets.GCP_SA_KEY }} - name: "Set up Cloud SDK" - uses: "google-github-actions/setup-gcloud@v2" + uses: "google-github-actions/setup-gcloud@v3" with: version: ">= 363.0.0" - - name: Install Poetry - if: steps.cache.outputs.cache-hit != true - run: | - curl -sSL https://install.python-poetry.org | python3 - - - name: Install dependencies - run: poetry install --extras "all" + run: uv sync --extra test - name: Wait for Ollama to be ready run: | @@ -73,11 +69,15 @@ jobs: REGION: ${{ vars.REGION }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} - run: poetry run pytest --cov-branch --cov-report=xml + GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }} + AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }} + AZURE_API_VERSION: ${{ secrets.AZURE_API_VERSION }} + AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }} + TEST_MODEL: ${{ matrix.model }} # Pass the model as an environment variable + run: uv run pytest --cov-branch --cov-report=xml - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v5 with: token: ${{ secrets.CODECOV_TOKEN }} fail_ci_if_error: true - diff --git a/.gitignore b/.gitignore index ab174994..f452699e 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,11 @@ output logs falkordb-data -weaviate-data \ No newline at end of file +weaviate-data +.deepeval_telemtry.txt + +.claude + +# Build artifacts +dist/ +build/ diff --git a/.wordlist.txt b/.wordlist.txt index 709f17c1..cd32c3b1 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -14,6 +14,7 @@ orchestrators LLM env JSONL +JSON CSV Orchestrator orchestrator @@ -25,6 +26,7 @@ html https www faq +codebase Ollama ollama Cypher @@ -40,3 +42,6 @@ NLP graphrag gemini codecov +gpt +uv +hatchling diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a03ac48d..4a72ca16 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -52,9 +52,16 @@ To set up **GraphRAG-SDK** for local development: cd GraphRAG-SDK ``` -2. Install the required dependencies: +2. Install uv (if not already installed): ```bash - poetry install --extras "all" + pip install uv + # Or on macOS/Linux: + # curl -LsSf https://astral.sh/uv/install.sh | sh + ``` + +3. Install the required dependencies: + ```bash + uv sync --all-extras ``` ## Pull Request Guidelines @@ -99,7 +106,7 @@ Testing is essential for ensuring code quality in **GraphRAG-SDK**. Before submi export REGION=${REGION} export OPENAI_API_KEY=${OPENAI_API_KEY} export GOOGLE_API_KEY=${GOOGLE_API_KEY} - poetry run pytest + uv run pytest ``` ## Thank You! diff --git a/README.md b/README.md index f4712d7e..9dc32477 100644 --- a/README.md +++ b/README.md @@ -1,291 +1,380 @@ -# GraphRAG -[![Dockerhub](https://img.shields.io/docker/pulls/falkordb/falkordb?label=Docker)](https://hub.docker.com/r/falkordb/falkordb/) -[![pypi](https://badge.fury.io/py/graphrag_sdk.svg)](https://pypi.org/project/graphrag_sdk/) -[![codecov](https://codecov.io/gh/falkordb/graphrag-sdk/graph/badge.svg?token=UKXzlNBark)](https://codecov.io/gh/falkordb/graphrag-sdk) -[![Discord](https://img.shields.io/discord/1146782921294884966?style=flat-square)](https://discord.gg/6M4QwDXn2w) -[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CODE_OF_CONDUCT.md) - -

- FalkorDB GraphRAG-SDK README Banner -

- -### Build fast and accurate GenAI apps with GraphRAG SDK at scale - -Simplify the development of your next GenAI application with GraphRAG-SDK, a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and state-of-the-art LLMs to deliver accurate, efficient, and customizable RAG workflows. - -# GraphRAG Setup -### Database Setup - -[![Try Free](https://img.shields.io/badge/Try%20Free-FalkorDB%20Cloud-FF8101?labelColor=FDE900&style=for-the-badge&link=https://app.falkordb.cloud)](https://app.falkordb.cloud) - -Or use on premise with Docker: - -```sh -docker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/data falkordb/falkordb:latest -``` - -### Dependencies: -```sh -# For all LLM providers -pip install graphrag_sdk[all] - -# For specific LLM provider (Example: LiteLLM) -pip install graphrag_sdk[litellm] -``` - -### Configure Credentials. See [.env](.env.template) for examples. - -* [LiteLLM](https://docs.litellm.ai): A framework supporting inference of large language models, allowing flexibility in deployment and use cases. - To choose vendor use the prefix "specific_vendor/your_model", for example "gemini/gemini-2.0-flash-exp". -* [OpenAI](https://openai.com/index/openai-api) Recommended model:`gpt-4o` -* [Google](https://makersuite.google.com/app/apikey) Recommended model:`gemini-2.0-flash-exp` -* [Azure-OpenAI](https://ai.azure.com) Recommended model:`gpt-4o` -* [Ollama](https://ollama.com/) Available only to the Q&A step. Recommended models: `llama3`. Ollama models are suitable for the Q&A step only (after the knowledge graph (KG) created). - - -# How to use -[![Get started](https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/get-started-badge.svg)](https://lightning.ai/muhammadqadora/studios/build-fast-accurate-genai-apps-advanced-rag-with-falkordb) -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FalkorDB/GraphRAG-SDK/blob/main/examples/movies/demo-movies.ipynb) - -### Step 1: Creating Ontologies -Automate ontology creation from unstructured data or define it manually - See [example](https://github.com/falkordb/GraphRAG-SDK/blob/main/examples/trip/demo_orchestrator_trip.ipynb) - -```python -from dotenv import load_dotenv -import json -from graphrag_sdk.source import URL -from graphrag_sdk import KnowledgeGraph, Ontology -from graphrag_sdk.models.litellm import LiteModel -from graphrag_sdk.model_config import KnowledgeGraphModelConfig -load_dotenv() - -# Import Data -urls = ["https://www.rottentomatoes.com/m/side_by_side_2012", -"https://www.rottentomatoes.com/m/matrix", -"https://www.rottentomatoes.com/m/matrix_revolutions", -"https://www.rottentomatoes.com/m/matrix_reloaded", -"https://www.rottentomatoes.com/m/speed_1994", -"https://www.rottentomatoes.com/m/john_wick_chapter_4"] - -sources = [URL(url) for url in urls] - -# Model - vendor: gemini, model: gemini-2.0-flash-exp -> gemini/gemini-2.0-flash-exp -model = LiteModel(model_name="gemini/gemini-2.0-flash-exp") - -# Ontology Auto-Detection -ontology = Ontology.from_sources( - sources=sources, - model=model, -) -# Save the ontology to the disk as a json file. -with open("ontology.json", "w", encoding="utf-8") as file: - file.write(json.dumps(ontology.to_json(), indent=2)) -``` - -### Step 2: Creating a knowledge graph agent -Build, query, and manage knowledge graphs optimized for retrieval and augmentation tasks. -Leverages FalkorDB for high-performance graph querying and multi-tenancy. - -```python -# After approving the ontology, load it from disk. -ontology_file = "ontology.json" -with open(ontology_file, "r", encoding="utf-8") as file: - ontology = Ontology.from_json(json.loads(file.read())) - -kg = KnowledgeGraph( - name="kg_name", - model_config=KnowledgeGraphModelConfig.with_model(model), - ontology=ontology, - host="127.0.0.1", - port=6379, - # username=falkor_username, # optional - # password=falkor_password # optional -) - -kg.process_sources(sources) -``` - -### Step 3: Query your Graph RAG - -At this point, you have a Knowledge Graph that can be queried using this SDK. Use the method `chat_session` for start a conversation. - -```python -# Conversation -chat = kg.chat_session() -response = chat.send_message("Who is the director of the movie The Matrix?") -print(response) -response = chat.send_message("How this director connected to Keanu Reeves?") -print(response) -``` - -## Next Steps -With these 3 steps now completed, you're ready to interact and query your knowledge graph. Here are suggestions for use cases: -

- GraphRAG-SDK Use Cases Banner from FalkorDB -

- -**Need help with your use case? let's [talk](https://www.falkordb.com/get-demo/)** - -
- -# AI Agents with GraphRAG - -### Orchestrator -The GraphRAG-SDK supports Knowledge Graph-based agents. Each agent is an expert in his domain, and the orchestrator orchestrates the agents. - -Check out the example: - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FalkorDB/GraphRAG-SDK/blob/main/examples/trip/demo_orchestrator_trip.ipynb) - - -### Agents - -See the [Step 1](#how-to-use) section to understand how to create Knowledge Graph objects for the agents. - -```python -# Define the model -model = LiteModel(model_name="gemini/gemini-2.0-flash-exp") - -# Create the Knowledge Graph from the predefined ontology. -# In this example, we will use the restaurants agent and the attractions agent. -restaurants_kg = KnowledgeGraph( - name="restaurants", - ontology=restaurants_ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - host="127.0.0.1", - port=6379, - # username=falkor_username, # optional - # password=falkor_password # optional -) -attractions_kg = KnowledgeGraph( - name="attractions", - ontology=attractions_ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - host="127.0.0.1", - port=6379, - # username=falkor_username, # optional - # password=falkor_password # optional -) - - -# The following agent is specialized in finding restaurants. -restaurants_agent = KGAgent( - agent_id="restaurants_agent", - kg=restaurants_kg, - introduction="I'm a restaurant agent, specialized in finding the best restaurants for you.", -) - -# The following agent is specialized in finding tourist attractions. -attractions_agent = KGAgent( - agent_id="attractions_agent", - kg=attractions_kg, - introduction="I'm an attractions agent, specialized in finding the best tourist attractions for you.", -) -``` - -### Orchestrator - Multi-Agent System - -The orchestrator manages the usage of agents and handles questioning. - -```python -# Initialize the orchestrator while giving it the backstory. -orchestrator = Orchestrator( - model, - backstory="You are a trip planner, and you want to provide the best possible itinerary for your clients.", -) - -# Register the agents that we created above. -orchestrator.register_agent(restaurants_agent) -orchestrator.register_agent(attractions_agent) - -# Query the orchestrator. -runner = orchestrator.ask("Create a two-day itinerary for a trip to Rome. Please don't ask me any questions; just provide the best itinerary you can.") -print(runner.output) - -``` -## Community - -Have questions or feedback? Reach out via: -- [GitHub Issues](https://github.com/FalkorDB/GraphRAG-SDK/issues) -- Join our [Discord](https://discord.com/invite/6M4QwDXn2w) - -⭐️ If you find this repository helpful, please consider giving it a star! - -## Additional Enhancement: Configuring your prompts -When creating your Knowledge Graph (KG) agent, you can customize the prompts to tailor its behavior. - -πŸ’‘ This step is optional but can enhance functionality. - -There are five types of prompts: - -1. **`cypher_system_instruction`** - - System instructions for the Cypher generation step. - - **Note:** Ensure your prompt includes `{ontology}`. - -2. **`qa_system_instruction`** - - System instructions for the Q&A step. - -3. **`cypher_gen_prompt`** - - The prompt used during the Cypher generation step. - - **Note:** Include `{question}` in your prompt. - -4. **`cypher_gen_prompt_history`** - - The prompt for Cypher generation when history needs to be considered. - - **Note:** Include `{question}` and `{last_answer}` in your prompt. - -5. **`qa_prompt`** - - The prompt used during the Q&A step. - - **Note:** Include `{question}`, `{context}`, and `{cypher}` in your prompt. - -Here’s an example configuration: - -```python -kg = KnowledgeGraph( - name="kg_name", - model_config=KnowledgeGraphModelConfig.with_model(model), - ontology=ontology, - cypher_system_instruction=cypher_system_instruction, - qa_system_instruction=qa_system_instruction, - cypher_gen_prompt=cypher_gen_prompt, - cypher_gen_prompt_history=cypher_gen_prompt_history, - qa_prompt=qa_prompt - host="127.0.0.1", - port=6379, - # username=falkor_username, # optional - # password=falkor_password # optional -) -``` - - -## FAQ -**Which databases are supported?** - -GraphRAG-SDK is optimized for FalkorDB. Other backends may require adapters. - -**How scalable is the SDK?** - -GraphRAG-SDK is designed for multi-tenancy and large-scale applications. Performance depends on FalkorDB deployment configuration. - -**How does this SDK improve retrieval-augmented generation?** - -By leveraging knowledge graphs, GraphRAG-SDK enables semantic relationships and ontology-driven queries that go beyond standard vector similarity. - -**Which file formats does the SDK support?** - -Supported formats include PDF, JSONL, CSV, HTML, TEXT, and URLs. - -**How does the SDK handle latency?** - -The SDK is optimized for low-latency operations through FalkorDB, using techniques like query optimization and in-memory processing. - -**Does the SDK support multi-graph querying?** - -Yes. Multi-graph querying is supported with APIs designed for cross-domain and hierarchical graph exploration. - -
- -# License - -This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. - -Keywords: RAG, graphrag, Retrieval-Augmented Generation, NLP, AI, Information Retrieval, Natural Language Processing, LLM, Embeddings, Semantic Search +# GraphRAG +[![Dockerhub](https://img.shields.io/docker/pulls/falkordb/falkordb?label=Docker)](https://hub.docker.com/r/falkordb/falkordb/) +[![pypi](https://badge.fury.io/py/graphrag_sdk.svg)](https://pypi.org/project/graphrag_sdk/) +[![codecov](https://codecov.io/gh/falkordb/graphrag-sdk/graph/badge.svg?token=UKXzlNBark)](https://codecov.io/gh/falkordb/graphrag-sdk) +[![Discord](https://img.shields.io/discord/1146782921294884966?style=flat-square)](https://discord.gg/6M4QwDXn2w) +[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CODE_OF_CONDUCT.md) + +

+ FalkorDB GraphRAG-SDK README Banner +

+ +### Build fast and accurate GenAI apps with GraphRAG SDK at scale + +Simplify the development of your next GenAI application with GraphRAG-SDK, a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and state-of-the-art LLMs to deliver accurate, efficient, and customizable RAG workflows. + +# GraphRAG Setup +### Database Setup + +[![Try Free](https://img.shields.io/badge/Try%20Free-FalkorDB%20Cloud-FF8101?labelColor=FDE900&style=for-the-badge&link=https://app.falkordb.cloud)](https://app.falkordb.cloud) + +Or use on premise with Docker: + +```sh +docker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/data falkordb/falkordb:latest +``` + +### Dependencies: +```sh +pip install graphrag_sdk +``` + +### Configure Credentials. See [.env](.env.template) for examples. + +* [LiteLLM](https://docs.litellm.ai): A framework supporting inference of large language models, allowing flexibility in deployment and use cases. + To choose vendor use the prefix "specific_vendor/your_model", for example "openai/gpt-4.1". +* [OpenAI](https://openai.com/index/openai-api) Recommended model:`gpt-4.1` +* [Google](https://makersuite.google.com/app/apikey) Recommended model:`gemini-2.0-flash` +* [Azure-OpenAI](https://ai.azure.com) Recommended model:`gpt-4.1` +* [Ollama](https://ollama.com/) Available only to the Q&A step. Recommended models: `llama3`. Ollama models are suitable for the Q&A step only (after the knowledge graph (KG) created). + + +# How to use +[![Get started](https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/get-started-badge.svg)](https://lightning.ai/muhammadqadora/studios/build-fast-accurate-genai-apps-advanced-rag-with-falkordb) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FalkorDB/GraphRAG-SDK/blob/main/examples/movies/demo-movies.ipynb) + +### Environment Configuration + +Before using the SDK, configure your environment variables: + +```bash +# FalkorDB Connection (defaults are for on-premises) +export FALKORDB_HOST="localhost" +export FALKORDB_PORT=6379 +export FALKORDB_USERNAME="your-username" # optional for on-premises +export FALKORDB_PASSWORD="your-password" # optional for on-premises + +# LLM Provider (choose one) +export OPENAI_API_KEY="your-key" # or GOOGLE_API_KEY, GROQ_API_KEY, etc. +``` + +## Quick Start with Existing Knowledge Graph + +If you already have a knowledge graph in FalkorDB, you can quickly set up GraphRAG by extracting the ontology from your existing graph: + +```python +import os +from falkordb import FalkorDB +from graphrag_sdk import KnowledgeGraph +from graphrag_sdk.ontology import Ontology +from graphrag_sdk.models.litellm import LiteModel +from graphrag_sdk.model_config import KnowledgeGraphModelConfig + +graph_name = "my_existing_graph" + +# Connect to FalkorDB using environment variables +db = FalkorDB( + host=os.getenv("FALKORDB_HOST", "localhost"), + port=int(os.getenv("FALKORDB_PORT", 6379)), + username=os.getenv("FALKORDB_USERNAME"), # optional for on-premises + password=os.getenv("FALKORDB_PASSWORD") # optional for on-premises +) + +# Select graph +graph = db.select_graph(graph_name) + +# Extract ontology from existing knowledge graph +ontology = Ontology.from_kg_graph(graph) + +# Configure model and create GraphRAG instance +model = LiteModel() # Default is OpenAI GPT-4.1, can specify different model +model_config = KnowledgeGraphModelConfig.with_model(model) + +# Create KnowledgeGraph instance +kg = KnowledgeGraph( + name=graph_name, + model_config=model_config, + ontology=ontology, + host=os.getenv("FALKORDB_HOST", "localhost"), + port=int(os.getenv("FALKORDB_PORT", 6379)), + username=os.getenv("FALKORDB_USERNAME"), + password=os.getenv("FALKORDB_PASSWORD") +) + +# Start chat session +chat = kg.chat_session() + +# Ask questions +response = chat.send_message("What products are available?") +print(response["response"]) + +# Ask follow-up questions +response = chat.send_message("Tell me which one of them is the most expensive") +print(response["response"]) +``` + +## Creating Knowledge Graphs from Scratch + +### Step 1: Creating Ontologies +Automate ontology creation from unstructured data or define it manually - See [example](https://github.com/falkordb/GraphRAG-SDK/blob/main/examples/trip/demo_orchestrator_trip.ipynb) + +```python +from dotenv import load_dotenv +import json +from graphrag_sdk.source import URL +from graphrag_sdk import KnowledgeGraph, Ontology +from graphrag_sdk.models.litellm import LiteModel +from graphrag_sdk.model_config import KnowledgeGraphModelConfig +load_dotenv() + +# Import Data +urls = ["https://www.rottentomatoes.com/m/side_by_side_2012", +"https://www.rottentomatoes.com/m/matrix", +"https://www.rottentomatoes.com/m/matrix_revolutions", +"https://www.rottentomatoes.com/m/matrix_reloaded", +"https://www.rottentomatoes.com/m/speed_1994", +"https://www.rottentomatoes.com/m/john_wick_chapter_4"] + +sources = [URL(url) for url in urls] + +# Model - vendor: openai, model: gpt-4.1 -> openai/gpt-4.1 +model = LiteModel(model_name="openai/gpt-4.1") + +# Ontology Auto-Detection +ontology = Ontology.from_sources( + sources=sources, + model=model, +) +# Save the ontology to the disk as a json file. +with open("ontology.json", "w", encoding="utf-8") as file: + file.write(json.dumps(ontology.to_json(), indent=2)) +``` + +### Step 2: Creating a knowledge graph agent +Build, query, and manage knowledge graphs optimized for retrieval and augmentation tasks. +Leverages FalkorDB for high-performance graph querying and multi-tenancy. + +```python +# After approving the ontology, load it from disk. +ontology_file = "ontology.json" +with open(ontology_file, "r", encoding="utf-8") as file: + ontology = Ontology.from_json(json.loads(file.read())) + +kg = KnowledgeGraph( + name="kg_name", + model_config=KnowledgeGraphModelConfig.with_model(model), + ontology=ontology, + host="127.0.0.1", + port=6379, + # username=falkor_username, # optional + # password=falkor_password # optional +) + +kg.process_sources(sources) +``` + +### Step 3: Query your Graph RAG + +At this point, you have a Knowledge Graph that can be queried using this SDK. Use the method `chat_session` for start a conversation. + +```python +# Conversation +chat = kg.chat_session() +response = chat.send_message("Who is the director of the movie The Matrix?") +print(response) +response = chat.send_message("How this director connected to Keanu Reeves?") +print(response) +``` + +## Next Steps +With these 3 steps now completed, you're ready to interact and query your knowledge graph. Here are suggestions for use cases: +

+ GraphRAG-SDK Use Cases Banner from FalkorDB +

+ +**Need help with your use case? let's [talk](https://www.falkordb.com/get-demo/)** + +
+ +# Using Ollama + +Ollama models are suitable for the Q&A step only (after the knowledge graph has been created). + +## Setup + +```python +from graphrag_sdk.models.ollama import OllamaGenerativeModel + +# Local Ollama (default: http://localhost:11434) +qa_model = OllamaGenerativeModel(model_name="llama3:8b") + +# Remote Ollama +qa_model = OllamaGenerativeModel( + model_name="llama3:8b", + api_base="http://remote-host:11434" +) +``` + +
+ +# AI Agents with GraphRAG + +### Orchestrator +The GraphRAG-SDK supports Knowledge Graph-based agents. Each agent is an expert in his domain, and the orchestrator orchestrates the agents. + +Check out the example: + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FalkorDB/GraphRAG-SDK/blob/main/examples/trip/demo_orchestrator_trip.ipynb) + + +### Agents + +See the [Step 1](#how-to-use) section to understand how to create Knowledge Graph objects for the agents. + +```python +# Define the model +model = LiteModel(model_name="openai/gpt-4.1") + +# Create the Knowledge Graph from the predefined ontology. +# In this example, we will use the restaurants agent and the attractions agent. +restaurants_kg = KnowledgeGraph( + name="restaurants", + ontology=restaurants_ontology, + model_config=KnowledgeGraphModelConfig.with_model(model), + host="127.0.0.1", + port=6379, + # username=falkor_username, # optional + # password=falkor_password # optional +) +attractions_kg = KnowledgeGraph( + name="attractions", + ontology=attractions_ontology, + model_config=KnowledgeGraphModelConfig.with_model(model), + host="127.0.0.1", + port=6379, + # username=falkor_username, # optional + # password=falkor_password # optional +) + + +# The following agent is specialized in finding restaurants. +restaurants_agent = KGAgent( + agent_id="restaurants_agent", + kg=restaurants_kg, + introduction="I'm a restaurant agent, specialized in finding the best restaurants for you.", +) + +# The following agent is specialized in finding tourist attractions. +attractions_agent = KGAgent( + agent_id="attractions_agent", + kg=attractions_kg, + introduction="I'm an attractions agent, specialized in finding the best tourist attractions for you.", +) +``` + +### Orchestrator - Multi-Agent System + +The orchestrator manages the usage of agents and handles questioning. + +```python +# Initialize the orchestrator while giving it the backstory. +orchestrator = Orchestrator( + model, + backstory="You are a trip planner, and you want to provide the best possible itinerary for your clients.", +) + +# Register the agents that we created above. +orchestrator.register_agent(restaurants_agent) +orchestrator.register_agent(attractions_agent) + +# Query the orchestrator. +runner = orchestrator.ask("Create a two-day itinerary for a trip to Rome. Please don't ask me any questions; just provide the best itinerary you can.") +print(runner.output) + +``` +## Community + +Have questions or feedback? Reach out via: +- [GitHub Issues](https://github.com/FalkorDB/GraphRAG-SDK/issues) +- Join our [Discord](https://discord.com/invite/6M4QwDXn2w) + +⭐️ If you find this repository helpful, please consider giving it a star! + +## Additional Enhancement: Configuring your prompts +When creating your Knowledge Graph (KG) agent, you can customize the prompts to tailor its behavior. + +πŸ’‘ This step is optional but can enhance functionality. + +There are five types of prompts: + +1. **`cypher_system_instruction`** + - System instructions for the Cypher generation step. + - **Note:** Ensure your prompt includes `{ontology}`. + +2. **`qa_system_instruction`** + - System instructions for the Q&A step. + +3. **`cypher_gen_prompt`** + - The prompt used during the Cypher generation step. + - **Note:** Include `{question}` in your prompt. + +4. **`cypher_gen_prompt_history`** + - The prompt for Cypher generation when history needs to be considered. + - **Note:** Include `{question}` and `{last_answer}` in your prompt. + +5. **`qa_prompt`** + - The prompt used during the Q&A step. + - **Note:** Include `{question}`, `{context}`, and `{cypher}` in your prompt. + +Here’s an example configuration: + +```python +kg = KnowledgeGraph( + name="kg_name", + model_config=KnowledgeGraphModelConfig.with_model(model), + ontology=ontology, + cypher_system_instruction=cypher_system_instruction, + qa_system_instruction=qa_system_instruction, + cypher_gen_prompt=cypher_gen_prompt, + cypher_gen_prompt_history=cypher_gen_prompt_history, + qa_prompt=qa_prompt + host="127.0.0.1", + port=6379, + # username=falkor_username, # optional + # password=falkor_password # optional +) +``` + + +## FAQ +**Which databases are supported?** + +GraphRAG-SDK is optimized for FalkorDB. Other backends may require adapters. + +**How scalable is the SDK?** + +GraphRAG-SDK is designed for multi-tenancy and large-scale applications. Performance depends on FalkorDB deployment configuration. + +**How does this SDK improve retrieval-augmented generation?** + +By leveraging knowledge graphs, GraphRAG-SDK enables semantic relationships and ontology-driven queries that go beyond standard vector similarity. + +**Which file formats does the SDK support?** + +Supported formats include PDF, JSONL, CSV, HTML, TEXT, and URLs. + +**How does the SDK handle latency?** + +The SDK is optimized for low-latency operations through FalkorDB, using techniques like query optimization and in-memory processing. + +**Does the SDK support multi-graph querying?** + +Yes. Multi-graph querying is supported with APIs designed for cross-domain and hierarchical graph exploration. + +
+ +# License + +This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. + +Keywords: RAG, graphrag, Retrieval-Augmented Generation, NLP, AI, Information Retrieval, Natural Language Processing, LLM, Embeddings, Semantic Search diff --git a/examples/data/ttl/sports.ttl b/examples/data/ttl/sports.ttl new file mode 100644 index 00000000..d86a9c1b --- /dev/null +++ b/examples/data/ttl/sports.ttl @@ -0,0 +1,147 @@ +@prefix : . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix rdfs: . + +# ======================================== +# ONTOLOGY DECLARATION +# ======================================== + + + rdf:type owl:Ontology . + + +# ======================================== +# OBJECT PROPERTIES +# ======================================== + +:associatedTo rdf:type owl:ObjectProperty ; + rdfs:domain :SportsPerson ; + rdfs:range :Sport ; + rdfs:comment "Links a sports person to the sport they play" . + +:partOf rdf:type owl:ObjectProperty ; + rdfs:domain :Workouts ; + rdfs:range :FocusedAreas ; + rdfs:comment "Relates workouts to their focused training areas" . + + +# ======================================== +# DATATYPE PROPERTIES (Person Attributes) +# ======================================== + +:personDatatypes rdf:type owl:DatatypeProperty ; + rdfs:comment "Parent property for all person-related datatype properties" . + +:Age rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf :personDatatypes ; + rdfs:comment "Age of the sports person" . + +:Gender rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf :personDatatypes ; + rdfs:comment "Gender of the sports person" . + +:Height rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf :personDatatypes ; + rdfs:comment "Height of the sports person" . + +:Weight rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf :personDatatypes ; + rdfs:comment "Weight of the sports person" . + + +# ======================================== +# MAIN CLASSES +# ======================================== + +:SportsPerson rdf:type owl:Class ; + rdfs:comment "An individual who participates in sports" . + +:Sport rdf:type owl:Class ; + rdfs:comment "A physical activity or game with competitive elements" . + +:Equipments rdf:type owl:Class ; + rdfs:comment "Tools and gear used in sports activities" . + +:Workouts rdf:type owl:Class ; + rdfs:comment "Training exercises and routines for athletic development" . + +:FocusedAreas rdf:type owl:Class ; + rdfs:subClassOf [ rdf:type owl:Restriction ; + owl:onProperty :personDatatypes ; + owl:someValuesFrom xsd:string + ] ; + rdfs:comment "Specific physical attributes targeted by training" . + + +# ======================================== +# SPORT TYPES (Subclasses of Sport) +# ======================================== + +:Cricket rdf:type owl:Class ; + rdfs:subClassOf :Sport ; + rdfs:comment "Bat-and-ball game played between two teams" . + +:Football rdf:type owl:Class ; + rdfs:subClassOf :Sport ; + rdfs:comment "Team sport played with a spherical ball" . + +:Tennis rdf:type owl:Class ; + rdfs:subClassOf :Sport ; + rdfs:comment "Racket sport played individually or in pairs" . + +:Badminton rdf:type owl:Class ; + rdfs:subClassOf :Sport ; + rdfs:comment "Racket sport using a shuttlecock" . + + +# ======================================== +# WORKOUT TYPES (Subclasses of Workouts) +# ======================================== + +:UpperBody rdf:type owl:Class ; + rdfs:subClassOf :Workouts ; + rdfs:comment "Exercises targeting chest, shoulders, arms, and back" . + +:LowerBody rdf:type owl:Class ; + rdfs:subClassOf :Workouts ; + rdfs:comment "Exercises targeting legs, glutes, and hips" . + +:Core rdf:type owl:Class ; + rdfs:subClassOf :Workouts ; + rdfs:comment "Exercises targeting abdominal and back muscles" . + +:Functional rdf:type owl:Class ; + rdfs:subClassOf :Workouts ; + rdfs:comment "Multi-joint exercises that mimic everyday movements" . + + +# ======================================== +# FOCUSED AREAS (Subclasses of FocusedAreas) +# ======================================== + +:Agility rdf:type owl:Class ; + rdfs:subClassOf :FocusedAreas ; + rdfs:comment "Ability to move quickly and change direction effectively" . + +:Balance rdf:type owl:Class ; + rdfs:subClassOf :FocusedAreas ; + rdfs:comment "Ability to maintain body equilibrium" . + +:Endurance rdf:type owl:Class ; + rdfs:subClassOf :FocusedAreas ; + rdfs:comment "Ability to sustain prolonged physical effort" . + +:Flexibility rdf:type owl:Class ; + rdfs:subClassOf :FocusedAreas ; + rdfs:comment "Range of motion in joints and muscles" . + +:MusclePower rdf:type owl:Class ; + rdfs:subClassOf :FocusedAreas ; + rdfs:comment "Combination of strength and speed in muscle contraction" . + +:Speed rdf:type owl:Class ; + rdfs:subClassOf :FocusedAreas ; + rdfs:comment "Rate of movement or action" . \ No newline at end of file diff --git a/examples/movies/demo-movies.ipynb b/examples/movies/demo-movies.ipynb index 276d84c5..6ca2668b 100644 --- a/examples/movies/demo-movies.ipynb +++ b/examples/movies/demo-movies.ipynb @@ -22,7 +22,17 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shubeli/miniconda3/envs/sdk/lib/python3.10/site-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:\n", + "* 'fields' has been removed\n", + " warnings.warn(message, UserWarning)\n" + ] + } + ], "source": [ "import os\n", "import logging\n", @@ -46,14 +56,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# Get your Credentails from google website, see https://aistudio.google.com/\n", - "os.environ[\"GEMINI_API_KEY\"] = \"\"\n", + "# Set your OpenAI credential in .env file\n", + "# OPENAI_API_KEY=your_api_key\n", "\n", - "# Get your Credentails from Falkor website, see https://app.falkordb.cloud\n", + "# Get your Credentials from Falkor website, see https://app.falkordb.cloud\n", "falkor_host = \"\"\n", "falkor_port = None\n", "falkor_username = \"\"\n", @@ -98,9 +108,17 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Process Documents: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 7/7 [00:43<00:00, 6.16s/it]\n" + ] + } + ], "source": [ - "model = LiteModel(model_name=\"gemini/gemini-2.0-flash-exp\")\n", + "model = LiteModel(model_name=\"openai/gpt-4.1\")\n", "\n", "boundaries = \"\"\"\n", " Extract only the most relevant information about all the movies, actors, and directors over the text.\n", @@ -127,7 +145,15 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Process Documents: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 6/6 [00:59<00:00, 9.86s/it]\n" + ] + } + ], "source": [ "kg = KnowledgeGraph(\n", " name=\"movies\",\n", @@ -197,7 +223,7 @@ ], "metadata": { "kernelspec": { - "display_name": "graphrag-sdk-ceBr3E7y-py3.12", + "display_name": "sdk", "language": "python", "name": "python3" }, @@ -211,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/examples/sports/demo-sports.ipynb b/examples/sports/demo-sports.ipynb new file mode 100644 index 00000000..ea56a6b1 --- /dev/null +++ b/examples/sports/demo-sports.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "34e270a4", + "metadata": {}, + "source": [ + "# Sports Ontology Extraction from TTL File\n", + "\n", + "This notebook demonstrates how to:\n", + "1. Extract an ontology from a TTL (Turtle) RDF schema file\n", + "2. Inspect the entities, relations, and attributes\n", + "3. Export the ontology to JSON format\n", + "\n", + "The sports.ttl file contains an OWL ontology defining sports-related concepts including SportsPerson, Sport, Workouts, and their relationships." + ] + }, + { + "cell_type": "markdown", + "id": "d6218be8", + "metadata": {}, + "source": [ + "## Step 1: Import Required Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d4264f31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TTL file path: /Users/dipanjanchowdhury/Labs/OpenScience/GraphRAG-SDK/examples/data/ttl/sports.ttl\n", + "File exists: True\n" + ] + } + ], + "source": [ + "import os\n", + "from pathlib import Path\n", + "from graphrag_sdk import Ontology\n", + "import json\n", + "\n", + "from dotenv import load_dotenv\n", + "# Load environment variables\n", + "load_dotenv()\n", + "\n", + "# Get the path to the sports.ttl file\n", + "ttl_file_path = os.path.join(Path(os.getcwd()).parent.parent, \"examples\", \"data/ttl\", \"sports.ttl\")\n", + "print(f\"TTL file path: {ttl_file_path}\")\n", + "print(f\"File exists: {os.path.exists(ttl_file_path)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e32d0b3c", + "metadata": {}, + "source": [ + "## Step 2: Extract Ontology from TTL File\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0a6fd10f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ontology from sports.ttl...\n", + " Successfully extracted ontology!\n", + " - Entities: 19\n", + " - Relations: 2\n" + ] + } + ], + "source": [ + "# Extract ontology from TTL file\n", + "print(\"Extracting ontology from sports.ttl...\")\n", + "ontology = Ontology.from_ttl(ttl_file_path)\n", + "\n", + "print(f\" Successfully extracted ontology!\")\n", + "print(f\" - Entities: {len(ontology.entities)}\")\n", + "print(f\" - Relations: {len(ontology.relations)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "078f3221", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Entities:\n", + "\f- (:Workouts {})\n", + "- (:LowerBody {})\n", + "- (:Sport {})\n", + "- (:FocusedAreas {})\n", + "- (:Flexibility {})\n", + "- (:Badminton {})\n", + "- (:Agility {})\n", + "- (:Equipments {})\n", + "- (:SportsPerson {})\n", + "- (:MusclePower {})\n", + "- (:Cricket {})\n", + "- (:Endurance {})\n", + "- (:UpperBody {})\n", + "- (:Core {})\n", + "- (:Functional {})\n", + "- (:Football {})\n", + "- (:Speed {})\n", + "- (:Tennis {})\n", + "- (:Balance {})\n", + "\n", + "Edges:\n", + "\f- (:Workouts)-[:PARTOF {}]->(:FocusedAreas)\n", + "- (:SportsPerson)-[:ASSOCIATEDTO {}]->(:Sport)\n" + ] + } + ], + "source": [ + "print(ontology)" + ] + }, + { + "cell_type": "markdown", + "id": "bd7d3e3e", + "metadata": {}, + "source": [ + "## Step 3: Inspect Extracted Entities\n", + "\n", + "Display all entities (classes) extracted from the TTL file with their attributes and descriptions." