diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 493936ef10..0000000000 --- a/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 Azure Samples - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/SECURITY.md b/SECURITY.md deleted file mode 100644 index 388e9ad471..0000000000 --- a/SECURITY.md +++ /dev/null @@ -1,41 +0,0 @@ - - -## Security - -Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). - -If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](), please report it to us as described below. - -## Reporting Security Issues - -**Please do not report security vulnerabilities through public GitHub issues.** - -Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report). - -If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/msrc/pgp-key-msrc). - -You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). - -Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: - -- Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) -- Full paths of source file(s) related to the manifestation of the issue -- The location of the affected source code (tag/branch/commit or direct URL) -- Any special configuration required to reproduce the issue -- Step-by-step instructions to reproduce the issue -- Proof-of-concept or exploit code (if possible) -- Impact of the issue, including how an attacker might exploit the issue - -This information will help us triage your report more quickly. - -If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) page for more details about our active programs. - -## Preferred Languages - -We prefer all communications to be in English. - -## Policy - -Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/msrc/cvd). - - diff --git a/app/backend/.dockerignore b/app/backend/.dockerignore index 9008115fc8..f5a745bcdf 100644 --- a/app/backend/.dockerignore +++ b/app/backend/.dockerignore @@ -1,7 +1,34 @@ +# Git .git +.gitignore + +# Python __pycache__ *.pyc *.pyo *.pyd .Python env +.env +.venv +*.egg-info/ +.pytest_cache/ +.coverage +.mypy_cache/ +.ruff_cache/ + +# Development files +*.md +.vscode/ +.idea/ + +# Docker +Dockerfile +.dockerignore + +# UV cache (not needed in container) +.uv-cache/ + +# OS +.DS_Store +Thumbs.db diff --git a/app/backend/.python-version b/app/backend/.python-version new file mode 100644 index 0000000000..b6d8b7612f --- /dev/null +++ b/app/backend/.python-version @@ -0,0 +1 @@ +3.11.8 diff --git a/app/backend/Dockerfile b/app/backend/Dockerfile index a84bd6e0b7..94c1791155 100644 --- a/app/backend/Dockerfile +++ b/app/backend/Dockerfile @@ -1,11 +1,39 @@ -FROM python:3.11-bullseye +FROM python:3.11-slim as builder +# Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Set environment variables for uv +ENV UV_COMPILE_BYTECODE=1 +ENV UV_LINK_MODE=copy + +# Create app directory WORKDIR /app -COPY ./ /app +# Copy essential project files +# Copy dependency files +COPY pyproject.toml ./pyproject.toml +COPY uv.lock ./uv.lock + +# Install dependencies with uv (creates .venv) +RUN uv sync --frozen --no-dev + +# Production stage +FROM python:3.11-slim + +# Copy the virtual environment from builder stage +COPY --from=builder /app/.venv /app/.venv + +# Set working directory +WORKDIR /app -RUN python -m pip install -r requirements.txt +# Copy application code +COPY . . -RUN python -m pip install gunicorn +# Ensure we use the venv and set Python path +ENV PATH="/app/.venv/bin:$PATH" +ENV PYTHONPATH="/app:$PYTHONPATH" -CMD ["python3", "-m", "gunicorn", "-b", "0.0.0.0:8000", "main:app"] +# Run the application +EXPOSE 8000 +CMD ["python", "-m", "gunicorn", "-c", "gunicorn.conf.py", "hrchatbot.main:app"] diff --git a/app/backend/approaches/chatreadretrievereadvision.py b/app/backend/approaches/chatreadretrievereadvision.py deleted file mode 100644 index f8aaf3c37d..0000000000 --- a/app/backend/approaches/chatreadretrievereadvision.py +++ /dev/null @@ -1,222 +0,0 @@ -from collections.abc import Awaitable -from typing import Any, Callable, Optional, Union, cast - -from azure.search.documents.aio import SearchClient -from azure.storage.blob.aio import ContainerClient -from openai import AsyncOpenAI, AsyncStream -from openai.types.chat import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessageParam, - ChatCompletionToolParam, -) - -from approaches.approach import DataPoints, ExtraInfo, ThoughtStep -from approaches.chatapproach import ChatApproach -from approaches.promptmanager import PromptManager -from core.authentication import AuthenticationHelper -from core.imageshelper import fetch_image - - -class ChatReadRetrieveReadVisionApproach(ChatApproach): - """ - A multi-step approach that first uses OpenAI to turn the user's question into a search query, - then uses Azure AI Search to retrieve relevant documents, and then sends the conversation history, - original user question, and search results to OpenAI to generate a response. - """ - - def __init__( - self, - *, - search_client: SearchClient, - blob_container_client: ContainerClient, - openai_client: AsyncOpenAI, - auth_helper: AuthenticationHelper, - chatgpt_model: str, - chatgpt_deployment: Optional[str], # Not needed for non-Azure OpenAI - gpt4v_deployment: Optional[str], # Not needed for non-Azure OpenAI - gpt4v_model: str, - embedding_deployment: Optional[str], # Not needed for non-Azure OpenAI or for retrieval_mode="text" - embedding_model: str, - embedding_dimensions: int, - embedding_field: str, - sourcepage_field: str, - content_field: str, - query_language: str, - query_speller: str, - vision_endpoint: str, - vision_token_provider: Callable[[], Awaitable[str]], - prompt_manager: PromptManager, - ): - self.search_client = search_client - self.blob_container_client = blob_container_client - self.openai_client = openai_client - self.auth_helper = auth_helper - self.chatgpt_model = chatgpt_model - self.chatgpt_deployment = chatgpt_deployment - self.gpt4v_deployment = gpt4v_deployment - self.gpt4v_model = gpt4v_model - self.embedding_deployment = embedding_deployment - self.embedding_model = embedding_model - self.embedding_dimensions = embedding_dimensions - self.embedding_field = embedding_field - self.sourcepage_field = sourcepage_field - self.content_field = content_field - self.query_language = query_language - self.query_speller = query_speller - self.vision_endpoint = vision_endpoint - self.vision_token_provider = vision_token_provider - self.prompt_manager = prompt_manager - self.query_rewrite_prompt = self.prompt_manager.load_prompt("chat_query_rewrite.prompty") - self.query_rewrite_tools = self.prompt_manager.load_tools("chat_query_rewrite_tools.json") - self.answer_prompt = self.prompt_manager.load_prompt("chat_answer_question_vision.prompty") - # Currently disabled due to issues with rendering token usage in the UI - self.include_token_usage = False - - async def run_until_final_call( - self, - messages: list[ChatCompletionMessageParam], - overrides: dict[str, Any], - auth_claims: dict[str, Any], - should_stream: bool = False, - ) -> tuple[ExtraInfo, Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]]]: - seed = overrides.get("seed", None) - use_text_search = overrides.get("retrieval_mode") in ["text", "hybrid", None] - use_vector_search = overrides.get("retrieval_mode") in ["vectors", "hybrid", None] - use_semantic_ranker = True if overrides.get("semantic_ranker") else False - use_query_rewriting = True if overrides.get("query_rewriting") else False - use_semantic_captions = True if overrides.get("semantic_captions") else False - top = overrides.get("top", 3) - minimum_search_score = overrides.get("minimum_search_score", 0.0) - minimum_reranker_score = overrides.get("minimum_reranker_score", 0.0) - filter = self.build_filter(overrides, auth_claims) - - vector_fields = overrides.get("vector_fields", "textAndImageEmbeddings") - send_text_to_gptvision = overrides.get("gpt4v_input") in ["textAndImages", "texts", None] - send_images_to_gptvision = overrides.get("gpt4v_input") in ["textAndImages", "images", None] - - original_user_query = messages[-1]["content"] - if not isinstance(original_user_query, str): - raise ValueError("The most recent message content must be a string.") - - # Use prompty to prepare the query prompt - query_messages = self.prompt_manager.render_prompt( - self.query_rewrite_prompt, {"user_query": original_user_query, "past_messages": messages[:-1]} - ) - tools: list[ChatCompletionToolParam] = self.query_rewrite_tools - - # STEP 1: Generate an optimized keyword search query based on the chat history and the last question - chat_completion: ChatCompletion = await self.openai_client.chat.completions.create( - messages=query_messages, - # Azure OpenAI takes the deployment name as the model name - model=self.chatgpt_deployment if self.chatgpt_deployment else self.chatgpt_model, - temperature=0.0, # Minimize creativity for search query generation - max_tokens=100, - n=1, - tools=tools, - seed=seed, - ) - - query_text = self.get_search_query(chat_completion, original_user_query) - - # STEP 2: Retrieve relevant documents from the search index with the GPT optimized query - - # If retrieval mode includes vectors, compute an embedding for the query - vectors = [] - if use_vector_search: - if vector_fields == "textEmbeddingOnly" or vector_fields == "textAndImageEmbeddings": - vectors.append(await self.compute_text_embedding(query_text)) - if vector_fields == "imageEmbeddingOnly" or vector_fields == "textAndImageEmbeddings": - vectors.append(await self.compute_image_embedding(query_text)) - - results = await self.search( - top, - query_text, - filter, - vectors, - use_text_search, - use_vector_search, - use_semantic_ranker, - use_semantic_captions, - minimum_search_score, - minimum_reranker_score, - use_query_rewriting, - ) - - # STEP 3: Generate a contextual and content specific answer using the search results and chat history - text_sources = [] - image_sources = [] - if send_text_to_gptvision: - text_sources = self.get_sources_content(results, use_semantic_captions, use_image_citation=True) - if send_images_to_gptvision: - for result in results: - url = await fetch_image(self.blob_container_client, result) - if url: - image_sources.append(url) - - messages = self.prompt_manager.render_prompt( - self.answer_prompt, - self.get_system_prompt_variables(overrides.get("prompt_template")) - | { - "include_follow_up_questions": bool(overrides.get("suggest_followup_questions")), - "past_messages": messages[:-1], - "user_query": original_user_query, - "text_sources": text_sources, - "image_sources": image_sources, - }, - ) - - extra_info = ExtraInfo( - DataPoints(text=text_sources, images=image_sources), - [ - ThoughtStep( - "Prompt to generate search query", - query_messages, - ( - {"model": self.chatgpt_model, "deployment": self.chatgpt_deployment} - if self.chatgpt_deployment - else {"model": self.chatgpt_model} - ), - ), - ThoughtStep( - "Search using generated search query", - query_text, - { - "use_semantic_captions": use_semantic_captions, - "use_semantic_ranker": use_semantic_ranker, - "use_query_rewriting": use_query_rewriting, - "top": top, - "filter": filter, - "vector_fields": vector_fields, - "use_text_search": use_text_search, - }, - ), - ThoughtStep( - "Search results", - [result.serialize_for_results() for result in results], - ), - ThoughtStep( - "Prompt to generate answer", - messages, - ( - {"model": self.gpt4v_model, "deployment": self.gpt4v_deployment} - if self.gpt4v_deployment - else {"model": self.gpt4v_model} - ), - ), - ], - ) - - chat_coroutine = cast( - Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]], - self.openai_client.chat.completions.create( - model=self.gpt4v_deployment if self.gpt4v_deployment else self.gpt4v_model, - messages=messages, - temperature=overrides.get("temperature", 0.3), - max_tokens=1024, - n=1, - stream=should_stream, - seed=seed, - ), - ) - return (extra_info, chat_coroutine) diff --git a/app/backend/approaches/retrievethenread.py b/app/backend/approaches/retrievethenread.py deleted file mode 100644 index d59f903b0e..0000000000 --- a/app/backend/approaches/retrievethenread.py +++ /dev/null @@ -1,232 +0,0 @@ -from typing import Any, Optional, cast - -from azure.search.documents.agent.aio import KnowledgeAgentRetrievalClient -from azure.search.documents.aio import SearchClient -from azure.search.documents.models import VectorQuery -from openai import AsyncOpenAI -from openai.types.chat import ChatCompletion, ChatCompletionMessageParam - -from approaches.approach import Approach, DataPoints, ExtraInfo, ThoughtStep -from approaches.promptmanager import PromptManager -from core.authentication import AuthenticationHelper - - -class RetrieveThenReadApproach(Approach): - """ - Simple retrieve-then-read implementation, using the AI Search and OpenAI APIs directly. It first retrieves - top documents from search, then constructs a prompt with them, and then uses OpenAI to generate an completion - (answer) with that prompt. - """ - - def __init__( - self, - *, - search_client: SearchClient, - search_index_name: str, - agent_model: Optional[str], - agent_deployment: Optional[str], - agent_client: KnowledgeAgentRetrievalClient, - auth_helper: AuthenticationHelper, - openai_client: AsyncOpenAI, - chatgpt_model: str, - chatgpt_deployment: Optional[str], # Not needed for non-Azure OpenAI - embedding_model: str, - embedding_deployment: Optional[str], # Not needed for non-Azure OpenAI or for retrieval_mode="text" - embedding_dimensions: int, - embedding_field: str, - sourcepage_field: str, - content_field: str, - query_language: str, - query_speller: str, - prompt_manager: PromptManager, - reasoning_effort: Optional[str] = None, - ): - self.search_client = search_client - self.search_index_name = search_index_name - self.agent_model = agent_model - self.agent_deployment = agent_deployment - self.agent_client = agent_client - self.chatgpt_deployment = chatgpt_deployment - self.openai_client = openai_client - self.auth_helper = auth_helper - self.chatgpt_model = chatgpt_model - self.embedding_model = embedding_model - self.embedding_dimensions = embedding_dimensions - self.chatgpt_deployment = chatgpt_deployment - self.embedding_deployment = embedding_deployment - self.embedding_field = embedding_field - self.sourcepage_field = sourcepage_field - self.content_field = content_field - self.query_language = query_language - self.query_speller = query_speller - self.prompt_manager = prompt_manager - self.answer_prompt = self.prompt_manager.load_prompt("ask_answer_question.prompty") - self.reasoning_effort = reasoning_effort - self.include_token_usage = True - - async def run( - self, - messages: list[ChatCompletionMessageParam], - session_state: Any = None, - context: dict[str, Any] = {}, - ) -> dict[str, Any]: - overrides = context.get("overrides", {}) - auth_claims = context.get("auth_claims", {}) - use_agentic_retrieval = True if overrides.get("use_agentic_retrieval") else False - q = messages[-1]["content"] - if not isinstance(q, str): - raise ValueError("The most recent message content must be a string.") - - if use_agentic_retrieval: - extra_info = await self.run_agentic_retrieval_approach(messages, overrides, auth_claims) - else: - extra_info = await self.run_search_approach(messages, overrides, auth_claims) - - # Process results - messages = self.prompt_manager.render_prompt( - self.answer_prompt, - self.get_system_prompt_variables(overrides.get("prompt_template")) - | {"user_query": q, "text_sources": extra_info.data_points.text}, - ) - - chat_completion = cast( - ChatCompletion, - await self.create_chat_completion( - self.chatgpt_deployment, - self.chatgpt_model, - messages=messages, - overrides=overrides, - response_token_limit=self.get_response_token_limit(self.chatgpt_model, 1024), - ), - ) - extra_info.thoughts.append( - self.format_thought_step_for_chatcompletion( - title="Prompt to generate answer", - messages=messages, - overrides=overrides, - model=self.chatgpt_model, - deployment=self.chatgpt_deployment, - usage=chat_completion.usage, - ) - ) - return { - "message": { - "content": chat_completion.choices[0].message.content, - "role": chat_completion.choices[0].message.role, - }, - "context": extra_info, - "session_state": session_state, - } - - async def run_search_approach( - self, messages: list[ChatCompletionMessageParam], overrides: dict[str, Any], auth_claims: dict[str, Any] - ): - use_text_search = overrides.get("retrieval_mode") in ["text", "hybrid", None] - use_vector_search = overrides.get("retrieval_mode") in ["vectors", "hybrid", None] - use_semantic_ranker = True if overrides.get("semantic_ranker") else False - use_query_rewriting = True if overrides.get("query_rewriting") else False - use_semantic_captions = True if