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3d308940", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "ENTITIES (Classes)\n", + "================================================================================\n", + "\n", + " Workouts\n", + " Description: Training exercises and routines for athletic development\n", + " No attributes defined\n", + "\n", + " LowerBody\n", + " Description: Exercises targeting legs, glutes, and hips\n", + " No attributes defined\n", + "\n", + " Sport\n", + " Description: A physical activity or game with competitive elements\n", + " No attributes defined\n", + "\n", + " FocusedAreas\n", + " Description: Specific physical attributes targeted by training\n", + " No attributes defined\n", + "\n", + " Flexibility\n", + " Description: Range of motion in joints and muscles\n", + " No attributes defined\n", + "\n", + " Badminton\n", + " Description: Racket sport using a shuttlecock\n", + " No attributes defined\n", + "\n", + " Agility\n", + " Description: Ability to move quickly and change direction effectively\n", + " No attributes defined\n", + "\n", + " Equipments\n", + " Description: Tools and gear used in sports activities\n", + " No attributes defined\n", + "\n", + " SportsPerson\n", + " Description: An individual who participates in sports\n", + " No attributes defined\n", + "\n", + " MusclePower\n", + " Description: Combination of strength and speed in muscle contraction\n", + " No attributes defined\n", + "\n", + " Cricket\n", + " Description: Bat-and-ball game played between two teams\n", + " No attributes defined\n", + "\n", + " Endurance\n", + " Description: Ability to sustain prolonged physical effort\n", + " No attributes defined\n", + "\n", + " UpperBody\n", + " Description: Exercises targeting chest, shoulders, arms, and back\n", + " No attributes defined\n", + "\n", + " Core\n", + " Description: Exercises targeting abdominal and back muscles\n", + " No attributes defined\n", + "\n", + " Functional\n", + " Description: Multi-joint exercises that mimic everyday movements\n", + " No attributes defined\n", + "\n", + " Football\n", + " Description: Team sport played with a spherical ball\n", + " No attributes defined\n", + "\n", + " Speed\n", + " Description: Rate of movement or action\n", + " No attributes defined\n", + "\n", + " Tennis\n", + " Description: Racket sport played individually or in pairs\n", + " No attributes defined\n", + "\n", + " Balance\n", + " Description: Ability to maintain body equilibrium\n", + " No attributes defined\n" + ] + } + ], + "source": [ + "print(\"=\" * 80)\n", + "print(\"ENTITIES (Classes)\")\n", + "print(\"=\" * 80)\n", + "\n", + "for entity in ontology.entities:\n", + " print(f\"\\n {entity.label}\")\n", + " if entity.description:\n", + " print(f\" Description: {entity.description}\")\n", + " if entity.attributes:\n", + " print(f\" Attributes ({len(entity.attributes)}):\")\n", + " for attr in entity.attributes:\n", + " print(f\" - {attr.name}: {attr.type.value}\")\n", + " else:\n", + " print(\" No attributes defined\")" + ] + }, + { + "cell_type": "markdown", + "id": "861f893e", + "metadata": {}, + "source": [ + "## Step 4: Inspect Extracted Relations\n", + "\n", + "Display all relations (object properties) extracted from the TTL file." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ead7c3a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "RELATIONS (Object Properties)\n", + "================================================================================\n", + "\n", + "PARTOF\n", + " (:Workouts) --[PARTOF]--> (:FocusedAreas)\n", + "\n", + "ASSOCIATEDTO\n", + " (:SportsPerson) --[ASSOCIATEDTO]--> (:Sport)\n" + ] + } + ], + "source": [ + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"RELATIONS (Object Properties)\")\n", + "print(\"=\" * 80)\n", + "\n", + "for relation in ontology.relations:\n", + " print(f\"\\n{relation.label}\")\n", + " print(f\" {relation.source} --[{relation.label}]--> {relation.target}\")\n", + " if hasattr(relation, 'attributes') and relation.attributes:\n", + " print(f\" Relation attributes: {[a.name for a in relation.attributes]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "994db559", + "metadata": {}, + "source": [ + "## Step 5: Export Ontology to JSON\n", + "\n", + "Export the extracted ontology to JSON format for storage or further processing." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "64d8ee34", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ONTOLOGY JSON EXPORT\n", + "================================================================================\n", + "{\n", + " \"entities\": [\n", + " {\n", + " \"label\": \"Workouts\",\n", + " \"attributes\": [],\n", + " \"description\": \"Training exercises and routines for athletic development\"\n", + " },\n", + " {\n", + " \"label\": \"LowerBody\",\n", + " \"attributes\": [],\n", + " \"description\": \"Exercises targeting legs, glutes, and hips\"\n", + " },\n", + " {\n", + " \"label\": \"Sport\",\n", + " \"attributes\": [],\n", + " \"description\": \"A physical activity or game with competitive elements\"\n", + " },\n", + " {\n", + " \"label\": \"FocusedAreas\",\n", + " \"attributes\": [],\n", + " \"description\": \"Specific physical attributes targeted by training\"\n", + " },\n", + " {\n", + " \"label\": \"Flexibility\",\n", + " \"attributes\": [],\n", + " \"description\": \"Range of motion in joints and muscles\"\n", + " },\n", + " {\n", + " \"label\": \"Badminton\",\n", + " \"attributes\": [],\n", + " \"description\": \"Racket sport using a shuttlecock\"\n", + " },\n", + " {\n", + " \"label\": \"Agility\",\n", + " \"attributes\": [],\n", + " \"description\": \"Ability to move quickly and change direction effectively\"\n", + " },\n", + " {\n", + " \"label\": \"Equipments\",\n", + " \"attributes\": [],\n", + " \"description\": \"Tools and gear used in sports activities\"\n", + " },\n", + " {\n", + " \"label\": \"SportsPerson\",\n", + " \"attributes\": [],\n", + " \"description\": \"An individual who participates in sports\"\n", + " },\n", + " {\n", + " \"label\": \"MusclePower\",\n", + " \"attributes\": [],\n", + " \"description\": \"Combination of strength and speed in muscle contraction\"\n", + " },\n", + " {\n", + " \"label\": \"Cricket\",\n", + " \"attributes\": [],\n", + " \"description\": \"Bat-and-ball game played between two teams\"\n", + " },\n", + " {\n", + " \"label\": \"Endurance\",\n", + " \"attributes\": [],\n", + " \"description\": \"Ability to sustain prolonged physical effort\"\n", + " },\n", + " {\n", + " \"label\": \"UpperBody\",\n", + " \"attributes\": [],\n", + " \"description\": \"Exercises targeting chest, shoulders, arms, and back\"\n", + " },\n", + " {\n", + " \"label\": \"Core\",\n", + " \"attributes\": [],\n", + " \"description\": \"Exercises targeting abdominal and back muscles\"\n", + " },\n", + " {\n", + " \"label\": \"Functional\",\n", + " \"attributes\": [],\n", + " \"description\": \"Multi-joint exercises that mimic everyday movements\"\n", + " },\n", + " {\n", + " \"label\": \"Football\",\n", + " \"attributes\": [],\n", + " \"description\": \"Team sport played with a spherical ball\"\n", + " },\n", + " {\n", + " \"label\": \"Speed\",\n", + " \"attributes\": [],\n", + " \"description\": \"Rate of movement or action\"\n", + " },\n", + " {\n", + " \"label\": \"Tennis\",\n", + " \"attributes\": [],\n", + " \"description\": \"Racket sport played individually or in pairs\"\n", + " },\n", + " {\n", + " \"label\": \"Balance\",\n", + " \"attributes\": [],\n", + " \"description\": \"Ability to maintain body equilibrium\"\n", + " }\n", + " ],\n", + " \"relations\": [\n", + " {\n", + " \"label\": \"PARTOF\",\n", + " \"source\": {\n", + " \"label\": \"Workouts\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"FocusedAreas\"\n", + " },\n", + " \"attributes\": []\n", + " },\n", + " {\n", + " \"label\": \"ASSOCIATEDTO\",\n", + " \"source\": {\n", + " \"label\": \"SportsPerson\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"Sport\"\n", + " },\n", + " \"attributes\": []\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "# Export to JSON\n", + "ontology_json = ontology.to_json()\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"ONTOLOGY JSON EXPORT\")\n", + "print(\"=\" * 80)\n", + "print(json.dumps(ontology_json, indent=2))\n", + "\n", + "# Optionally save to file (not executed by default)\n", + "# output_file = \"sports_ontology.json\"\n", + "# with open(output_file, 'w') as f:\n", + "# json.dump(ontology_json, f, indent=2)\n", + "# print(f\"\\n Ontology saved to {output_file}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f6e6f511", + "metadata": {}, + "source": [ + "## Step 6: Summary Statistics\n", + "\n", + "Get a quick overview of the extracted ontology." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fa7c5ba4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "ONTOLOGY SUMMARY\n", + "================================================================================\n", + "\n", + " Statistics:\n", + " Total Entities: 19\n", + " Total Relations: 2\n", + " Entities with Attributes: 0\n", + " Total Attributes: 0\n", + "\n", + " Entity List:\n", + " - Workouts (0 attributes)\n", + " - LowerBody (0 attributes)\n", + " - Sport (0 attributes)\n", + " - FocusedAreas (0 attributes)\n", + " - Flexibility (0 attributes)\n", + " - Badminton (0 attributes)\n", + " - Agility (0 attributes)\n", + " - Equipments (0 attributes)\n", + " - SportsPerson (0 attributes)\n", + " - MusclePower (0 attributes)\n", + " - Cricket (0 attributes)\n", + " - Endurance (0 attributes)\n", + " - UpperBody (0 attributes)\n", + " - Core (0 attributes)\n", + " - Functional (0 attributes)\n", + " - Football (0 attributes)\n", + " - Speed (0 attributes)\n", + " - Tennis (0 attributes)\n", + " - Balance (0 attributes)\n", + "\n", + " Relation List:\n", + " - (:Workouts) β†’ PARTOF β†’ (:FocusedAreas)\n", + " - (:SportsPerson) β†’ ASSOCIATEDTO β†’ (:Sport)\n", + "\n", + " Ontology extraction complete!\n" + ] + } + ], + "source": [ + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"ONTOLOGY SUMMARY\")\n", + "print(\"=\" * 80)\n", + "\n", + "def summarize_ontology_stats(ontology):\n", + "\n", + " total_entities = len(ontology.entities)\n", + " total_relations = len(ontology.relations)\n", + " entities_with_attrs = sum(1 for e in ontology.entities if e.attributes)\n", + " total_attributes = sum(len(e.attributes) for e in ontology.entities)\n", + "\n", + " print(f\"\\n Statistics:\")\n", + " print(f\" Total Entities: {total_entities}\")\n", + " print(f\" Total Relations: {total_relations}\")\n", + " print(f\" Entities with Attributes: {entities_with_attrs}\")\n", + " print(f\" Total Attributes: {total_attributes}\")\n", + "\n", + " print(f\"\\n Entity List:\")\n", + " for entity in ontology.entities:\n", + " attr_count = len(entity.attributes) if entity.attributes else 0\n", + " print(f\" - {entity.label} ({attr_count} attributes)\")\n", + "\n", + " print(f\"\\n Relation List:\")\n", + " for relation in ontology.relations:\n", + " print(f\" - {relation.source} β†’ {relation.label} β†’ {relation.target}\")\n", + "\n", + " print(\"\\n Ontology extraction complete!\")\n", + "\n", + "summarize_ontology_stats(ontology)" + ] + }, + { + "cell_type": "markdown", + "id": "b8b36772", + "metadata": {}, + "source": [ + "## Step 7: Configure FalkorDB Connection\n", + "\n", + "Set your FalkorDB credentials. You can use a local instance or get credentials from [FalkorDB Cloud](https://app.falkordb.cloud)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aed7c0a5", + "metadata": {}, + "outputs": [], + "source": [ + "# Set your OpenAI credential in .env file\n", + "# OPENAI_API_KEY=your_api_key\n", + "\n", + "# Get your Credentials from Falkor website, see https://app.falkordb.cloud\n", + "falkor_host = \"\"\n", + "falkor_port = None\n", + "falkor_username = \"\"\n", + "falkor_password = \"\"\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "beddd528", + "metadata": {}, + "source": [ + "## Step 8: Create Knowledge Graph\n", + "\n", + "Two ways to create a Knowledge Graph with the TTL ontology:" + ] + }, + { + "cell_type": "markdown", + "id": "7a7432aa", + "metadata": {}, + "source": [ + "### Option 1: Using Ontology.from_ttl() + KnowledgeGraph()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11d13b78", + "metadata": {}, + "outputs": [], + "source": [ + "from litellm import models_by_provider\n", + "from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig\n", + "from graphrag_sdk.models.litellm import LiteModel\n", + "\n", + "# Initialize the model\n", + "model = LiteModel(model_name=\"openai/gpt-4.1\")\n", + "\n", + "# Method 1: Use pre-extracted ontology\n", + "kg = KnowledgeGraph(\n", + " name=\"sports_kg\",\n", + " model_config=KnowledgeGraphModelConfig.with_model(model),\n", + " ontology=ontology, # Use the ontology extracted in Step 2\n", + " host=falkor_host,\n", + " port=falkor_port,\n", + " username=falkor_username,\n", + " password=falkor_password\n", + ")\n", + "\n", + "print(f\"βœ“ Knowledge Graph created!\")\n", + "print(f\" - {len(ontology.entities)} entities, {len(ontology.relations)} relations\")" + ] + }, + { + "cell_type": "markdown", + "id": "865dfdc0", + "metadata": {}, + "source": [ + "### Option 2: Using KnowledgeGraph.from_ttl() (One-Step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "112d9d3b", + "metadata": {}, + "outputs": [], + "source": [ + "# Method 2: Create KG directly from TTL file (alternative approach)\n", + "\n", + "\n", + "kg = KnowledgeGraph.from_ttl(\n", + " path=ttl_file_path,\n", + " name=\"sports_kg\",\n", + " model_config=KnowledgeGraphModelConfig.with_model(model),\n", + " host=falkor_host,\n", + " port=falkor_port,\n", + " username=falkor_username,\n", + " password=falkor_password\n", + ")\n", + "\n", + "print(f\"βœ“ Knowledge Graph created directly from TTL!\")\n", + "print(f\" - {len(kg.ontology.entities)} entities, {len(kg.ontology.relations)} relations\")" + ] + }, + { + "cell_type": "markdown", + "id": "1a321ade", + "metadata": {}, + "source": [ + "## Step 9: Graph RAG - Query with Chat\n", + "\n", + "Utilize the `chat_session` method to start conversations and query your Knowledge Graph using natural language." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa23fafc", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a chat session\n", + "chat = kg.chat_session()\n", + "\n", + "# Example queries about the sports ontology\n", + "answer = chat.send_message(\"What entities are defined in the sports ontology?\")\n", + "print(f\"Q: {answer['question']}\\nA: {answer['response']}\\n\")\n", + "\n", + "answer = chat.send_message(\"What are the relationships between SportsPerson and Sport?\")\n", + "print(f\"Q: {answer['question']}\\nA: {answer['response']}\\n\")\n", + "\n", + "answer = chat.send_message(\"List all attributes of the Team entity.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b6adc8d", + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to delete the Knowledge Graph\n", + "# kg.delete()\n", + "# print(\" Knowledge Graph 'sports_kg' deleted successfully\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GraphRAG-SDK", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/trip/demo_orchestrator_trip.ipynb b/examples/trip/demo_orchestrator_trip.ipynb index 989197f3..b99d101c 100644 --- a/examples/trip/demo_orchestrator_trip.ipynb +++ b/examples/trip/demo_orchestrator_trip.ipynb @@ -15,14 +15,23 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install graphrag_sdk[openai]" + "!pip install graphrag_sdk[litellm]" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shubeli/miniconda3/envs/sdk/lib/python3.10/site-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:\n", + "* 'fields' has been removed\n", + " warnings.warn(message, UserWarning)\n" + ] + }, { "data": { "text/plain": [ @@ -39,7 +48,7 @@ "from dotenv import load_dotenv\n", "from graphrag_sdk.orchestrator import Orchestrator\n", "from graphrag_sdk.agents.kg_agent import KGAgent\n", - "from graphrag_sdk.models.openai import OpenAiGenerativeModel\n", + "from graphrag_sdk.models.litellm import LiteModel\n", "from graphrag_sdk import (\n", " Ontology, Entity, Relation, Attribute, AttributeType, KnowledgeGraph, KnowledgeGraphModelConfig\n", ")\n", @@ -48,7 +57,8 @@ "load_dotenv()\n", "\n", "# Configuration\n", - "# OPENAI_API_KEY = \"sk-\"# OpenAI API key" + "# Set your OpenAI credential in .env file\n", + "# OPENAI_API_KEY=your_api_key" ] }, { @@ -247,12 +257,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define the model\n", - "model = OpenAiGenerativeModel(\"gpt-4o\")\n", + "# Using OpenAI with gpt-4.1 as default (temperature=0 by default)\n", + "model = LiteModel()\n", "\n", "# Create the KG from the predefined ontology.\n", "# Restaurants KG\n", @@ -359,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -389,13 +400,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ + "model_orchestrator = LiteModel(\"o3-mini\")\n", + "\n", "# Initialize the orchestrator and register agents\n", "orchestrator = Orchestrator(\n", - " model,\n", + " model_orchestrator,\n", " backstory=\"You are a trip planner, and you want to provide the best possible itinerary for your clients.\",\n", ")\n", "orchestrator.register_agent(restaurants_agent)\n", @@ -407,42 +420,53 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Certainly! Here's a detailed two-day itinerary for your trip to Rome, ensuring you make the most out of your visit to this historically rich and vibrant city. We’ve arranged a balanced mix of iconic attractions and delightful dining experiences for your enjoyment.\n", + "Dear Traveler,\n", + "\n", + "Here is your detailed two-day itinerary for a memorable trip to Rome, designed to help you experience the city's rich history, culture, and culinary delights. I’ve combined the top attractions with excellent dining recommendations to ensure you have a well-rounded experience.\n", + "\n", + "────────────────────────────\n", + "Day 1:\n", + "────────────────────────────\n", + "β€’ Morning – Begin your day with a visit to the Colosseum. This iconic ancient amphitheater not only offers a glimpse into the grandeur of ancient Rome but also provides a stirring reminder of the gladiatorial contests and public spectacles that once took place there.\n", + "\n", + "β€’ Afternoon – Head over to the Vatican Museums. This renowned collection of art and historical artifacts, accumulated by Popes over centuries, is a must-see. The museums offer fascinating insights into Renaissance art and history, making it a perfect cultural stop.\n", "\n", - "**Day 1: Discovering Ancient Rome and Modern Flavors**\n", - "- **Morning: Colosseum**\n", - " Start your day by visiting the Colosseum, the iconic ancient amphitheater known for its gladiatorial contests and grand spectacles. Allow yourself a few hours to explore the structure and absorb the rich history of ancient Rome that it represents.\n", + "β€’ Lunch Suggestion – Enjoy lunch at Trattoria da Enzo. This well-loved eatery is known for its traditional Roman dishes and warm, inviting atmosphere, making it an ideal spot to recharge after your cultural explorations.\n", "\n", - "- **Lunch: Tonnarello**\n", - " After exploring, head to Tonnarello, a restaurant praised for its casual ambiance and delicious Roman cuisine. Enjoy dishes like homemade pasta and meatballs while soaking in the atmosphere of authentic Roman hospitality. \n", + "β€’ Evening – Conclude the day with a leisurely stroll to the Trevi Fountain. As you admire its exquisite Baroque artistry, take part in the charming tradition of tossing a coin for good luck.\n", "\n", - "- **Afternoon: Trevi Fountain**\n", - " Rejuvenated from lunch, stroll towards the Trevi Fountain. This Baroque masterpiece not only offers stunning sculptures but also the tradition of coin tossing, promising a future return trip to Rome.\n", + "β€’ Dinner Suggestion – For dinner, consider dining at Il Pagliaccio, a Michelin-starred restaurant offering contemporary Italian cuisine, which promises a refined dining experience to cap off your day.\n", "\n", - "- **Dinner: Il Pagliaccio**\n", - " Conclude your first day with dinner at Il Pagliaccio. This Michelin-starred restaurant offers a contemporary Italian dining experience, perfect for indulging in exquisite flavors and creative dishes.\n", + "────────────────────────────\n", + "Day 2:\n", + "────────────────────────────\n", + "β€’ Morning – Start your day with a visit to the Basilica di Santa Maria Maggiore. This magnificent basilica, celebrated for its rich history and impressive architecture, provides a serene and inspiring start to your morning.\n", "\n", - "**Day 2: Religious Artistry and Cozy Feasts**\n", - "- **Morning: Vatican Museums**\n", - " Begin your second day by exploring the Vatican Museums, home to centuries of art collected by the Popes. Allocate sufficient time here to truly appreciate the vast collections, including the Sistine Chapel's renowned ceiling.\n", + "β€’ Afternoon – Spend your afternoon revisiting your favorite spots from Day 1 or simply enjoy a relaxed, wandering walk through Rome’s picturesque historic streets. This free time allows you to soak in the city’s atmosphere at your own pace.\n", "\n", - "- **Lunch: Trattoria da Enzo**\n", - " For a cozy and traditional Roman dining experience, visit Trattoria da Enzo. Known for its welcoming atmosphere and scrumptious local dishes, it provides the perfect lunchtime escape.\n", + "β€’ Lunch Suggestion – For a casual yet satisfying meal, try Tonnarello. This local restaurant offers authentic Roman dishes like pasta and meatballs, perfect for a midday break with a welcoming ambiance.\n", "\n", - "- **Afternoon: Basilica di Santa Maria Maggiore**\n", - " Spend your afternoon visiting the Basilica di Santa Maria Maggiore. This major basilica is renowned for its impressive architecture and deep historical roots, representing a significant piece of Rome's religious heritage.\n", + "β€’ Evening – Unwind by relaxing at a local cafΓ©. Enjoy a leisurely evening soak in the Mediterranean atmosphere, reflecting on your experiences and the vibrant life of Rome.\n", "\n", - "- **Evening: Stroll through Rome**\n", - " Conclude your Roman adventure with a leisurely evening walk through the city's streets, taking in the lively Italian spirit as you reminisce over the sights and experiences from the past two days.\n", + "β€’ Dinner Suggestion – You have the option for dinner at either Il Pagliaccio for a fine dining experience or Trattoria da Enzo if you prefer a more traditional Roman meal. Both options promise delightful flavors and excellent service.\n", "\n", - "This itinerary ensures a wonderful balance between exploration of Rome's historical grandeur and indulgence in its exquisite culinary offerings. Enjoy your trip!\n" + "────────────────────────────\n", + "Reasoning Behind the Itinerary:\n", + "β€’ Historical and Cultural Immersion: Visits to classic landmarks such as the Colosseum, Vatican Museums, and Basilica di Santa Maria Maggiore ensure you experience Rome's rich history and art.\n", + "β€’ Culinary Experiences: Restaurant recommendations have been carefully selected to provide both traditional Roman cuisine and modern, refined dining experiences, catering to varying moods and palates.\n", + "β€’ Balanced Timing: The itinerary is structured to provide a mix of guided tours and personal leisure time, allowing you to explore at your own pace while still taking in the must-see attractions.\n", + "\n", + "I hope this itinerary meets your expectations and helps you enjoy a truly engaging and delicious trip to Rome. Enjoy your travels and feel free to refer back to this plan whenever you need inspiration during your visit!\n", + "\n", + "Best wishes for a wonderful journey,\n", + "Your Trip Planning Team\n" ] } ], @@ -452,14 +476,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Certainly! Based on the two-day itinerary for Rome that was previously provided to you, the restaurant mentioned for dinner on the first day is \"Il Pagliaccio.\" This restaurant is renowned for its elegant ambiance and Michelin-starred contemporary Italian cuisine, offering a perfect dining experience to conclude your day of exploring ancient Rome. Enjoy your dinner and the remarkable culinary delights it has to offer!\n" + "Dear Traveler,\n", + "\n", + "Thank you for your inquiry. Based on the itinerary details provided, the recommended restaurant for dinner on Day 1 is \"Il Pagliaccio.\" This Michelin-starred restaurant was selected for its elegant ambiance and contemporary Italian cuisine, ensuring a refined dining experience that complements the rich cultural exploration of your trip.\n", + "\n", + "I hope this clear and concise response helps you in planning your dining experience. Enjoy your journey and the delightful culinary offerings of Rome!\n", + "\n", + "Best regards,\n", + "Your Trip Planning Team\n" ] } ], @@ -470,42 +501,41 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Certainly! I've updated your two-day itinerary for Rome with a new dinner option for the first day. Here is the revised itinerary:\n", - "\n", - "**Day 1: Discovering Ancient Rome and Modern Flavors**\n", - "- **Morning: Colosseum**\n", - " Begin your day with a visit to the iconic Colosseum, where you'll delve into the historical essence of ancient Rome. Take a few hours to explore this magnificent structure and the stories it holds about the past.\n", + "Dear Traveler,\n", "\n", - "- **Lunch: Tonnarello**\n", - " Enjoy your lunch at Tonnarello, a restaurant highly praised for its casual ambiance and delicious Roman cuisines such as pasta and meatballs. This spot also features patio seating and acoustic guitar, enhancing your dining experience.\n", + "Thank you for your request. I have updated your itinerary by replacing the original Day 1 dinner restaurant with an alternative option. Please find your updated itinerary below:\n", "\n", - "- **Afternoon: Trevi Fountain**\n", - " After lunch, take a leisurely walk to the Trevi Fountain. Revel in the beauty of this Baroque masterpiece, famous for its stunning sculptures and the tradition of tossing a coin for a promise to return.\n", + "────────────────────────────\n", + "Day 1:\n", + "────────────────────────────\n", + "β€’ Lunch – Enjoy a delightful meal at Trattoria da Enzo, a cozy trattoria celebrated for its traditional Roman dishes and warm atmosphere.\n", "\n", - "- **Dinner: Tonnarello (Alternative choice)**\n", - " For dinner, continue enjoying the delightful offerings at Tonnarello. With its refreshing and laid-back atmosphere, you can relish more of their simple yet satisfying Roman dishes, ensuring a memorable end to your day.\n", + "β€’ Dinner – For dinner, you'll now have the chance to experience Tonnarello, a charming restaurant offering a casual setting with delicious pasta, meatballs, and other classic Roman dishes. This venue features patio seating accompanied by acoustic guitar, providing a relaxed yet engaging dining experience.\n", "\n", - "**Day 2: Religious Artistry and Cozy Feasts**\n", - "- **Morning: Vatican Museums**\n", - " Begin your second day by exploring the Vatican Museums, where vast collections of art amassed by the Popes await you. Take your time to appreciate these historical artifacts, including the Sistine Chapel's awe-inspiring ceiling.\n", + "────────────────────────────\n", + "Day 2:\n", + "────────────────────────────\n", + "β€’ Lunch – Once again, Tonnarello offers a satisfying meal with its array of authentic Roman dishes, ideal for a casual stop.\n", "\n", - "- **Lunch: Trattoria da Enzo**\n", - " Experience a cozy and traditional Roman ambiance at Trattoria da Enzo for lunch. Known for its welcoming atmosphere and scrumptious local dishes, it serves as a perfect midday retreat.\n", + "β€’ Dinner – Conclude your Day 2 dining experience at Trattoria da Enzo, which ensures you continue to enjoy the comfort and traditional flavors of Roman cuisine.\n", "\n", - "- **Afternoon: Basilica di Santa Maria Maggiore**\n", - " Spend the afternoon at the Basilica di Santa Maria Maggiore. This basilica is celebrated for its impressive architecture and historical significance, providing a deep dive into Rome's religious heritage.\n", + "────────────────────────────\n", + "Reasoning Behind the Update:\n", + "β€’ By replacing the Day 1 dinner restaurant with Tonnarello, the itinerary now offers variety and caters to a preference for a more casual dining environment with a lively ambiance.\n", + "β€’ The updated plan maintains a balanced culinary experience by mixing both traditional and casual dining options, giving you a more diverse taste of Rome’s vibrant food culture.\n", + "β€’ The overall itinerary is structured to enhance your travel experience while allowing you to immerse yourself in the local flavors and atmosphere of Rome.\n", "\n", - "- **Evening: Stroll through Rome**\n", - " End your Roman adventure with a relaxed evening stroll through the city's lively streets, taking in the sights and sounds while reminiscing about your enriching experiences over the past two days.\n", + "I hope you enjoy this updated itinerary and that it meets your expectations. Have a wonderful trip and enjoy every moment of your Roman adventure!\n", "\n", - "This updated itinerary ensures you have a fulfilled and delightful experience while exploring the cultural and culinary richness of Rome. Enjoy your trip!\n" + "Best regards,\n", + "Your Trip Planning Team\n" ] } ], @@ -516,26 +546,33 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Certainly! For the two-day itinerary in Rome that was previously provided, the attraction mentioned for the morning on the first day is the **Colosseum**. This iconic ancient amphitheater is known for its gladiatorial contests and grand spectacles, making it a must-see when exploring the history and culture of ancient Rome. Visiting the Colosseum allows you to delve into Rome's fascinating past and appreciate the architectural marvels and historical significance of this world-renowned site. Enjoy your exploration of this magnificent landmark!\n" + "Dear Traveler,\n", + "\n", + "Thank you for your inquiry. Based on the itinerary details, the attraction scheduled for the morning of Day 1 is the Colosseum. The Colosseum, an iconic monument of ancient Rome, was chosen as it provides a firsthand look into the rich history and architectural prowess of the Roman Empire, setting a perfect tone for your day of exploration.\n", + "\n", + "I hope this detailed explanation helps you understand your itinerary better. Enjoy your visit to this magnificent historical site and have a wonderful trip!\n", + "\n", + "Best regards,\n", + "Your Trip Planning Team\n" ] } ], "source": [ - "runner = orchestrator.ask(\"Please tell me attraction on the morning at the first day that you mention in the itinerary of the trip\")\n", + "runner = orchestrator.ask(\"Please tell me the attraction on the morning at the first day that you mention in the itinerary of the trip\")\n", "print(runner.output)" ] } ], "metadata": { "kernelspec": { - "display_name": "graphrag-sdk-ceBr3E7y-py3.12", + "display_name": "sdk", "language": "python", "name": "python3" }, @@ -549,7 +586,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/examples/ufc/demo-ufc.ipynb b/examples/ufc/demo-ufc.ipynb index 43eca283..2fa95529 100644 --- a/examples/ufc/demo-ufc.ipynb +++ b/examples/ufc/demo-ufc.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "True" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -41,14 +41,15 @@ "from dotenv import load_dotenv\n", "from graphrag_sdk.source import Source\n", "from graphrag_sdk import KnowledgeGraph, Ontology\n", - "from graphrag_sdk.models.openai import OpenAiGenerativeModel\n", + "from graphrag_sdk.models.litellm import LiteModel\n", "from graphrag_sdk.model_config import KnowledgeGraphModelConfig\n", "\n", "# Load environment variables\n", "load_dotenv()\n", "\n", "# Configuration\n", - "# OPENAI_API_KEY = \"sk-\"# OpenAI API key" + "# Set your OpenAI credential in .env file\n", + "# OPENAI_API_KEY = \"your-api-key\"" ] }, { @@ -62,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +89,15 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Process Documents: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2/2 [00:35<00:00, 17.79s/it]\n" + ] + } + ], "source": [ "# Define the percentage of files that will be used to auto-create the ontology.\n", "percent = 0.1 # This represents 10%. You can adjust this value (e.g., 0.2 for 20%).\n", @@ -99,7 +108,8 @@ "\"\"\"\n", "\n", "# Define the model to be used for the ontology\n", - "model = OpenAiGenerativeModel(model_name=\"gpt-4o\")\n", + "# Using OpenAI with gpt-4.1 as default (temperature=0 by default)\n", + "model = LiteModel()\n", "\n", "# Randomly select a percentage of files from sources.