overrides.get("semantic_captions") else False - top = overrides.get("top", 3) - minimum_search_score = overrides.get("minimum_search_score", 0.0) - minimum_reranker_score = overrides.get("minimum_reranker_score", 0.0) - filter = self.build_filter(overrides, auth_claims) - q = str(messages[-1]["content"]) - - # If retrieval mode includes vectors, compute an embedding for the query - vectors: list[VectorQuery] = [] - if use_vector_search: - vectors.append(await self.compute_text_embedding(q)) - - results = await self.search( - top, - q, - filter, - vectors, - use_text_search, - use_vector_search, - use_semantic_ranker, - use_semantic_captions, - minimum_search_score, - minimum_reranker_score, - use_query_rewriting, - ) - - text_sources = self.get_sources_content(results, use_semantic_captions, use_image_citation=False) - - return ExtraInfo( - DataPoints(text=text_sources), - thoughts=[ - ThoughtStep( - "Search using user query", - q, - { - "use_semantic_captions": use_semantic_captions, - "use_semantic_ranker": use_semantic_ranker, - "use_query_rewriting": use_query_rewriting, - "top": top, - "filter": filter, - "use_vector_search": use_vector_search, - "use_text_search": use_text_search, - }, - ), - ThoughtStep( - "Search results", - [result.serialize_for_results() for result in results], - ), - ], - ) - - async def run_agentic_retrieval_approach( - self, - messages: list[ChatCompletionMessageParam], - overrides: dict[str, Any], - auth_claims: dict[str, Any], - ): - minimum_reranker_score = overrides.get("minimum_reranker_score", 0) - search_index_filter = self.build_filter(overrides, auth_claims) - top = overrides.get("top", 3) - max_subqueries = overrides.get("max_subqueries", 10) - results_merge_strategy = overrides.get("results_merge_strategy", "interleaved") - # 50 is the amount of documents that the reranker can process per query - max_docs_for_reranker = max_subqueries * 50 - - response, results = await self.run_agentic_retrieval( - messages, - self.agent_client, - search_index_name=self.search_index_name, - top=top, - filter_add_on=search_index_filter, - minimum_reranker_score=minimum_reranker_score, - max_docs_for_reranker=max_docs_for_reranker, - results_merge_strategy=results_merge_strategy, - ) - - text_sources = self.get_sources_content(results, use_semantic_captions=False, use_image_citation=False) - - extra_info = ExtraInfo( - DataPoints(text=text_sources), - thoughts=[ - ThoughtStep( - "Use agentic retrieval", - messages, - { - "reranker_threshold": minimum_reranker_score, - "max_docs_for_reranker": max_docs_for_reranker, - "results_merge_strategy": results_merge_strategy, - "filter": search_index_filter, - }, - ), - ThoughtStep( - f"Agentic retrieval results (top {top})", - [result.serialize_for_results() for result in results], - { - "query_plan": ( - [activity.as_dict() for activity in response.activity] if response.activity else None - ), - "model": self.agent_model, - "deployment": self.agent_deployment, - }, - ), - ], - ) - return extra_info diff --git a/app/backend/approaches/retrievethenreadvision.py b/app/backend/approaches/retrievethenreadvision.py deleted file mode 100644 index a021537c52..0000000000 --- a/app/backend/approaches/retrievethenreadvision.py +++ /dev/null @@ -1,181 +0,0 @@ -from collections.abc import Awaitable -from typing import Any, Callable, Optional - -from azure.search.documents.aio import SearchClient -from azure.storage.blob.aio import ContainerClient -from openai import AsyncOpenAI -from openai.types.chat import ( - ChatCompletionMessageParam, -) - -from approaches.approach import Approach, DataPoints, ExtraInfo, ThoughtStep -from approaches.promptmanager import PromptManager -from core.authentication import AuthenticationHelper -from core.imageshelper import fetch_image - - -class RetrieveThenReadVisionApproach(Approach): - """ - Simple retrieve-then-read implementation, using the AI Search and OpenAI APIs directly. It first retrieves - top documents including images from search, then constructs a prompt with them, and then uses OpenAI to generate an completion - (answer) with that prompt. - """ - - def __init__( - self, - *, - search_client: SearchClient, - blob_container_client: ContainerClient, - openai_client: AsyncOpenAI, - auth_helper: AuthenticationHelper, - gpt4v_deployment: Optional[str], - gpt4v_model: str, - embedding_deployment: Optional[str], # Not needed for non-Azure OpenAI or for retrieval_mode="text" - embedding_model: str, - embedding_dimensions: int, - embedding_field: str, - sourcepage_field: str, - content_field: str, - query_language: str, - query_speller: str, - vision_endpoint: str, - vision_token_provider: Callable[[], Awaitable[str]], - prompt_manager: PromptManager, - ): - self.search_client = search_client - self.blob_container_client = blob_container_client - self.openai_client = openai_client - self.auth_helper = auth_helper - self.embedding_model = embedding_model - self.embedding_deployment = embedding_deployment - self.embedding_dimensions = embedding_dimensions - self.embedding_field = embedding_field - self.sourcepage_field = sourcepage_field - self.content_field = content_field - self.gpt4v_deployment = gpt4v_deployment - self.gpt4v_model = gpt4v_model - self.query_language = query_language - self.query_speller = query_speller - self.vision_endpoint = vision_endpoint - self.vision_token_provider = vision_token_provider - self.prompt_manager = prompt_manager - self.answer_prompt = self.prompt_manager.load_prompt("ask_answer_question_vision.prompty") - # Currently disabled due to issues with rendering token usage in the UI - self.include_token_usage = False - - async def run( - self, - messages: list[ChatCompletionMessageParam], - session_state: Any = None, - context: dict[str, Any] = {}, - ) -> dict[str, Any]: - q = messages[-1]["content"] - if not isinstance(q, str): - raise ValueError("The most recent message content must be a string.") - - overrides = context.get("overrides", {}) - seed = overrides.get("seed", None) - auth_claims = context.get("auth_claims", {}) - use_text_search = overrides.get("retrieval_mode") in ["text", "hybrid", None] - use_vector_search = overrides.get("retrieval_mode") in ["vectors", "hybrid", None] - use_semantic_ranker = True if overrides.get("semantic_ranker") else False - use_query_rewriting = True if overrides.get("query_rewriting") else False - use_semantic_captions = True if overrides.get("semantic_captions") else False - top = overrides.get("top", 3) - minimum_search_score = overrides.get("minimum_search_score", 0.0) - minimum_reranker_score = overrides.get("minimum_reranker_score", 0.0) - filter = self.build_filter(overrides, auth_claims) - - vector_fields = overrides.get("vector_fields", "textAndImageEmbeddings") - send_text_to_gptvision = overrides.get("gpt4v_input") in ["textAndImages", "texts", None] - send_images_to_gptvision = overrides.get("gpt4v_input") in ["textAndImages", "images", None] - - # If retrieval mode includes vectors, compute an embedding for the query - vectors = [] - if use_vector_search: - if vector_fields == "textEmbeddingOnly" or vector_fields == "textAndImageEmbeddings": - vectors.append(await self.compute_text_embedding(q)) - if vector_fields == "imageEmbeddingOnly" or vector_fields == "textAndImageEmbeddings": - vectors.append(await self.compute_image_embedding(q)) - - results = await self.search( - top, - q, - filter, - vectors, - use_text_search, - use_vector_search, - use_semantic_ranker, - use_semantic_captions, - minimum_search_score, - minimum_reranker_score, - use_query_rewriting, - ) - - # Process results - text_sources = [] - image_sources = [] - if send_text_to_gptvision: - text_sources = self.get_sources_content(results, use_semantic_captions, use_image_citation=True) - if send_images_to_gptvision: - for result in results: - url = await fetch_image(self.blob_container_client, result) - if url: - image_sources.append(url) - - messages = self.prompt_manager.render_prompt( - self.answer_prompt, - self.get_system_prompt_variables(overrides.get("prompt_template")) - | {"user_query": q, "text_sources": text_sources, "image_sources": image_sources}, - ) - - chat_completion = await self.openai_client.chat.completions.create( - model=self.gpt4v_deployment if self.gpt4v_deployment else self.gpt4v_model, - messages=messages, - temperature=overrides.get("temperature", 