\n", "sampled_sources = random.sample(sources, round(len(sources) * percent))\n", @@ -134,7 +144,7 @@ "{\n", " \"entities\": [\n", " {\n", - " \"label\": \"Person\",\n", + " \"label\": \"Event\",\n", " \"attributes\": [\n", " {\n", " \"name\": \"name\",\n", @@ -143,46 +153,40 @@ " \"required\": true\n", " },\n", " {\n", - " \"name\": \"nickname\",\n", + " \"name\": \"date\",\n", " \"type\": \"string\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"location\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": true\n", " }\n", " ],\n", " \"description\": \"\"\n", " },\n", " {\n", - " \"label\": \"FightEvent\",\n", + " \"label\": \"Fight\",\n", " \"attributes\": [\n", " {\n", - " \"name\": \"name\",\n", + " \"name\": \"fight_id\",\n", " \"type\": \"string\",\n", " \"unique\": true,\n", " \"required\": true\n", " },\n", " {\n", - " \"name\": \"date\",\n", - " \"type\": \"string\",\n", + " \"name\": \"title_bout\",\n", + " \"type\": \"boolean\",\n", " \"unique\": false,\n", " \"required\": true\n", " },\n", " {\n", - " \"name\": \"location\",\n", - " \"type\": \"string\",\n", - " \"unique\": false,\n", - " \"required\": true\n", - " }\n", - " ],\n", - " \"description\": \"\"\n", - " },\n", - " {\n", - " \"label\": \"Bout\",\n", - " \"attributes\": [\n", - " {\n", " \"name\": \"weight_class\",\n", " \"type\": \"string\",\n", " \"unique\": false,\n", - " \"required\": true\n", + " \"required\": false\n", " },\n", " {\n", " \"name\": \"method\",\n", @@ -206,127 +210,376 @@ " \"name\": \"time_format\",\n", " \"type\": \"string\",\n", " \"unique\": false,\n", - " \"required\": true\n", + " \"required\": false\n", " },\n", " {\n", - " \"name\": \"referee\",\n", + " \"name\": \"details\",\n", " \"type\": \"string\",\n", " \"unique\": false,\n", " \"required\": false\n", " }\n", " ],\n", " \"description\": \"\"\n", - " }\n", - " ],\n", - " \"relations\": [\n", + " },\n", " {\n", - " \"label\": \"FOUGHT_IN\",\n", - " \"source\": {\n", - " \"label\": \"Person\"\n", - " },\n", - " \"target\": {\n", - " \"label\": \"Bout\"\n", - " },\n", + " \"label\": \"Person\",\n", " \"attributes\": [\n", " {\n", - " \"name\": \"result\",\n", + " \"name\": \"name\",\n", + " \"type\": \"string\",\n", + " \"unique\": true,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"nickname\",\n", " \"type\": \"string\",\n", " \"unique\": false,\n", + " \"required\": false\n", + " }\n", + " ],\n", + " \"description\": \"\"\n", + " },\n", + " {\n", + " \"label\": \"Referee\",\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"name\",\n", + " \"type\": \"string\",\n", + " \"unique\": true,\n", + " \"required\": true\n", + " }\n", + " ],\n", + " \"description\": \"\"\n", + " },\n", + " {\n", + " \"label\": \"FightStatistics\",\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"statistics_id\",\n", + " \"type\": \"string\",\n", + " \"unique\": true,\n", " \"required\": true\n", " },\n", " {\n", " \"name\": \"knockdowns\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"significant_strikes\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"significant_strikes_attempted\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"significant_strikes_percentage\",\n", - " \"type\": \"string\",\n", + " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"total_strikes\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"total_strikes_attempted\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"takedowns\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"takedowns_attempted\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"takedown_percentage\",\n", - " \"type\": \"string\",\n", + " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"submissions_attempted\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"passes\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", - " \"required\": false\n", + " \"required\": true\n", " },\n", " {\n", " \"name\": \"reversals\",\n", " \"type\": \"number\",\n", " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ],\n", + " \"description\": \"\"\n", + " },\n", + " {\n", + " \"label\": \"RoundStatistics\",\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"round_statistics_id\",\n", + " \"type\": \"string\",\n", + " \"unique\": true,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"round_number\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"knockdowns\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"significant_strikes\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"significant_strikes_attempted\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"significant_strikes_percentage\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"takedown_percentage\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"submissions_attempted\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"reversals\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ],\n", + " \"description\": \"\"\n", + " },\n", + " {\n", + " \"label\": \"SignificantStrikeBreakdown\",\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"breakdown_id\",\n", + " \"type\": \"string\",\n", + " \"unique\": true,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"head\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"body\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"leg\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"distance\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"clinch\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " },\n", + " {\n", + " \"name\": \"ground\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ],\n", + " \"description\": \"\"\n", + " }\n", + " ],\n", + " \"relations\": [\n", + " {\n", + " \"label\": \"HOSTED\",\n", + " \"source\": {\n", + " \"label\": \"Event\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"Fight\"\n", + " },\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"main_event\",\n", + " \"type\": \"boolean\",\n", + " \"unique\": false,\n", " \"required\": false\n", " }\n", " ]\n", " },\n", " {\n", - " \"label\": \"PART_OF_EVENT\",\n", + " \"label\": \"PARTICIPATED\",\n", " \"source\": {\n", - " \"label\": \"Bout\"\n", + " \"label\": \"Person\"\n", " },\n", " \"target\": {\n", - " \"label\": \"FightEvent\"\n", + " \"label\": \"Fight\"\n", " },\n", - " \"attributes\": []\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"result\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ]\n", " },\n", " {\n", - " \"label\": \"PART_OF\",\n", + " \"label\": \"OFFICIATED\",\n", " \"source\": {\n", - " \"label\": \"Bout\"\n", + " \"label\": \"Referee\"\n", " },\n", " \"target\": {\n", - " \"label\": \"FightEvent\"\n", + " \"label\": \"Fight\"\n", " },\n", - " \"attributes\": []\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"role\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"label\": \"HAS_STATISTICS\",\n", + " \"source\": {\n", + " \"label\": \"Fight\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"FightStatistics\"\n", + " },\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"fighter_name\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"label\": \"HAS_ROUND_STATISTICS\",\n", + " \"source\": {\n", + " \"label\": \"Fight\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"RoundStatistics\"\n", + " },\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"fighter_name\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"label\": \"HAS_BREAKDOWN\",\n", + " \"source\": {\n", + " \"label\": \"FightStatistics\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"SignificantStrikeBreakdown\"\n", + " },\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"round_number\",\n", + " \"type\": \"number\",\n", + " \"unique\": false,\n", + " \"required\": false\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"label\": \"HAS_ROUND_BREAKDOWN\",\n", + " \"source\": {\n", + " \"label\": \"RoundStatistics\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"SignificantStrikeBreakdown\"\n", + " },\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"breakdown_id\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": true\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"label\": \"PARTICIPATED_AS_REFEREE\",\n", + " \"source\": {\n", + " \"label\": \"Referee\"\n", + " },\n", + " \"target\": {\n", + " \"label\": \"Event\"\n", + " },\n", + " \"attributes\": [\n", + " {\n", + " \"name\": \"role\",\n", + " \"type\": \"string\",\n", + " \"unique\": false,\n", + " \"required\": false\n", + " }\n", + " ]\n", " }\n", " ]\n", "}\n" @@ -350,7 +603,15 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Process Documents: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 8/8 [01:18<00:00, 9.77s/it]\n" + ] + } + ], "source": [ "# After approving the ontology, load it from disk.\n", "ontology_file = \"ontology.json\"\n", @@ -383,8 +644,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'question': 'Who is Salsa Boy?', 'response': 'Waldo Cortes-Acosta is Salsa Boy.', 'context': '[[\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\']]', 'cypher': \"MATCH (p:Person) WHERE p.nickname CONTAINS 'Salsa Boy' RETURN p\"}\n", - "{'question': 'Tell me about one of his fights?', 'response': 'Waldo Cortes-Acosta, also known as Salsa Boy, fought in a bout at UFC 284: Makhachev vs. Volkanovski, held on February 11, 2023, in Perth, Western Australia, Australia. He won the fight by submission in the second round, at 4:05. In this fight, he displayed impressive skills with 52 significant strikes out of 101 attempted, achieving a 51.49% success rate. He also attempted 4 takedowns, successfully completing 3 for a 75.00% success rate, and attempted 1 submission.', 'context': '[[\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\', \\'()-[:FOUGHT_IN{knockdowns:0,passes:0,result:\"Win\",reversals:0,significant_strikes:52,significant_strikes_attempted:101,significant_strikes_percentage:\"51.49%\",submissions_attempted:1,takedown_percentage:\"75.00%\",takedowns:3,takedowns_attempted:4,total_strikes:130,total_strikes_attempted:204}]->()\\', \\'(:Bout{method:\"Submission\",referee:\"Osiris Maia\",round:2,time:\"4:05\",time_format:\"3 Rounds (5-5-5)\",weight_class:\"Lightweight\"})\\', \\'(:FightEvent{date:\"2023-02-11\",location:\"Perth, Western Australia, Australia\",name:\"UFC 284: Makhachev vs. Volkanovski\"})\\']]', 'cypher': \"MATCH (p:Person)-[f:FOUGHT_IN]->(b:Bout)-[:PART_OF_EVENT]->(e:FightEvent) WHERE p.nickname CONTAINS 'Salsa Boy' RETURN p, f, b, e LIMIT 1\"}\n" + "{'question': 'Who is Salsa Boy?', 'response': 'Salsa Boy is Waldo Cortes-Acosta.', 'context': '[[\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\']]', 'cypher': \"\\nMATCH (p:Person)\\nWHERE p.nickname CONTAINS 'Salsa Boy'\\nRETURN p\\n\"}\n", + "{'question': 'Tell me about one of his fights?', 'response': 'Waldo Cortes-Acosta, also known as Salsa Boy, fought Lukasz Brzeski at UFC Fight Night: Holloway vs. The Korean Zombie on August 26, 2023. He won the fight by KO/TKO with punches to the head at distance in the first round at 3:01. The bout was in the Heavyweight division and was scheduled for 3 rounds.', 'context': '[[\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\', \\'()-[:PARTICIPATED{result:\"Win\"}]->()\\', \\'(:Fight{details:\"Punches to Head At Distance\",fight_id:\"Waldo Cortes-Acosta v Lukasz Brzeski UFC Fight Night: Holloway vs. The Korean Zombie 2023-08-26\",method:\"KO/TKO\",round:1,time:\"3:01\",time_format:\"3 Rounds (5-5-5)\",title_bout:False,weight_class:\"Heavyweight\"})\\'], [\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\', \\'()-[:PARTICIPATED{result:\"Win\"}]->()\\', \\'(:Fight{details:\"Decision\",fight_id:\"Arlovski_vs_Cortes-Acosta_UFCFN_2024-01-13\",method:\"Decision - Unanimous\",round:3,time:\"5:00\",time_format:\"3 Rounds (5-5-5)\",title_bout:False,weight_class:\"Heavyweight\"})\\'], [\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\', \\'()-[:PARTICIPATED{result:\"Win\"}]->()\\', \\'(:Fight{details:\"Decision\",fight_id:\"WaldoCortesAcosta_vs_RobelisDespaigne_UFCFN_Lewis_Nascimento_2024-05-11\",method:\"Decision - Unanimous\",round:3,time:\"5:00\",time_format:\"3 Rounds (5-5-5)\",title_bout:False,weight_class:\"Heavyweight\"})\\'], [\\'(:Person{name:\"Waldo Cortes-Acosta\",nickname:\"Salsa Boy\"})\\', \\'()-[:PARTICIPATED{result:\"Loss\"}]->()\\', \\'(:Fight{details:\"Decision\",fight_id:\"MarcosRogerioDeLima_vs_WaldoCortesAcosta_UFCFightNightSongVsSimon_2023-04-29\",method:\"Decision - Unanimous\",round:3,time:\"5:00\",time_format:\"3 Rounds (5-5-5)\",title_bout:False,weight_class:\"Heavyweight\"})\\']]', 'cypher': \"\\nMATCH (p:Person)-[pa:PARTICIPATED]->(f:Fight)\\nWHERE p.name CONTAINS 'Waldo Cortes-Acosta'\\nRETURN p, pa, f\\n\"}\n" ] } ], @@ -400,7 +661,7 @@ ], "metadata": { "kernelspec": { - "display_name": "graphrag-sdk-ceBr3E7y-py3.12", + "display_name": "sdk", "language": "python", "name": "python3" }, @@ -414,7 +675,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/examples/ufc/ontology.json b/examples/ufc/ontology.json index 8565fe68..d0ee2570 100644 --- a/examples/ufc/ontology.json +++ b/examples/ufc/ontology.json @@ -4,7 +4,7 @@ "label": "Event", "attributes": [ { - "name": "title", + "name": "name", "type": "string", "unique": true, "required": true @@ -28,11 +28,23 @@ "label": "Fight", "attributes": [ { - "name": "title", + "name": "fight_id", "type": "string", "unique": true, "required": true }, + { + "name": "title_bout", + "type": "boolean", + "unique": false, + "required": true + }, + { + "name": "weight_class", + "type": "string", + "unique": false, + "required": false + }, { "name": "method", "type": "string", @@ -40,28 +52,34 @@ "required": true }, { - "name": "rounds", + "name": "round", "type": "number", "unique": false, "required": true }, { - "name": "referee", + "name": "time", "type": "string", "unique": false, "required": true }, { - "name": "result_details", + "name": "time_format", "type": "string", "unique": false, - "required": true + "required": false + }, + { + "name": "details", + "type": "string", + "unique": false, + "required": false } ], "description": "" }, { - "label": "Fighter", + "label": "Person", "attributes": [ { "name": "name", @@ -74,11 +92,17 @@ "type": "string", "unique": false, "required": false - }, + } + ], + "description": "" + }, + { + "label": "Referee", + "attributes": [ { - "name": "outcome", + "name": "name", "type": "string", - "unique": false, + "unique": true, "required": true } ], @@ -88,7 +112,7 @@ "label": "FightStatistics", "attributes": [ { - "name": "fighter_name", + "name": "statistics_id", "type": "string", "unique": true, "required": true @@ -167,48 +191,252 @@ } ], "description": "" + }, + { + "label": "RoundStatistics", + "attributes": [ + { + "name": "round_statistics_id", + "type": "string", + "unique": true, + "required": true + }, + { + "name": "round_number", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "knockdowns", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "significant_strikes", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "significant_strikes_attempted", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "significant_strikes_percentage", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "takedown_percentage", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "submissions_attempted", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "reversals", + "type": "number", + "unique": false, + "required": true + } + ], + "description": "" + }, + { + "label": "SignificantStrikeBreakdown", + "attributes": [ + { + "name": "breakdown_id", + "type": "string", + "unique": true, + "required": true + }, + { + "name": "head", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "body", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "leg", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "distance", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "clinch", + "type": "number", + "unique": false, + "required": true + }, + { + "name": "ground", + "type": "number", + "unique": false, + "required": true + } + ], + "description": "" } ], "relations": [ { - "label": "PART_OF", + "label": "HOSTED", "source": { + "label": "Event" + }, + "target": { "label": "Fight" }, + "attributes": [ + { + "name": "main_event", + "type": "boolean", + "unique": false, + "required": false + } + ] + }, + { + "label": "PARTICIPATED", + "source": { + "label": "Person" + }, "target": { - "label": "Event" + "label": "Fight" }, - "attributes": [] + "attributes": [ + { + "name": "result", + "type": "string", + "unique": false, + "required": true + } + ] }, { - "label": "PARTICIPATED_IN", + "label": "OFFICIATED", "source": { - "label": "Fighter" + "label": "Referee" }, "target": { "label": "Fight" }, - "attributes": [] + "attributes": [ + { + "name": "role", + "type": "string", + "unique": false, + "required": true + } + ] }, { - "label": "RELATED_STATS", + "label": "HAS_STATISTICS", "source": { - "label": "FightStatistics" + "label": "Fight" }, "target": { - "label": "Fighter" + "label": "FightStatistics" }, - "attributes": [] + "attributes": [ + { + "name": "fighter_name", + "type": "string", + "unique": false, + "required": true + } + ] }, { - "label": "HAS_STATS", + "label": "HAS_ROUND_STATISTICS", "source": { "label": "Fight" }, "target": { + "label": "RoundStatistics" + }, + "attributes": [ + { + "name": "fighter_name", + "type": "string", + "unique": false, + "required": true + } + ] + }, + { + "label": "HAS_BREAKDOWN", + "source": { "label": "FightStatistics" }, - "attributes": [] + "target": { + "label": "SignificantStrikeBreakdown" + }, + "attributes": [ + { + "name": "round_number", + "type": "number", + "unique": false, + "required": false + } + ] + }, + { + "label": "HAS_ROUND_BREAKDOWN", + "source": { + "label": "RoundStatistics" + }, + "target": { + "label": "SignificantStrikeBreakdown" + }, + "attributes": [ + { + "name": "breakdown_id", + "type": "string", + "unique": false, + "required": true + } + ] + }, + { + "label": "PARTICIPATED_AS_REFEREE", + "source": { + "label": "Referee" + }, + "target": { + "label": "Event" + }, + "attributes": [ + { + "name": "role", + "type": "string", + "unique": false, + "required": false + } + ] } ] } \ No newline at end of file diff --git a/graphrag_sdk/agents/agent.py b/graphrag_sdk/agents/agent.py index 54c95fa0..d896c08c 100644 --- a/graphrag_sdk/agents/agent.py +++ b/graphrag_sdk/agents/agent.py @@ -1,6 +1,4 @@ from abc import ABC, abstractmethod -from graphrag_sdk.models.model import GenerativeModelChatSession - class AgentResponseCode: """ diff --git a/graphrag_sdk/agents/kg_agent.py b/graphrag_sdk/agents/kg_agent.py index 908303ac..ee5d8bc4 100644 --- a/graphrag_sdk/agents/kg_agent.py +++ b/graphrag_sdk/agents/kg_agent.py @@ -1,6 +1,5 @@ -from graphrag_sdk.kg import KnowledgeGraph from .agent import Agent -from graphrag_sdk.models import GenerativeModelChatSession +from graphrag_sdk.kg import KnowledgeGraph class KGAgent(Agent): @@ -56,15 +55,12 @@ def agent_id(self) -> str: return self._agent_id @agent_id.setter - def agent_id(self, value): + def agent_id(self, value) -> None: """ Sets the agent ID. - Parameters: - value (str): The ID of the agent. - - Returns: - None + Args: + value (str): The ID of the agent. """ self._agent_id = value @@ -73,21 +69,18 @@ def introduction(self) -> str: """ Returns the introduction of the agent. - :return: The introduction of the agent. - :rtype: str + Returns: + str: The introduction of the agent. """ return self._introduction @introduction.setter - def introduction(self, value): + def introduction(self, value) -> None: """ Sets the introduction of the agent. - Parameters: - value (str): The introduction of the agent. - - Returns: - None + Args: + value (str): The introduction of the agent. """ self._introduction = value @@ -112,15 +105,12 @@ def kg(self) -> KnowledgeGraph: return self._kg @kg.setter - def kg(self, value: KnowledgeGraph): + def kg(self, value: KnowledgeGraph) -> None: """ Sets the knowledge graph for the agent. - Parameters: + Args: value (KnowledgeGraph): The knowledge graph to be set. - - Returns: - None """ self._kg = value @@ -138,7 +128,7 @@ def run(self, params: dict) -> str: output = self.chat_session.send_message(params["prompt"]) return output['response'] - def __repr__(self): + def __repr__(self) -> str: """ Returns a string representation of the KGAgent object. diff --git a/graphrag_sdk/attribute.py b/graphrag_sdk/attribute.py index 402a10f5..614e3214 100644 --- a/graphrag_sdk/attribute.py +++ b/graphrag_sdk/attribute.py @@ -1,13 +1,18 @@ import re import json import logging +<<<<<<< HEAD from typing import Union from graphrag_sdk.fixtures.regex import * +======= +from enum import Enum +from typing import Union, Optional -logger = logging.getLogger(__name__) +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b +logger = logging.getLogger(__name__) -class AttributeType: +class AttributeType(Enum): """ Represents the types of attributes in the system. """ @@ -20,6 +25,7 @@ class AttributeType: MAP = "map" VECTOR = "vectorf32" +<<<<<<< HEAD # Synonyms for attribute types _SYNONYMS = { @@ -34,8 +40,10 @@ class AttributeType: "vectorf32": VECTOR, } +======= +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b @staticmethod - def from_string(txt: str): + def from_string(txt: str) -> "AttributeType": """ Converts a string representation of an attribute type to its corresponding AttributeType value. @@ -49,13 +57,29 @@ def from_string(txt: str): ValueError: If the provided attribute type is invalid. """ # Graph representation of the attribute type - normalized_txt = txt.lower() + normalized_txt = txt.lstrip('AttributeType.').lower() # Find the matching attribute type - if normalized_txt in AttributeType._SYNONYMS: - return AttributeType._SYNONYMS[normalized_txt] + if normalized_txt in _SYNONYMS: + return _SYNONYMS[normalized_txt] raise ValueError(f"Invalid attribute type: {txt}") +<<<<<<< HEAD +======= + +# Mapping of string representations to AttributeType enum members. +_SYNONYMS = { + "string": AttributeType.STRING, + "integer": AttributeType.NUMBER, + "float": AttributeType.NUMBER, + "number": AttributeType.NUMBER, + "boolean": AttributeType.BOOLEAN, + "list": AttributeType.LIST, + "point": AttributeType.POINT, + "map": AttributeType.MAP, + "vectorf32": AttributeType.VECTOR, +} +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b class Attribute: """ Represents an attribute of an entity or relation in the ontology. @@ -73,7 +97,11 @@ class Attribute: """ def __init__( +<<<<<<< HEAD self, name: str, attr_type: AttributeType, unique: bool = False, required: bool = False +======= + self, name: str, attr_type: AttributeType, unique: Optional[bool] = False, required: Optional[bool] = False +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ): """ Initialize a new Attribute object. @@ -81,8 +109,13 @@ def __init__( Args: name (str): The name of the attribute. attr_type (AttributeType): The type of the attribute. +<<<<<<< HEAD unique (bool, optional): Indicates whether the attribute should be unique. Defaults to False. required (bool, optional): Indicates whether the attribute is required. Defaults to False. +======= + unique (Optional[bool]): Indicates whether the attribute should be unique. Defaults to False. + required (Optional[bool]): Indicates whether the attribute is required. Defaults to False. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ self.name = re.sub(r"([^a-zA-Z0-9_])", "_", name) self.type = attr_type @@ -90,7 +123,11 @@ def __init__( self.required = required @staticmethod +<<<<<<< HEAD def from_json(txt: Union[str, dict]): +======= + def from_json(txt: Union[str, dict]) -> "Attribute": +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ Creates an Attribute object from a JSON string or dictionary. @@ -111,7 +148,7 @@ def from_json(txt: Union[str, dict]): ) @staticmethod - def from_string(txt: str): + def from_string(txt: str) -> "Attribute": """ Parses an attribute from a string. The "!" symbol indicates that the attribute is unique. @@ -138,7 +175,7 @@ def from_string(txt: str): return Attribute(name, AttributeType.from_string(attr_type), unique, required) - def to_json(self): + def to_json(self) -> dict: """ Converts the attribute object to a JSON representation. @@ -152,7 +189,7 @@ def to_json(self): """ json_data = { "name": self.name, - "type": self.type, + "type": self.type.value, "unique": self.unique, "required": self.required, } diff --git a/graphrag_sdk/chat_session.py b/graphrag_sdk/chat_session.py index a4cce9e4..2db0a324 100644 --- a/graphrag_sdk/chat_session.py +++ b/graphrag_sdk/chat_session.py @@ -1,10 +1,13 @@ import json from falkordb import Graph +from typing import Iterator from graphrag_sdk.ontology import Ontology from graphrag_sdk.steps.qa_step import QAStep +from graphrag_sdk.steps.stream_qa_step import StreamingQAStep from graphrag_sdk.model_config import KnowledgeGraphModelConfig from graphrag_sdk.steps.graph_query_step import GraphQueryGenerationStep +CYPHER_ERROR_RES = "Sorry, I could not find the answer to your question" class ChatSession: """ @@ -56,17 +59,47 @@ def __init__(self, model_config: KnowledgeGraphModelConfig, ontology: Ontology, self.qa_prompt = qa_prompt self.cypher_prompt_with_history = cypher_gen_prompt_history - self.cypher_chat_session = ( - model_config.cypher_generation.with_system_instruction( + self.cypher_chat_session = model_config.cypher_generation.start_chat( cypher_system_instruction - ).start_chat() - ) - self.qa_chat_session = model_config.qa.with_system_instruction( + ) + self.qa_chat_session = model_config.qa.start_chat( qa_system_instruction - ).start_chat() - self.last_answer = None + ) + self.last_complete_response = { + "question": None, + "response": None, + "context": None, + "cypher": None + } + + # Metadata to store additional information about the chat session (currently only last query execution time) + self.metadata = {"last_query_execution_time": None} + + def _generate_cypher_query(self, message: str) -> tuple: + """ + Generate a Cypher query for the given message. + + Args: + message (str): The message to generate a query for. + + Returns: + tuple: A tuple containing (context, cypher) + """ + cypher_step = GraphQueryGenerationStep( + graph=self.graph, + chat_session=self.cypher_chat_session, + ontology=self.ontology, + last_answer=self.last_complete_response["response"], + cypher_prompt=self.cypher_prompt, + cypher_prompt_with_history=self.cypher_prompt_with_history + ) + + (context, cypher, query_execution_time) = cypher_step.run(message) + self.metadata["last_query_execution_time"] = query_execution_time + + return (context, cypher) - def send_message(self, message: str): + def send_message(self, message: str) -> dict: """ Sends a message to the chat session. @@ -80,39 +113,78 @@ def send_message(self, message: str): "context": context, "cypher": cypher} """ - cypher_step = GraphQueryGenerationStep( - graph=self.graph, - chat_session=self.cypher_chat_session, - ontology=self.ontology, - last_answer=self.last_answer, - cypher_prompt=self.cypher_prompt, - cypher_prompt_with_history=self.cypher_prompt_with_history - ) - - (context, cypher) = cypher_step.run(message) + (context, cypher) = self._generate_cypher_query(message) + # If the cypher is empty, return an error message if not cypher or len(cypher) == 0: - return { + self.last_complete_response = { "question": message, - "response": "I am sorry, I could not find the answer to your question", + "response": CYPHER_ERROR_RES, "context": None, "cypher": None - } - + } + return self.last_complete_response + qa_step = QAStep( chat_session=self.qa_chat_session, qa_prompt=self.qa_prompt, ) answer = qa_step.run(message, cypher, context) - self.last_answer = answer - - return { + + self.last_complete_response = { "question": message, "response": answer, "context": context, "cypher": cypher +<<<<<<< HEAD +======= + } + + return self.last_complete_response + + def send_message_stream(self, message: str) -> Iterator[str]: + + """ + Sends a message to the chat session and streams the response. + + Args: + message (str): The message to send. + + Yields: + str: Chunks of the response as they're generated. + """ + (context, cypher) = self._generate_cypher_query(message) + + if not cypher or len(cypher) == 0: + # Stream the error message for consistency with successful responses + yield CYPHER_ERROR_RES + + self.last_complete_response = { + "question": message, + "response": CYPHER_ERROR_RES, + "context": None, + "cypher": None +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b } + return + + qa_step = StreamingQAStep( + chat_session=self.qa_chat_session, + qa_prompt=self.qa_prompt, + ) + + # Yield chunks of the response as they're generated + for chunk in qa_step.run(message, cypher, context): + yield chunk + + # Set the last answer using chat history to ensure we have the complete response + self.last_complete_response = { + "question": message, + "response": qa_step.chat_session.get_chat_history()[-1]['content'], + "context": context, + "cypher": cypher + } def clean_ontology_for_prompt(self, ontology: dict) -> str: """ diff --git a/graphrag_sdk/document_loaders/csv.py b/graphrag_sdk/document_loaders/csv.py index d8e37057..96eb7b5d 100644 --- a/graphrag_sdk/document_loaders/csv.py +++ b/graphrag_sdk/document_loaders/csv.py @@ -1,4 +1,5 @@ import csv +from math import ceil from typing import Iterator from graphrag_sdk.document import Document @@ -13,11 +14,11 @@ def __init__(self, path: str, rows_per_document: int = 500): self.rows_per_document = rows_per_document def load(self) -> Iterator[Document]: - with open(self.path, "r") as f: + with open(self.path, "r", encoding='utf-8') as f: reader = csv.reader(f) rows = [row for row in reader] num_rows = len(rows) - num_documents = num_rows // self.rows_per_document + num_documents = ceil(num_rows / self.rows_per_document) for i in range(num_documents): content = "\n".join( [ diff --git a/graphrag_sdk/document_loaders/html.py b/graphrag_sdk/document_loaders/html.py index 04ea4dc3..f7899eaa 100644 --- a/graphrag_sdk/document_loaders/html.py +++ b/graphrag_sdk/document_loaders/html.py @@ -14,7 +14,7 @@ def __init__(self, path: str) -> None: """ Initialize loader - Parameters: + Args: path (str): path to HTML. """ diff --git a/graphrag_sdk/document_loaders/jsonl.py b/graphrag_sdk/document_loaders/jsonl.py index 1742b7c0..9ba615f8 100644 --- a/graphrag_sdk/document_loaders/jsonl.py +++ b/graphrag_sdk/document_loaders/jsonl.py @@ -1,3 +1,4 @@ +from math import ceil from typing import Iterator from graphrag_sdk.document import Document @@ -15,7 +16,7 @@ def load(self) -> Iterator[Document]: with open(self.path, "r") as f: rows = f.readlines() num_rows = len(rows) - num_documents = num_rows // self.rows_per_document + num_documents = ceil(num_rows / self.rows_per_document) for i in range(num_documents): content = "\n".join( rows[ diff --git a/graphrag_sdk/document_loaders/pdf.py b/graphrag_sdk/document_loaders/pdf.py index 538b9eeb..c5000024 100644 --- a/graphrag_sdk/document_loaders/pdf.py +++ b/graphrag_sdk/document_loaders/pdf.py @@ -10,7 +10,7 @@ def __init__(self, path: str) -> None: """ Initialize loader - Parameters: + Args: path (str): path to PDF. """ diff --git a/graphrag_sdk/document_loaders/string.py b/graphrag_sdk/document_loaders/string.py index 166fb86a..e65753c9 100644 --- a/graphrag_sdk/document_loaders/string.py +++ b/graphrag_sdk/document_loaders/string.py @@ -10,7 +10,7 @@ def __init__(self, string: str) -> None: """ Initialize loader - Parameters: + Args: string (str): string from memory. """ self.string = string diff --git a/graphrag_sdk/document_loaders/text.py b/graphrag_sdk/document_loaders/text.py index 930a2202..79c3b26b 100644 --- a/graphrag_sdk/document_loaders/text.py +++ b/graphrag_sdk/document_loaders/text.py @@ -10,7 +10,7 @@ def __init__(self, path: str) -> None: """ Initialize loader - Parameters: + Args: path (str): path to Text. """ diff --git a/graphrag_sdk/document_loaders/url.py b/graphrag_sdk/document_loaders/url.py index 496ea5a5..5a39e107 100644 --- a/graphrag_sdk/document_loaders/url.py +++ b/graphrag_sdk/document_loaders/url.py @@ -13,7 +13,7 @@ def __init__(self, url: str) -> None: """ Initialize loader - Parameters: + Args: url (str): url. """ diff --git a/graphrag_sdk/entity.py b/graphrag_sdk/entity.py index 34cc533f..f80d6617 100644 --- a/graphrag_sdk/entity.py +++ b/graphrag_sdk/entity.py @@ -1,15 +1,14 @@ +import re import json import logging from typing import Union from .attribute import Attribute from falkordb import Node as GraphNode -import re -logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) descriptionKey = "__description__" - class Entity: """ Represents an entity in the knowledge graph. @@ -42,7 +41,7 @@ def __init__(self, label: str, attributes: list[Attribute], description: str = " self.description = description @staticmethod - def from_graph(entity: GraphNode): + def from_graph(entity: GraphNode) -> "Entity": """ Converts a GraphNode object to an Entity object. @@ -65,12 +64,20 @@ def from_graph(entity: GraphNode): ) @staticmethod +<<<<<<< HEAD def from_json(txt: Union[dict, str]): +======= + def from_json(txt: Union[dict, str]) -> "Entity": +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ Create an Entity object from a JSON representation. Args: +<<<<<<< HEAD txt Union[dict, str]: The JSON representation of the Entity. It can be either a dictionary or a string. +======= + txt (Union[dict, str]): The JSON representation of the Entity. It can be either a dictionary or a string. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b Returns: Entity: The Entity object created from the JSON representation. @@ -100,7 +107,7 @@ def to_json(self) -> dict: "description": self.description, } - def merge(self, entity2: "Entity"): + def merge(self, entity2: "Entity") -> "Entity": """Overwrite attributes of self with attributes of entity2. Args: @@ -122,7 +129,7 @@ def merge(self, entity2: "Entity"): return self - def get_unique_attributes(self): + def get_unique_attributes(self) -> list[Attribute]: """ Returns a list of unique attributes for the entity. @@ -131,7 +138,7 @@ def get_unique_attributes(self): """ return [attr for attr in self.attributes if attr.unique] - def to_graph_query(self): + def to_graph_query(self) -> str: """ Generates a Cypher query string for creating or updating a node in a graph database. @@ -145,7 +152,9 @@ def to_graph_query(self): [str(attr) for attr in self.attributes if not attr.unique] ) if self.description: - non_unique_attributes += f"{', ' if len(non_unique_attributes) > 0 else ''} {descriptionKey}: '{self.description}'" + # Escape special characters to prevent Cypher syntax errors + escaped_description = self.description.replace("\\", "\\\\").replace("'", "\\'").replace('"', '\\"') + non_unique_attributes += f"{', ' if len(non_unique_attributes) > 0 else ''} {descriptionKey}: '{escaped_description}'" return f"MERGE (n:{self.label} {{{unique_attributes}}}) SET n += {{{non_unique_attributes}}} RETURN n" def __str__(self) -> str: diff --git a/graphrag_sdk/fixtures/prompts.py b/graphrag_sdk/fixtures/prompts.py index 929af0af..78c0444c 100644 --- a/graphrag_sdk/fixtures/prompts.py +++ b/graphrag_sdk/fixtures/prompts.py @@ -171,7 +171,7 @@ CREATE_ONTOLOGY_PROMPT = """ Given the following text, create the ontology that represents the entities and relationships in the data. Extract as many entities and relations as possible to fully describe the data. -Extract as attributes as possible to fully describe the entities and relationships in the text. +Extract as many attributes as possible to fully describe the entities and relationships in the text. Attributes should be extracted as entities or relations whenever possible. For example, when describing a Movie entity, the "director" attribute can be extracted as a entity "Person" and connected to the "Movie" entity with an relation labeled "DIRECTED". For example, when describing a Movie entity, you can extract attributes like title, release year, genre, and more. Make sure to connect all related entities in the ontology. For example, if a Person PLAYED a Character in a Movie, make sure to connect the Character back to the Movie, otherwise we won't be able to say which Movie the Character is from. @@ -433,12 +433,9 @@ For example, given the question "Which managers own Neo4j stocks?", the OpenCypher statement should look like this: -``` MATCH (m:Manager)-[:OWNS]->(s:Stock) WHERE s.name CONTAINS 'Neo4j' -RETURN m, s -``` -""" +RETURN m, s""" CYPHER_GEN_PROMPT = """ Using the ontology provided, generate an OpenCypher statement to query the graph database returning all relevant entities, relationships, and attributes to answer the question below. @@ -447,7 +444,7 @@ Please think if your answer is a valid Cypher query, and correct it if it is not. Question: {question} -""" +Your generated Cypher: """ CYPHER_GEN_PROMPT_WITH_ERROR = """ @@ -472,9 +469,8 @@ Respect the order of the relationships; the arrows should always point from the "source" to the "target". Last Answer: {last_answer} - Question: {question} -""" +Your generated Cypher: """ GRAPH_QA_SYSTEM = """ You are an assistant that helps to form nice and human understandable answers. diff --git a/graphrag_sdk/graph_metrics.py b/graphrag_sdk/graph_metrics.py new file mode 100644 index 00000000..0d15e338 --- /dev/null +++ b/graphrag_sdk/graph_metrics.py @@ -0,0 +1,635 @@ +""" +This module is based on DeepEval framwork and provides metrics for evaluating Knowledge Graph performance. + +This module provides two metrics: +1. GraphContextualRelevancy - Measures how relevant the retrieved context is to the input query +2. GraphContextualRecall - Measures how well the expected output is supported by the retrieved context +""" + +from typing import Optional +from deepeval.metrics import BaseMetric +from typing import Optional, List, Union +from deepeval.utils import prettify_list +from deepeval.test_case import LLMTestCase +from deepeval.models import DeepEvalBaseLLM +from deepeval.metrics.indicator import metric_progress_indicator +from deepeval.metrics.contextual_relevancy.schema import ContextualRelevancyVerdicts +from deepeval.metrics.contextual_recall.schema import ( + Reason, + Verdicts, + ContextualRecallVerdict,) +from deepeval.test_case import ( + LLMTestCase, + LLMTestCaseParams, + ConversationalTestCase,) +from deepeval.metrics.utils import ( + construct_verbose_logs, + trimAndLoadJson, + check_llm_test_case_params, + initialize_model,) +REQUIRED_PARAMS: List[LLMTestCaseParams] = [ + LLMTestCaseParams.INPUT, + LLMTestCaseParams.ACTUAL_OUTPUT, + LLMTestCaseParams.RETRIEVAL_CONTEXT, + LLMTestCaseParams.EXPECTED_OUTPUT, +] + + + +class GraphContextualRecall(BaseMetric): + """ + Evaluates how well the expected output is supported by the retrieval context. + + A high score indicates that most sentences in the expected output + can be attributed to information in the context. + """ + + def __init__( + self, + threshold: float = 0.5, + model: Optional[Union[str, DeepEvalBaseLLM]] = None, + include_reason: bool = True, + strict_mode: bool = False, + ): + """ + Initialize the GraphContextualRecall metric. + + Args: + threshold: Minimum score to consider the test successful (0.0-1.0) + model: LLM to use for evaluation (string name or DeepEvalBaseLLM instance) + include_reason: Whether to generate a reason for the score + strict_mode: If True, requires perfect score to pass + verbose_mode: If True, includes detailed logs in the output + """ + self.threshold = 1 if strict_mode else threshold + self.model, self.using_native_model = initialize_model(model) + self.evaluation_model = self.model.get_model_name() + self.include_reason = include_reason + self.strict_mode = strict_mode + + def measure( + self, + test_case: LLMTestCase, + _show_indicator: bool = True, + ) -> float: + """ + Measure the contextual recall score. + + Args: + test_case: The test case to evaluate + _show_indicator: Whether to show a progress indicator + + Returns: + The recall score (0.0-1.0) + """ + # Validate test case has required parameters + check_llm_test_case_params(test_case, REQUIRED_PARAMS, self) + + self.evaluation_cost = 0 if self.using_native_model else None + with metric_progress_indicator(self, _show_indicator=_show_indicator): + # Generate verdicts + self.verdicts: List[ContextualRecallVerdict] = ( + self._generate_verdicts( + test_case.expected_output, test_case.retrieval_context, test_case.additional_metadata + ) + ) + + # Calculate the score and generate the reason + self.score = self._calculate_score() + self.reason = self._generate_reason(test_case.expected_output) + self.success = self.score >= self.threshold + # Generate verbose logs + self.verbose_logs = construct_verbose_logs( + self, + steps=[ + f"Verdicts:\n{prettify_list(self.verdicts)}", + f"Score: {self.score}\nReason: {self.reason}", + ], + ) + + return self.score + + def _generate_reason(self, expected_output: str): + """ + Generate a human-readable reason for the metric score. + + Args: + expected_output: The expected output to evaluate + + Returns: + A string explaining the score + """ + if self.include_reason is False: + return None + + # Collect supportive and unsupportive reasons + supportive_reasons = [] + unsupportive_reasons = [] + for verdict in self.verdicts: + if verdict.verdict.lower() == "yes": + supportive_reasons.append(verdict.reason) + else: + unsupportive_reasons.append(verdict.reason) + + # Generate the prompt for the reason + prompt = GraphContextualRecallTemplate.generate_reason( + expected_output=expected_output, + supportive_reasons=supportive_reasons, + unsupportive_reasons=unsupportive_reasons, + score=format(self.score, ".2f"), + ) + + # Generate the reason using the appropriate model + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + return data["reason"] + else: + try: + res: Reason = self.model.generate(prompt, schema=Reason) + return res.reason + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + return data["reason"] + + def _calculate_score(self): + """ + Calculate the recall score based on the verdicts. + + Returns: + A score between 0.0 and 1.0 + """ + number_of_verdicts = len(self.verdicts) + if number_of_verdicts == 0: + return 0 + + justified_sentences = 0 + for verdict in self.verdicts: + if verdict.verdict.lower() == "yes": + justified_sentences += 1 + + score = justified_sentences / number_of_verdicts + + # In strict mode, return 0 if below threshold + return 0 if self.strict_mode and score < self.threshold else score + + def _generate_verdicts( + self, expected_output: str, retrieval_context: List[str], cypher_query: Optional[str] = None + ) -> List[ContextualRecallVerdict]: + """ + Generate verdicts for each sentence in the expected output. + + Args: + expected_output: The expected output to evaluate + retrieval_context: List of retrieval contexts to check against + cypher_query: Optional Cypher query used to generate the context + + Returns: + List of ContextualRecallVerdict for each sentence + """ + prompt = GraphContextualRecallTemplate.generate_verdicts( + expected_output=expected_output, retrieval_context=retrieval_context, cypher_query=cypher_query + ) + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + verdicts = [ + ContextualRecallVerdict(**item) for item in data["verdicts"] + ] + return verdicts + else: + try: + res: Verdicts = self.model.generate(prompt, schema=Verdicts) + verdicts: Verdicts = [item for item in res.verdicts] + return verdicts + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + verdicts = [ + ContextualRecallVerdict(**item) for item in data["verdicts"] + ] + return verdicts + + def is_successful(self) -> bool: + """ + Check if the metric meets the success threshold. + + Returns: + True if the score meets or exceeds the threshold, False otherwise + """ + if self.error is not None: + self.success = False + else: + try: + self.success = self.score >= self.threshold + except: + self.success = False + return self.success + + @property + def __name__(self): + return "Graph Contextual Recall" + +class GraphContextualRecallTemplate: + """ + Template for generating prompts for the GraphContextualRecall metric. + """ + + @staticmethod + def generate_reason( + expected_output, supportive_reasons, unsupportive_reasons, score + ): + """ + Generate a prompt for generating a reason for the recall score. + + Args: + expected_output: The original expected output + supportive_reasons: List of supportive reasons for the score + unsupportive_reasons: List of unsupportive reasons for the score + score: The calculated recall score + + Returns: + A formatted prompt for generating the reason + """ + return f""" +Given the original expected output, a list of supportive reasons, and a list of unsupportive reasons (which is deduced directly from the 'expected output'), and a contextual recall score (closer to 1 the better), summarize a CONCISE reason for the score. +A supportive reason is the reason why a certain sentence in the original expected output can be attributed to the node in the retrieval context. +An unsupportive reason is the reason why a certain sentence in the original expected output cannot be attributed to anything in the retrieval context. +In your reason, you should related supportive/unsupportive reasons to the sentence number in expected output, and info regarding the node number in retrieval context to support your final reason. The first mention of "node(s)" should specify "node(s) in retrieval context)". + +** +IMPORTANT: Please make sure to only return in JSON format, with the 'reason' key providing the reason. +Example JSON: +{{ + "reason": "The score is because ." +}} + +DO NOT mention 'supportive reasons' and 'unsupportive reasons' in your reason, these terms are just here for you to understand the broader scope of things. +If the score is 1, keep it short and say something positive with an upbeat encouraging tone (but don't overdo it otherwise it gets annoying). +** + +Contextual Recall Score: +{score} + +Expected Output: +{expected_output} + +Supportive Reasons: +{supportive_reasons} + +Unsupportive Reasons: +{unsupportive_reasons} + +JSON: +""" + + @staticmethod + def generate_verdicts(expected_output, retrieval_context, cypher_query): + """ + Generate a prompt for generating verdicts for the expected output. + + Args: + expected_output: The original expected output + retrieval_context: List of retrieval contexts to check against + cypher_query: Optional Cypher query used to generate the context + + Returns: + A formatted prompt for generating the verdicts + """ + return f""" +For EACH sentence in the given expected output below, determine whether the sentence can be attributed to the nodes of retrieval contexts (cypher query and its output). Please generate a list of JSON with two keys: `verdict` and `reason`. +The `verdict` key should STRICTLY be either a 'yes' or 'no'. Answer 'yes' if the sentence can be attributed to any parts of the retrieval context (cypher query and its output), else answer 'no'. +The `reason` key should provide a reason why to the verdict. In the reason, you should aim to include the node(s) count in the retrieval context (eg., 1st node, and 2nd node in the retrieval context) that is attributed to said sentence. You should also aim to quote the specific part of the retrieval context to justify your verdict, but keep it extremely concise and cut short the quote with an ellipsis if possible. + + +** +IMPORTANT: Please make sure to only return in JSON format, with the 'verdicts' key as a list of JSON objects, each with two keys: `verdict` and `reason`. + +{{ + "verdicts": [ + {{ + "verdict": "yes", + "reason": "..." + }}, + ... + ] +}} + +Since you are going to generate a verdict for each sentence, the number of 'verdicts' SHOULD BE STRICTLY EQUAL to the number of sentences in of `expected output`. +** + +Expected Output: +{expected_output} + +Cypher Query: +{cypher_query} + +Retrieval Context: +{retrieval_context} + +JSON: +""" + +class GraphContextualRelevancy(BaseMetric): + """ + Evaluates how relevant the retrieval context is to the input query. + + A high score indicates that most of the statements in the context + are relevant to answering the query. + """ + + def __init__( + self, + threshold: float = 0.5, + model: Optional[Union[str, DeepEvalBaseLLM]] = None, + include_reason: bool = True, + strict_mode: bool = False, + ): + """ + Initialize the GraphContextualRelevancy metric. + + Args: + threshold: Minimum score to consider the test successful (0.0-1.0) + model: LLM to use for evaluation (string name or DeepEvalBaseLLM instance) + include_reason: Whether to generate a reason for the score + strict_mode: If True, requires perfect score to pass + verbose_mode: If True, includes detailed logs in the output + """ + self.threshold = 1 if strict_mode else threshold + self.model, self.using_native_model = initialize_model(model) + self.evaluation_model = self.model.get_model_name() + self.include_reason = include_reason + self.strict_mode = strict_mode + + def measure( + self, + test_case: Union[LLMTestCase, ConversationalTestCase], + _show_indicator: bool = True, + ) -> float: + """ + Measure the contextual relevancy score. + + Args: + test_case: The test case to evaluate + _show_indicator: Whether to show a progress indicator + + Returns: + The relevancy score (0.0-1.0) + """ + # Convert conversational test case to standard test case if needed + if isinstance(test_case, ConversationalTestCase): + test_case = test_case.turns[0] + + # Validate test case has required parameters + check_llm_test_case_params(test_case, REQUIRED_PARAMS, self) + + # Initialize cost tracking for native models + self.evaluation_cost = 0 if self.using_native_model else None + with metric_progress_indicator(self, _show_indicator=_show_indicator): + # Generate verdicts for each context + self.verdicts_list: List[ContextualRelevancyVerdicts] = [ + (self._generate_verdicts(test_case.input, context, test_case.additional_metadata)) + for context in test_case.retrieval_context + ] + # Calculate the score and generate the reason + self.score = self._calculate_score() + self.reason = self._generate_reason(test_case.input) + self.success = self.score >= self.threshold + # Generate verbose logs + self.verbose_logs = construct_verbose_logs( + self, + steps=[ + f"Verdicts:\n{prettify_list(self.verdicts_list)}", + f"Score: {self.score}\nReason: {self.reason}", + ], + ) + + return self.score + + def _generate_reason(self, input: str): + """ + Generate a human-readable reason for the metric score. + + Args: + input_query: The original input query + + Returns: + A string explaining the score + """ + if self.include_reason is False: + return None + + # Collect irrelevant and relevant statements + irrelevancies = [] + relevant_statements = [] + for verdicts in self.verdicts_list: + for verdict in verdicts.verdicts: + if verdict.verdict.lower() == "no": + irrelevancies.append(verdict.reason) + else: + relevant_statements.append(verdict.statement) + + # Generate the prompt for the reason + prompt: dict = GraphContextualRelevancyTemplate.generate_reason( + input=input, + irrelevancies=irrelevancies, + relevant_statements=relevant_statements, + score=format(self.score, ".2f"), + ) + + # Generate the reason using the appropriate model + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + return data["reason"] + else: + try: + res: Reason = self.model.generate(prompt, schema=Reason) + return res.reason + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + return data["reason"] + + def _calculate_score(self): + """ + Calculate the relevancy score based on the verdicts. + + Returns: + A score between 0.0 and 1.0 + """ + total_verdicts = 0 + relevant_statements = 0 + for verdicts in self.verdicts_list: + for verdict in verdicts.verdicts: + total_verdicts += 1 + if verdict.verdict.lower() == "yes": + relevant_statements += 1 + + if total_verdicts == 0: + return 0 + + score = relevant_statements / total_verdicts + + # In strict mode, return 0 if below threshold + return 0 if self.strict_mode and score < self.threshold else score + + def _generate_verdicts( + self, input: str, context: str, cypher_query: Optional[str] = None + ) -> ContextualRelevancyVerdicts: + """ + Generate verdicts for a single context. + + Args: + input_query: The original input query + context: The retrieval context to evaluate + cypher_query: Optional Cypher query used to generate the context + + Returns: + ContextualRelevancyVerdicts containing verdicts for each statement + """ + prompt = GraphContextualRelevancyTemplate.generate_verdicts( + input=input, context=context, cypher_query=cypher_query + ) + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + return ContextualRelevancyVerdicts(**data) + else: + try: + res = self.model.generate( + prompt, schema=ContextualRelevancyVerdicts + ) + return res + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + return ContextualRelevancyVerdicts(**data) + + def is_successful(self) -> bool: + """ + Check if the metric meets the success threshold. + + Returns: + True if the score meets or exceeds the threshold, False otherwise + """ + if self.error is not None: + self.success = False + else: + try: + self.success = self.score >= self.threshold + except: + self.success = False + return self.success + + @property + def __name__(self): + return "Graph Contextual Relevancy" + +class GraphContextualRelevancyTemplate: + """ + Template for generating prompts for the GraphContextualRelevancy metric. + """ + @staticmethod + def generate_reason( + input: str, + irrelevancies: List[str], + relevant_statements: List[str], + score: float, + ): + """ + Generate a prompt for generating a reason for the relevancy score. + + Args: + input: The original input query + irrelevancies: List of irrelevant statements in the context + relevant_statements: List of relevant statements in the context + score: The calculated relevancy score + + Returns: + A formatted prompt for generating the reason + """ + return f"""Based on the given input, reasons for why the retrieval context is irrelevant to the input, the statements in the retrieval context that is actually relevant to the retrieval context, and the contextual relevancy score (the closer to 1 the better), please generate a CONCISE reason for the score. +In your reason, you should quote data provided in the reasons for irrelevancy and relevant statements to support your point. + +** +IMPORTANT: Please make sure to only return in JSON format, with the 'reason' key providing the reason. +Example JSON: +{{ + "reason": "The score is because ." +}} + +If the score is 1, keep it short and say something positive with an upbeat encouraging tone (but don't overdo it otherwise it gets annoying). +** + + +Contextual Relevancy Score: +{score} + +Input: +{input} + +Reasons for why the retrieval context is irrelevant to the input: +{irrelevancies} + +Statement in the retrieval context that is relevant to the input: +{relevant_statements} + +JSON: +""" + + @staticmethod + def generate_verdicts(input: str, context: str, cypher_query: Optional[str] = None): + """ + Generate a prompt for generating verdicts for the context. + + Args: + input: The original input query + context: The retrieval context to evaluate + cypher_query: Optional Cypher query used to generate the context + + Returns: + A formatted prompt for generating the verdicts + """ + return f"""Based on the input and context (cypher and query output), please generate a JSON object to indicate whether each statement found in the context is relevant to the provided input. The JSON will be a list of 'verdicts', with 2 mandatory fields: 'verdict' and 'statement', and 1 optional field: 'reason'. +You should first extract statements found in the context, which are high level information found in the context, before deciding on a verdict and optionally a reason for each statement. +The 'verdict' key should STRICTLY be either 'yes' or 'no', and states whether the statement is relevant to the input. +Provide a 'reason' ONLY IF verdict is no. You MUST quote the irrelevant parts of the statement to back up your reason. + +** +IMPORTANT: Please make sure to only return in JSON format. +Example Context: "Einstein won the Nobel Prize for his discovery of the photoelectric effect. He won the Nobel Prize in 1968. There was a cat." +Example Input: "What were some of Einstein's achievements?" + +Example: +{{ + "verdicts": [ + {{ + "verdict": "yes", + "statement": "Einstein won the Nobel Prize for his discovery of the photoelectric effect in 1968", + }}, + {{ + "verdict": "no", + "statement": "There was a cat.", + "reason": "The retrieval context contained the information 'There was a cat' when it has nothing to do with Einstein's achievements." + }} + ] +}} +** + +Input: +{input} + +Cypher Query: +{cypher_query} + +Context: +{context} + +JSON: +""" diff --git a/graphrag_sdk/helpers.py b/graphrag_sdk/helpers.py index 9794954f..f5698c0e 100644 --- a/graphrag_sdk/helpers.py +++ b/graphrag_sdk/helpers.py @@ -1,13 +1,35 @@ import re import logging +<<<<<<< HEAD import graphrag_sdk from typing import Union +======= +from typing import Union, Optional, TYPE_CHECKING +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b from fix_busted_json import repair_json +if TYPE_CHECKING: + from graphrag_sdk import Ontology + + logger = logging.getLogger(__name__) +<<<<<<< HEAD def extract_json(text: Union[str, dict], skip_repair=False) -> str: +======= +def extract_json(text: Union[str, dict], skip_repair: Optional[bool] = False) -> str: + """ + Extracts JSON from a string or dictionary, optionally skipping JSON repair. + + Args: + text (Union[str, dict]): The input text or dictionary. + skip_repair (Optional[bool]): Flag to skip JSON repair. Defaults to False. + + Returns: + str: The extracted JSON as a string. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b if not isinstance(text, str): text = str(text) regex = r"(?:```)?(?:json)?([^`]*)(?:\\n)?(?:```)?" @@ -19,8 +41,35 @@ def extract_json(text: Union[str, dict], skip_repair=False) -> str: logger.error(f"Failed to repair JSON: {e} - {text}") return "".join(matches) - -def map_dict_to_cypher_properties(d: dict): +def extract_name_from_uri(uri: str) -> str: + """ + Extract local name from URI, stripping namespace. + + Args: + uri (str): Full URI or literal value. + + Returns: + str: Local name without namespace prefix. + """ + # Handle common URI patterns + if "#" in uri: + return uri.split("#")[-1] + elif "/" in uri: + return uri.split("/")[-1] + else: + # Already a local name or literal + return uri + +def map_dict_to_cypher_properties(d: dict) -> str: + """ + Maps a dictionary to Cypher query properties. + + Args: + d (dict): The dictionary to map. + + Returns: + str: A Cypher-formatted string of properties. + """ cypher = "{" if isinstance(d, list): if len(d) == 0: @@ -50,7 +99,16 @@ def map_dict_to_cypher_properties(d: dict): return cypher -def stringify_falkordb_response(response): +def stringify_falkordb_response(response: Union[list, str]) -> str: + """ + Converts FalkorDB response to a string. + + Args: + response (Union[list, str]): The response to stringify. + + Returns: + str: The stringified response. + """ if not isinstance(response, list) or len(response) == 0: data = str(response).strip() elif not isinstance(response[0], list): @@ -67,7 +125,16 @@ def stringify_falkordb_response(response): return data -def extract_cypher(text: str): +def extract_cypher(text: str) -> str: + """ + Extracts Cypher query from a text block. + + Args: + text (str): The text containing a Cypher query. + + Returns: + str: The extracted Cypher query. + """ if not text.startswith("```"): return text @@ -79,8 +146,23 @@ def extract_cypher(text: str): def validate_cypher( +<<<<<<< HEAD cypher: str, ontology: graphrag_sdk.Ontology ) -> Union[list[str], None]: +======= + cypher: str, ontology: "Ontology" +) -> Optional[list[str]]: + """ + Validates a Cypher query against the ontology. + + Args: + cypher (str): The Cypher query. + ontology (Ontology): The ontology to validate against. + + Returns: + Optional[list[str]]: A list of validation errors, or None if valid. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b try: if not cypher or len(cypher) == 0: return ["Cypher statement is empty"] @@ -105,7 +187,17 @@ def validate_cypher( return None -def validate_cypher_entities_exist(cypher: str, ontology: graphrag_sdk.Ontology): +def validate_cypher_entities_exist(cypher: str, ontology: "Ontology") -> list[str]: + """ + Validates whether entities in the Cypher query exist in the ontology. + + Args: + cypher (str): The Cypher query. + ontology (Ontology): The ontology to validate against. + + Returns: + list[str]: A list of errors if entities are not found. + """ # Check if entities exist in ontology not_found_entity_labels = [] entity_labels = re.findall(r"\(:(.*?)\)", cypher) @@ -120,7 +212,17 @@ def validate_cypher_entities_exist(cypher: str, ontology: graphrag_sdk.Ontology) ] -def validate_cypher_relations_exist(cypher: str, ontology: graphrag_sdk.Ontology): +def validate_cypher_relations_exist(cypher: str, ontology: "Ontology") -> list[str]: + """ + Validates whether relations in the Cypher query exist in the ontology. + + Args: + cypher (str): The Cypher query. + ontology (Ontology): The ontology to validate against. + + Returns: + list[str]: A list of errors if relations are not found. + """ # Check if relations exist in ontology not_found_relation_labels = [] relation_labels = re.findall(r"\[:(.*?)\]", cypher) @@ -141,9 +243,18 @@ def validate_cypher_relations_exist(cypher: str, ontology: graphrag_sdk.Ontology def validate_cypher_relation_directions( - cypher: str, ontology: graphrag_sdk.Ontology -): - + cypher: str, ontology: "Ontology" +) -> list[str]: + """ + Validates relation directions in a Cypher query. + + Args: + cypher (str): The Cypher query. + ontology (Ontology): The ontology to validate against. + + Returns: + list[str]: A list of errors if relation directions are incorrect. + """ errors = [] relations = list(re.finditer(r"\[.*?\]", cypher)) i = 0 diff --git a/graphrag_sdk/kg.py b/graphrag_sdk/kg.py index 4f966265..bf7c9432 100644 --- a/graphrag_sdk/kg.py +++ b/graphrag_sdk/kg.py @@ -1,5 +1,9 @@ import logging import warnings +<<<<<<< HEAD +======= +import time +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b from falkordb import FalkorDB from typing import Optional, Union from graphrag_sdk.ontology import Ontology @@ -12,10 +16,10 @@ from graphrag_sdk.fixtures.prompts import (GRAPH_QA_SYSTEM, CYPHER_GEN_SYSTEM, CYPHER_GEN_PROMPT, GRAPH_QA_PROMPT, CYPHER_GEN_PROMPT_WITH_HISTORY) + logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) - class KnowledgeGraph: """Knowledge Graph model data as a network of entities and relations To create one it is best to provide a ontology which will define the graph's ontology @@ -40,11 +44,12 @@ def __init__( """ Initialize Knowledge Graph - Parameters: + Args: name (str): Knowledge graph name. model (GenerativeModel): The Google GenerativeModel to use. host (str): FalkorDB hostname. port (int): FalkorDB port number. +<<<<<<< HEAD username (Union[str, None]): FalkorDB username. password (Union[str, None]): FalkorDB password. ontology (Union[Ontology, None]): Ontology to use. @@ -53,6 +58,16 @@ def __init__( cypher_gen_prompt (Union[str, None]): Cypher generation prompt. Make sure you have {question} in the prompt. qa_prompt (Union[str, None]): QA prompt. Make sure you have {question}, {context} and {cypher} in the prompt. cypher_gen_prompt_history (Union[str, None]): Cypher generation prompt with history. Make sure you have {question} and {last_answer} in the prompt. +======= + username (Optional[str]): FalkorDB username. + password (Optional[str]): FalkorDB password. + ontology (Optional[str]): Ontology to use. + cypher_system_instruction (Optional[str]): Cypher system instruction. Make sure you have {ontology} in the instruction. + qa_system_instruction (Optional[str]): QA system instruction. + cypher_gen_prompt (Optional[str]): Cypher generation prompt. Make sure you have {question} in the prompt. + qa_prompt (Optional[str]): QA prompt. Make sure you have {question}, {context} and {cypher} in the prompt. + cypher_gen_prompt_history (Optional[str]): Cypher generation prompt with history. Make sure you have {question} and {last_answer} in the prompt. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ if not isinstance(name, str) or name == "": @@ -61,18 +76,22 @@ def __init__( # Connect to database self.db = FalkorDB(host=host, port=port, username=username, password=password) self.graph = self.db.select_graph(name) - ontology_graph = self.db.select_graph("{" + name + "}" + "_schema") + self.ontology_graph = self.db.select_graph("{" + name + "}" + "_schema") # Load / Save ontology to database if ontology is None: # Load ontology from DB - ontology = Ontology.from_schema_graph(ontology_graph) + ontology = Ontology.from_schema_graph(self.ontology_graph) if len(ontology.entities) == 0: raise Exception("The ontology is empty. Load a valid ontology or create one using the ontology module.") else: # Save ontology to DB +<<<<<<< HEAD ontology.save_to_graph(ontology_graph) +======= + ontology.save_to_graph(self.ontology_graph) +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b self._ontology = ontology self._name = name @@ -117,6 +136,62 @@ def __init__( self.qa_prompt = qa_prompt self.cypher_gen_prompt_history = cypher_gen_prompt_history + @staticmethod + def from_ttl( + path: str, + name: str, + model_config: KnowledgeGraphModelConfig, + host: Optional[str] = "127.0.0.1", + port: Optional[int] = 6379, + username: Optional[str] = None, + password: Optional[str] = None, + cypher_system_instruction: Optional[str] = None, + qa_system_instruction: Optional[str] = None, + cypher_gen_prompt: Optional[str] = None, + qa_prompt: Optional[str] = None, + cypher_gen_prompt_history: Optional[str] = None, + ) -> "KnowledgeGraph": + """ + Create a KnowledgeGraph from a TTL (Turtle) RDF schema file. + + Args: + path (str): Path to the TTL file. + name (str): Knowledge graph name. + model_config (KnowledgeGraphModelConfig): Model configuration. + host (Optional[str]): FalkorDB hostname. + port (Optional[int]): FalkorDB port number. + username (Optional[str]): FalkorDB username. + password (Optional[str]): FalkorDB password. + cypher_system_instruction (Optional[str]): Cypher system instruction. + qa_system_instruction (Optional[str]): QA system instruction. + cypher_gen_prompt (Optional[str]): Cypher generation prompt. + qa_prompt (Optional[str]): QA prompt. + cypher_gen_prompt_history (Optional[str]): Cypher generation prompt with history. + + Returns: + KnowledgeGraph: New instance with extracted ontology. + """ + logger.info(f"Creating KnowledgeGraph from TTL file: {path}") + + # Extract ontology from TTL file + ontology = Ontology.from_ttl(path) + + # Create and return the KnowledgeGraph instance + return KnowledgeGraph( + name=name, + model_config=model_config, + ontology=ontology, + host=host, + port=port, + username=username, + password=password, + cypher_system_instruction=cypher_system_instruction, + qa_system_instruction=qa_system_instruction, + cypher_gen_prompt=cypher_gen_prompt, + qa_prompt=qa_prompt, + cypher_gen_prompt_history=cypher_gen_prompt_history, + ) + # Attributes @property @@ -146,15 +221,16 @@ def list_sources(self) -> list[AbstractSource]: return [s.source for s in self.sources] def process_sources( - self, sources: list[AbstractSource], instructions: str = None, hide_progress: bool = False + self, sources: list[AbstractSource], instructions: Optional[str] = None, hide_progress: Optional[bool] = False, delay: Optional[float] = 0 ) -> None: """ Add entities and relations found in sources into the knowledge-graph - Parameters: - sources (list[AbstractSource]): list of sources to extract knowledge from - instructions (str): instructions to use for extraction - hide_progress (bool): hide progress bar + Args: + sources (list[AbstractSource]): list of sources to extract knowledge from + instructions (Optional[str]): Instructions for processing. + hide_progress (Optional[bool]): hide progress bar + delay (float): seconds to delay each iteration through sources to avoid rate limits """ if self.ontology is None: @@ -162,12 +238,32 @@ def process_sources( # Create graph with sources self._create_graph_with_sources(sources, instructions, hide_progress) +<<<<<<< HEAD def _create_graph_with_sources( self, sources: Union[list[AbstractSource], None] = None, instructions: str = None, hide_progress: bool = False ) -> None: +======= + + # Add processed sources + for src in sources: + self.sources.add(src) + time.sleep(delay) + + + def _create_graph_with_sources( + self, sources: Optional[list[AbstractSource]] = None, instructions: Optional[str] = None, hide_progress: Optional[bool] = False + ) -> None: + """ + Create a graph using the provided sources. + + Args: + sources (Optional[list[AbstractSource]]): List of sources. + instructions (Optional[str]): Instructions for the graph creation. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b step = ExtractDataStep( sources=list(sources), ontology=self.ontology, @@ -195,14 +291,36 @@ def delete(self) -> None: setattr(self, key, None) def chat_session(self) -> ChatSession: + """ + Create a new chat session. + + Returns: + ChatSession: A new chat session instance. + """ chat_session = ChatSession(self._model_config, self.ontology, self.graph, self.cypher_system_instruction, self.qa_system_instruction, self.cypher_gen_prompt, self.qa_prompt, self.cypher_gen_prompt_history) return chat_session - def add_node(self, entity: str, attributes: dict): + + def refresh_ontology(self) -> None: + """ + Refresh the ontology by reloading it from the database. + This is useful when the schema has been updated. + + Raises: + Exception: If the refreshed ontology is empty and no fallback is available. + """ + # Reload ontology from database + refreshed_ontology = Ontology.from_schema_graph(self.ontology_graph) + + # Always update the ontology, even if it's empty + # This allows users to intentionally clear the ontology if needed + self._ontology = refreshed_ontology + + def add_node(self, entity: str, attributes: dict) -> None: """ Add a node to the knowledge graph, checking if it matches the ontology - Parameters: + Args: label (str): label of the node attributes (dict): node attributes """ @@ -219,20 +337,20 @@ def add_edge( relation: str, source: str, target: str, - source_attr: dict = None, - target_attr: dict = None, - attributes: dict = None, - ): + source_attr: Optional[dict] = None, + target_attr: Optional[dict] = None, + attributes: Optional[dict] = None, + ) -> None: """ Add an edge to the knowledge graph, checking if it matches the ontology - Parameters: + Args: relation (str): relation label source (str): source entity label target (str): target entity label - source_attr (dict): source entity attributes - target_attr (dict): target entity attributes - attributes (dict): relation attributes + source_attr (Optional[dict]): Source entity attributes. + target_attr (Optional[dict]): Target entity attributes. + attributes (Optional[dict]): Relation attributes. """ source_attr = source_attr or {} @@ -248,7 +366,14 @@ def add_edge( f"MATCH (s:{source} {map_dict_to_cypher_properties(source_attr)}) MATCH (t:{target} {map_dict_to_cypher_properties(target_attr)}) MERGE (s)-[r:{relation} {map_dict_to_cypher_properties(attributes)}]->(t)" ) - def _validate_entity(self, entity: str, attributes: str): + def _validate_entity(self, entity: str, attributes: str) -> None: + """ + Validate if the entity exists in the ontology and check its attributes. + + Args: + entity (str): Entity label. + attributes (dict): Entity attributes. + """ ontology_entity = self.ontology.get_entity_with_label(entity) if ontology_entity is None: @@ -264,7 +389,18 @@ def _validate_relation( source_attr: dict, target_attr: dict, attributes: dict, - ): + ) -> None: + """ + Validate if the relation exists in the ontology and check its attributes. + + Args: + relation (str): Relation label. + source (str): Source entity label. + target (str): Target entity label. + source_attr (dict): Source entity attributes. + target_attr (dict): Target entity attributes. + attributes (dict): Relation attributes. + """ ontology_relations = self.ontology.get_relations_with_label(relation) found_relation = [ diff --git a/graphrag_sdk/model_config.py b/graphrag_sdk/model_config.py index e1f0722a..859bbd5e 100644 --- a/graphrag_sdk/model_config.py +++ b/graphrag_sdk/model_config.py @@ -1,3 +1,4 @@ +import copy from graphrag_sdk.models import GenerativeModel @@ -26,14 +27,19 @@ def __init__( cypher_generation (GenerativeModel): The generative model for Cypher query generation. qa (GenerativeModel): The generative model for question answering. """ + # Ensure the extract_data model is configured for structured data extraction + extract_data.generation_config.response_format = {"type": "json_object"} + self.extract_data = extract_data self.cypher_generation = cypher_generation self.qa = qa @staticmethod - def with_model(model: GenerativeModel): + def with_model(model: GenerativeModel) -> "KnowledgeGraphModelConfig": """ Creates a new KnowledgeGraphModelConfig instance with the given generative model. + + The extract_data model will be configured with JSON response format for structured data extraction. Args: model (GenerativeModel): The generative model to use. @@ -42,8 +48,22 @@ def with_model(model: GenerativeModel): KnowledgeGraphModelConfig: The new KnowledgeGraphModelConfig instance. """ + # Ensure the extract_data model is configured for structured data extraction + try: + extract_data_model = copy.deepcopy(model) + except TypeError as te: + # Handle models that cannot be deep-copied (e.g., Ollama models with threading locks) + # Check if this is an Ollama model by checking its type + from .models.ollama import OllamaGenerativeModel + if isinstance(model, OllamaGenerativeModel): + extract_data_model = OllamaGenerativeModel.from_json(model.to_json()) + else: + raise te + extract_data_model.generation_config.response_format = {"type": "json_object"} + + return KnowledgeGraphModelConfig( - extract_data=model, + extract_data=extract_data_model, cypher_generation=model, qa=model, ) diff --git a/graphrag_sdk/models/azure_openai.py b/graphrag_sdk/models/azure_openai.py index 222e657f..547e81bc 100644 --- a/graphrag_sdk/models/azure_openai.py +++ b/graphrag_sdk/models/azure_openai.py @@ -1,27 +1,28 @@ -import os +from typing import Any from typing import Optional -from openai import AzureOpenAI +from .litellm import LiteModel, LiteModelChatSession from .model import ( - OutputMethod, GenerativeModel, GenerativeModelConfig, - GenerationResponse, - FinishReason, GenerativeModelChatSession, ) -class AzureOpenAiGenerativeModel(GenerativeModel): + +class AzureOpenAiGenerativeModel(LiteModel): """ A generative model that interfaces with Azure's OpenAI API for chat completions. + + Inherits from LiteModel and automatically converts Azure OpenAI model names to + LiteLLM format internally (e.g., "gpt-4o" -> "azure/gpt-4o") while + exposing the original model name through the public API. """ - client: AzureOpenAI = None - def __init__( self, - model_name: str, + model_name: str = "gpt-4.1", generation_config: Optional[GenerativeModelConfig] = None, system_instruction: Optional[str] = None, + **kwargs: Any, ): """ Initialize the AzureOpenAiGenerativeModel with required parameters. @@ -30,114 +31,46 @@ def __init__( model_name (str): Name of the Azure OpenAI model. generation_config (Optional[GenerativeModelConfig]): Configuration settings for generation. system_instruction (Optional[str]): System-level instruction for the model. - """ - self.model_name = model_name - self.generation_config = generation_config or GenerativeModelConfig() - self.system_instruction = system_instruction - - # Credentials - self.api_key = os.getenv("AZURE_OPENAI_API_KEY") - self.azure_endpoint = os.getenv("AZURE_ENDPOINT") - self.api_version = os.getenv("AZURE_API_VERSION") - - if not self.api_key or not self.azure_endpoint or not self.api_version: - raise ValueError( - "Missing credentials in the environment: AZURE_OPENAI_API_KEY, AZURE_ENDPOINT, or AZURE_API_VERSION." - ) - - - def _connect_to_model(self) -> None: - """ - Establish a connection to the Azure OpenAI model by initializing the AzureOpenAI client. - """ - self.client = AzureOpenAI(azure_endpoint=self.azure_endpoint, - api_version=self.api_version, - api_key=self.api_key, - ) - - def with_system_instruction(self, system_instruction: str) -> "GenerativeModel": - """ - Set or update the system instruction and connect to the Azure OpenAI model. - - Args: - system_instruction (str): System instructions for the model. + kwargs (Any): Additional arguments required by Azure OpenAI API. + """ + # Convert to LiteLLM format and call parent constructor + lite_model_name = f"azure/{model_name}" + super().__init__( + model_name=lite_model_name, + generation_config=generation_config, + system_instruction=system_instruction, + additional_params=kwargs + ) - Returns: - GenerativeModel: The updated model instance. - """ - self.system_instruction = system_instruction - self._connect_to_model() + # Store original model name for compatibility (without the azure/ prefix) + self._original_model_name = model_name - return self + @property + def model_name(self) -> str: + """Get the original model name (without azure/ prefix).""" + return self._original_model_name - def start_chat(self, args: Optional[dict] = None) -> GenerativeModelChatSession: + def start_chat(self, system_instruction: Optional[str] = None) -> GenerativeModelChatSession: """ Start a new chat session. - + Args: - args (Optional[dict]): Additional arguments for the chat session. - + system_instruction (Optional[str]): Optional system instruction to guide the chat session. + Returns: GenerativeModelChatSession: A new instance of the chat session. """ - return AzureOpenAiChatSession(self, args) - - def ask(self, message: str) -> GenerationResponse: - """ - Send a message to the model and receive a response. - - Args: - message (str): The user's message input. - - Returns: - GenerationResponse: The model's generated response. - """ - response = self.client.chat.completions.create( - model=self.model_name, - messages=[ - {"role": "system", "content": self.system_instruction}, - {"role": "user", "content": message[:14385]}, - ], - max_tokens=self.generation_config.max_output_tokens, - temperature=self.generation_config.temperature, - top_p=self.generation_config.top_p, - top_k=self.generation_config.top_k, - stop=self.generation_config.stop_sequences, - ) - return self._parse_generate_content_response(response) - - def _parse_generate_content_response(self, response: any) -> GenerationResponse: - """ - Parse the model's response and extract content for the user. - - Args: - response (any): The raw response from the model. - - Returns: - GenerationResponse: Parsed response containing the generated text and finish reason. - """ - return GenerationResponse( - text=response.choices[0].message.content, - finish_reason=( - FinishReason.STOP - if response.choices[0].finish_reason == "stop" - else ( - FinishReason.MAX_TOKENS - if response.choices[0].finish_reason == "length" - else FinishReason.OTHER - ) - ), - ) + return AzureOpenAiChatSession(self, system_instruction) def to_json(self) -> dict: """ Serialize the model's configuration and state to JSON format. Returns: - dict: The serialized JSON data. + dict: The serialized JSON data with clean Azure OpenAI model names. """ return { - "model_name": self.model_name, + "model_name": self.model_name, # Return original model name without azure/ prefix "generation_config": self.generation_config.to_json(), "system_instruction": self.system_instruction, } @@ -151,7 +84,7 @@ def from_json(json: dict) -> "GenerativeModel": json (dict): The serialized JSON data. Returns: - GenerativeModel: A new instance of the model. + GenerativeModel: A new instance of AzureOpenAiGenerativeModel. """ return AzureOpenAiGenerativeModel( json["model_name"], @@ -162,100 +95,21 @@ def from_json(json: dict) -> "GenerativeModel": ) -class AzureOpenAiChatSession(GenerativeModelChatSession): +class AzureOpenAiChatSession(LiteModelChatSession): """ - A chat session for interacting with the Azure OpenAI model, maintaining conversation history. + A chat session for interacting with the Azure OpenAI model. + + This implementation inherits from LiteModelChatSession to leverage all chat functionality + without any code duplication. All methods (send_message, send_message_stream, + get_chat_history, delete_last_message) are inherited from the parent class. """ - - _history = [] - - def __init__(self, model: AzureOpenAiGenerativeModel, args: Optional[dict] = None): + def __init__(self, model: AzureOpenAiGenerativeModel, system_instruction: Optional[str] = None) -> None: """ Initialize the chat session and set up the conversation history. - + Args: model (AzureOpenAiGenerativeModel): The model instance for the session. - args (Optional[dict]): Additional arguments for customization. - """ - self._model = model - self._args = args - self._history = ( - [{"role": "system", "content": self._model.system_instruction}] - if self._model.system_instruction is not None - else [] - ) - - def send_message(self, message: str, output_method: OutputMethod = OutputMethod.DEFAULT) -> GenerationResponse: - """ - Send a message in the chat session and receive the model's response. - - Args: - message (str): The message to send. - output_method (OutputMethod): Format for the model's output. - - Returns: - GenerationResponse: The generated response. - """ - generation_config = self._get_generation_config(output_method) - prompt = [] - prompt.extend(self._history) - prompt.append({"role": "user", "content": message[:14385]}) - response = self._model.client.chat.completions.create( - model=self._model.model_name, - messages=prompt, - **generation_config - ) - content = self._model._parse_generate_content_response(response) - self._history.append({"role": "user", "content": message}) - self._history.append({"role": "assistant", "content": content.text}) - return content - - def _get_generation_config(self, output_method: OutputMethod): - """ - Adjust the generation configuration based on the output method. - - Args: - output_method (OutputMethod): The desired output method (e.g., default or JSON). - - Returns: - dict: The configuration settings for generation. - """ - config = self._model.generation_config.to_json() - if output_method == OutputMethod.JSON: - config['temperature'] = 0 - config['response_format'] = { "type": "json_object" } - - return config - - def delete_last_message(self): + system_instruction (Optional[str]): Optional system instruction. """ - Deletes the last message exchange (user message and assistant response) from the chat history. - Preserves the system message if present. - - Example: - Before: - [ - {"role": "system", "content": "System message"}, - {"role": "user", "content": "User message"}, - {"role": "assistant", "content": "Assistant response"}, - ] - After: - [ - {"role": "system", "content": "System message"}, - ] - - Note: Does nothing if the chat history is empty or contains only a system message. - """ - # Keep at least the system message if present - min_length = 1 if self._model.system_instruction else 0 - if len(self._history) - 2 >= min_length: - self._history.pop() - self._history.pop() - else: - # Reset to initial state with just system message if present - self._history = ( - [{"role": "system", "content": self._model.system_instruction}] - if self._model.system_instruction is not None - else [] - ) + super().__init__(model, system_instruction) diff --git a/graphrag_sdk/models/gemini.py b/graphrag_sdk/models/gemini.py index fddbe77f..e87110f2 100644 --- a/graphrag_sdk/models/gemini.py +++ b/graphrag_sdk/models/gemini.py @@ -1,30 +1,31 @@ +<<<<<<< HEAD import os from typing import Union +======= +from typing import Optional +from .litellm import LiteModel, LiteModelChatSession +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b from .model import ( - OutputMethod, GenerativeModel, GenerativeModelConfig, - GenerationResponse, - FinishReason, GenerativeModelChatSession, ) -from google.generativeai import ( - GenerativeModel as GoogleGenerativeModel, - GenerationConfig as GoogleGenerationConfig, - configure, - protos, - types,) - -class GeminiGenerativeModel(GenerativeModel): - - _model: GoogleGenerativeModel = None +class GeminiGenerativeModel(LiteModel): + """ + A generative model that interfaces with GoogleAI API for chat completions. + + Inherits from LiteModel and automatically converts Gemini model names to + LiteLLM format internally (e.g., "gemini-2.0-flash" -> "gemini/gemini-2.0-flash") while + exposing the original model name through the public API. + """ def __init__( self, model_name: str, +<<<<<<< HEAD generation_config: Union[GoogleGenerationConfig, None] = None, system_instruction: Union[str, None] = None, ): @@ -79,19 +80,73 @@ def parse_generate_content_response( else FinishReason.OTHER ) ), +======= + generation_config: Optional[GenerativeModelConfig] = None, + system_instruction: Optional[str] = None, + ): + """ + Initialize the GeminiGenerativeModel with required parameters. + + Args: + model_name (str): Name of the Gemini model. + generation_config (Optional[GenerativeModelConfig]): Configuration settings for generation. + system_instruction (Optional[str]): System-level instruction for the model. + """ + # Convert to LiteLLM format and call parent constructor + lite_model_name = f"gemini/{model_name}" + super().__init__( + model_name=lite_model_name, + generation_config=generation_config, + system_instruction=system_instruction +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ) + + # Store original model name for compatibility (without the gemini/ prefix) + self._original_model_name = model_name + + @property + def model_name(self) -> str: + """Get the original model name (without gemini/ prefix).""" + return self._original_model_name + + def start_chat(self, system_instruction: Optional[str] = None) -> GenerativeModelChatSession: + """ + Start a new chat session. + + Args: + system_instruction (Optional[str]): Optional system instruction to guide the chat session. + + Returns: + GenerativeModelChatSession: A new instance of the chat session. + """ + return GeminiChatSession(self, system_instruction) def to_json(self) -> dict: + """ + Serialize the model's configuration and state to JSON format. + + Returns: + dict: The serialized JSON data with clean Gemini model names. + """ return { - "model_name": self._model_name, - "generation_config": self._generation_config.to_json(), - "system_instruction": self._system_instruction, + "model_name": self.model_name, # Return original model name without gemini/ prefix + "generation_config": self.generation_config.to_json(), + "system_instruction": self.system_instruction, } @staticmethod def from_json(json: dict) -> "GenerativeModel": + """ + Deserialize a JSON object to create an instance of GeminiGenerativeModel. + + Args: + json (dict): The serialized JSON data. + + Returns: + GenerativeModel: A new instance of GeminiGenerativeModel. + """ return GeminiGenerativeModel( - model_name=json["model_name"], + json["model_name"], generation_config=GenerativeModelConfig.from_json( json["generation_config"] ), @@ -99,6 +154,7 @@ def from_json(json: dict) -> "GenerativeModel": ) +<<<<<<< HEAD class GeminiChatSession(GenerativeModelChatSession): def __init__(self, model: GeminiGenerativeModel, args: Union[dict, None] = None): @@ -111,34 +167,23 @@ def send_message(self, message: str, output_method: OutputMethod = OutputMethod. generation_config = self._get_generation_config(output_method) response = self._chat_session.send_message(message, generation_config=generation_config) return self._model.parse_generate_content_response(response) +======= +class GeminiChatSession(LiteModelChatSession): + """ + A chat session for interacting with the Gemini model. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b - def _get_generation_config(self, output_method: OutputMethod): - if output_method == OutputMethod.JSON: - return { - "response_mime_type": "application/json", - "temperature": 0 - } - return self._model._generation_config + This implementation inherits from LiteModelChatSession to leverage all chat functionality + without any code duplication. All methods (send_message, send_message_stream, + get_chat_history, delete_last_message) are inherited from the parent class. + """ - def delete_last_message(self): + def __init__(self, model: GeminiGenerativeModel, system_instruction: Optional[str] = None) -> None: """ - Deletes the last message exchange (user message and assistant response) from the chat history. - Preserves the system message if present. + Initialize the chat session and set up the conversation history. - Example: - Before: - [ - {"role": "user", "content": "User message"}, - {"role": "assistant", "content": "Assistant response"}, - ] - After: - [] - - Note: Does nothing if the chat history is empty or contains only a system message. + Args: + model (GeminiGenerativeModel): The model instance for the session. + system_instruction (Optional[str]): Optional system instruction. """ - if len(self._chat_session.history) >= 2: - self._chat_session.history.pop() - self._chat_session.history.pop() - else: - self._chat_session.history = [] - + super().__init__(model, system_instruction) diff --git a/graphrag_sdk/models/litellm.py b/graphrag_sdk/models/litellm.py index 288e6a1d..78f0d0f6 100644 --- a/graphrag_sdk/models/litellm.py +++ b/graphrag_sdk/models/litellm.py @@ -1,9 +1,8 @@ import logging -from ollama import Client -from typing import Optional +from typing import Optional, Iterator from litellm import completion, validate_environment, utils as litellm_utils + from .model import ( - OutputMethod, GenerativeModel, GenerativeModelConfig, GenerationResponse, @@ -21,42 +20,51 @@ class LiteModel(GenerativeModel): def __init__( self, - model_name: str, + model_name: str = "openai/gpt-4.1", generation_config: Optional[GenerativeModelConfig] = None, system_instruction: Optional[str] = None, + additional_params: Optional[dict] = None, ): """ Initialize the LiteModel with the required parameters. LiteLLM model_name format: / Examples: - - openai/gpt-4o - - azure/gpt-4o - - gemini/gemini-1.5-pro + - openai/gpt-4.1 + - azure/gpt-4.1 + - gemini/gemini-2.0-flash - ollama/llama3:8b Args: - model_name (str): The name and the provider for the LiteLLM client. + model_name (str): The name and the provider for the LiteLLM client. Defaults to "openai/gpt-4.1". generation_config (Optional[GenerativeModelConfig]): Configuration settings for generation. system_instruction (Optional[str]): Instruction to guide the model. + additional_params (Optional[dict]): Additional provider-specific parameters. """ - + api_base = additional_params.get('api_base') if additional_params else None + env_val = validate_environment(model_name, api_base=api_base) - env_val = validate_environment(model_name) if not env_val['keys_in_environment']: raise ValueError(f"Missing {env_val['missing_keys']} in the environment.") - self.model_name, provider, _, _ = litellm_utils.get_llm_provider(model_name) + self._internal_model_name, provider, _, _ = litellm_utils.get_llm_provider(model_name) self.model = model_name - if provider == "ollama": - self.ollama_client = Client() - self.check_and_pull_model() if not self.check_valid_key(model_name): raise ValueError(f"Invalid keys for model {model_name}.") - + if self._internal_model_name == "gpt-4.1": + # Set default temperature to 0 for gpt-4.1 + if generation_config is None: + generation_config = GenerativeModelConfig(temperature=0) self.generation_config = generation_config or GenerativeModelConfig() + self.system_instruction = system_instruction + self.additional_params = additional_params or {} + + @property + def model_name(self) -> str: + """Get the model name.""" + return self._internal_model_name def check_valid_key(self, model: str): """ @@ -71,63 +79,23 @@ def check_valid_key(self, model: str): messages = [{"role": "user", "content": "Hey, how's it going?"}] try: completion( - model=model, messages=messages, max_tokens=10 + model=model, messages=messages, max_completion_tokens=10 ) return True - except: + except Exception as e: return False - def check_and_pull_model(self) -> None: - """ - Checks if the specified model is available locally, and pulls it if not. - - Logs: - - Info: If the model is already available or after successfully pulling the model. - - Error: If there is a failure in pulling the model. - - Raises: - Exception: If there is an error during the model pull process. - """ - # Get the list of available models - response = self.ollama_client.list() # This returns a dictionary - available_models = [model['name'] for model in response['models']] # Extract model names - - # Check if the model is already pulled - if self.model_name in available_models: - logger.info(f"The model '{self.model_name}' is already available.") - else: - logger.info(f"Pulling the model '{self.model_name}'...") - try: - self.ollama_client.pull(self.model_name) # Pull the model - logger.info(f"Model '{self.model_name}' pulled successfully.") - except Exception as e: - logger.error(f"Failed to pull the model '{self.model_name}': {e}") - raise ValueError(f"Failed to pull the model '{self.model_name}': {e}") - - def with_system_instruction(self, system_instruction: str) -> "GenerativeModel": - """ - Set or update the system instruction for new model instance. - - Args: - system_instruction (str): Instruction for guiding the model's behavior. - - Returns: - GenerativeModel: The updated model instance. - """ - self.system_instruction = system_instruction - return self - - def start_chat(self, args: Optional[dict] = None) -> GenerativeModelChatSession: + def start_chat(self, system_instruction: Optional[str] = None) -> GenerativeModelChatSession: """ Start a new chat session. - + Args: - args (Optional[dict]): Additional arguments for the chat session. - + system_instruction (Optional[str]): Optional system instruction to guide the chat session. + Returns: GenerativeModelChatSession: A new instance of the chat session. """ - return LiteModelChatSession(self, args) + return LiteModelChatSession(self, system_instruction) def parse_generate_content_response(self, response: any) -> GenerationResponse: """ @@ -190,49 +158,38 @@ class LiteModelChatSession(GenerativeModelChatSession): A chat session for interacting with the LiteLLM model, maintaining conversation history. """ - def __init__(self, model: LiteModel, args: Optional[dict] = None): + def __init__(self, model: LiteModel, system_instruction: Optional[str] = None): """ Initialize the chat session and set up the conversation history. Args: - model (LiteLLMGenerativeModel): The model instance for the session. - args (Optional[dict]): Additional arguments for customization. + model (OllamaGenerativeModel): The model instance for the session. + system_instruction (Optional[str]): Optional system instruction """ self._model = model - self._args = args self._chat_history = ( - [{"role": "system", "content": self._model.system_instruction}] - if self._model.system_instruction is not None + [{"role": "system", "content": system_instruction}] + if system_instruction is not None else [] ) - - def get_chat_history(self) -> list[dict]: - """ - Retrieve the conversation history for the current chat session. - - Returns: - list[dict]: The chat session's conversation history. - """ - return self._chat_history.copy() - def send_message(self, message: str, output_method: OutputMethod = OutputMethod.DEFAULT) -> GenerationResponse: + def send_message(self, message: str) -> GenerationResponse: """ Send a message in the chat session and receive the model's response. Args: message (str): The message to send. - output_method (OutputMethod): Format for the model's output. Returns: GenerationResponse: The generated response. """ - generation_config = self._adjust_generation_config(output_method) self._chat_history.append({"role": "user", "content": message}) try: response = completion( model=self._model.model, messages=self._chat_history, - **generation_config + **self._model.generation_config.to_json(), + **self._model.additional_params ) except Exception as e: raise ValueError(f"Error during completion request, please check the credentials - {e}") @@ -240,23 +197,54 @@ def send_message(self, message: str, output_method: OutputMethod = OutputMethod. self._chat_history.append({"role": "assistant", "content": content.text}) return content - def _adjust_generation_config(self, output_method: OutputMethod): + def send_message_stream(self, message: str) -> Iterator[str]: """ - Adjust the generation configuration based on the specified output method. + Send a message and receive the response in a streaming fashion. Args: - output_method (OutputMethod): The desired output method (e.g., default or JSON). + message (str): The message to send. - Returns: - dict: The adjusted configuration settings for generation. + Yields: + str: Streamed chunks of the model's response. """ - config = self._model.generation_config.to_json() - if output_method == OutputMethod.JSON: - config['temperature'] = 0 - config['response_format'] = { "type": "json_object" } + self._chat_history.append({"role": "user", "content": message}) + + try: + response_stream = completion( + model=self._model.model, + messages=self._chat_history, + stream=True, # Enable streaming mode + **self._model.generation_config.to_json(), + **self._model.additional_params + ) + + chunks = [] + for chunk in response_stream: + if not chunk or "choices" not in chunk or not chunk["choices"]: + continue # Skip empty or malformed chunks + + content = chunk["choices"][0].get("delta", {}).get("content", "") + if content: + chunks.append(content) + yield content # Yield streamed response chunks - return config + # Save the final response to chat history + full_response = "".join(chunks) # Collect full response + self._chat_history.append({"role": "assistant", "content": full_response}) + + except Exception as e: + raise ValueError(f"Error during streaming request, check credentials - {e}") from e + + def get_chat_history(self) -> list[dict]: + """ + Retrieve the conversation history for the current chat session. + + Returns: + list[dict]: The chat session's conversation history. + """ + return self._chat_history.copy() + def delete_last_message(self): """ Deletes the last message exchange (user message and assistant response) from the chat history. diff --git a/graphrag_sdk/models/model.py b/graphrag_sdk/models/model.py index a41d4189..adfd893e 100644 --- a/graphrag_sdk/models/model.py +++ b/graphrag_sdk/models/model.py @@ -1,6 +1,11 @@ +<<<<<<< HEAD from enum import Enum from typing import Union from abc import ABC, abstractmethod +======= +from abc import ABC, abstractmethod +from typing import Optional, Iterator +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b class FinishReason: @@ -8,65 +13,85 @@ class FinishReason: STOP = "STOP" OTHER = "OTHER" -class OutputMethod(Enum): - JSON = 'json' - DEFAULT = 'default' - class GenerativeModelConfig: """ - Configuration for a generative model - - Args: - temperature (float): The temperature to use for sampling. - top_p (float): The top-p value to use for sampling. - top_k (int): The top-k value to use for sampling. - max_output_tokens (int): The maximum number of tokens to generate. - stop_sequences (list[str]): The stop sequences to use for sampling. - response_format (dict): The format of the response. + Configuration for a generative model. - Examples: + This configuration follows OpenAI-style parameter naming but is designed to be + compatible with other generative models. Supports both predefined and arbitrary parameters. - >>> config = GenerativeModelConfig(temperature=0.5, top_p=0.9, top_k=50, max_output_tokens=100, stop_sequences=[".", "?", "!"]) + Args: + temperature (Optional[float]): Controls randomness of the output. + top_p (Optional[float]): Nucleus sampling parameter. + top_k (Optional[int]): Limits sampling to the top-k most probable tokens. + max_completion_tokens (Optional[int]): Maximum number of tokens to generate. + stop (Optional[list[str]]): Stop sequences. + response_format (Optional[dict]): Desired response format. + **kwargs: Any additional parameters not explicitly defined. + + Example: + >>> config = GenerativeModelConfig( + ... temperature=0.5, + ... max_completion_tokens=100, + ... stop=["\n", "END"] + ... ) + >>> config.to_json() + {'temperature': 0.5, 'max_completion_tokens': 100, 'stop': ['\n', 'END']} """ def __init__( self, +<<<<<<< HEAD temperature: Union[float, None] = None, top_p: Union[float, None] = None, top_k: Union[int, None] = None, max_output_tokens: Union[int, None] = None, stop_sequences: Union[list[str], None] = None, response_format: Union[dict, None] = None, +======= + temperature: Optional[float] = None, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + max_completion_tokens: Optional[int] = None, + stop: Optional[list] = None, + response_format: Optional[dict] = None, + **kwargs, +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ): self.temperature = temperature self.top_p = top_p self.top_k = top_k - self.max_output_tokens = max_output_tokens - self.stop_sequences = stop_sequences + self.max_completion_tokens = max_completion_tokens + self.stop = stop self.response_format = response_format - def __str__(self) -> str: - return f"GenerativeModelConfig(temperature={self.temperature}, top_p={self.top_p}, top_k={self.top_k}, max_output_tokens={self.max_output_tokens}, stop_sequences={self.stop_sequences})" + # Store extra parameters + for key, value in kwargs.items(): + setattr(self, key, value) + + def __str__(self) -> str: + return f"GenerativeModelConfig({', '.join(f'{k}={v}' for k, v in self.to_json().items())})" + def to_json(self) -> dict: - return { - "temperature": self.temperature, - "top_p": self.top_p, - "max_tokens": self.max_output_tokens, - "stop": self.stop_sequences, - "response_format": self.response_format, - } + """ + Serialize the configuration to a dictionary, excluding any fields with None values. + + Returns: + dict: A dictionary containing only the parameters that are explicitly set + (i.e., not None). + + Example: + >>> config = GenerativeModelConfig(temperature=0.7, max_completion_tokens=100) + >>> config.to_json() + {'temperature': 0.7, 'max_completion_tokens': 100} + """ + return {k: v for k, v in vars(self).items() if v is not None} @staticmethod def from_json(json: dict) -> "GenerativeModelConfig": - return GenerativeModelConfig( - temperature=json.get("temperature"), - top_p=json.get("top_p"), - top_k=json.get("top_k"), - max_output_tokens=json.get("max_tokens"), - stop_sequences=json.get("stop"), - ) - + # Simply pass all JSON data as kwargs - the constructor will handle it + return GenerativeModelConfig(**json) class GenerationResponse: @@ -90,9 +115,12 @@ def __init__(self, model: "GenerativeModel"): self.model = model @abstractmethod - def send_message(self, message: str, output_method: OutputMethod = OutputMethod.DEFAULT) -> GenerationResponse: + def send_message(self, message: str) -> GenerationResponse: pass + def send_message_stream(self, message: str) -> Iterator[str]: + raise NotImplementedError("Streaming not supported by this API implementation.") + class GenerativeModel(ABC): """ @@ -100,13 +128,17 @@ class GenerativeModel(ABC): """ @abstractmethod - def with_system_instruction(self, system_instruction: str) -> "GenerativeModel": + def start_chat(self, system_instruction: Optional[str] = None) -> GenerativeModelChatSession: pass +<<<<<<< HEAD @abstractmethod def start_chat(self, args: Union[dict, None]) -> GenerativeModelChatSession: pass +======= + +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b @staticmethod @abstractmethod def from_json(json: dict) -> "GenerativeModel": diff --git a/graphrag_sdk/models/ollama.py b/graphrag_sdk/models/ollama.py index b053067b..3b3e23e6 100644 --- a/graphrag_sdk/models/ollama.py +++ b/graphrag_sdk/models/ollama.py @@ -1,30 +1,31 @@ import logging +import os from typing import Optional -from ollama import Client, Options +from .litellm import LiteModel, LiteModelChatSession from .model import ( - OutputMethod, GenerativeModel, GenerativeModelConfig, - GenerationResponse, - FinishReason, GenerativeModelChatSession, ) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -class OllamaGenerativeModel(GenerativeModel): +class OllamaGenerativeModel(LiteModel): """ A generative model that interfaces with the Ollama Client for chat completions. + + Inherits from LiteModel and automatically converts Ollama model names to + LiteLLM format internally (e.g., "llama3:8b" -> "ollama/llama3:8b") while + exposing the original model name through the public API. """ - client: Client = None - def __init__( self, model_name: str, generation_config: Optional[GenerativeModelConfig] = None, system_instruction: Optional[str] = None, + api_base: Optional[str] = None, host: Optional[str] = None, ): """ @@ -33,100 +34,69 @@ def __init__( Args: model_name (str): The name of the Ollama model. generation_config (Optional[GenerativeModelConfig]): Configuration settings for generation. - system_instruction (Optional[str]): Instruction to guide the model. - host (Optional[str]): Host for connecting to the Ollama API. - """ - self.model_name = model_name - self.generation_config = generation_config or GenerativeModelConfig() - self.system_instruction = system_instruction - self._host = host - - def _connect_to_client(self) -> None: + system_instruction (Optional[str]): System-level instruction for the model. + api_base (Optional[str]): Base URL for the Ollama API (e.g., "http://localhost:11434"). + If not provided, will use OLLAMA_API_BASE environment variable. + host (Optional[str]): DEPRECATED: Use api_base instead. Base URL for the Ollama API. """ - Initializing the connection to the Ollama client. - """ - self.client = Client(host=self._host) - - def with_system_instruction(self, system_instruction: str) -> "GenerativeModel": - """ - Set or update the system instruction and connect to the Ollama model. - - Args: - system_instruction (str): Instruction for guiding the model's behavior. + # Convert to LiteLLM format + lite_model_name = f"ollama/{model_name}" - Returns: - GenerativeModel: The updated model instance. - """ - self.system_instruction = system_instruction - self._connect_to_client() - self.check_and_pull_model() - - return self - - def check_and_pull_model(self) -> None: - """ - Checks if the specified model is available locally, and pulls it if not. - - Logs: - - Info: If the model is already available or after successfully pulling the model. - - Error: If there is a failure in pulling the model. + # Handle backward compatibility for host parameter + if host is not None and api_base is not None: + logger.warning("Both 'host' and 'api_base' parameters provided. Using 'api_base' and ignoring 'host'.") + elif host is not None and api_base is None: + logger.warning("Parameter 'host' is deprecated. Please use 'api_base' instead.") + api_base = host + + # Use environment variable if no api_base is provided + if api_base is None: + api_base = os.getenv('OLLAMA_API_BASE') + + # Handle api_base parameter for Ollama + additional_params = {} + if api_base is not None: + additional_params['api_base'] = api_base + + # Call parent constructor + super().__init__( + model_name=lite_model_name, + generation_config=generation_config, + system_instruction=system_instruction, + additional_params=additional_params + ) + + # Store original model name for compatibility (without the ollama/ prefix) + self._original_model_name = model_name - Raises: - Exception: If there is an error during the model pull process. - """ - # Get the list of available models - response = self.client.list() # This returns a dictionary - available_models = [model['name'] for model in response['models']] # Extract model names + @property + def model_name(self) -> str: + """Get the original model name (without ollama/ prefix).""" + return self._original_model_name - # Check if the model is already pulled - if self.model_name in available_models: - logger.info(f"The model '{self.model_name}' is already available.") - else: - logger.info(f"Pulling the model '{self.model_name}'...") - try: - self.client.pull(self.model_name) # Pull the model - logger.info(f"Model '{self.model_name}' pulled successfully.") - except Exception as e: - logger.error(f"Failed to pull the model '{self.model_name}': {e}") - - def start_chat(self, args: Optional[dict] = None) -> GenerativeModelChatSession: + def start_chat(self, system_instruction: Optional[str] = None) -> GenerativeModelChatSession: """ Start a new chat session. - + Args: - args (Optional[dict]): Additional arguments for the chat session. - + system_instruction (Optional[str]): Optional system instruction to guide the chat session. + Returns: GenerativeModelChatSession: A new instance of the chat session. """ - return OllamaChatSession(self, args) - - def _parse_generate_content_response(self, response: any) -> GenerationResponse: - """ - Parse the model's response and extract content for the user. - - Args: - response (any): The raw response from the model. - - Returns: - GenerationResponse: Parsed response containing the generated text. - """ - return GenerationResponse( - text=response["message"]["content"], - finish_reason=FinishReason.STOP - ) + return OllamaChatSession(self, system_instruction) def to_json(self) -> dict: """ Serialize the model's configuration and state to JSON format. Returns: - dict: The serialized JSON data. + dict: The serialized JSON data with clean Ollama model names. """ return { - "model_name": self._model_name, - "generation_config": self._generation_config.to_json(), - "system_instruction": self._system_instruction, + "model_name": self.model_name, # Return original model name without ollama/ prefix + "generation_config": self.generation_config.to_json(), + "system_instruction": self.system_instruction, } @staticmethod @@ -138,113 +108,32 @@ def from_json(json: dict) -> "GenerativeModel": json (dict): The serialized JSON data. Returns: - GenerativeModel: A new instance of the model. + GenerativeModel: A new instance of OllamaGenerativeModel. """ return OllamaGenerativeModel( - model_name=json["model_name"], + json["model_name"], generation_config=GenerativeModelConfig.from_json( json["generation_config"] ), system_instruction=json["system_instruction"], ) -class OllamaChatSession(GenerativeModelChatSession): - """ - A chat session for interacting with the Ollama model, maintaining conversation history. + +class OllamaChatSession(LiteModelChatSession): """ + A chat session for interacting with the Ollama model. - def __init__(self, model: OllamaGenerativeModel, args: Optional[dict] = None): + This implementation inherits from LiteModelChatSession to leverage all chat functionality + without any code duplication. All methods (send_message, send_message_stream, + get_chat_history, delete_last_message) are inherited from the parent class. + """ + + def __init__(self, model: OllamaGenerativeModel, system_instruction: Optional[str] = None) -> None: """ Initialize the chat session and set up the conversation history. Args: model (OllamaGenerativeModel): The model instance for the session. - args (Optional[dict]): Additional arguments for customization. - """ - self._model = model - self._args = args - self._chat_history = ( - [{"role": "system", "content": self._model.system_instruction}] - if self._model.system_instruction is not None - else [] - ) - - def get_chat_history(self) -> list[dict]: - """ - Retrieve the conversation history for the current chat session. - - Returns: - list[dict]: The chat session's conversation history. - """ - return self._chat_history.copy() - - def send_message(self, message: str, output_method: OutputMethod = OutputMethod.DEFAULT) -> GenerationResponse: - """ - Send a message in the chat session and receive the model's response. - - Args: - message (str): The message to send. - output_method (OutputMethod): Format for the model's output. - - Returns: - GenerationResponse: The generated response. - """ - generation_config = self._adjust_generation_config(output_method) - self._chat_history.append({"role": "user", "content": message[:14385]}) - response = self._model.client.chat( - model=self._model.model_name, - messages=self._chat_history, - options=Options(**generation_config) - ) - content = self._model._parse_generate_content_response(response) - self._chat_history.append({"role": "assistant", "content": content.text}) - return content - - def _adjust_generation_config(self, output_method: OutputMethod) -> dict: - """ - Adjust the generation configuration based on the specified output method. - - Args: - output_method (OutputMethod): The desired output method (e.g., default or JSON). - - Returns: - dict: The adjusted configuration settings for generation. - """ - config = self._model.generation_config.to_json() - if output_method == OutputMethod.JSON: - config['temperature'] = 0 - config['format'] = 'json' - - return config - - def delete_last_message(self): - """ - Deletes the last message exchange (user message and assistant response) from the chat history. - Preserves the system message if present. - - Example: - Before: - [ - {"role": "system", "content": "System message"}, - {"role": "user", "content": "User message"}, - {"role": "assistant", "content": "Assistant response"}, - ] - After: - [ - {"role": "system", "content": "System message"}, - ] - - Note: Does nothing if the chat history is empty or contains only a system message. + system_instruction (Optional[str]): Optional system instruction. """ - # Keep at least the system message if present - min_length = 1 if self._model.system_instruction else 0 - if len(self._chat_history) - 2 >= min_length: - self._chat_history.pop() - self._chat_history.pop() - else: - # Reset to initial state with just system message if present - self._chat_history = ( - [{"role": "system", "content": self._model.system_instruction}] - if self._model.system_instruction is not None - else [] - ) \ No newline at end of file + super().