0.3), - max_tokens=1024, - n=1, - seed=seed, - ) - - extra_info = ExtraInfo( - DataPoints(text=text_sources, images=image_sources), - [ - ThoughtStep( - "Search using user query", - q, - { - "use_semantic_captions": use_semantic_captions, - "use_semantic_ranker": use_semantic_ranker, - "use_query_rewriting": use_query_rewriting, - "top": top, - "filter": filter, - "vector_fields": vector_fields, - "use_vector_search": use_vector_search, - "use_text_search": use_text_search, - }, - ), - ThoughtStep( - "Search results", - [result.serialize_for_results() for result in results], - ), - ThoughtStep( - "Prompt to generate answer", - messages, - ( - {"model": self.gpt4v_model, "deployment": self.gpt4v_deployment} - if self.gpt4v_deployment - else {"model": self.gpt4v_model} - ), - ), - ], - ) - - return { - "message": { - "content": chat_completion.choices[0].message.content, - "role": chat_completion.choices[0].message.role, - }, - "context": extra_info, - "session_state": session_state, - } diff --git a/app/backend/gunicorn.conf.py b/app/backend/gunicorn.conf.py index 9144e3cc00..5ff90daa06 100644 --- a/app/backend/gunicorn.conf.py +++ b/app/backend/gunicorn.conf.py @@ -15,4 +15,4 @@ workers = 1 else: workers = (num_cpus * 2) + 1 -worker_class = "custom_uvicorn_worker.CustomUvicornWorker" +worker_class = "hrchatbot.custom_uvicorn_worker.CustomUvicornWorker" diff --git a/app/backend/approaches/__init__.py b/app/backend/hrchatbot/__init__.py similarity index 100% rename from app/backend/approaches/__init__.py rename to app/backend/hrchatbot/__init__.py diff --git a/app/backend/app.py b/app/backend/hrchatbot/app.py similarity index 73% rename from app/backend/app.py rename to app/backend/hrchatbot/app.py index 1b4563bb98..3abadaff7a 100644 --- a/app/backend/app.py +++ b/app/backend/hrchatbot/app.py @@ -50,14 +50,11 @@ ) from quart_cors import cors -from approaches.approach import Approach -from approaches.chatreadretrieveread import ChatReadRetrieveReadApproach -from approaches.chatreadretrievereadvision import ChatReadRetrieveReadVisionApproach -from approaches.promptmanager import PromptyManager -from approaches.retrievethenread import RetrieveThenReadApproach -from approaches.retrievethenreadvision import RetrieveThenReadVisionApproach -from chat_history.cosmosdb import chat_history_cosmosdb_bp -from config import ( +from hrchatbot.approaches.approach import Approach +from hrchatbot.approaches.chatreadretrieveread import ChatReadRetrieveReadApproach +from hrchatbot.approaches.promptmanager import PromptyManager +from hrchatbot.chat_history.cosmosdb import chat_history_cosmosdb_bp +from hrchatbot.config import ( CONFIG_AGENT_CLIENT, CONFIG_AGENTIC_RETRIEVAL_ENABLED, CONFIG_ASK_APPROACH, @@ -90,20 +87,20 @@ CONFIG_USER_UPLOAD_ENABLED, CONFIG_VECTOR_SEARCH_ENABLED, ) -from core.authentication import AuthenticationHelper -from core.sessionhelper import create_session_id -from decorators import authenticated, authenticated_path -from error import error_dict, error_response -from prepdocs import ( +from hrchatbot.core.authentication import AuthenticationHelper +from hrchatbot.core.sessionhelper import create_session_id +from hrchatbot.decorators import authenticated, authenticated_path +from hrchatbot.error import error_dict, error_response +from hrchatbot.prepdocs import ( clean_key_if_exists, setup_embeddings_service, setup_file_processors, setup_search_info, ) -from prepdocslib.filestrategy import UploadUserFileStrategy -from prepdocslib.listfilestrategy import File +from hrchatbot.prepdocslib.filestrategy import UploadUserFileStrategy +from hrchatbot.prepdocslib.listfilestrategy import File -bp = Blueprint("routes", __name__, static_folder="static") +bp = Blueprint("routes", __name__, static_folder="../static") # Fix Windows registry issue with mimetypes mimetypes.add_type("application/javascript", ".js") mimetypes.add_type("text/css", ".css") @@ -128,7 +125,9 @@ async def favicon(): @bp.route("/assets/") async def assets(path): - return await send_from_directory(Path(__file__).resolve().parent / "static" / "assets", path) + return await send_from_directory( + Path(__file__).resolve().parent.parent / "static" / "assets", path + ) @bp.route("/content/") @@ -147,7 +146,9 @@ async def content_file(path: str, auth_claims: dict[str, Any]): path_parts = path.rsplit("#page=", 1) path = path_parts[0] current_app.logger.info("Opening file %s", path) - blob_container_client: ContainerClient = current_app.config[CONFIG_BLOB_CONTAINER_CLIENT] + blob_container_client: ContainerClient = current_app.config[ + CONFIG_BLOB_CONTAINER_CLIENT + ] blob: Union[BlobDownloader, DatalakeDownloader] try: blob = await blob_container_client.get_blob_client(path).download_blob() @@ -156,8 +157,12 @@ async def content_file(path: str, auth_claims: dict[str, Any]): if current_app.config[CONFIG_USER_UPLOAD_ENABLED]: try: user_oid = auth_claims["oid"] - user_blob_container_client = current_app.config[CONFIG_USER_BLOB_CONTAINER_CLIENT] - user_directory_client: FileSystemClient = user_blob_container_client.get_directory_client(user_oid) + user_blob_container_client = current_app.config[ + CONFIG_USER_BLOB_CONTAINER_CLIENT + ] + user_directory_client: FileSystemClient = ( + user_blob_container_client.get_directory_client(user_oid) + ) file_client = user_directory_client.get_file_client(path) blob = await file_client.download_file() except ResourceNotFoundError: @@ -173,7 +178,9 @@ async def content_file(path: str, auth_claims: dict[str, Any]): blob_file = io.BytesIO() await blob.readinto(blob_file) blob_file.seek(0) - return await send_file(blob_file, mimetype=mime_type, as_attachment=False, attachment_filename=path) + return await send_file( + blob_file, mimetype=mime_type, as_attachment=False, attachment_filename=path + ) @bp.route("/ask", methods=["POST"]) @@ -192,7 +199,9 @@ async def ask(auth_claims: dict[str, Any]): else: approach = cast(Approach, current_app.config[CONFIG_ASK_APPROACH]) r = await approach.run( - request_json["messages"], context=context, session_state=request_json.get("session_state") + request_json["messages"], + context=context, + session_state=request_json.get("session_state"), ) return jsonify(r) except Exception as error: @@ -298,20 +307,38 @@ def config(): return jsonify( { "showGPT4VOptions": current_app.config[CONFIG_GPT4V_DEPLOYED], - "showSemanticRankerOption": current_app.config[CONFIG_SEMANTIC_RANKER_DEPLOYED], - "showQueryRewritingOption": current_app.config[CONFIG_QUERY_REWRITING_ENABLED], - "showReasoningEffortOption": current_app.config[CONFIG_REASONING_EFFORT_ENABLED], + "showSemanticRankerOption": current_app.config[ + CONFIG_SEMANTIC_RANKER_DEPLOYED + ], + "showQueryRewritingOption": current_app.config[ + CONFIG_QUERY_REWRITING_ENABLED + ], + "showReasoningEffortOption": current_app.config[ + CONFIG_REASONING_EFFORT_ENABLED + ], "streamingEnabled": current_app.config[CONFIG_STREAMING_ENABLED], - "defaultReasoningEffort": current_app.config[CONFIG_DEFAULT_REASONING_EFFORT], + "defaultReasoningEffort": current_app.config[ + CONFIG_DEFAULT_REASONING_EFFORT + ], "showVectorOption": current_app.config[CONFIG_VECTOR_SEARCH_ENABLED], "showUserUpload": current_app.config[CONFIG_USER_UPLOAD_ENABLED], "showLanguagePicker": current_app.config[CONFIG_LANGUAGE_PICKER_ENABLED], "showSpeechInput": current_app.config[CONFIG_SPEECH_INPUT_ENABLED], - "showSpeechOutputBrowser": current_app.config[CONFIG_SPEECH_OUTPUT_BROWSER_ENABLED], - "showSpeechOutputAzure": current_app.config[CONFIG_SPEECH_OUTPUT_AZURE_ENABLED], - "showChatHistoryBrowser": current_app.config[CONFIG_CHAT_HISTORY_BROWSER_ENABLED], - "showChatHistoryCosmos": current_app.config[CONFIG_CHAT_HISTORY_COSMOS_ENABLED], - "showAgenticRetrievalOption": current_app.config[CONFIG_AGENTIC_RETRIEVAL_ENABLED], + "showSpeechOutputBrowser": current_app.config[ + CONFIG_SPEECH_OUTPUT_BROWSER_ENABLED + ], + "showSpeechOutputAzure": current_app.config[ + CONFIG_SPEECH_OUTPUT_AZURE_ENABLED + ], + "showChatHistoryBrowser": current_app.config[ + CONFIG_CHAT_HISTORY_BROWSER_ENABLED + ], + "showChatHistoryCosmos": current_app.config[ + CONFIG_CHAT_HISTORY_COSMOS_ENABLED + ], + "showAgenticRetrievalOption": current_app.config[ + CONFIG_AGENTIC_RETRIEVAL_ENABLED + ], } ) @@ -339,9 +366,16 @@ async def speech(): + "#" + current_app.config[CONFIG_SPEECH_SERVICE_TOKEN].token ) - speech_config = SpeechConfig(auth_token=auth_token, region=current_app.config[CONFIG_SPEECH_SERVICE_LOCATION]) - speech_config.speech_synthesis_voice_name = current_app.config[CONFIG_SPEECH_SERVICE_VOICE] - speech_config.speech_synthesis_output_format = SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3 + speech_config = SpeechConfig( + auth_token=auth_token, + region=current_app.config[CONFIG_SPEECH_SERVICE_LOCATION], + ) + speech_config.speech_synthesis_voice_name = current_app.config[ + CONFIG_SPEECH_SERVICE_VOICE + ] + speech_config.speech_synthesis_output_format = ( + SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3 + ) synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=None) result: SpeechSynthesisResult = synthesizer.speak_text_async(text).get() if result.reason == ResultReason.SynthesizingAudioCompleted: @@ -349,7 +383,9 @@ async def speech(): elif result.reason == ResultReason.Canceled: cancellation_details = result.cancellation_details current_app.logger.error( - "Speech synthesis canceled: %s %s", cancellation_details.reason, cancellation_details.error_details + "Speech synthesis canceled: %s %s", + cancellation_details.reason, + cancellation_details.error_details, ) raise Exception("Speech synthesis canceled. Check logs for details.") else: @@ -366,11 +402,15 @@ async def upload(auth_claims: dict[str, Any]): request_files = await request.files if "file" not in request_files: # If no files were included in the request, return an error response - return jsonify({"message": "No file part in the request", "status": "failed"}), 400 + return jsonify( + {"message": "No file part in the request", "status": "failed"} + ), 400 user_oid = auth_claims["oid"] file = request_files.getlist("file")[0] - user_blob_container_client: FileSystemClient = current_app.config[CONFIG_USER_BLOB_CONTAINER_CLIENT] + user_blob_container_client: FileSystemClient = current_app.config[ + CONFIG_USER_BLOB_CONTAINER_CLIENT + ] user_directory_client = user_blob_container_client.get_directory_client(user_oid) try: await user_directory_client.get_directory_properties() @@ -382,10 +422,14 @@ async def upload(auth_claims: dict[str, Any]): file_io = file file_io.name = file.filename file_io = io.BufferedReader(file_io) - await file_client.upload_data(file_io, overwrite=True, metadata={"UploadedBy": user_oid}) + await file_client.upload_data( + file_io, overwrite=True, metadata={"UploadedBy": user_oid} + ) file_io.seek(0) ingester: UploadUserFileStrategy = current_app.config[CONFIG_INGESTER] - await ingester.add_file(File(content=file_io, acls={"oids": [user_oid]}, url=file_client.url)) + await ingester.add_file( + File(content=file_io, acls={"oids": [user_oid]}, url=file_client.url) + ) return jsonify({"message": "File uploaded successfully"}), 200 @@ -395,7 +439,9 @@ async def delete_uploaded(auth_claims: dict[str, Any]): request_json = await request.get_json() filename = request_json.get("filename") user_oid = auth_claims["oid"] - user_blob_container_client: FileSystemClient = current_app.config[CONFIG_USER_BLOB_CONTAINER_CLIENT] + user_blob_container_client: FileSystemClient = current_app.config[ + CONFIG_USER_BLOB_CONTAINER_CLIENT + ] user_directory_client = user_blob_container_client.get_directory_client(user_oid) file_client = user_directory_client.get_file_client(filename) await file_client.delete_file() @@ -408,7 +454,9 @@ async def delete_uploaded(auth_claims: dict[str, Any]): @authenticated async def list_uploaded(auth_claims: dict[str, Any]): user_oid = auth_claims["oid"] - user_blob_container_client: FileSystemClient = current_app.config[CONFIG_USER_BLOB_CONTAINER_CLIENT] + user_blob_container_client: FileSystemClient = current_app.config[ + CONFIG_USER_BLOB_CONTAINER_CLIENT + ] files = [] try: all_paths = user_blob_container_client.get_paths(path=user_oid) @@ -435,8 +483,12 @@ async def setup_clients(): OPENAI_HOST = os.getenv("OPENAI_HOST", "azure") OPENAI_CHATGPT_MODEL = os.environ["AZURE_OPENAI_CHATGPT_MODEL"] AZURE_OPENAI_SEARCHAGENT_MODEL = os.getenv("AZURE_OPENAI_SEARCHAGENT_MODEL") - AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT = os.getenv("AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT") - OPENAI_EMB_MODEL = os.getenv("AZURE_OPENAI_EMB_MODEL_NAME", "text-embedding-ada-002") + AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT = os.getenv( + "AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT" + ) + OPENAI_EMB_MODEL = os.getenv( + "AZURE_OPENAI_EMB_MODEL_NAME", "text-embedding-ada-002" + ) OPENAI_EMB_DIMENSIONS = int(os.getenv("AZURE_OPENAI_EMB_DIMENSIONS") or 1536) OPENAI_REASONING_EFFORT = os.getenv("AZURE_OPENAI_REASONING_EFFORT") # Used with Azure OpenAI deployments @@ -444,9 +496,15 @@ async def setup_clients(): AZURE_OPENAI_GPT4V_DEPLOYMENT = os.environ.get("AZURE_OPENAI_GPT4V_DEPLOYMENT") AZURE_OPENAI_GPT4V_MODEL = os.environ.get("AZURE_OPENAI_GPT4V_MODEL") AZURE_OPENAI_CHATGPT_DEPLOYMENT = ( - os.getenv("AZURE_OPENAI_CHATGPT_DEPLOYMENT") if OPENAI_HOST.startswith("azure") else None + os.getenv("AZURE_OPENAI_CHATGPT_DEPLOYMENT") + if OPENAI_HOST.startswith("azure") + else None + ) + AZURE_OPENAI_EMB_DEPLOYMENT = ( + os.getenv("AZURE_OPENAI_EMB_DEPLOYMENT") + if OPENAI_HOST.startswith("azure") + else None ) - AZURE_OPENAI_EMB_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMB_DEPLOYMENT") if OPENAI_HOST.startswith("azure") else None AZURE_OPENAI_CUSTOM_URL = os.getenv("AZURE_OPENAI_CUSTOM_URL") # https://learn.microsoft.com/azure/ai-services/openai/api-version-deprecation#latest-ga-api-release AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION") or "2024-10-21" @@ -456,10 +514,18 @@ async def setup_clients(): OPENAI_ORGANIZATION = os.getenv("OPENAI_ORGANIZATION") AZURE_TENANT_ID = os.getenv("AZURE_TENANT_ID") - AZURE_USE_AUTHENTICATION = os.getenv("AZURE_USE_AUTHENTICATION", "").lower() == "true" - AZURE_ENFORCE_ACCESS_CONTROL = os.getenv("AZURE_ENFORCE_ACCESS_CONTROL", "").lower() == "true" - AZURE_ENABLE_GLOBAL_DOCUMENT_ACCESS = os.getenv("AZURE_ENABLE_GLOBAL_DOCUMENT_ACCESS", "").lower() == "true" - AZURE_ENABLE_UNAUTHENTICATED_ACCESS = os.getenv("AZURE_ENABLE_UNAUTHENTICATED_ACCESS", "").lower() == "true" + AZURE_USE_AUTHENTICATION = ( + os.getenv("AZURE_USE_AUTHENTICATION", "").lower() == "true" + ) + AZURE_ENFORCE_ACCESS_CONTROL = ( + os.getenv("AZURE_ENFORCE_ACCESS_CONTROL", "").lower() == "true" + ) + AZURE_ENABLE_GLOBAL_DOCUMENT_ACCESS = ( + os.getenv("AZURE_ENABLE_GLOBAL_DOCUMENT_ACCESS", "").lower() == "true" + ) + AZURE_ENABLE_UNAUTHENTICATED_ACCESS = ( + os.getenv("AZURE_ENABLE_UNAUTHENTICATED_ACCESS", "").lower() == "true" + ) AZURE_SERVER_APP_ID = os.getenv("AZURE_SERVER_APP_ID") AZURE_SERVER_APP_SECRET = os.getenv("AZURE_SERVER_APP_SECRET") AZURE_CLIENT_APP_ID = os.getenv("AZURE_CLIENT_APP_ID") @@ -470,51 +536,78 @@ async def setup_clients(): AZURE_SEARCH_QUERY_LANGUAGE = os.getenv("AZURE_SEARCH_QUERY_LANGUAGE") or "en-us" AZURE_SEARCH_QUERY_SPELLER = os.getenv("AZURE_SEARCH_QUERY_SPELLER") or "lexicon" - AZURE_SEARCH_SEMANTIC_RANKER = os.getenv("AZURE_SEARCH_SEMANTIC_RANKER", "free").lower() - AZURE_SEARCH_QUERY_REWRITING = os.getenv("AZURE_SEARCH_QUERY_REWRITING", "false").lower() + AZURE_SEARCH_SEMANTIC_RANKER = os.getenv( + "AZURE_SEARCH_SEMANTIC_RANKER", "free" + ).lower() + AZURE_SEARCH_QUERY_REWRITING = os.getenv( + "AZURE_SEARCH_QUERY_REWRITING", "false" + ).lower() # This defaults to the previous field name "embedding", for backwards compatibility - AZURE_SEARCH_FIELD_NAME_EMBEDDING = os.getenv("AZURE_SEARCH_FIELD_NAME_EMBEDDING", "embedding") + AZURE_SEARCH_FIELD_NAME_EMBEDDING = os.getenv( + "AZURE_SEARCH_FIELD_NAME_EMBEDDING", "embedding" + ) AZURE_SPEECH_SERVICE_ID = os.getenv("AZURE_SPEECH_SERVICE_ID") AZURE_SPEECH_SERVICE_LOCATION = os.getenv("AZURE_SPEECH_SERVICE_LOCATION") - AZURE_SPEECH_SERVICE_VOICE = os.getenv("AZURE_SPEECH_SERVICE_VOICE") or "en-US-AndrewMultilingualNeural" + AZURE_SPEECH_SERVICE_VOICE = ( + os.getenv("AZURE_SPEECH_SERVICE_VOICE") or "en-US-AndrewMultilingualNeural" + ) USE_GPT4V = os.getenv("USE_GPT4V", "").lower() == "true" USE_USER_UPLOAD = os.getenv("USE_USER_UPLOAD", "").lower() == "true" ENABLE_LANGUAGE_PICKER = os.getenv("ENABLE_LANGUAGE_PICKER", "").lower() == "true" - USE_SPEECH_INPUT_BROWSER = os.getenv("USE_SPEECH_INPUT_BROWSER", "").lower() == "true" - USE_SPEECH_OUTPUT_BROWSER = os.getenv("USE_SPEECH_OUTPUT_BROWSER", "").lower() == "true" + USE_SPEECH_INPUT_BROWSER = ( + os.getenv("USE_SPEECH_INPUT_BROWSER", "").lower() == "true" + ) + USE_SPEECH_OUTPUT_BROWSER = ( + os.getenv("USE_SPEECH_OUTPUT_BROWSER", "").lower() == "true" + ) USE_SPEECH_OUTPUT_AZURE = os.getenv("USE_SPEECH_OUTPUT_AZURE", "").lower() == "true" - USE_CHAT_HISTORY_BROWSER = os.getenv("USE_CHAT_HISTORY_BROWSER", "").lower() == "true" + USE_CHAT_HISTORY_BROWSER = ( + os.getenv("USE_CHAT_HISTORY_BROWSER", "").lower() == "true" + ) USE_CHAT_HISTORY_COSMOS = os.getenv("USE_CHAT_HISTORY_COSMOS", "").lower() == "true" USE_AGENTIC_RETRIEVAL = os.getenv("USE_AGENTIC_RETRIEVAL", "").lower() == "true" # WEBSITE_HOSTNAME is always set by App Service, RUNNING_IN_PRODUCTION is set in main.bicep - RUNNING_ON_AZURE = os.getenv("WEBSITE_HOSTNAME") is not None or os.getenv("RUNNING_IN_PRODUCTION") is not None + RUNNING_ON_AZURE = ( + os.getenv("WEBSITE_HOSTNAME") is not None + or os.getenv("RUNNING_IN_PRODUCTION") is not None + ) # Use the current user identity for keyless authentication to Azure services. # This assumes you use 'azd auth login' locally, and managed identity when deployed on Azure. # The managed identity is setup in the infra/ folder. azure_credential: Union[AzureDeveloperCliCredential, ManagedIdentityCredential] if RUNNING_ON_AZURE: - current_app.logger.info("Setting up Azure credential using ManagedIdentityCredential") + current_app.logger.info( + "Setting up Azure credential using ManagedIdentityCredential" + ) if AZURE_CLIENT_ID := os.getenv("AZURE_CLIENT_ID"): # ManagedIdentityCredential should use AZURE_CLIENT_ID if set in env, but its not working for some reason, # so we explicitly pass it in as the client ID here. This is necessary for user-assigned managed identities. current_app.logger.info( - "Setting up Azure credential using ManagedIdentityCredential with client_id %s", AZURE_CLIENT_ID + "Setting up Azure credential using ManagedIdentityCredential with client_id %s", + AZURE_CLIENT_ID, ) azure_credential = ManagedIdentityCredential(client_id=AZURE_CLIENT_ID) else: - current_app.logger.info("Setting up Azure credential using ManagedIdentityCredential") + current_app.logger.info( + "Setting up Azure credential using ManagedIdentityCredential" + ) azure_credential = ManagedIdentityCredential() elif AZURE_TENANT_ID: current_app.logger.info( - "Setting up Azure credential using AzureDeveloperCliCredential with tenant_id %s", AZURE_TENANT_ID + "Setting up Azure credential using AzureDeveloperCliCredential with tenant_id %s", + AZURE_TENANT_ID, + ) + azure_credential = AzureDeveloperCliCredential( + tenant_id=AZURE_TENANT_ID, process_timeout=60 ) - azure_credential = AzureDeveloperCliCredential(tenant_id=AZURE_TENANT_ID, process_timeout=60) else: - current_app.logger.info("Setting up Azure credential using AzureDeveloperCliCredential for home tenant") + current_app.logger.info( + "Setting up Azure credential using AzureDeveloperCliCredential for home tenant" + ) azure_credential = AzureDeveloperCliCredential(process_timeout=60) # Set the Azure credential in the app config for use in other parts of the app @@ -527,17 +620,23 @@ async def setup_clients(): credential=azure_credential, ) agent_client = KnowledgeAgentRetrievalClient( - endpoint=AZURE_SEARCH_ENDPOINT, agent_name=AZURE_SEARCH_AGENT, credential=azure_credential + endpoint=AZURE_SEARCH_ENDPOINT, + agent_name=AZURE_SEARCH_AGENT, + credential=azure_credential, ) blob_container_client = ContainerClient( - f"https://{AZURE_STORAGE_ACCOUNT}.blob.core.windows.net", AZURE_STORAGE_CONTAINER, credential=azure_credential + f"https://{AZURE_STORAGE_ACCOUNT}.blob.core.windows.net", + AZURE_STORAGE_CONTAINER, + credential=azure_credential, ) # Set up authentication helper search_index = None if AZURE_USE_AUTHENTICATION: - current_app.logger.info("AZURE_USE_AUTHENTICATION is true, setting up search index client") + current_app.logger.info( + "AZURE_USE_AUTHENTICATION is true, setting up search index client" + ) search_index_client = SearchIndexClient( endpoint=AZURE_SEARCH_ENDPOINT, credential=azure_credential, @@ -557,7 +656,9 @@ async def setup_clients(): ) if USE_USER_UPLOAD: - current_app.logger.info("USE_USER_UPLOAD is true, setting up user upload feature") + current_app.logger.info( + "USE_USER_UPLOAD is true, setting up user upload feature" + ) if not AZURE_USERSTORAGE_ACCOUNT or not AZURE_USERSTORAGE_CONTAINER: raise ValueError( "AZURE_USERSTORAGE_ACCOUNT and AZURE_USERSTORAGE_CONTAINER must be set when USE_USER_UPLOAD is true" @@ -567,18 +668,24 @@ async def setup_clients(): AZURE_USERSTORAGE_CONTAINER, credential=azure_credential, ) - current_app.config[CONFIG_USER_BLOB_CONTAINER_CLIENT] = user_blob_container_client + current_app.config[CONFIG_USER_BLOB_CONTAINER_CLIENT] = ( + user_blob_container_client + ) # Set up ingester file_processors = setup_file_processors( azure_credential=azure_credential, - document_intelligence_service=os.getenv("AZURE_DOCUMENTINTELLIGENCE_SERVICE"), + document_intelligence_service=os.getenv( + "AZURE_DOCUMENTINTELLIGENCE_SERVICE" + ), local_pdf_parser=os.getenv("USE_LOCAL_PDF_PARSER", "").lower() == "true", local_html_parser=os.getenv("USE_LOCAL_HTML_PARSER", "").lower() == "true", search_images=USE_GPT4V, ) search_info = await setup_search_info( - search_service=AZURE_SEARCH_SERVICE, index_name=AZURE_SEARCH_INDEX, azure_credential=azure_credential + search_service=AZURE_SEARCH_SERVICE, + index_name=AZURE_SEARCH_INDEX, + azure_credential=azure_credential, ) text_embeddings_service = setup_embeddings_service( azure_credential=azure_credential, @@ -605,43 +712,69 @@ async def setup_clients(): openai_client: AsyncOpenAI if USE_SPEECH_OUTPUT_AZURE: - current_app.logger.info("USE_SPEECH_OUTPUT_AZURE is true, setting up Azure speech service") + current_app.logger.info( + "USE_SPEECH_OUTPUT_AZURE is true, setting up Azure speech service" + ) if not AZURE_SPEECH_SERVICE_ID or AZURE_SPEECH_SERVICE_ID == "": - raise ValueError("Azure speech resource not configured correctly, missing AZURE_SPEECH_SERVICE_ID") + raise ValueError( + "Azure speech resource not configured correctly, missing AZURE_SPEECH_SERVICE_ID" + ) if not AZURE_SPEECH_SERVICE_LOCATION or AZURE_SPEECH_SERVICE_LOCATION == "": - raise ValueError("Azure speech resource not configured correctly, missing AZURE_SPEECH_SERVICE_LOCATION") + raise ValueError( + "Azure speech resource not configured correctly, missing AZURE_SPEECH_SERVICE_LOCATION" + ) current_app.config[CONFIG_SPEECH_SERVICE_ID] = AZURE_SPEECH_SERVICE_ID - current_app.config[CONFIG_SPEECH_SERVICE_LOCATION] = AZURE_SPEECH_SERVICE_LOCATION + current_app.config[CONFIG_SPEECH_SERVICE_LOCATION] = ( + AZURE_SPEECH_SERVICE_LOCATION + ) current_app.config[CONFIG_SPEECH_SERVICE_VOICE] = AZURE_SPEECH_SERVICE_VOICE # Wait until token is needed to fetch for the first time current_app.config[CONFIG_SPEECH_SERVICE_TOKEN] = None if OPENAI_HOST.startswith("azure"): if OPENAI_HOST == "azure_custom": - current_app.logger.info("OPENAI_HOST is azure_custom, setting up Azure OpenAI custom client") + current_app.logger.info( + "OPENAI_HOST is azure_custom, setting up Azure OpenAI custom client" + ) if not AZURE_OPENAI_CUSTOM_URL: - raise ValueError("AZURE_OPENAI_CUSTOM_URL must be set when OPENAI_HOST is azure_custom") + raise ValueError( + "AZURE_OPENAI_CUSTOM_URL must be set when OPENAI_HOST is azure_custom" + ) endpoint = AZURE_OPENAI_CUSTOM_URL else: - current_app.logger.info("OPENAI_HOST is azure, setting up Azure OpenAI client") + current_app.logger.info( + "OPENAI_HOST is azure, setting up Azure OpenAI client" + ) if not AZURE_OPENAI_SERVICE: - raise ValueError("AZURE_OPENAI_SERVICE must be set when OPENAI_HOST is azure") + raise ValueError( + "AZURE_OPENAI_SERVICE must be set when OPENAI_HOST is azure" + ) endpoint = f"https://{AZURE_OPENAI_SERVICE}.openai.azure.com" if api_key := os.getenv("AZURE_OPENAI_API_KEY_OVERRIDE"): - current_app.logger.info("AZURE_OPENAI_API_KEY_OVERRIDE found, using as api_key for Azure OpenAI client") + current_app.logger.info( + "AZURE_OPENAI_API_KEY_OVERRIDE found, using as api_key for Azure OpenAI client" + ) openai_client = AsyncAzureOpenAI( - api_version=AZURE_OPENAI_API_VERSION, azure_endpoint=endpoint, api_key=api_key + api_version=AZURE_OPENAI_API_VERSION, + azure_endpoint=endpoint, + api_key=api_key, ) else: - current_app.logger.info("Using Azure credential (passwordless authentication) for Azure OpenAI client") - token_provider = get_bearer_token_provider(azure_credential, "https://cognitiveservices.azure.com/.default") + current_app.logger.info( + "Using Azure credential (passwordless authentication) for Azure OpenAI client" + ) + token_provider = get_bearer_token_provider( + azure_credential, "https://cognitiveservices.azure.com/.default" + ) openai_client = AsyncAzureOpenAI( api_version=AZURE_OPENAI_API_VERSION, azure_endpoint=endpoint, azure_ad_token_provider=token_provider, ) elif OPENAI_HOST == "local": - current_app.logger.info("OPENAI_HOST is local, setting up local OpenAI client for OPENAI_BASE_URL with no key") + current_app.logger.info( + "OPENAI_HOST is local, setting up local OpenAI client for OPENAI_BASE_URL with no key" + ) openai_client = AsyncOpenAI( base_url=os.environ["OPENAI_BASE_URL"], api_key="no-key-required", @@ -662,18 +795,25 @@ async def setup_clients(): current_app.config[CONFIG_AUTH_CLIENT] = auth_helper current_app.config[CONFIG_GPT4V_DEPLOYED] = bool(USE_GPT4V) - current_app.config[CONFIG_SEMANTIC_RANKER_DEPLOYED] = AZURE_SEARCH_SEMANTIC_RANKER != "disabled" + current_app.config[CONFIG_SEMANTIC_RANKER_DEPLOYED] = ( + AZURE_SEARCH_SEMANTIC_RANKER != "disabled" + ) current_app.config[CONFIG_QUERY_REWRITING_ENABLED] = ( - AZURE_SEARCH_QUERY_REWRITING == "true" and AZURE_SEARCH_SEMANTIC_RANKER != "disabled" + AZURE_SEARCH_QUERY_REWRITING == "true" + and AZURE_SEARCH_SEMANTIC_RANKER != "disabled" ) current_app.config[CONFIG_DEFAULT_REASONING_EFFORT] = OPENAI_REASONING_EFFORT - current_app.config[CONFIG_REASONING_EFFORT_ENABLED] = OPENAI_CHATGPT_MODEL in Approach.GPT_REASONING_MODELS + current_app.config[CONFIG_REASONING_EFFORT_ENABLED] = ( + OPENAI_CHATGPT_MODEL in Approach.GPT_REASONING_MODELS + ) current_app.config[CONFIG_STREAMING_ENABLED] = ( bool(USE_GPT4V) or OPENAI_CHATGPT_MODEL not in Approach.GPT_REASONING_MODELS or Approach.GPT_REASONING_MODELS[OPENAI_CHATGPT_MODEL].streaming ) - current_app.config[CONFIG_VECTOR_SEARCH_ENABLED] = os.getenv("USE_VECTORS", "").lower() != "false" + current_app.config[CONFIG_VECTOR_SEARCH_ENABLED] = ( + os.getenv("USE_VECTORS", "").lower() != "false" + ) current_app.config[CONFIG_USER_UPLOAD_ENABLED] = bool(USE_USER_UPLOAD) current_app.config[CONFIG_LANGUAGE_PICKER_ENABLED] = ENABLE_LANGUAGE_PICKER current_app.config[CONFIG_SPEECH_INPUT_ENABLED] = USE_SPEECH_INPUT_BROWSER @@ -685,29 +825,6 @@ async def setup_clients(): prompt_manager = PromptyManager() - # Set up the two default RAG approaches for /ask and /chat - # RetrieveThenReadApproach is used by /ask for single-turn Q&A - current_app.config[CONFIG_ASK_APPROACH] = RetrieveThenReadApproach( - search_client=search_client, - search_index_name=AZURE_SEARCH_INDEX, - agent_model=AZURE_OPENAI_SEARCHAGENT_MODEL, - agent_deployment=AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT, - agent_client=agent_client, - openai_client=openai_client, - auth_helper=auth_helper, - chatgpt_model=OPENAI_CHATGPT_MODEL, - chatgpt_deployment=AZURE_OPENAI_CHATGPT_DEPLOYMENT, - embedding_model=OPENAI_EMB_MODEL, - embedding_deployment=AZURE_OPENAI_EMB_DEPLOYMENT, - embedding_dimensions=OPENAI_EMB_DIMENSIONS, - embedding_field=AZURE_SEARCH_FIELD_NAME_EMBEDDING, - sourcepage_field=KB_FIELDS_SOURCEPAGE, - content_field=KB_FIELDS_CONTENT, - query_language=AZURE_SEARCH_QUERY_LANGUAGE, - query_speller=AZURE_SEARCH_QUERY_SPELLER, - prompt_manager=prompt_manager, - reasoning_effort=OPENAI_REASONING_EFFORT, - ) # ChatReadRetrieveReadApproach is used by /chat for multi-turn conversation current_app.config[CONFIG_CHAT_APPROACH] = ChatReadRetrieveReadApproach( @@ -735,7 +852,9 @@ async def setup_clients(): if USE_GPT4V: current_app.logger.info("USE_GPT4V is true, setting up GPT4V approach") if not AZURE_OPENAI_GPT4V_MODEL: - raise ValueError("AZURE_OPENAI_GPT4V_MODEL must be set when USE_GPT4V is true") + raise ValueError( + "AZURE_OPENAI_GPT4V_MODEL must be set when USE_GPT4V is true" + ) if any( model in Approach.GPT_REASONING_MODELS for model in [ @@ -749,48 +868,8 @@ async def setup_clients(): "AZURE_OPENAI_CHATGPT_MODEL and AZURE_OPENAI_GPT4V_MODEL must not be a reasoning model when USE_GPT4V is true" ) - token_provider = get_bearer_token_provider(azure_credential, "https://cognitiveservices.azure.com/.default") - - current_app.config[CONFIG_ASK_VISION_APPROACH] = RetrieveThenReadVisionApproach( - search_client=search_client, - openai_client=openai_client, - blob_container_client=blob_container_client, - auth_helper=auth_helper, - vision_endpoint=AZURE_VISION_ENDPOINT, - vision_token_provider=token_provider, - gpt4v_deployment=AZURE_OPENAI_GPT4V_DEPLOYMENT, - gpt4v_model=AZURE_OPENAI_GPT4V_MODEL, - embedding_model=OPENAI_EMB_MODEL, - embedding_deployment=AZURE_OPENAI_EMB_DEPLOYMENT, - embedding_dimensions=OPENAI_EMB_DIMENSIONS, - embedding_field=AZURE_SEARCH_FIELD_NAME_EMBEDDING, - sourcepage_field=KB_FIELDS_SOURCEPAGE, - content_field=KB_FIELDS_CONTENT, - query_language=AZURE_SEARCH_QUERY_LANGUAGE, - query_speller=AZURE_SEARCH_QUERY_SPELLER, - prompt_manager=prompt_manager, - ) - - current_app.config[CONFIG_CHAT_VISION_APPROACH] = ChatReadRetrieveReadVisionApproach( - search_client=search_client, - openai_client=openai_client, - blob_container_client=blob_container_client, - auth_helper=auth_helper, - vision_endpoint=AZURE_VISION_ENDPOINT, - vision_token_provider=token_provider, - chatgpt_model=OPENAI_CHATGPT_MODEL, - chatgpt_deployment=AZURE_OPENAI_CHATGPT_DEPLOYMENT, - gpt4v_deployment=AZURE_OPENAI_GPT4V_DEPLOYMENT, - gpt4v_model=AZURE_OPENAI_GPT4V_MODEL, - embedding_model=OPENAI_EMB_MODEL, - embedding_deployment=AZURE_OPENAI_EMB_DEPLOYMENT, - embedding_dimensions=OPENAI_EMB_DIMENSIONS, - embedding_field=AZURE_SEARCH_FIELD_NAME_EMBEDDING, - sourcepage_field=KB_FIELDS_SOURCEPAGE, - content_field=KB_FIELDS_CONTENT, - query_language=AZURE_SEARCH_QUERY_LANGUAGE, - query_speller=AZURE_SEARCH_QUERY_SPELLER, - prompt_manager=prompt_manager, + token_provider = get_bearer_token_provider( + azure_credential, "https://cognitiveservices.azure.com/.default" ) @@ -808,7 +887,9 @@ def create_app(): app.register_blueprint(chat_history_cosmosdb_bp) if os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING"): - app.logger.info("APPLICATIONINSIGHTS_CONNECTION_STRING is set, enabling Azure Monitor") + app.logger.info( + "APPLICATIONINSIGHTS_CONNECTION_STRING is set, enabling Azure Monitor" + ) configure_azure_monitor() # This tracks HTTP requests made by aiohttp: AioHttpClientInstrumentor().instrument() diff --git a/app/backend/chat_history/__init__.py b/app/backend/hrchatbot/approaches/__init__.py similarity index 100% rename from app/backend/chat_history/__init__.py rename to app/backend/hrchatbot/approaches/__init__.py diff --git a/app/backend/approaches/approach.py b/app/backend/hrchatbot/approaches/approach.py similarity index 84% rename from app/backend/approaches/approach.py rename to app/backend/hrchatbot/approaches/approach.py index ab58ba528a..df23bc6aed 100644 --- a/app/backend/approaches/approach.py +++ b/app/backend/hrchatbot/approaches/approach.py @@ -33,8 +33,8 @@ ChatCompletionToolParam, ) -from approaches.promptmanager import PromptManager -from core.authentication import AuthenticationHelper +from hrchatbot.approaches.promptmanager import PromptManager +from hrchatbot.core.authentication import AuthenticationHelper @dataclass @@ -116,7 +116,9 @@ def from_completion_usage(cls, usage: CompletionUsage) -> "TokenUsageProps": prompt_tokens=usage.prompt_tokens, completion_tokens=usage.completion_tokens, reasoning_tokens=( - usage.completion_tokens_details.reasoning_tokens if usage.completion_tokens_details else None + usage.completion_tokens_details.reasoning_tokens + if usage.completion_tokens_details + else None ), total_tokens=usage.total_tokens, ) @@ -148,7 +150,9 @@ def __init__( auth_helper: AuthenticationHelper, query_language: Optional[str], query_speller: Optional[str], - embedding_deployment: Optional[str], # Not needed for non-Azure OpenAI or for retrieval_mode="text" + embedding_deployment: Optional[ + str + ], # Not needed for non-Azure OpenAI or for retrieval_mode="text" embedding_model: str, embedding_dimensions: int, embedding_field: str, @@ -174,15 +178,23 @@ def __init__( self.reasoning_effort = reasoning_effort