__init__(model, system_instruction) diff --git a/graphrag_sdk/models/openai.py b/graphrag_sdk/models/openai.py index e630e3dc..11421a63 100644 --- a/graphrag_sdk/models/openai.py +++ b/graphrag_sdk/models/openai.py @@ -1,21 +1,29 @@ +from typing import Optional +from .litellm import LiteModel, LiteModelChatSession from .model import ( - OutputMethod, GenerativeModel, GenerativeModelConfig, - GenerationResponse, - FinishReason, GenerativeModelChatSession, ) +<<<<<<< HEAD from typing import Union from openai import OpenAI +======= +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b -class OpenAiGenerativeModel(GenerativeModel): - - client: OpenAI = None +class OpenAiGenerativeModel(LiteModel): + """ + A generative model that interfaces with OpenAI's API for chat completions. + + Inherits from LiteModel and automatically converts OpenAI model names to + LiteLLM format internally (e.g., "gpt-4o" -> "openai/gpt-4o") while + exposing the original model name through the public API. + """ def __init__( self, +<<<<<<< HEAD model_name: str, generation_config: Union[GenerativeModelConfig, None] = None, system_instruction: Union[str, None] = None, @@ -52,17 +60,71 @@ def parse_generate_content_response(self, response: any) -> GenerationResponse: else FinishReason.OTHER ) ), +======= + model_name: str = "gpt-4.1", + generation_config: Optional[GenerativeModelConfig] = None, + system_instruction: Optional[str] = None, + ): + """ + Initialize the OpenAiGenerativeModel with required parameters. + + Args: + model_name (str): Name of the OpenAI model. + generation_config (Optional[GenerativeModelConfig]): Configuration settings for generation. + system_instruction (Optional[str]): System-level instruction for the model. + """ + # Convert to LiteLLM format and call parent constructor + lite_model_name = f"openai/{model_name}" + super().__init__( + model_name=lite_model_name, + generation_config=generation_config, + system_instruction=system_instruction +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ) + + # Store original model name for compatibility (without the openai/ prefix) + self._original_model_name = model_name + + @property + def model_name(self) -> str: + """Get the original model name (without openai/ prefix).""" + return self._original_model_name + + def start_chat(self, system_instruction: Optional[str] = None) -> GenerativeModelChatSession: + """ + Start a new chat session. + + Args: + system_instruction (Optional[str]): Optional system instruction to guide the chat session. + Returns: + GenerativeModelChatSession: A new instance of the chat session. + """ + return OpenAiChatSession(self, system_instruction) def to_json(self) -> dict: + """ + Serialize the model's configuration and state to JSON format. + + Returns: + dict: The serialized JSON data with clean OpenAI model names. + """ return { - "model_name": self.model_name, + "model_name": self.model_name, # Return original model name without openai/ prefix "generation_config": self.generation_config.to_json(), "system_instruction": self.system_instruction, } @staticmethod def from_json(json: dict) -> "GenerativeModel": + """ + Deserialize a JSON object to create an instance of OpenAiGenerativeModel. + + Args: + json (dict): The serialized JSON data. + + Returns: + GenerativeModel: A new instance of OpenAiGenerativeModel. + """ return OpenAiGenerativeModel( json["model_name"], generation_config=GenerativeModelConfig.from_json( @@ -72,6 +134,7 @@ def from_json(json: dict) -> "GenerativeModel": ) +<<<<<<< HEAD class OpenAiChatSession(GenerativeModelChatSession): _history = [] @@ -99,43 +162,23 @@ def send_message(self, message: str, output_method: OutputMethod = OutputMethod. self._history.append({"role": "user", "content": message}) self._history.append({"role": "assistant", "content": content.text}) return content +======= +class OpenAiChatSession(LiteModelChatSession): + """ + A chat session for interacting with the OpenAI model. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b - def _get_generation_config(self, output_method: OutputMethod): - config = self._model.generation_config.to_json() - if output_method == OutputMethod.JSON: - config['temperature'] = 0 - config['response_format'] = { "type": "json_object" } - - return config + This implementation inherits from LiteModelChatSession to leverage all chat functionality + without any code duplication. All methods (send_message, send_message_stream, + get_chat_history, delete_last_message) are inherited from the parent class. + """ - def delete_last_message(self): + def __init__(self, model: OpenAiGenerativeModel, system_instruction: Optional[str] = None) -> None: """ - Deletes the last message exchange (user message and assistant response) from the chat history. - Preserves the system message if present. + Initialize the chat session and set up the conversation history. - Example: - Before: - [ - {"role": "system", "content": "System message"}, - {"role": "user", "content": "User message"}, - {"role": "assistant", "content": "Assistant response"}, - ] - After: - [ - {"role": "system", "content": "System message"}, - ] - - Note: Does nothing if the chat history is empty or contains only a system message. + Args: + model (OpenAiGenerativeModel): The model instance for the session. + system_instruction (Optional[str]): Optional system instruction. """ - # Keep at least the system message if present - min_length = 1 if self._model.system_instruction else 0 - if len(self._history) - 2 >= min_length: - self._history.pop() - self._history.pop() - else: - # Reset to initial state with just system message if present - self._history = ( - [{"role": "system", "content": self._model.system_instruction}] - if self._model.system_instruction is not None - else [] - ) + super().__init__(model, system_instruction) diff --git a/graphrag_sdk/ontology.py b/graphrag_sdk/ontology.py index f3b8fbbc..0c49a15f 100644 --- a/graphrag_sdk/ontology.py +++ b/graphrag_sdk/ontology.py @@ -1,10 +1,15 @@ import json import logging import graphrag_sdk +<<<<<<< HEAD from .entity import Entity from falkordb import Graph -from typing import Optional, Union +======= +from falkordb import Graph +from .entity import Entity from .relation import Relation +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b +from typing import Optional, Union from graphrag_sdk.source import AbstractSource from graphrag_sdk.models import GenerativeModel from .attribute import Attribute, AttributeType @@ -41,13 +46,18 @@ class Ontology(object): relations (list[Relation]): The list of relations in the ontology. """ - def __init__(self, entities: list[Entity] = None, relations: list[Relation] = None): + def __init__(self, entities: Optional[list[Entity]] = None, relations: Optional[list[Relation]] = None): """ Initialize the Ontology class. Args: +<<<<<<< HEAD entities (list[Entity], optional): List of Entity objects. Defaults to None. relations (list[Relation], optional): List of Relation objects. Defaults to None. +======= + entities (Optional[list[Entity]]): List of Entity objects. Defaults to None. + relations (Optional[list[Relation]]): List of Relation objects. Defaults to None. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ self.entities = entities or [] self.relations = relations or [] @@ -81,7 +91,11 @@ def from_sources( return step.run(boundaries=boundaries) @staticmethod +<<<<<<< HEAD def from_json(txt: Union[dict, str]): +======= + def from_json(txt: Union[dict, str]) -> "Ontology": +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ Creates an Ontology object from a JSON representation. @@ -101,7 +115,11 @@ def from_json(txt: Union[dict, str]): ) @staticmethod +<<<<<<< HEAD def from_schema_graph(graph: Graph): +======= + def from_schema_graph(graph: Graph) -> "Ontology": +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ Creates an Ontology object from a given schema graph. @@ -125,7 +143,7 @@ def from_schema_graph(graph: Graph): return ontology @staticmethod - def from_kg_graph(graph: Graph, sample_size: int = 100,): + def from_kg_graph(graph: Graph, sample_size: Optional[int] = 100) -> "Ontology": """ Constructs an Ontology object from a given Knowledge Graph. @@ -135,7 +153,7 @@ def from_kg_graph(graph: Graph, sample_size: int = 100,): Args: graph (Graph): The graph object representing the knowledge graph. - sample_size (int): The sample size for the attribute extraction. + sample_size (Optional[int]): The maximum number of attributes to sample for each entity and relationship. Defaults to 100. Returns: Ontology: The Ontology object constructed from the Knowledge Graph. @@ -173,7 +191,59 @@ def from_kg_graph(graph: Graph, sample_size: int = 100,): return ontology +<<<<<<< HEAD def add_entity(self, entity: Entity): +======= + @staticmethod + def from_ttl(path: str) -> "Ontology": + """ + Creates an Ontology object from a TTL/Turtle RDF schema file. + + This method parses an RDF schema file (RDFS or OWL) and extracts: + 1. Classes as entities with their datatype properties as attributes + 2. Object properties as relations between entities + 3. Labels (rdfs:label) and descriptions (rdfs:comment) + + Args: + path (str): Path to the TTL file containing the RDF schema + + Returns: + Ontology: The Ontology object extracted from the RDF schema + + Raises: + ValueError: If the file contains individual instances (only schema definitions allowed) + ImportError: If rdflib is not installed + Exception: If the TTL file cannot be parsed + + """ + try: + from rdflib import Graph as RDFGraph + from graphrag_sdk.rdf_extractor import RDFOntologyExtractor + except ImportError as e: + raise ImportError( + f"Required packages not found: {e}. " + "Please ensure rdflib is installed with `pip install rdflib`" + ) + + # Parse the RDF graph from TTL file + try: + graph = RDFGraph() + graph.parse(path, format="turtle") + except (FileNotFoundError, OSError, SyntaxError) as e: + raise ValueError( + f"Failed to parse TTL file: {e}. " + "Please ensure the file is valid TTL format." + ) + + # Extract ontology using RDFOntologyExtractor + extractor = RDFOntologyExtractor(graph) + ontology = extractor.extract() + + logger.info(f"Extracted ontology from TTL file: {len(ontology.entities)} entities, {len(ontology.relations)} relations") + return ontology + + def add_entity(self, entity: Entity) -> None: +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ Adds an entity to the ontology. @@ -182,7 +252,7 @@ def add_entity(self, entity: Entity): """ self.entities.append(entity) - def add_relation(self, relation: Relation): + def add_relation(self, relation: Relation) -> None: """ Adds a relation to the ontology. @@ -298,7 +368,7 @@ def discard_relations_without_entities(self): return self - def validate_entities(self): + def validate_entities(self) -> bool: """ Validates the entities in the ontology. @@ -324,7 +394,7 @@ def validate_entities(self): return False return True - def get_entity_with_label(self, label: str): + def get_entity_with_label(self, label: str) -> Optional[Entity]: """ Retrieves the entity with the specified label. @@ -336,7 +406,7 @@ def get_entity_with_label(self, label: str): """ return next((n for n in self.entities if n.label == label), None) - def get_relations_with_label(self, label: str): + def get_relations_with_label(self, label: str) -> list[Relation]: """ Returns a list of relations with the specified label. @@ -348,7 +418,7 @@ def get_relations_with_label(self, label: str): """ return [e for e in self.relations if e.label == label] - def has_entity_with_label(self, label: str): + def has_entity_with_label(self, label: str) -> bool: """ Checks if the ontology has an entity with the given label. @@ -360,7 +430,7 @@ def has_entity_with_label(self, label: str): """ return any(n.label == label for n in self.entities) - def has_relation_with_label(self, label: str): + def has_relation_with_label(self, label: str) -> bool: """ Checks if the ontology has a relation with the given label. @@ -386,7 +456,7 @@ def __str__(self) -> str: relations="\n- ".join([str(relation) for relation in self.relations]), ) - def save_to_graph(self, graph: Graph): + def save_to_graph(self, graph: Graph) -> None: """ Saves the entities and relations to the specified graph. diff --git a/graphrag_sdk/orchestrator/execution_plan.py b/graphrag_sdk/orchestrator/execution_plan.py index b624f079..5e771cf2 100644 --- a/graphrag_sdk/orchestrator/execution_plan.py +++ b/graphrag_sdk/orchestrator/execution_plan.py @@ -1,22 +1,51 @@ from json import loads from typing import Union +<<<<<<< HEAD from graphrag_sdk.orchestrator.step import PlanStep, StepBlockType +======= +from graphrag_sdk.orchestrator.step import PlanStep +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b class ExecutionPlan: - + """ + Represents an execution plan consisting of a sequence of steps. + """ + steps: list[PlanStep] = [] def __init__(self, steps: list[PlanStep]): + """ + Args: + steps (List[PlanStep]): The list of steps in the execution plan. + """ self.steps = steps @staticmethod def from_json(json: Union[str, dict]) -> "ExecutionPlan": +<<<<<<< HEAD +======= + """ + Create an ExecutionPlan instance from a JSON dictionary or string. + + Args: + json (Union[str, dict]): The input JSON string or dictionary. + + Returns: + ExecutionPlan: An instance of ExecutionPlan. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b if isinstance(json, str): json = loads(json) return ExecutionPlan([PlanStep.from_json(step) for step in json]) def to_json(self) -> dict: + """ + Convert the execution plan to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the execution plan. + """ return {"steps": [step.to_json() for step in self.steps]} def __str__(self) -> str: diff --git a/graphrag_sdk/orchestrator/orchestrator.py b/graphrag_sdk/orchestrator/orchestrator.py index 1db76cae..9f1d73bc 100644 --- a/graphrag_sdk/orchestrator/orchestrator.py +++ b/graphrag_sdk/orchestrator/orchestrator.py @@ -1,53 +1,100 @@ +import logging +from typing import Optional from graphrag_sdk.agents import Agent -from graphrag_sdk.models import GenerativeModel -from graphrag_sdk.models.model import OutputMethod -from .orchestrator_runner import OrchestratorRunner -from graphrag_sdk.fixtures.prompts import ( - ORCHESTRATOR_SYSTEM, - ORCHESTRATOR_EXECUTION_PLAN_PROMPT, -) from graphrag_sdk.helpers import extract_json +from graphrag_sdk.models import GenerativeModel, GenerativeModelChatSession +from .orchestrator_runner import OrchestratorRunner, OrchestratorResult from .execution_plan import ( ExecutionPlan, ) -import logging +from graphrag_sdk.fixtures.prompts import ( + ORCHESTRATOR_SYSTEM, + ORCHESTRATOR_EXECUTION_PLAN_PROMPT, +) -logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) class Orchestrator: + """ + The Orchestrator class is responsible for managing agents and generating an execution plan + based on the user's input question. It interacts with a generative model to produce the plan + and assigns agents to execute tasks. + """ - def __init__(self, model: GenerativeModel, backstory: str = ""): + def __init__(self, model: GenerativeModel, backstory: Optional[str] = ""): + """ + Initialize the Orchestrator with a generative model and an optional backstory. + + Args: + model (GenerativeModel): The model that powers the orchestration process. + backstory (Optional[str]): Optional backstory or context to be included in the orchestration system. + """ self._model = model self._backstory = backstory self._agents = [] self._chat = None - def _get_chat(self): + def _get_chat(self) -> GenerativeModelChatSession: + """ + Internal method to get or initialize a chat session with the model. + + Returns: + GenerativeModelChatSession: The chat session used for communication with the model. + """ if self._chat is None: - self._chat = self._model.with_system_instruction( + self._chat = self._model.start_chat( ORCHESTRATOR_SYSTEM.replace("#BACKSTORY", self._backstory).replace( "#AGENTS", - ",".join([str(agent) for agent in self._agents]), - ) - ).start_chat({"response_validation": False}) + ",".join([str(agent) for agent in self._agents])) + ) return self._chat def register_agent(self, agent: Agent): self._agents.append(agent) - def ask(self, question: str): + def ask(self, question: str) -> OrchestratorResult: + """ + Ask the orchestrator a question and run the corresponding execution plan. + + Args: + question (str): The user's question. + + Returns: + OrchestratorResult: The result of executing the plan. + """ return self.runner(question).run() def runner(self, question: str) -> OrchestratorRunner: + """ + Create an OrchestratorRunner to execute the plan based on the user's question. + + Args: + question (str): The user's input question. + + Returns: + OrchestratorRunner: A runner that will handle the execution of the plan. + """ plan = self._create_execution_plan(question) return OrchestratorRunner( self._get_chat(), self._agents, plan, user_question=question ) - def _create_execution_plan(self, question: str): + def _create_execution_plan(self, question: str) -> ExecutionPlan: + """ + Generate an execution plan based on the user's question by interacting with the model. + + Args: + question (str): The question or prompt for which the execution plan is generated. + + Returns: + ExecutionPlan: The generated execution plan. + + Raises: + Exception: If the plan generation fails. + """ try: response = self._get_chat().send_message( ORCHESTRATOR_EXECUTION_PLAN_PROMPT.replace("#QUESTION", question) diff --git a/graphrag_sdk/orchestrator/orchestrator_decision.py b/graphrag_sdk/orchestrator/orchestrator_decision.py index 65b6adb2..215751f8 100644 --- a/graphrag_sdk/orchestrator/orchestrator_decision.py +++ b/graphrag_sdk/orchestrator/orchestrator_decision.py @@ -1,15 +1,32 @@ from json import loads +<<<<<<< HEAD from typing import Union +======= +from typing import Union, Optional +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b from graphrag_sdk.orchestrator.step import PlanStep class OrchestratorDecisionCode: + CONTINUE = "continue" END = "end" UPDATE_STEP = "update_step" @staticmethod def from_str(code: str) -> str: + """ + Convert a string representation of a decision code to the corresponding constant. + + Args: + code (str): The string representation of a decision code. + + Returns: + str: The corresponding decision code. + + Raises: + ValueError: If the code is unknown. + """ if code == OrchestratorDecisionCode.CONTINUE: return OrchestratorDecisionCode.CONTINUE elif code == OrchestratorDecisionCode.END: @@ -21,13 +38,34 @@ def from_str(code: str) -> str: class OrchestratorDecision: + """ + Represents a decision made by the orchestrator. + """ + def __init__( +<<<<<<< HEAD self, code: OrchestratorDecisionCode, new_step: Union[PlanStep, None] = None +======= + self, code: OrchestratorDecisionCode, new_step: Optional[PlanStep] = None +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ): + """ + Initialize a new OrchestratorDecision object. + + Args: + code (OrchestratorDecisionCode): The decision code. + new_step (Optional[PlanStep]): The new step to execute, if applicable. + """ self.code = code self.new_step = new_step def to_json(self) -> dict: + """ + Convert the orchestrator decision to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the orchestrator decision. + """ return { "code": self.code, "new_step": self.new_step.to_json() if self.new_step else None, @@ -35,6 +73,18 @@ def to_json(self) -> dict: @staticmethod def from_json(json: Union[dict, str]) -> "OrchestratorDecision": +<<<<<<< HEAD +======= + """ + Create an OrchestratorDecision instance from a JSON dictionary or string. + + Args: + json (Union[dict, str]): The input dictionary or string containing decision data. + + Returns: + OrchestratorDecision: An instance of OrchestratorDecision. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b json = json if isinstance(json, dict) else loads(json) return OrchestratorDecision( OrchestratorDecisionCode.from_str(json["code"]), diff --git a/graphrag_sdk/orchestrator/orchestrator_runner.py b/graphrag_sdk/orchestrator/orchestrator_runner.py index 2b97b8c9..05cb8384 100644 --- a/graphrag_sdk/orchestrator/orchestrator_runner.py +++ b/graphrag_sdk/orchestrator/orchestrator_runner.py @@ -1,3 +1,4 @@ +<<<<<<< HEAD from typing import Union from graphrag_sdk.agents import Agent from graphrag_sdk.models import GenerativeModelChatSession @@ -6,17 +7,42 @@ from .orchestrator_decision import OrchestratorDecision, OrchestratorDecisionCode from graphrag_sdk.fixtures.prompts import ORCHESTRATOR_DECISION_PROMPT from graphrag_sdk.helpers import extract_json +======= +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b import logging +from typing import Optional +from graphrag_sdk.agents import Agent +from .execution_plan import ExecutionPlan +from graphrag_sdk.helpers import extract_json +from .step import StepResult, PlanStep, StepBlockType +from graphrag_sdk.models import GenerativeModelChatSession +from graphrag_sdk.fixtures.prompts import ORCHESTRATOR_DECISION_PROMPT +from .orchestrator_decision import OrchestratorDecision, OrchestratorDecisionCode -logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) class OrchestratorResult(StepResult): + """ + Represents the result of an orchestrator execution step. + """ def __init__(self, output: str): + """ + Initialize a new OrchestratorResult object. + + Args: + _output (str): The output generated by the orchestrator. + """ self._output = output def to_json(self) -> dict: + """ + Convert the orchestrator result to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the orchestrator result. + """ return { "output": self._output, } @@ -35,10 +61,20 @@ def __repr__(self) -> str: @property def output(self) -> str: + """ + Get the output of the orchestrator result. + + Returns: + str: The output generated by the orchestrator. + """ return self._output class OrchestratorRunner: + """ + Manages the execution of an orchestrator plan, coordinating the involved agents. + """ + _runner_log: list[tuple[PlanStep, StepResult]] = [] _agent_sessions: dict[str, GenerativeModelChatSession] = {} @@ -47,9 +83,19 @@ def __init__( chat: GenerativeModelChatSession, agents: list[Agent], plan: ExecutionPlan, - user_question: str = "", - config: dict = None, + user_question: Optional[str] = None, + config: Optional[dict] = None, ): + """ + Initialize a new OrchestratorRunner object. + + Args: + chat (GenerativeModelChatSession): The chat session used for communication with the model. + agents (list[Agent]): List of registered agents for executing tasks. + plan (ExecutionPlan): The execution plan that dictates the steps to be executed. + user_question (str): The user's original question. + config (dict): Configuration settings for the orchestrator. + """ self._chat = chat self._agents = agents self._plan = plan @@ -60,38 +106,111 @@ def __init__( @property def plan(self) -> ExecutionPlan: + """ + Get the execution plan. + + Returns: + ExecutionPlan: The current execution plan. + """ return self._plan @property def chat(self) -> GenerativeModelChatSession: + """ + Get the chat session. + + Returns: + GenerativeModelChatSession: The current chat session. + """ return self._chat @property def runner_log(self) -> list[tuple[PlanStep, StepResult]]: + """ + Get the log of executed steps and their results. + + Returns: + list[tuple[PlanStep, StepResult]]: The log of steps executed. + """ return self._runner_log @property def user_question(self) -> str: + """ + Get the user's original question. + + Returns: + str: The user's question. + """ return self._user_question def get_agent(self, agent_id: str) -> Agent: + """ + Retrieve an agent by its ID. + + Args: + agent_id (str): The ID of the agent to retrieve. + + Returns: + Agent: The agent corresponding to the provided ID. + """ return next(agent for agent in self._agents if agent.agent_id == agent_id) +<<<<<<< HEAD def get_session(self, session_id: str) -> Union[GenerativeModelChatSession, None]: +======= + def get_session(self, session_id: str) -> Optional[GenerativeModelChatSession]: + """ + Retrieve the chat session for a specific agent. + + Args: + session_id (str): The ID of the session to retrieve. + + Returns: + Optional[GenerativeModelChatSession]: The corresponding session or None if not found. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b return self._agent_sessions.get(session_id, None) def set_session(self, session_id: str, session: GenerativeModelChatSession): + """ + Set the chat session for a specific agent. + + Args: + session_id (str): The ID of the session to set. + session (GenerativeModelChatSession): The session to associate with the agent. + """ + self._agent_sessions[session_id] = session def get_user_input(self, question: str) -> str: + """ + Get user input based on a prompt. + + Args: + question (str): The prompt to display to the user. + + Returns: + str: The user's input. + """ + return input(question) def run(self) -> OrchestratorResult: + """ + Execute the orchestrator plan and return the result. + + Returns: + OrchestratorResult: The result of the orchestrator execution. + + Raises: + ValueError: If the execution plan is empty. + """ - first_step = self._plan.steps[0] if len(self._plan.steps) > 0 else None + if not self._plan.steps: + raise ValueError("Execution plan contains no steps to execute.") - if first_step is None: - return OrchestratorResult("No steps to run") + first_step = self._plan.steps[0] first_step_result = first_step.run(self) @@ -105,23 +224,39 @@ def run(self) -> OrchestratorResult: return loop_response def _run_loop(self, steps: list[PlanStep]) -> StepResult: + """ + Run a loop through the steps of the execution plan and handle decisions. + + Args: + steps (list[PlanStep]): The remaining steps to execute. + + Returns: + StepResult: The result of the execution. + """ decision = self._get_orchestrator_decision( steps[0] if len(steps) > 0 else None, ) if decision.code == OrchestratorDecisionCode.END: return self._handle_end_decision() - - if decision.code == OrchestratorDecisionCode.CONTINUE: + elif decision.code == OrchestratorDecisionCode.CONTINUE: return self._handle_continue_decision(steps) - - if decision.code == OrchestratorDecisionCode.UPDATE_STEP: + elif decision.code == OrchestratorDecisionCode.UPDATE_STEP: return self._handle_update_step_decision(decision.new_step) + else: + raise ValueError(f"Unhandled OrchestratorDecisionCode: {decision.code}") def _handle_end_decision(self) -> StepResult: + """ + Handle the decision to end the orchestration. + + Returns: + StepResult: The result of the orchestration upon ending. + """ last_step = self._runner_log[-1][0] if len(self._runner_log) > 0 else None if last_step is None: - return OrchestratorResult("No steps to run") + return OrchestratorResult("Execution plan contains no steps to execute.") + if last_step.block != StepBlockType.SUMMARY: return self._call_summary_step() last_result = self._runner_log[-1][1] if len(self._runner_log) > 0 else None @@ -133,6 +268,15 @@ def _handle_end_decision(self) -> StepResult: ) def _handle_continue_decision(self, steps: list[PlanStep]) -> StepResult: + """ + Handle the decision to continue with the next step in the execution. + + Args: + steps (list[PlanStep]): The remaining steps to execute. + + Returns: + StepResult: The result of the next step execution. + """ if len(steps) == 0: return self._handle_end_decision() @@ -142,11 +286,26 @@ def _handle_continue_decision(self, steps: list[PlanStep]) -> StepResult: return self._run_loop(steps[1:]) def _handle_update_step_decision(self, new_step: PlanStep) -> StepResult: + """ + Handle the decision to update the current step. + + Args: + new_step (PlanStep): The new step to execute. + + Returns: + StepResult: The result of the new step execution. + """ next_step_result = new_step.run(self) self._runner_log.append((new_step, next_step_result)) return self._run_loop([]) def _call_summary_step(self): + """ + Call the summary step at the end of the orchestration. + + Returns: + StepResult: The result of the summary step execution. + """ return self._run_loop( [ PlanStep.from_json( @@ -161,8 +320,21 @@ def _call_summary_step(self): def _get_orchestrator_decision( self, +<<<<<<< HEAD next_step: Union[PlanStep, None] = None, +======= + next_step: Optional[PlanStep] = None, +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ) -> OrchestratorDecision: + """ + Get the orchestrator's decision based on the current step and context. + + Args: + next_step (Optional[PlanStep]): The next step to evaluate for decision making. + + Returns: + OrchestratorDecision: The decision made by the orchestrator. + """ response = self.chat.send_message( ORCHESTRATOR_DECISION_PROMPT.replace( diff --git a/graphrag_sdk/orchestrator/step.py b/graphrag_sdk/orchestrator/step.py index 5396049a..0c9d1bbb 100644 --- a/graphrag_sdk/orchestrator/step.py +++ b/graphrag_sdk/orchestrator/step.py @@ -2,8 +2,15 @@ from typing import Union from .step_result import StepResult from abc import ABC, abstractmethod +<<<<<<< HEAD +======= + +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b class StepBlockType: + """ + Enum representing different types of step blocks in a plan. + """ PARALLEL = "parallel" AGENT = "agent" SUMMARY = "summary" @@ -11,6 +18,18 @@ class StepBlockType: @staticmethod def from_str(text: str) -> "StepBlockType": + """ + Convert a string to a corresponding StepBlockType enum value. + + Args: + text (str): The string representation of the step block type. + + Returns: + StepBlockType: The corresponding block type. + + Raises: + ValueError: If the input string does not match any known step block type. + """ if text == StepBlockType.PARALLEL: return StepBlockType.PARALLEL elif text == StepBlockType.AGENT: @@ -24,6 +43,10 @@ def from_str(text: str) -> "StepBlockType": class PlanStep(ABC): + """ + Abstract base class for a PlanStep, which defines a step in the execution plan. + Each subclass must implement the following properties and methods. + """ @property @abstractmethod @@ -42,6 +65,21 @@ def properties(self) -> any: @staticmethod def from_json(json: Union[dict, str]) -> "PlanStep": +<<<<<<< HEAD +======= + """ + Factory method to create a PlanStep instance from a JSON object or string. + + Args: + json (Union[dict, str]): The JSON representation of the step. + + Returns: + PlanStep: The corresponding step instance. + + Raises: + ValueError: If the block type is unknown or if step_type is None. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b json = json if isinstance(json, dict) else loads(json) from graphrag_sdk.orchestrator.steps import PLAN_STEP_TYPE_MAP block = StepBlockType.from_str(json["block"]) diff --git a/graphrag_sdk/orchestrator/steps/agent.py b/graphrag_sdk/orchestrator/steps/agent.py index 65172a05..b320fb46 100644 --- a/graphrag_sdk/orchestrator/steps/agent.py +++ b/graphrag_sdk/orchestrator/steps/agent.py @@ -1,19 +1,36 @@ +import logging +from typing import Optional from graphrag_sdk.agents.agent import AgentResponseCode from graphrag_sdk.orchestrator.step_result import StepResult from graphrag_sdk.orchestrator.step import PlanStep, StepBlockType from graphrag_sdk.orchestrator.orchestrator_runner import OrchestratorRunner -import logging -logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) class AgentStepResult(StepResult): - + """ + Represents the result of executing an agent step. + """ + def __init__(self, response_code: AgentResponseCode, payload: dict): + """ + Initializes a new AgentStepResult object. + + Args: + response_code (AgentResponseCode): The response code from the agent. + payload (dict): The payload containing the result data. + """ self.response_code = response_code self.payload = payload def to_json(self) -> dict: + """ + Convert the agent step result to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the agent step result. + """ return { "response_code": self.response_code, "payload": self.payload, @@ -21,6 +38,15 @@ def to_json(self) -> dict: @staticmethod def from_json(json: dict) -> "AgentStepResult": + """ + Create an AgentStepResult instance from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + AgentStepResult: An instance of AgentStepResult. + """ return AgentStepResult( AgentResponseCode.from_str(json["response_code"]), json["payload"], @@ -38,14 +64,34 @@ def output(self) -> str: class AgentProperties: - - def __init__(self, agent_id: str, session_id: str = None, payload: dict = None): + """ + Represents the properties for an agent step. + """ + + def __init__(self, agent_id: str, session_id: Optional[str] = None, payload: Optional[dict] = None): + """ + Represents the properties for an agent step. + + Args: + agent_id (str): The identifier for the agent. + session_id (Optional[str]): The session identifier. + payload (Optional[dict]): Additional payload data for the agent. + """ self.agent_id = agent_id self.session_id = session_id self.payload = payload @staticmethod def from_json(json: dict) -> "AgentProperties": + """ + Create AgentProperties from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + AgentProperties: An instance of AgentProperties. + """ return AgentProperties( json["agent_id"], json.get("session_id", None), @@ -53,6 +99,12 @@ def from_json(json: dict) -> "AgentProperties": ) def to_json(self) -> dict: + """ + Convert the agent properties to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the agent properties. + """ return { "agent_id": self.agent_id, "session_id": self.session_id, @@ -67,8 +119,18 @@ def __repr__(self) -> str: class AgentStep(PlanStep): + """ + Represents a step that interacts with an agent. + """ def __init__(self, id: str, properties: AgentProperties): + """ + Initializes a new AgentStep object. + + Args: + id (str): The identifier for the step. + properties (AgentProperties): The properties of the agent step. + """ self._id = id self._properties = properties @@ -85,6 +147,12 @@ def properties(self) -> AgentProperties: return self._properties def to_json(self) -> dict: + """ + Convert the agent step to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the agent step. + """ return { "id": self.id, "block": self.block, @@ -93,6 +161,15 @@ def to_json(self) -> dict: @staticmethod def from_json(json: dict) -> "AgentStep": + """ + Create an AgentStep from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + AgentStep: An instance of AgentStep. + """ return AgentStep(json["id"], AgentProperties.from_json(json["properties"])) def __str__(self) -> str: @@ -105,6 +182,15 @@ def run( self, runner: "OrchestratorRunner", ) -> AgentStepResult: + """ + Run the agent step, executing the agent with the provided properties. + + Args: + runner (OrchestratorRunner): The orchestrator runner instance. + + Returns: + AgentStepResult: The result of the agent step execution. + """ logger.info(f"Running agent {self.properties.agent_id}, step: {self.id}, payload: {self.properties.payload}") agent = runner.get_agent(self.properties.agent_id) diff --git a/graphrag_sdk/orchestrator/steps/parallel.py b/graphrag_sdk/orchestrator/steps/parallel.py index 90a33620..24e6cac6 100644 --- a/graphrag_sdk/orchestrator/steps/parallel.py +++ b/graphrag_sdk/orchestrator/steps/parallel.py @@ -1,20 +1,47 @@ +from typing import Optional import graphrag_sdk.orchestrator.step +from graphrag_sdk.orchestrator.step import PlanStep from concurrent.futures import ThreadPoolExecutor, wait from graphrag_sdk.orchestrator.step_result import StepResult from graphrag_sdk.orchestrator.orchestrator_runner import OrchestratorRunner class ParallelStepResult(StepResult): + """ + Represents the result of executing parallel steps. + """ + results: list[StepResult] def __init__(self, results: list[StepResult]): + """ + Initializes a new ParallelStepResult object. + + Args: + results (list[StepResult]): The results of the parallel steps. + """ self.results = results def to_json(self) -> dict: + """ + Convert the parallel step result to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the parallel step result. + """ return {"results": [result.to_json() for result in self.results]} @staticmethod def from_json(json: dict) -> "ParallelStepResult": + """ + Create a ParallelStepResult instance from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + ParallelStepResult: An instance of ParallelStepResult. + """ return ParallelStepResult( [ StepResult.from_json(result) @@ -34,13 +61,32 @@ def output(self) -> str: class ParallelProperties: + """ + Represents properties for parallel execution of steps. + """ + steps: list["PlanStep"] def __init__(self, steps: list["PlanStep"]): + """ + Initializes a new ParallelProperties object. + + Args: + steps (list[PlanStep]): The list of steps to execute in parallel. + """ self.steps = steps @staticmethod def from_json(json: dict) -> "ParallelProperties": + """ + Create ParallelProperties from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + ParallelProperties: An instance of ParallelProperties. + """ return ParallelProperties( [ graphrag_sdk.orchestrator.step.PlanStep.from_json(step) @@ -49,6 +95,12 @@ def from_json(json: dict) -> "ParallelProperties": ) def to_json(self) -> dict: + """ + Convert the parallel properties to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the parallel properties. + """ return {"steps": [step.to_json() for step in self.steps]} def __str__(self) -> str: @@ -59,8 +111,18 @@ def __repr__(self) -> str: class ParallelStep(graphrag_sdk.orchestrator.step.PlanStep): + """ + Represents a step that executes multiple sub-steps in parallel. + """ def __init__(self, id: str, properties: ParallelProperties): + """ + Initializes a new ParallelStep object. + + Args: + id (str): The identifier for the step. + properties (ParallelProperties): The properties of the parallel step. + """ self._id = id self._properties = properties @@ -77,6 +139,12 @@ def properties(self) -> ParallelProperties: return self._properties def to_json(self) -> dict: + """ + Convert the parallel step to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the parallel step. + """ return { "id": self.id, "block": self.block, @@ -85,6 +153,15 @@ def to_json(self) -> dict: @staticmethod def from_json(json: dict) -> "ParallelStep": + """ + Create a ParallelStep from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + ParallelStep: An instance of ParallelStep. + """ return ParallelStep( json["id"], ParallelProperties.from_json(json["properties"]) ) @@ -96,8 +173,18 @@ def __repr__(self) -> str: return str(self) def run( - self, runner: OrchestratorRunner, config: dict = None + self, runner: OrchestratorRunner, config: Optional[dict] = None ) -> ParallelStepResult: + """ + Run the parallel step, executing sub-steps concurrently. + + Args: + runner (OrchestratorRunner): The orchestrator runner instance. + config (Optional[dict]): Configuration options. Defaults to None. + + Returns: + ParallelStepResult: The result of the parallel step execution. + """ config = config or {"parallel_max_workers": 16} tasks = [] with ThreadPoolExecutor( diff --git a/graphrag_sdk/orchestrator/steps/summary.py b/graphrag_sdk/orchestrator/steps/summary.py index edeb5475..c72b1a8c 100644 --- a/graphrag_sdk/orchestrator/steps/summary.py +++ b/graphrag_sdk/orchestrator/steps/summary.py @@ -1,26 +1,49 @@ +import logging import graphrag_sdk.orchestrator.step from graphrag_sdk.orchestrator.step_result import StepResult -from concurrent.futures import ThreadPoolExecutor, wait from graphrag_sdk.orchestrator.orchestrator_runner import OrchestratorRunner from graphrag_sdk.fixtures.prompts import ORCHESTRATOR_SUMMARY_PROMPT -import logging - logger = logging.getLogger(__name__) class SummaryResult(StepResult): + """ + Represents the result of a summary step. + """ def __init__(self, output: str): + """ + Initializes a new SummaryResult object. + + Args: + output (str): The summary output. + """ self._output = output def to_json(self) -> dict: + """ + Convert the summary result to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the summary result. + """ return { "output": self._output, } @staticmethod def from_json(json: dict) -> "SummaryResult": + """ + Create a SummaryResult instance from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + SummaryResult: An instance of SummaryResult. + """ + return SummaryResult( json["output"], ) @@ -37,8 +60,18 @@ def output(self) -> str: class SummaryStep(graphrag_sdk.orchestrator.step.PlanStep): + """ + Represents a step that generates a summary. + """ def __init__(self, id: str, properties: any): + """ + Initializes a new SummaryStep object. + + Args: + id (str): The identifier for the step. + properties (any): The properties of the summary step. + """ self._id = id self._properties = {} @@ -55,6 +88,12 @@ def properties(self) -> any: return self._properties def to_json(self) -> dict: + """ + Convert the summary step to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the summary step. + """ return { "id": self.id, "block": self.block, @@ -63,6 +102,15 @@ def to_json(self) -> dict: @staticmethod def from_json(json: dict) -> "SummaryStep": + """ + Create a SummaryStep from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + SummaryStep: An instance of SummaryStep. + """ return SummaryStep(json["id"], {}) def __str__(self) -> str: @@ -75,6 +123,15 @@ def run( self, runner: OrchestratorRunner, ) -> SummaryResult: + """ + Run the summary step, generating a summary based on execution logs. + + Args: + runner (OrchestratorRunner): The orchestrator runner instance. + + Returns: + SummaryResult: The result of the summary step. + """ response = runner.chat.send_message( ORCHESTRATOR_SUMMARY_PROMPT.replace( "#USER_QUESTION", str(runner.user_question) diff --git a/graphrag_sdk/orchestrator/steps/user_input.py b/graphrag_sdk/orchestrator/steps/user_input.py index b3c5c7da..5106b454 100644 --- a/graphrag_sdk/orchestrator/steps/user_input.py +++ b/graphrag_sdk/orchestrator/steps/user_input.py @@ -1,23 +1,47 @@ +import logging from graphrag_sdk.orchestrator.step import PlanStep, StepBlockType from graphrag_sdk.orchestrator.step_result import StepResult from graphrag_sdk.orchestrator.orchestrator_runner import OrchestratorRunner -import logging -logger = logging.getLogger(__name__) +logger = logging.getLogger(__name__) class UserInputResult(StepResult): + """ + Represents the result of a user input step. + """ def __init__(self, output: str): + """ + Initializes a new UserInputResult object. + + Args: + output (str): The user's input. + """ self._output = output def to_json(self) -> dict: + """ + Convert the user input result to a JSON-serializable dictionary. + + Returns: + Dict: A dictionary representation of the user input result. + """ return { "output": self._output, } @staticmethod def from_json(json: dict) -> "UserInputResult": + """ + Create a UserInputResult instance from a JSON dictionary. + + Args: + json (Dict): The input JSON dictionary. + + Returns: + UserInputResult: An instance of UserInputResult. + """ return UserInputResult( json["output"], ) @@ -34,17 +58,41 @@ def output(self) -> str: class UserInputProperties: + """ + Represents the properties required for a user input step. + """ def __init__(self, question: str): + """ + Initialize a new UserInputProperties object. + + Args: + question (str): The question to prompt the user. + """ self.question = question @staticmethod def from_json(json: dict) -> "UserInputProperties": + """ + Create UserInputProperties from a JSON dictionary. + + Args: + json (Dict): The input JSON dictionary. + + Returns: + UserInputProperties: An instance of UserInputProperties. + """ return UserInputProperties( json["question"], ) def to_json(self) -> dict: + """ + Convert the user input properties to a JSON-serializable dictionary. + + Returns: + Dict: A dictionary representation of the user input properties. + """ return { "question": self.question, } @@ -57,8 +105,18 @@ def __repr__(self) -> str: class UserInputStep(PlanStep): + """ + Represents a step that requires user input. + """ def __init__(self, id: str, properties: UserInputProperties): + """ + Initializes a new UserInputStep object. + + Args: + id (str): The identifier for the step. + properties (UserInputProperties): The properties of the user input step. + """ self._id = id self._properties = properties @@ -75,6 +133,12 @@ def properties(self) -> UserInputProperties: return self._properties def to_json(self) -> dict: + """ + Convert the user input step to a JSON-serializable dictionary. + + Returns: + dict: A dictionary representation of the user input step. + """ return { "id": self.id, "block": self.block, @@ -83,6 +147,15 @@ def to_json(self) -> dict: @staticmethod def from_json(json: dict) -> "UserInputStep": + """ + Create a UserInputStep from a JSON dictionary. + + Args: + json (dict): The input JSON dictionary. + + Returns: + UserInputStep: An instance of UserInputStep. + """ return UserInputStep( json["id"], UserInputProperties.from_json(json["properties"]) ) @@ -97,5 +170,14 @@ def run( self, runner: OrchestratorRunner, ) -> UserInputResult: + """ + Run the user input step, prompting the user for input. + + Args: + runner (OrchestratorRunner): The orchestrator runner instance. + + Returns: + UserInputResult: The result of the user input step. + """ logger.info(f"Running user input step: {self.id}") return UserInputResult(runner.get_user_input(self.properties.question)) diff --git a/graphrag_sdk/rdf_extractor.py b/graphrag_sdk/rdf_extractor.py new file mode 100644 index 00000000..fdd492ff --- /dev/null +++ b/graphrag_sdk/rdf_extractor.py @@ -0,0 +1,301 @@ +""" +RDF Ontology Extractor for extracting ontologies from RDF/OWL schema files. +""" + +import logging +from typing import List, Optional, Dict, Set, Tuple +from rdflib import Graph, URIRef, Namespace +from rdflib.namespace import RDF, RDFS, OWL, XSD + +from graphrag_sdk.entity import Entity +from graphrag_sdk.relation import Relation +from graphrag_sdk.attribute import Attribute, AttributeType +from graphrag_sdk.ontology import Ontology +from graphrag_sdk.helpers import extract_name_from_uri + + +logger = logging.getLogger(__name__) + + +class RDFOntologyExtractor: + """ + Extracts ontology (entities, relations, attributes) from RDF/OWL schema graphs. + + This extractor processes RDF graphs containing RDFS or OWL class and property + definitions to generate a GraphRAG-compatible ontology. It enforces that only + schema definitions are present (no individual instances). + + Key Features: + - Extracts classes as entities with labels and comments + - Extracts object properties as relations with domain/range + - Extracts datatype properties as entity attributes + - Validates that no individuals are present in the schema + - Supports RDFS and OWL vocabularies + """ + + def __init__(self, graph: Graph): + """ + Initialize the RDF Ontology Extractor. + + Args: + graph: An rdflib.Graph containing the RDF schema + + Raises: + ValueError: If the graph contains individual instances + """ + self.graph = graph + self.entities: Dict[str, Entity] = {} + self._check_for_individuals() + + def extract(self) -> Ontology: + """ + Extract complete ontology from the RDF graph. + + Returns: + Ontology object containing all entities and relations + """ + entities = self.extract_entities() + relations = self.extract_relations() + + return Ontology( + entities=entities, + relations=relations + ) + + def extract_entities(self) -> List[Entity]: + """ + Extract all classes as entities. + + Returns: + List of Entity objects + """ + entity_list = [] + seen_classes = set() + + # Find all classes (RDFS and OWL) + classes = set() + for s in self.graph.subjects(RDF.type, RDFS.Class): + classes.add(s) + for s in self.graph.subjects(RDF.type, OWL.Class): + classes.add(s) + + for class_uri in classes: + # Skip blank nodes + if not isinstance(class_uri, URIRef): + continue + + if class_uri in seen_classes: + continue + seen_classes.add(class_uri) + + # Extract label and comment + label = self._extract_label(class_uri) + comment = self._extract_comment(class_uri) + local_name = extract_name_from_uri(str(class_uri)) + + # Use label if available, otherwise local name + entity_name = label if label else local_name + + # Extract attributes (properties with this class as domain) + attributes = self._infer_attributes_for_entity(class_uri) + + entity = Entity( + label=entity_name, + attributes=attributes, + description=comment or "" + ) + + self.entities[str(class_uri)] = entity + entity_list.append(entity) + + logger.info(f"Extracted {len(entity_list)} entities from RDF schema") + return entity_list + + def extract_relations(self) -> List[Relation]: + """ + Extract all object properties as relations. + + Returns: + List of Relation objects + """ + relations = [] + + # Find all properties that look like relations + properties = set() + for s in self.graph.subjects(RDF.type, OWL.ObjectProperty): + properties.add(s) + # Also check generic properties that link two classes + for s in self.graph.subjects(RDF.type, RDF.Property): + properties.add(s) + + for prop_uri in properties: + if not isinstance(prop_uri, URIRef): + continue + + domain = self._get_domain(prop_uri) + range_val = self._get_range(prop_uri) + + # Skip if domain or range is missing + if not domain or not range_val: + # Only warn if it's explicitly an ObjectProperty + if (prop_uri, RDF.type, OWL.ObjectProperty) in self.graph: + label = self._extract_label(prop_uri) or extract_name_from_uri(str(prop_uri)) + logger.warning( + f"Property '{label}' is missing domain or range. " + f"This relation will be skipped." + ) + continue + + # Check if domain/range are properties (property chains) + if self._is_property(domain) or self._is_property(range_val): + logger.warning(f"Property {extract_name_from_uri(str(prop_uri))} has property as domain/range - skipping (property chains not supported)") + continue + + # Check if domain and range are known classes (or at least not datatypes) + if self._is_datatype(range_val): + continue + + # We need to map URIs to Entity names + # If we have processed entities, we can use their names + # Otherwise we extract names from URIs + + domain_str = str(domain) + range_str = str(range_val) + + source_name = self.entities[domain_str].label if domain_str in self.entities else (self._extract_label(domain) or extract_name_from_uri(domain_str)) + target_name = self.entities[range_str].label if range_str in self.entities else (self._extract_label(range_val) or extract_name_from_uri(range_str)) + + label = self._extract_label(prop_uri) + local_name = extract_name_from_uri(str(prop_uri)) + relation_name = label if label else local_name + + relation = Relation( + label=relation_name, + source=source_name, + target=target_name + ) + relations.append(relation) + + logger.info(f"Extracted {len(relations)} relations from RDF schema") + return relations + + def _infer_attributes_for_entity(self, entity_uri: URIRef) -> List[Attribute]: + """ + Find properties where rdfs:domain is this entity. + """ + attributes = [] + + # Find properties with this domain + for prop_uri in self.graph.subjects(RDFS.domain, entity_uri): + # Skip if not a URIRef + if not isinstance(prop_uri, URIRef): + continue + + # Check if it's a datatype property (attribute) or object property (relation) + # If range is a literal/datatype, it's an attribute + range_val = self._get_range(prop_uri) + + if not range_val: + # Default to string attribute if no range + attr_type = AttributeType.STRING + elif self._is_datatype(range_val): + attr_type = self._map_xsd_type(range_val) + else: + # It's likely a relation (range is a class), skip here + continue + + label = self._extract_label(prop_uri) + local_name = extract_name_from_uri(str(prop_uri)) + attr_name = label if label else local_name + + attributes.append(Attribute(name=attr_name, attr_type=attr_type, required=False)) + + return attributes + + def _check_for_individuals(self) -> None: + """ + Check if the graph contains any individual instances. + + Raises: + ValueError: If individuals are found in the graph + """ + # Look for instances (subjects that are instances of a class) + individuals = set() + + # Get all classes defined in the schema + classes = set() + for s in self.graph.subjects(RDF.type, RDFS.Class): + classes.add(s) + for s in self.graph.subjects(RDF.type, OWL.Class): + classes.add(s) + + # Now check if there are any subjects that are instances of these classes + for cls in classes: + for individual in self.graph.subjects(RDF.type, cls): + # Make sure this is not a class definition itself + if not (individual, RDF.type, RDFS.Class) in self.graph and \ + not (individual, RDF.type, OWL.Class) in self.graph: + individuals.add(individual) + + if individuals: + individual_list = [extract_name_from_uri(str(ind)) for ind in list(individuals)[:5]] + error_msg = ( + f"The RDF file contains {len(individuals)} individual instance(s), " + f"but only schema definitions (classes and properties) are allowed. " + f"Examples: {', '.join(individual_list)}" + f"{'...' if len(individuals) > 5 else ''}. " + f"Please provide an RDF schema file (RDFS/OWL) without individual instances." + ) + raise ValueError(error_msg) + + def _get_domain(self, prop_uri: URIRef) -> Optional[URIRef]: + for obj in self.graph.objects(prop_uri, RDFS.domain): + if isinstance(obj, URIRef): + return obj + return None + + def _get_range(self, prop_uri: URIRef) -> Optional[URIRef]: + for obj in self.graph.objects(prop_uri, RDFS.range): + if isinstance(obj, URIRef): + return obj + return None + + def _is_property(self, uri: URIRef) -> bool: + """Check if URI is a property definition""" + return (uri, RDF.type, RDF.Property) in self.graph or \ + (uri, RDF.type, OWL.ObjectProperty) in self.graph or \ + (uri, RDF.type, OWL.DatatypeProperty) in self.graph + + def _is_datatype(self, uri: URIRef) -> bool: + """Check if URI is a datatype (XSD)""" + return str(uri).startswith(str(XSD)) + + def _map_xsd_type(self, xsd_uri: URIRef) -> AttributeType: + """Map XSD types to AttributeType""" + uri_str = str(xsd_uri) + + if uri_str in [str(XSD.integer), str(XSD.int), str(XSD.long), + str(XSD.float), str(XSD.decimal), str(XSD.double), + str(XSD.nonNegativeInteger), str(XSD.positiveInteger), + str(XSD.unsignedLong), str(XSD.unsignedInt), + str(XSD.unsignedShort), str(XSD.unsignedByte), + str(XSD.nonPositiveInteger), str(XSD.negativeInteger), + str(XSD.short), str(XSD.byte)]: + return AttributeType.NUMBER + elif uri_str == str(XSD.boolean): + return AttributeType.BOOLEAN + else: + return AttributeType.STRING + + def _extract_label(self, uri: URIRef) -> Optional[str]: + """Extract rdfs:label""" + for label in self.graph.objects(uri, RDFS.label): + return str(label) + return None + + def _extract_comment(self, uri: URIRef) -> Optional[str]: + """Extract rdfs:comment""" + for comment in self.graph.objects(uri, RDFS.comment): + return str(comment) + return None + diff --git a/graphrag_sdk/relation.py b/graphrag_sdk/relation.py index e39c3cc8..b3df8c6c 100644 --- a/graphrag_sdk/relation.py +++ b/graphrag_sdk/relation.py @@ -1,8 +1,12 @@ -import json import re +import json import logging +<<<<<<< HEAD from typing import Union +======= +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b from .attribute import Attribute +from typing import Union, Optional from falkordb import Node as GraphNode, Edge as GraphEdge from graphrag_sdk.fixtures.regex import ( EDGE_LABEL_REGEX, @@ -13,7 +17,6 @@ logger = logging.getLogger(__name__) - class _RelationEntity: """ Represents a relation entity. @@ -35,7 +38,7 @@ def __init__(self, label: str): self.label = re.sub(r"([^a-zA-Z0-9_])", "", label) @staticmethod - def from_json(txt: str): + def from_json(txt: str) -> "_RelationEntity": """ Creates a _RelationEntity object from a JSON string. @@ -48,7 +51,7 @@ def from_json(txt: str): txt = txt if isinstance(txt, dict) else json.loads(txt) return _RelationEntity(txt.get("label", txt)) - def to_json(self): + def to_json(self) -> dict: """ Converts the _RelationEntity object to a JSON string. @@ -77,7 +80,11 @@ class Relation: label (str): The label of the relation. source (Union[_RelationEntity, str]): The source entity of the relation. target (Union[_RelationEntity, str]): The target entity of the relation. +<<<<<<< HEAD attributes (list[Attribute]): The attributes associated with the relation. +======= + attributes (Optional[list[Attribute]]): The attributes associated with the relation. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b Methods: from_graph(relation: GraphEdge, entities: list[GraphNode]) -> Relation: @@ -101,7 +108,11 @@ def __init__( label: str, source: Union[_RelationEntity, str], target: Union[_RelationEntity, str], +<<<<<<< HEAD attributes: list[Attribute] = None, +======= + attributes: Optional[list[Attribute]] = None, +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b ): """ Initializes a Relation object. @@ -110,7 +121,11 @@ def __init__( label (str): The label of the relation. source (Union[_RelationEntity, str]): The source entity of the relation. target (Union[_RelationEntity, str]): The target entity of the relation. +<<<<<<< HEAD attributes (list[Attribute], optional): The attributes associated with the relation. Defaults to None. +======= + attributes (Optional[list[Attribute]]): The attributes associated with the relation. Defaults to None. +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ attributes = attributes or [] if isinstance(source, str): @@ -129,7 +144,7 @@ def __init__( self.attributes = attributes @staticmethod - def from_graph(relation: GraphEdge, entities: list[GraphNode]): + def from_graph(relation: GraphEdge, entities: list[GraphNode]) -> "Relation": """ Creates a Relation object from a graph edge and a list of graph nodes. @@ -156,7 +171,11 @@ def from_graph(relation: GraphEdge, entities: list[GraphNode]): ) @staticmethod +<<<<<<< HEAD def from_json(txt: Union[dict, str]): +======= + def from_json(txt: Union[dict, str]) -> "Relation": +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b """ Creates a Relation object from a JSON string or dictionary. @@ -179,7 +198,7 @@ def from_json(txt: Union[dict, str]): ) @staticmethod - def from_string(txt: str): + def from_string(txt: str) -> "Relation": """ Creates a Relation object from a string representation. @@ -220,7 +239,7 @@ def to_json(self) -> dict: "attributes": [attr.to_json() for attr in self.attributes], } - def combine(self, relation2: "Relation"): + def combine(self, relation2: "Relation") -> "Relation": """ Overwrites attributes of self with attributes of relation2. @@ -240,7 +259,7 @@ def combine(self, relation2: "Relation"): return self - def to_graph_query(self): + def to_graph_query(self) -> str: """ Generates a Cypher query string for creating the relation in a graph database. diff --git a/graphrag_sdk/source.py b/graphrag_sdk/source.py index 148a222f..0ef884d0 100644 --- a/graphrag_sdk/source.py +++ b/graphrag_sdk/source.py @@ -1,5 +1,9 @@ from abc import ABC +<<<<<<< HEAD from typing import Optional, Iterator +======= +from typing import Iterator, Optional +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b from graphrag_sdk.document import Document from graphrag_sdk.document_loaders import ( PDFLoader, @@ -18,12 +22,11 @@ def Source(path: str, instruction: Optional[str] = None) -> "AbstractSource": Args: path (str): path to source - instruction (str): source specific instruction for the LLM - + instruction (Optional[str]): source specific instruction for the LLM + Returns: AbstractSource: A source object corresponding to the input path format. """ - if not isinstance(path, str) or path == "": raise Exception("Invalid argument, path should be a none empty string.") diff --git a/graphrag_sdk/steps/Step.py b/graphrag_sdk/steps/Step.py index 5ceec5d6..7f0471af 100644 --- a/graphrag_sdk/steps/Step.py +++ b/graphrag_sdk/steps/Step.py @@ -1,4 +1,4 @@ class Step: - def run(self): + def run(self) -> None: pass diff --git a/graphrag_sdk/steps/create_ontology_step.py b/graphrag_sdk/steps/create_ontology_step.py index 5df9ea8d..3b31f1e8 100644 --- a/graphrag_sdk/steps/create_ontology_step.py +++ b/graphrag_sdk/steps/create_ontology_step.py @@ -24,13 +24,12 @@ FIX_JSON_PROMPT, BOUNDARIES_PREFIX, ) -RENDER_STEP_SIZE = 0.5 +RENDER_STEP_SIZE = 0.5 logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) - class CreateOntologyStep(Step): """ Create Ontology Step @@ -41,25 +40,56 @@ def __init__( sources: list[AbstractSource], ontology: Ontology, model: GenerativeModel, - config: dict = { - "max_workers": 16, - "max_input_tokens": 500000, - "max_output_tokens": 8192, - }, - hide_progress: bool = False, + config: Optional[dict] = None, + hide_progress: Optional[bool] = False, ) -> None: + """ + Initialize the CreateOntologyStep. + + Args: + sources (List[AbstractSource]): List of sources from which ontology is created. + ontology (Ontology): The initial ontology to be merged with new extracted data. + model (GenerativeModel): The generative model used for processing and creating the ontology. + config (Optional[dict]): Configuration for the step, including thread workers and token limits. Defaults to standard config. + hide_progress (bool): Flag to hide the progress bar. Defaults to False. + """ self.sources = sources self.ontology = ontology - self.model = model.with_system_instruction(CREATE_ONTOLOGY_SYSTEM) - self.config = config + self.model = model + if config is None: + self.config = { + "max_workers": 16, + "max_input_tokens": 500000, + "max_output_tokens": 8192, + } + else: + self.config = config self.hide_progress = hide_progress self.process_files = 0 self.counter_lock = Lock() - def _create_chat(self): - return self.model.start_chat({"response_validation": False}) + def _create_chat(self) -> GenerativeModelChatSession: + """ + Create a new chat session with the generative model. + + Returns: + GenerativeModelChatSession: A session for interacting with the generative model. + """ + return self.model.start_chat(CREATE_ONTOLOGY_SYSTEM) def run(self, boundaries: Optional[str] = None): + """ + Execute the ontology creation process by extracting data from all sources. + + Args: + boundaries (Optional[str]): Additional boundaries or constraints for the ontology creation. + + Returns: + Ontology: The final ontology after merging with extracted data. + + Raises: + Exception: If ontology creation fails and no entities are found. + """ tasks: list[Future[Ontology]] = [] with tqdm(total=len(self.sources) + 1, desc="Process Documents", disable=self.hide_progress) as pbar: @@ -104,8 +134,21 @@ def _process_source( source: AbstractSource, o: Ontology, boundaries: Optional[str] = None, - retries: int = 1, + retries: Optional[int] = 1, ): + """ + Process a single document and extract ontology data. + + Args: + chat_session (GenerativeModelChatSession): The chat session for interacting with the generative model. + document (Document): The document to extract data from. + o (Ontology): The current ontology to be merged with extracted data. + boundaries (Optional[str]): Constraints for data extraction. + retries (Optional[int]) Number of retries for processing the document. + + Returns: + Ontology: The updated ontology after processing the document. + """ try: document = next(source.load()) @@ -170,6 +213,16 @@ def _process_source( return o def _fix_ontology(self, chat_session: GenerativeModelChatSession, o: Ontology): + """ + Fix and validate the ontology using the generative model. + + Args: + chat_session (GenerativeModelChatSession): The chat session for interacting with the model. + o (Ontology): The ontology to fix and validate. + + Returns: + Ontology: The fixed and validated ontology. + """ logger.debug(f"Fixing ontology...") user_message = FIX_ONTOLOGY_PROMPT.format(ontology=o) @@ -230,8 +283,22 @@ def _call_model( self, chat_session: GenerativeModelChatSession, prompt: str, - retry=6, + retry: Optional[int] = 6, ): + """ + Call the generative model with retries and rate limiting. + + Args: + chat_session (GenerativeModelChatSession): The chat session for interacting with the model. + prompt (str): The prompt to send to the model. + retry (Optional[int]): Number of retries if quota is exceeded or errors occur. + + Returns: + GenerationResponse: The model's response. + + Raises: + Exception: If the model fails after exhausting retries. + """ try: return chat_session.send_message(prompt) except Exception as e: diff --git a/graphrag_sdk/steps/extract_data_step.py b/graphrag_sdk/steps/extract_data_step.py index 65258968..b2bd047e 100644 --- a/graphrag_sdk/steps/extract_data_step.py +++ b/graphrag_sdk/steps/extract_data_step.py @@ -6,11 +6,11 @@ from uuid import uuid4 from falkordb import Graph from threading import Lock +from typing import Optional from graphrag_sdk.steps.Step import Step from graphrag_sdk.document import Document from ratelimit import limits, sleep_and_retry from graphrag_sdk.source import AbstractSource -from graphrag_sdk.models.model import OutputMethod from concurrent.futures import Future, ThreadPoolExecutor from graphrag_sdk.helpers import extract_json, map_dict_to_cypher_properties from graphrag_sdk.ontology import Ontology @@ -44,19 +44,32 @@ def __init__( ontology: Ontology, model: GenerativeModel, graph: Graph, - config: dict = { - "max_workers": 16, - "max_input_tokens": 500000, - "max_output_tokens": 8192, - }, - hide_progress: bool = False, + config: Optional[dict] = None, + hide_progress: Optional[bool] = False, ) -> None: + """ + Initialize the ExtractDataStep. + + Args: + sources (list[AbstractSource]): List of data sources to process. + ontology (Ontology): The ontology associated with the knowledge graph. + model (GenerativeModel): The generative model used for data extraction. + graph (Graph): The FalkorDB graph instance. + config (Optional[dict]): Configuration options for the step. + hide_progress (Optional[bool]): Flag to hide progress bar. Defaults to False. + """ self.sources = sources self.ontology = ontology self.config = config - self.model = model.with_system_instruction( - EXTRACT_DATA_SYSTEM.replace("#ONTOLOGY", str(self.ontology.to_json())) - ) + if config is None: + self.config = { + "max_workers": 16, + "max_input_tokens": 500000, + "max_output_tokens": 8192, + } + else: + self.config = config + self.model = model self.graph = graph self.hide_progress = hide_progress self.process_files = 0 @@ -64,11 +77,21 @@ def __init__( if not os.path.exists("logs"): os.makedirs("logs") - def _create_chat(self): - return self.model.start_chat({"response_validation": False}) + def _create_chat(self) -> GenerativeModelChatSession: + return self.model.start_chat(EXTRACT_DATA_SYSTEM.replace("#ONTOLOGY", str(self.ontology.to_json()))) +<<<<<<< HEAD def run(self, instructions: str = None) -> list[AbstractSource]: +======= + def run(self, instructions: Optional[str] = None): + """ + Run the data extraction process. + + Args: + instructions (Optional[str]): Optional additional instructions for data extraction. + """ +>>>>>>> b2aa07fc70e298ca25ae07c67c1e8af35dd2953b # Each task is represented by a tuple containing: # 1. A Future object (the asynchronous processing task) # 2. A string (the ID of the document being processed) @@ -117,11 +140,24 @@ def _process_document( document: Document, ontology: Ontology, graph: Graph, - source_instructions: str = "", - instructions: str = "", - retries: int = 1, + source_instructions: Optional[str] = "", + instructions: Optional[str] = "", + retries: Optional[int] = 1, ): try: + """ + Process a single source document and extract entities and relations. + + Args: + task_id (str): The unique ID for the task. + chat_session (GenerativeModelChatSession): The chat session for the extraction. + document (Document): The document to process. + ontology (Ontology): The ontology associated with the graph. + graph (Graph): The FalkorDB graph instance. + source_instructions (Optional[str]): Instructions specific to the source. + instructions (Optional[str]): Additional instructions. + retries (Optional[int]): Number of times to retry if the model stops unexpectedly. + """ _task_logger = logging.getLogger(task_id) _task_logger.setLevel(logging.DEBUG) @@ -156,14 +192,14 @@ def _process_document( responses: list[GenerationResponse] = [] response_idx = 0 - responses.append(self._call_model(chat_session, user_message, output_method=OutputMethod.JSON)) + responses.append(self._call_model(chat_session, user_message)) _task_logger.debug(f"Model response: {responses[response_idx].text}") while responses[response_idx].finish_reason == FinishReason.MAX_TOKENS and response_idx < retries: _task_logger.debug("Asking model to continue") response_idx += 1 - responses.append(self._call_model(chat_session, COMPLETE_DATA_EXTRACTION, output_method=OutputMethod.JSON)) + responses.append(self._call_model(chat_session, COMPLETE_DATA_EXTRACTION)) _task_logger.debug( f"Model response after continue: {responses[response_idx].text}" ) @@ -187,7 +223,6 @@ def _process_document( json_fix_response = self._call_model( self._create_chat(), FIX_JSON_PROMPT.format(json=last_respond, error=str(e)), - output_method=OutputMethod.JSON, ) data = json.loads(extract_json(json_fix_response.text)) _task_logger.debug(f"Fixed JSON: {data}") @@ -220,7 +255,15 @@ def _process_document( with self.counter_lock: self.process_files += 1 - def _create_entity(self, graph: Graph, args: dict, ontology: Ontology): + def _create_entity(self, graph: Graph, args: dict, ontology: Ontology) -> None: + """ + Create an entity in the graph based on the extracted data. + + Args: + graph (Graph): The graph instance to create the entity in. + args (dict): The entity data extracted from the source. + ontology (Ontology): The ontology to validate the entity type. + """ # Get unique attributes from entity entity = ontology.get_entity_with_label(args["label"]) if entity is None: @@ -252,7 +295,15 @@ def _create_entity(self, graph: Graph, args: dict, ontology: Ontology): result = graph.query(query) return result - def _create_relation(self, graph: Graph, args: dict, ontology: Ontology): + def _create_relation(self, graph: Graph, args: dict, ontology: Ontology) -> None: + """ + Create a relation in the graph based on the extracted data. + + Args: + graph (Graph): The graph instance to create the relation in. + args (dict): The relation data extracted from the source. + ontology (Ontology): The ontology to validate the relation type. + """ relations = ontology.get_relations_with_label(args["label"]) if len(relations) == 0: print(f"Relations with label {args['label']} not found in ontology") @@ -303,10 +354,23 @@ def _call_model( chat_session: GenerativeModelChatSession, prompt: str, retry: int = 6, - output_method: OutputMethod = OutputMethod.DEFAULT - ): + ) -> GenerationResponse: + """ + Call the generative model with rate limiting and retries. + + Args: + chat_session (GenerativeModelChatSession): The chat session for interacting with the model. + prompt (str): The prompt to send to the model. + retry (Optional[int]): Number of retries in case of quota exceeded or errors. + + Returns: + GenerationResponse: The model's response. + + Raises: + Exception: If an error occurs after exhausting retries. + """ try: - return chat_session.send_message(prompt, output_method=output_method) + return chat_session.send_message(prompt) except Exception as e: # If exception is caused by quota exceeded, wait 10 seconds and try again for 6 times if "Quota exceeded" in str(e) and retry > 0: diff --git a/graphrag_sdk/steps/graph_query_step.py b/graphrag_sdk/steps/graph_query_step.py index a9ac8bb2..e77da058 100644 --- a/graphrag_sdk/steps/graph_query_step.py +++ b/graphrag_sdk/steps/graph_query_step.py @@ -1,5 +1,6 @@ import logging from falkordb import Graph +from typing import Optional from graphrag_sdk.steps.Step import Step from graphrag_sdk.ontology import Ontology from graphrag_sdk.models import ( @@ -25,11 +26,23 @@ def __init__( graph: Graph, ontology: Ontology, chat_session: GenerativeModelChatSession, - config: dict = None, - last_answer: str = None, - cypher_prompt: str = None, - cypher_prompt_with_history: str = None, + config: Optional[dict] = None, + last_answer: Optional[str] = None, + cypher_prompt: Optional[str] = None, + cypher_prompt_with_history: Optional[str] = None, ) -> None: + """ + Initializes the GraphQueryGenerationStep object. + + Args: + graph (Graph): The graph object to query. + ontology (Ontology): The ontology object. + chat_session (GenerativeModelChatSession): The chat session object. + config (Optional[dict]): The configuration object. + last_answer (Optional[str]): The last answer. + cypher_prompt (Optional[str]): The Cypher prompt. + cypher_prompt_with_history (Optional[str]): The Cypher prompt with history. + """ self.ontology = ontology self.config = config or {} self.graph = graph @@ -38,7 +51,17 @@ def __init__( self.cypher_prompt = cypher_prompt self.cypher_prompt_with_history = cypher_prompt_with_history - def run(self, question: str, retries: int = 10): + def run(self, question: str, retries: Optional[int] = 10) -> tuple[Optional[str], Optional[str], Optional[int]]: + """ + Run the step to generate and validate a Cypher query. + + Args: + question (str): The question being asked to generate the query. + retries (Optional[int]): Number of retries allowed in case of errors. + + Returns: + tuple[Optional[str], Optional[str], Optional[int]]: The context, the generated Cypher query and the query execution time. + """ cypher = "" for i in range(retries): try: @@ -56,20 +79,22 @@ def run(self, question: str, retries: int = 10): logger.debug(f"Cypher: {cypher}") if not cypher or len(cypher) == 0: - return (None, None) + return (None, None, None) validation_errors = validate_cypher(cypher, self.ontology) if validation_errors is not None: raise Exception("\n".join(validation_errors)) if cypher is not None: - result_set = self.graph.query(cypher).result_set + query_result = self.graph.query(cypher) + result_set = query_result.result_set + execution_time = query_result.run_time_ms context = stringify_falkordb_response(result_set) logger.debug(f"Context: {context}") logger.debug(f"Context size: {len(result_set)}") logger.debug(f"Context characters: {len(str(context))}") - return (context, cypher) + return (context, cypher, execution_time) except Exception as e: logger.debug(f"Error: {e}") error = e diff --git a/graphrag_sdk/steps/qa_step.py b/graphrag_sdk/steps/qa_step.py index 286cfd8b..bf25e5c8 100644 --- a/graphrag_sdk/steps/qa_step.py +++ b/graphrag_sdk/steps/qa_step.py @@ -1,11 +1,12 @@ import logging +from typing import Optional from graphrag_sdk.steps.Step import Step from graphrag_sdk.models import GenerativeModelChatSession + logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) - class QAStep(Step): """ QA Step @@ -14,14 +15,33 @@ class QAStep(Step): def __init__( self, chat_session: GenerativeModelChatSession, - config: dict = None, - qa_prompt: str = None, + config: Optional[dict] = None, + qa_prompt: Optional[str] = None, ) -> None: + """ + Initialize the QA Step. + + Args: + chat_session (GenerativeModelChatSession): The chat session for handling the QA. + config (Optional[dict]): Optional configuration for the step. + qa_prompt (Optional[str]): The prompt template for question answering. + """ self.config = config or {} self.chat_session = chat_session self.qa_prompt = qa_prompt - def run(self, question: str, cypher: str, context: str): + def run(self, question: str, cypher: str, context: str) -> str: + """ + Run the QA step. + + Args: + question (str): The question being asked. + cypher (str): The Cypher query to run. + context (str): Context for the QA. + + Returns: + str: The response from the QA session. + """ qa_prompt = self.qa_prompt.format( context=context, cypher=cypher, question=question ) diff --git a/graphrag_sdk/steps/stream_qa_step.py b/graphrag_sdk/steps/stream_qa_step.py new file mode 100644 index 00000000..ab2d2bae --- /dev/null +++ b/graphrag_sdk/steps/stream_qa_step.py @@ -0,0 +1,51 @@ +import logging +from typing import Optional, Iterator +from graphrag_sdk.steps.Step import Step +from graphrag_sdk.models import GenerativeModelChatSession + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) + +class StreamingQAStep(Step): + """ + QA Step that supports streaming responses + """ + + def __init__( + self, + chat_session: GenerativeModelChatSession, + config: Optional[dict] = None, + qa_prompt: Optional[str] = None, + ) -> None: + """ + Initialize the QA Step. + + Args: + chat_session (GenerativeModelChatSession): The chat session for handling the QA. + config (Optional[dict]): Optional configuration for the step. + qa_prompt (Optional[str]): The prompt template for question answering. + """ + self.config = config or {} + self.chat_session = chat_session + self.qa_prompt = qa_prompt + + def run(self, question: str, cypher: str, context: str) -> Iterator[str]: + """ + Run the QA step and stream the response chunks. + + Args: + question (str): The question being asked. + cypher (str): The Cypher query to run. + context (str): Context for the QA. + + Returns: + Iterator[str]: A generator that yields response chunks. + """ + qa_prompt = self.qa_prompt.format( + context=context, cypher=cypher, question=question + ) + logger.debug(f"QA Prompt: {qa_prompt}") + # Send the message and stream the response + for chunk in self.chat_session.send_message_stream(qa_prompt): + yield chunk \ No newline at end of file diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 46b98ebe..00000000 --- a/poetry.lock +++ /dev/null @@ -1,4655 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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-description = "Backport of pathlib-compatible object wrapper for zip files" -optional = false -python-versions = ">=3.9" -files = [ - {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, - {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, -] - -[package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] -cover = ["pytest-cov"] -doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] -type = ["pytest-mypy"] - -[extras] -all = ["google-generativeai", "litellm", "ollama", "openai", "vertexai"] -google-generativeai = ["google-generativeai"] -litellm = ["litellm", "ollama"] -ollama = ["ollama"] -openai = ["openai"] -vertexai = ["vertexai"] - -[metadata] -lock-version = "2.0" -python-versions = "^3.9.0" -content-hash = "d53e56537233e2b2c26621582df70c2adbba414af24a25ccecfa8d430e692b95" diff --git a/pyproject.toml b/pyproject.toml index c1d5675a..2df7d93b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,54 +1,62 @@ -[tool.poetry] -name = "graphrag_sdk" -version = "0.6.1" -license = "MIT" +[project] +name = "graphrag-sdk" +version = "0.8.2" description = "Enables graph-based RAG and integration with LLMs for advanced querying and knowledge graph creation." -authors = ["FalkorDB "] +authors = [ + { name = "FalkorDB", email = "info@falkordb.com" } +] readme = "README.md" -repository = "https://github.com/FalkorDB/GraphRAG-SDK" -homepage = "http://RAG-SDK.readthedocs.io" -keywords = ['RAG', 'Graph-RAG', 'KnowledgeGraph'] +license = { text = "MIT" } +requires-python = ">=3.10,<3.14" +keywords = ["RAG", "Graph-RAG", "KnowledgeGraph"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] -[tool.poetry.dependencies] -python = "^3.9.0" -falkordb = "^1.0.9" -typing-extensions = "^4.12.1" -bs4 = "^0.0.2" -pypdf = "^4.2.0" -backoff = "^2.2.1" -requests = "^2.32.3" -python-abc = "^0.2.0" -ratelimit = "^2.2.1" -python-dotenv = "^1.0.1" -fix-busted-json = "^0.0.18" -ipykernel = "^6.29.5" -vertexai = { version = "^1.49.0", optional = true } -ollama = { version = "^0.2.1", optional = true } -openai = { version = "^1.35.9", optional = true } -google-generativeai = { version = "^0.8.1", optional = true } -litellm = { version = "^1.52.14", optional = true } +dependencies = [ + "falkordb>=1.0.9,<2.0.0", + "typing-extensions>=4.12.1,<5.0.0", + "beautifulsoup4>=4.12.3,<5.0.0", + "pypdf>=5.9.0,<7.0.0", + "backoff>=2.2.1,<3.0.0", + "requests>=2.32.3,<3.0.0", + "ratelimit>=2.2.1,<3.0.0", + "python-dotenv>=1.0.1,<2.0.0", + "fix-busted-json>=0.0.18,<0.0.19", + "rich>=13.9.4,<14.0.0", + "litellm>=1.73.1,<2.0.0", + "rdflib>=7.0.0,<8.0.0", +] -[tool.poetry.extras] -all = ["vertexai", "ollama", "openai", "google-generativeai", "litellm"] -ollama = ["ollama"] -openai = ["openai"] -vertexai = ["vertexai"] -google-generativeai = ["google-generativeai"] -litellm = ["litellm", "ollama"] +[project.optional-dependencies] +test = [ + "pytest>=8.2.1,<9.0.0", + "pytest-cov>=6.0.0,<7.0.0", + "deepeval>=2.2.6,<3.0.0", +] +dev = [ + "sphinx>=7.3.7,<8.0.0", + "sphinx-rtd-theme>=2.0.0,<4.0.0", + "pandas>=2.2.2,<3.0.0", + "jupyter>=1.0.0,<2.0.0", + "ipykernel>=6.29.5,<8.0.0", +] -[tool.poetry.group.test.dependencies] -pytest = "^8.2.1" -pytest-cov = "^6.0.0" - -[tool.poetry.group.dev.dependencies] -sphinx = "^7.3.7" -sphinx-rtd-theme = "^2.0.0" -pandas = "^2.2.2" -jupyter = "^1.0.0" +[project.urls] +Homepage = "http://RAG-SDK.readthedocs.io" +Repository = "https://github.com/FalkorDB/GraphRAG-SDK" +Documentation = "http://RAG-SDK.readthedocs.io" [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" [tool.pytest.ini_options] log_cli = true diff --git a/requirements.txt b/requirements.txt index 4fddf276..c952afd0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,54 +1,21 @@ -appnope==0.1.4 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" and platform_system == "Darwin" -asttokens==2.4.1 ; python_version >= "3.9" and python_full_version < "4.0.0" -async-timeout==5.0.1 ; python_full_version >= "3.9.0" and python_full_version < "3.11.3" -backoff==2.2.1 ; python_full_version >= "3.9.0" and python_version < "4.0" -beautifulsoup4==4.12.3 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -bs4==0.0.2 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -certifi==2024.8.30 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -cffi==1.17.1 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" and implementation_name == "pypy" -charset-normalizer==3.4.0 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -colorama==0.4.6 ; python_version >= "3.9" and python_full_version < "4.0.0" and sys_platform == "win32" -comm==0.2.2 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -debugpy==1.8.8 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -decorator==5.1.1 ; python_version >= "3.9" and python_full_version < "4.0.0" -exceptiongroup==1.2.2 ; python_version >= "3.9" and python_version < "3.11" -executing==2.1.0 ; python_version >= "3.9" and python_full_version < "4.0.0" -falkordb==1.0.9 ; python_full_version >= "3.9.0" and python_version < "4.0" -fix-busted-json==0.0.18 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -idna==3.10 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -importlib-metadata==8.5.0 ; python_full_version >= "3.9.0" and python_version < "3.10" -ipykernel==6.29.5 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -ipython==8.18.1 ; python_version >= "3.9" and python_full_version < "4.0.0" -jedi==0.19.2 ; python_version >= "3.9" and python_full_version < "4.0.0" -jupyter-client==8.6.3 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -jupyter-core==5.7.2 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -matplotlib-inline==0.1.7 ; python_version >= "3.9" and python_full_version < "4.0.0" -nest-asyncio==1.6.0 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -packaging==24.2 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -parso==0.8.4 ; python_version >= "3.9" and python_full_version < "4.0.0" -pexpect==4.9.0 ; python_version >= "3.9" and python_full_version < "4.0.0" and sys_platform != "win32" -platformdirs==4.3.6 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -prompt-toolkit==3.0.48 ; python_version >= "3.9" and python_full_version < "4.0.0" -psutil==6.1.0 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -ptyprocess==0.7.0 ; python_version >= "3.9" and python_full_version < "4.0.0" and sys_platform != "win32" -pure-eval==0.2.3 ; python_version >= "3.9" and python_full_version < "4.0.0" -pycparser==2.22 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" and implementation_name == "pypy" -pygments==2.18.0 ; python_version >= "3.9" and python_full_version < "4.0.0" -pypdf==4.3.1 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -python-abc==0.2.0 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -python-dateutil==2.9.0.post0 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -python-dotenv==1.0.1 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -pywin32==308 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_full_version >= "3.9.0" and python_full_version < "4.0.0" -pyzmq==26.2.0 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -ratelimit==2.2.1 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -redis==5.2.0 ; python_full_version >= "3.9.0" and python_version < "4.0" -requests==2.32.3 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -six==1.16.0 ; python_version >= "3.9" and python_full_version < "4.0.0" -soupsieve==2.6 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -stack-data==0.6.3 ; python_version >= "3.9" and python_full_version < "4.0.0" -tornado==6.4.1 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -traitlets==5.14.3 ; python_version >= "3.9" and python_full_version < "4.0.0" -typing-extensions==4.12.2 ; python_version >= "3.9" and python_full_version < "4.0.0" -urllib3==2.2.3 ; python_full_version >= "3.9.0" and python_full_version < "4.0.0" -wcwidth==0.2.13 ; python_version >= "3.9" and python_full_version < "4.0.0" -zipp==3.21.0 ; python_version >= "3.9" and python_version < "3.10" +# Auto-generated from pyproject.toml [project.dependencies] +# This file contains only DIRECT dependencies, not transitive ones +# Source of truth: pyproject.toml +# +# To install: +# pip install -r requirements.txt +# Or with uv: +# uv pip install -r requirements.txt + +backoff>=2.2.1,<3.0.0 +beautifulsoup4>=4.12.3,<5.0.0 +falkordb>=1.0.9,<2.0.0 +fix-busted-json>=0.0.18,<0.0.19 +litellm>=1.73.1,<2.0.0 +pypdf>=5.9.0,<7.0.0 +python-dotenv>=1.0.1,<2.0.0 +ratelimit>=2.2.1,<3.0.0 +rdflib>=7.0.0,<8.0.0 +requests>=2.32.3,<3.0.0 +rich>=13.9.4,<14.0.0 +typing-extensions>=4.12.1,<5.0.0 diff --git a/style.md b/style.md new file mode 100644 index 00000000..82d2db0f --- /dev/null +++ b/style.md @@ -0,0 +1,27 @@ + +# Refined Code Style Guidelines + +## 1. Code Structure +- **JSON Output Format**: Use a specific JSON output format for entity and relation extraction. + - Entities should have "label" and "attributes" fields. + - Relations should have "label", "source", "target", and "attributes" fields. +- **Code Organization**: Maintain a consistent and logical structure throughout the codebase. +- **Import Statement Simplification**: Optimize and simplify import statements for better readability and performance. + +## 2. Documentation +- **Documentation Clarity and Formatting**: + - Ensure all documentation is clear, concise, and well-formatted. + - Use proper Markdown formatting for consistency and readability. +- **Documentation Improvement**: Continuously update and improve documentation to reflect the current state of the project. + +## 3. Version Control and Dependency Management +- **Version Control and Semantic Versioning**: + - Use semantic versioning (MAJOR.MINOR.PATCH) for release management. + - Follow consistent version control practices. +- **Dependency Management**: + - Keep dependencies up-to-date and properly managed. + - Regularly review and update project dependencies. + +## 4. Project Management +- **Contributing Guidelines**: Provide clear guidelines for contributors to ensure consistency and quality of contributions. +- **Open Source Licensing**: Ensure proper open-source licensing is in place and clearly communicated. diff --git a/tests/test_auto_create_ontology.py b/tests/test_auto_create_ontology.py index 4a9ec6dc..8d83adb5 100644 --- a/tests/test_auto_create_ontology.py +++ b/tests/test_auto_create_ontology.py @@ -3,15 +3,12 @@ from graphrag_sdk.ontology import Ontology import unittest from graphrag_sdk.source import Source -from graphrag_sdk.models.gemini import GeminiGenerativeModel -import vertexai +from graphrag_sdk.models.litellm import LiteModel import os import logging logging.basicConfig(level=logging.DEBUG) -vertexai.init(project=os.getenv("PROJECT_ID"), location=os.getenv("REGION")) - class TestAutoDetectOntology(unittest.TestCase): """ @@ -24,7 +21,7 @@ def test_auto_detect_ontology(self): sources = [Source(file_path)] - model = GeminiGenerativeModel(model_name="gemini-1.5-flash-001") + model = LiteModel(model_name="gemini/gemini-2.0-flash") boundaries = """ Extract entities and relationships from each page diff --git a/tests/test_kg.py b/tests/test_kg.py index 3398eae4..05433def 100644 --- a/tests/test_kg.py +++ b/tests/test_kg.py @@ -68,7 +68,7 @@ def setUpClass(cls): cls.graph_name = "test_kg" - model = GeminiGenerativeModel(model_name="gemini-1.5-flash-001") + model = GeminiGenerativeModel(model_name="gemini-2.0-flash") cls.kg = KnowledgeGraph( name=cls.graph_name, ontology=cls.ontology, diff --git a/tests/test_kg_gemini.py b/tests/test_kg_gemini.py deleted file mode 100644 index cbfdf2b1..00000000 --- a/tests/test_kg_gemini.py +++ /dev/null @@ -1,108 +0,0 @@ -import re -import os -import logging -import unittest -import vertexai -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.relation import Relation -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.gemini import GeminiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig - -load_dotenv() - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -vertexai.init(project=os.getenv("PROJECT_ID"), location=os.getenv("REGION")) - - -class TestKGGemini(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - - cls.graph_name = "IMDB_gemini" - - model = GeminiGenerativeModel(model_name="gemini-1.5-flash-001") - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - ) - - def test_kg_creation(self): - file_path = "tests/data/madoff.txt" - - sources = [Source(file_path)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 10, "The number of actors found should be greater than 10" - - def test_kg_delete(self): - self.kg.delete() - - db = FalkorDB() - graphs = db.list_graphs() - self.assertNotIn(self.graph_name, graphs) diff --git a/tests/test_kg_litellm_openai.py b/tests/test_kg_litellm_openai.py deleted file mode 100644 index 253d7bfa..00000000 --- a/tests/test_kg_litellm_openai.py +++ /dev/null @@ -1,97 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source_FromRawText -from graphrag_sdk.relation import Relation -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.litellm import LiteModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig - -load_dotenv() - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -class TestKGLiteLLM(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - cls.graph_name = "IMDB_openai" - model = LiteModel(model_name="gpt-4o") - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - ) - - def test_kg_creation(self): - - file_path = "tests/data/madoff.txt" - with open(file_path) as f: - string = f.read() - - sources = [Source_FromRawText(string)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 10, "The number of actors found should be greater than 10" \ No newline at end of file diff --git a/tests/test_kg_ollama.py b/tests/test_kg_ollama.py deleted file mode 100644 index 06ec03ee..00000000 --- a/tests/test_kg_ollama.py +++ /dev/null @@ -1,109 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.relation import Relation -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.ollama import OllamaGenerativeModel -from graphrag_sdk.models.openai import OpenAiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig - -load_dotenv() - - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -class TestKGOllama(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - - cls.graph_name = "IMDB_ollama" - - model_ollama = OllamaGenerativeModel(model_name="llama3:8b", generation_config=GenerativeModelConfig(temperature=0)) - model_openai = OpenAiGenerativeModel(model_name="gpt-3.5-turbo") - - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig(extract_data=model_openai, cypher_generation=model_ollama, qa=model_ollama), - ) - - def test_kg_creation(self): - - file_path = "tests/data/madoff.txt" - - sources = [Source(file_path)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 5, "The number of actors found should be greater than 5" - - def test_kg_delete(self): - - self.kg.delete() - - db = FalkorDB() - graphs = db.list_graphs() - self.assertNotIn(self.graph_name, graphs) diff --git a/tests/test_kg_openai.py b/tests/test_kg_openai.py deleted file mode 100644 index 785a28df..00000000 --- a/tests/test_kg_openai.py +++ /dev/null @@ -1,102 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source -from graphrag_sdk.relation import Relation -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.openai import OpenAiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig - -load_dotenv() - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -class TestKGOpenAI(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - cls.graph_name = "IMDB_openai" - model = OpenAiGenerativeModel(model_name="gpt-3.5-turbo-0125") - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - ) - - def test_kg_creation(self): - - file_path = "tests/data/madoff.txt" - - sources = [Source(file_path)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 10, "The number of actors found should be greater than 10" - - def test_kg_delete(self): - self.kg.delete() - - db = FalkorDB() - graphs = db.list_graphs() - self.assertNotIn(self.graph_name, graphs) diff --git a/tests/test_multi_agent.py b/tests/test_multi_agent.py index 02ae5978..172197ef 100644 --- a/tests/test_multi_agent.py +++ b/tests/test_multi_agent.py @@ -6,10 +6,12 @@ from graphrag_sdk.relation import Relation from graphrag_sdk.ontology import Ontology from graphrag_sdk.agents.kg_agent import KGAgent +from graphrag_sdk.models.litellm import LiteModel from graphrag_sdk.orchestrator import Orchestrator from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.gemini import GeminiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig +from graphrag_sdk import (KnowledgeGraph, + KnowledgeGraphModelConfig) + load_dotenv() logging.basicConfig(level=logging.DEBUG) @@ -185,7 +187,7 @@ def setUpClass(cls): ) ) - cls.model = GeminiGenerativeModel("gemini-1.5-flash-001") + cls.model = LiteModel("azure/o3-mini") cls.restaurants_kg = KnowledgeGraph( name="restaurants", ontology=cls.restaurants_ontology, diff --git a/tests/test_ontology_from_kg.py b/tests/test_ontology_from_kg.py index 652359fe..761d7ae9 100644 --- a/tests/test_ontology_from_kg.py +++ b/tests/test_ontology_from_kg.py @@ -55,7 +55,7 @@ def ontology_kg_setup(): ontology.add_relation(Relation(label="IN_CITY", source="Restaurant", target="City")) # Create a model and a knowledge graph. - model = GeminiGenerativeModel("gemini-1.5-flash-001") + model = GeminiGenerativeModel("gemini-2.0-flash") kg = KnowledgeGraph( name="test_ontology", ontology=ontology, diff --git a/tests/test_rag.py b/tests/test_rag.py new file mode 100644 index 00000000..cd626aef --- /dev/null +++ b/tests/test_rag.py @@ -0,0 +1,207 @@ +import os +import pytest +import logging +from dotenv import load_dotenv +from graphrag_sdk.entity import Entity +from deepeval.test_case import LLMTestCase +from graphrag_sdk.relation import Relation +from graphrag_sdk.ontology import Ontology +from graphrag_sdk.models.litellm import LiteModel +from graphrag_sdk.source import Source_FromRawText +from graphrag_sdk.attribute import Attribute, AttributeType +# Custom metrics of DeepEval +from graphrag_sdk.graph_metrics import GraphContextualRelevancy, GraphContextualRecall +from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig + + +load_dotenv() +TEST_FILE = "tests/data/madoff.txt" + +# Test queries and expected results +BENCHMARK = [ + { + "query": "How many actors acted in a movie?", + "expected": "Over than 10 actors acted in a movie." + }, + { + "query": "Which actors acted in a movie?", + "expected": "Joseph Scotto, Melony Feliciano, and Donna Pastorello acted in a movie." + }, + { + "query": "What is the role of Joseph Scotto in a movie?", + "expected": "Joseph Scotto played the role of Bernie Madoff in a movie." + }, + { + "query": "Did Donna Pastorello act in a movie?", + "expected": "Yes, Donna Pastorello acted in a movie as Eleanor Squillari." + }, + { + "query": "Who played the role of Mark Madoff in a movie?", + "expected": "Alex Olson played the role of Mark Madoff in a movie." + }, + { + "query": "Did Melony Feliciano have a named role in a movie?", + "expected": "No, Melony Feliciano acted as a background extra in a movie." + },] + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger(__name__) + + +@pytest.fixture +def movie_actor_ontology(): + """ + Create and return an ontology for movies and actors with the ACTED_IN relationship. + """ + ontology = Ontology() + + # Add Actor entity with name attribute + ontology.add_entity( + Entity( + label="Actor", + attributes=[ + Attribute( + name="name", + attr_type=AttributeType.STRING, + unique=True, + required=True, + ), + ], + ) + ) + + # Add Movie entity with title attribute + ontology.add_entity( + Entity( + label="Movie", + attributes=[ + Attribute( + name="title", + attr_type=AttributeType.STRING, + unique=True, + required=True, + ), + ], + ) + ) + + # Add ACTED_IN relation between Actor and Movie with role attribute + ontology.add_relation( + Relation( + label="ACTED_IN", + source="Actor", + target="Movie", + attributes=[ + Attribute( + name="role", + attr_type=AttributeType.STRING, + unique=False, + required=True, + ), + ], + ) + ) + + return ontology + + +@pytest.fixture +def knowledge_graph_setup(movie_actor_ontology): + """ + Sets up the knowledge graph using the provided ontology. + """ + # Get the model name from the environment variable, with a default + model_name = os.getenv("TEST_MODEL", "openai/gpt-4.1") + graph_name = model_name.split("/")[0] + + # Configure the model with zero temperature for deterministic outputs + model = LiteModel( + model_name=model_name, + generation_config=GenerativeModelConfig(temperature=0) + ) + + # Initialize the knowledge graph with the model and ontology + kg = KnowledgeGraph( + name=graph_name, + ontology=movie_actor_ontology, + model_config=KnowledgeGraphModelConfig.with_model(model), + ) + + # URL for a movie on IMDB + with open(TEST_FILE) as f: + string = f.read() + + sources = [Source_FromRawText(string)] + + # Process the source to populate the knowledge graph + kg.process_sources(sources) + + return kg + + +@pytest.fixture +def delete_kg(): + """ + Returns a function that deletes a given knowledge graph. + """ + def cleanup(kg): + logger.info("Cleaning up test graph...") + kg.delete() + + return cleanup + + +class TestGraphRAGPipeline: + """ + Test Knowledge Graph using LiteLLM for query processing and evaluation. + Tests the ability of the graph to answer questions based on processed sources. + """ + + def test_movie_actor_queries(self, knowledge_graph_setup, delete_kg): + """ + Test the knowledge graph's ability to answer questions about actors and movies. + """ + kg = knowledge_graph_setup + + # Configure evaluation metrics + relevancy_metric = GraphContextualRelevancy(threshold=0.5) + recall_metric = GraphContextualRecall(threshold=0.5) + scores = [] + + # Run each test case and evaluate metrics + for case in BENCHMARK: + chat = kg.chat_session() + answer = chat.send_message(case["query"]) + + # Create a test case for evaluation + test_case = LLMTestCase( + input=case["query"], + actual_output=answer["response"], + retrieval_context=[answer["context"]], + context=[answer["context"]], + name="knowledge_graph_test", + expected_output=case["expected"], + additional_metadata=answer["cypher"], + ) + + # Measure metrics + relevancy_score = relevancy_metric.measure(test_case) + recall_score = recall_metric.measure(test_case) + + # Calculate and store average score + combined_score = (relevancy_score + recall_score) / 2 + scores.append(combined_score) + + # Log results for debugging + logger.info(f"Query: {case['query']}") + logger.info(f"Response: {answer['response']}") + logger.info(f"Combined Score: {combined_score}") + + # Clean up by deleting the graph + delete_kg(kg) + + # Verify that the average score meets the threshold + assert len(scores) > 0, "No scores were collected" + average_score = sum(scores) / len(scores) + logger.info(f"Average Score: {average_score}") + assert average_score >= 0.5, f"Average score {average_score} is below threshold of 0.5" \ No newline at end of file diff --git a/tests/test_streaming_response.py b/tests/test_streaming_response.py new file mode 100644 index 00000000..f6b2b9b4 --- /dev/null +++ b/tests/test_streaming_response.py @@ -0,0 +1,192 @@ +import os +import pytest +import logging +from dotenv import load_dotenv +from graphrag_sdk.entity import Entity +from deepeval.test_case import LLMTestCase +from graphrag_sdk.relation import Relation +from graphrag_sdk.ontology import Ontology +from graphrag_sdk.models.litellm import LiteModel +from graphrag_sdk.source import Source_FromRawText +from graphrag_sdk.attribute import Attribute, AttributeType +# Custom metrics of DeepEval +from graphrag_sdk.graph_metrics import GraphContextualRelevancy, GraphContextualRecall +from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig + + +load_dotenv() +TEST_FILE = "tests/data/madoff.txt" + +# Test case +USECASE = { + "query": "How many actors acted in a movie?\nPlease give me full details of the actors with a long output.", + "expected": "More than 10 actors acted in a movie." + } + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger(__name__) + + +@pytest.fixture +def movie_actor_ontology(): + """ + Create and return an ontology for movies and actors with the ACTED_IN relationship. + """ + ontology = Ontology() + + # Add Actor entity with name attribute + ontology.add_entity( + Entity( + label="Actor", + attributes=[ + Attribute( + name="name", + attr_type=AttributeType.STRING, + unique=True, + required=True, + ), + ], + ) + ) + + # Add Movie entity with title attribute + ontology.add_entity( + Entity( + label="Movie", + attributes=[ + Attribute( + name="title", + attr_type=AttributeType.STRING, + unique=True, + required=True, + ), + ], + ) + ) + + # Add ACTED_IN relation between Actor and Movie with role attribute + ontology.add_relation( + Relation( + label="ACTED_IN", + source="Actor", + target="Movie", + attributes=[ + Attribute( + name="role", + attr_type=AttributeType.STRING, + unique=False, + required=True, + ), + ], + ) + ) + + return ontology + + +@pytest.fixture +def knowledge_graph_setup(movie_actor_ontology): + """ + Sets up the knowledge graph using the provided ontology. + """ + # Get the model name from the environment variable, with a default + model_name = os.getenv("TEST_MODEL", "openai/gpt-4.1") + graph_name = model_name.split("/")[0] + + # Configure the model with zero temperature for deterministic outputs + model = LiteModel( + model_name=model_name, + generation_config=GenerativeModelConfig(temperature=0) + ) + + # Initialize the knowledge graph with the model and ontology + kg = KnowledgeGraph( + name=graph_name, + ontology=movie_actor_ontology, + model_config=KnowledgeGraphModelConfig.with_model(model), + ) + + # URL for a movie on IMDB + with open(TEST_FILE) as f: + string = f.read() + + sources = [Source_FromRawText(string)] + + # Process the source to populate the knowledge graph + kg.process_sources(sources) + + return kg + + +@pytest.fixture +def delete_kg(): + """ + Returns a function that deletes a given knowledge graph. + """ + def cleanup(kg): + logger.info("Cleaning up test graph...") + kg.delete() + + return cleanup + + +class TestStreamingResponse: + """ + Test the knowledge graph's ability to answer questions about actors and movies. + Test the streaming response of the knowledge graph. + """ + + def test_streaming(self, knowledge_graph_setup, delete_kg): + """ + Test the knowledge graph's ability to answer questions about actors and movies with streaming response. + """ + kg = knowledge_graph_setup + + # Configure evaluation metrics + relevancy_metric = GraphContextualRelevancy(threshold=0.5) + recall_metric = GraphContextualRecall(threshold=0.5) + + chat = kg.chat_session() + # Track that we received multiple chunks to verify streaming + received_chunks = [] + for chunk in chat.send_message_stream(USECASE["query"]): + logger.info(chunk) + received_chunks.append(chunk) + + # Verify that streaming actually occurred (received multiple chunks) + assert len(received_chunks) > 1, "Expected multiple chunks in streaming response" + + # Get the last complete response for the cypher and context + response_dict = chat.last_complete_response + answer = ''.join(received_chunks) + + assert answer.strip() == response_dict["response"].strip(), "Combined chunks (using join) should match last complete response" + assert chat.metadata.get("last_query_execution_time") is not None, "Expected last query execution time to be set" + + # Create a test case for evaluation + test_case = LLMTestCase( + input=USECASE["query"], + actual_output=answer, + retrieval_context=[response_dict["context"]], + context=[response_dict["context"]], + name="streaming_test", + expected_output=USECASE["expected"], + additional_metadata=response_dict["cypher"], + ) + + # Measure metrics + relevancy_score = relevancy_metric.measure(test_case) + recall_score = recall_metric.measure(test_case) + + # Calculate and store average score + combined_score = (relevancy_score + recall_score) / 2 + + # Log results for debugging + logger.info(f"Query: {USECASE['query']}") + logger.info(f"Response: {answer}") + logger.info(f"Combined Score: {combined_score}") + + # Clean up by deleting the graph + delete_kg(kg) + + assert combined_score >= 0.5, f"Average score {combined_score} is below 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