self.include_token_usage = True - def build_filter(self, overrides: dict[str, Any], auth_claims: dict[str, Any]) -> Optional[str]: + def build_filter( + self, overrides: dict[str, Any], auth_claims: dict[str, Any] + ) -> Optional[str]: include_category = overrides.get("include_category") exclude_category = overrides.get("exclude_category") - security_filter = self.auth_helper.build_security_filters(overrides, auth_claims) + security_filter = self.auth_helper.build_security_filters( + overrides, auth_claims + ) filters = [] if include_category: - filters.append("category eq '{}'".format(include_category.replace("'", "''"))) + filters.append( + "category eq '{}'".format(include_category.replace("'", "''")) + ) if exclude_category: - filters.append("category ne '{}'".format(exclude_category.replace("'", "''"))) + filters.append( + "category ne '{}'".format(exclude_category.replace("'", "''")) + ) if security_filter: filters.append(security_filter) return None if len(filters) == 0 else " and ".join(filters) @@ -208,7 +220,9 @@ async def search( search_text=search_text, filter=filter, top=top, - query_caption="extractive|highlight-false" if use_semantic_captions else None, + query_caption="extractive|highlight-false" + if use_semantic_captions + else None, query_rewrites="generative" if use_query_rewriting else None, vector_queries=search_vectors, query_type=QueryType.SEMANTIC, @@ -237,7 +251,9 @@ async def search( sourcefile=document.get("sourcefile"), oids=document.get("oids"), groups=document.get("groups"), - captions=cast(list[QueryCaptionResult], document.get("@search.captions")), + captions=cast( + list[QueryCaptionResult], document.get("@search.captions") + ), score=document.get("@search.score"), reranker_score=document.get("@search.reranker_score"), ) @@ -270,7 +286,10 @@ async def run_agentic_retrieval( retrieval_request=KnowledgeAgentRetrievalRequest( messages=[ KnowledgeAgentMessage( - role=str(msg["role"]), content=[KnowledgeAgentMessageTextContent(text=str(msg["content"]))] + role=str(msg["role"]), + content=[ + KnowledgeAgentMessageTextContent(text=str(msg["content"])) + ], ) for msg in messages if msg["role"] != "system" @@ -303,18 +322,25 @@ async def run_agentic_retrieval( if response and response.references: if results_merge_strategy == "interleaved": # Use interleaved reference order - references = sorted(response.references, key=lambda reference: int(reference.id)) + references = sorted( + response.references, key=lambda reference: int(reference.id) + ) else: # Default to descending strategy references = response.references for reference in references: - if isinstance(reference, KnowledgeAgentAzureSearchDocReference) and reference.source_data: + if ( + isinstance(reference, KnowledgeAgentAzureSearchDocReference) + and reference.source_data + ): results.append( Document( id=reference.doc_key, content=reference.source_data["content"], sourcepage=reference.source_data["sourcepage"], - search_agent_query=activity_mapping[reference.activity_source], + search_agent_query=activity_mapping[ + reference.activity_source + ], ) ) if top and len(results) == top: @@ -323,9 +349,11 @@ async def run_agentic_retrieval( return response, results def get_sources_content( - self, results: list[Document], use_semantic_captions: bool, use_image_citation: bool + self, + results: list[Document], + use_semantic_captions: bool, + use_image_citation: bool, ) -> list[str]: - def nonewlines(s: str) -> str: return s.replace("\n", " ").replace("\r", " ") @@ -333,12 +361,16 @@ def nonewlines(s: str) -> str: return [ (self.get_citation((doc.sourcepage or ""), use_image_citation)) + ": " - + nonewlines(" . ".join([cast(str, c.text) for c in (doc.captions or [])])) + + nonewlines( + " . ".join([cast(str, c.text) for c in (doc.captions or [])]) + ) for doc in results ] else: return [ - (self.get_citation((doc.sourcepage or ""), use_image_citation)) + ": " + nonewlines(doc.content or "") + (self.get_citation((doc.sourcepage or ""), use_image_citation)) + + ": " + + nonewlines(doc.content or "") for doc in results ] @@ -365,21 +397,29 @@ class ExtraArgs(TypedDict, total=False): dimensions: int dimensions_args: ExtraArgs = ( - {"dimensions": self.embedding_dimensions} if SUPPORTED_DIMENSIONS_MODEL[self.embedding_model] else {} + {"dimensions": self.embedding_dimensions} + if SUPPORTED_DIMENSIONS_MODEL[self.embedding_model] + else {} ) embedding = await self.openai_client.embeddings.create( # Azure OpenAI takes the deployment name as the model name - model=self.embedding_deployment if self.embedding_deployment else self.embedding_model, + model=self.embedding_deployment + if self.embedding_deployment + else self.embedding_model, input=q, **dimensions_args, ) query_vector = embedding.data[0].embedding # This performs an oversampling due to how the search index was setup, # so we do not need to explicitly pass in an oversampling parameter here - return VectorizedQuery(vector=query_vector, k_nearest_neighbors=50, fields=self.embedding_field) + return VectorizedQuery( + vector=query_vector, k_nearest_neighbors=50, fields=self.embedding_field + ) async def compute_image_embedding(self, q: str): - endpoint = urljoin(self.vision_endpoint, "computervision/retrieval:vectorizeText") + endpoint = urljoin( + self.vision_endpoint, "computervision/retrieval:vectorizeText" + ) headers = {"Content-Type": "application/json"} params = {"api-version": "2024-02-01", "model-version": "2023-04-15"} data = {"text": q} @@ -388,13 +428,21 @@ async def compute_image_embedding(self, q: str): async with aiohttp.ClientSession() as session: async with session.post( - url=endpoint, params=params, headers=headers, json=data, raise_for_status=True + url=endpoint, + params=params, + headers=headers, + json=data, + raise_for_status=True, ) as response: json = await response.json() image_query_vector = json["vector"] - return VectorizedQuery(vector=image_query_vector, k_nearest_neighbors=50, fields="imageEmbedding") + return VectorizedQuery( + vector=image_query_vector, k_nearest_neighbors=50, fields="imageEmbedding" + ) - def get_system_prompt_variables(self, override_prompt: Optional[str]) -> dict[str, str]: + def get_system_prompt_variables( + self, override_prompt: Optional[str] + ) -> dict[str, str]: # Allows client to replace the entire prompt, or to inject into the existing prompt using >>> if override_prompt is None: return {} @@ -433,7 +481,11 @@ def create_chat_completion( if supported_features.streaming and should_stream: params["stream"] = True params["stream_options"] = {"include_usage": True} - params["reasoning_effort"] = reasoning_effort or overrides.get("reasoning_effort") or self.reasoning_effort + params["reasoning_effort"] = ( + reasoning_effort + or overrides.get("reasoning_effort") + or self.reasoning_effort + ) else: # Include parameters that may not be supported for reasoning models diff --git a/app/backend/approaches/chatapproach.py b/app/backend/hrchatbot/approaches/chatapproach.py similarity index 78% rename from app/backend/approaches/chatapproach.py rename to app/backend/hrchatbot/approaches/chatapproach.py index 346c9f3b0a..e1e471025f 100644 --- a/app/backend/approaches/chatapproach.py +++ b/app/backend/hrchatbot/approaches/chatapproach.py @@ -11,20 +11,22 @@ ChatCompletionMessageParam, ) -from approaches.approach import ( +from hrchatbot.approaches.approach import ( Approach, ExtraInfo, ) class ChatApproach(Approach, ABC): - NO_RESPONSE = "0" @abstractmethod async def run_until_final_call( self, messages, overrides, auth_claims, should_stream - ) -> tuple[ExtraInfo, Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]]]: + ) -> tuple[ + ExtraInfo, + Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]], + ]: pass def get_search_query(self, chat_completion: ChatCompletion, user_query: str): @@ -60,14 +62,20 @@ async def run_without_streaming( extra_info, chat_coroutine = await self.run_until_final_call( messages, overrides, auth_claims, should_stream=False ) - chat_completion_response: ChatCompletion = await cast(Awaitable[ChatCompletion], chat_coroutine) + chat_completion_response: ChatCompletion = await cast( + Awaitable[ChatCompletion], chat_coroutine + ) content = chat_completion_response.choices[0].message.content role = chat_completion_response.choices[0].message.role if overrides.get("suggest_followup_questions"): content, followup_questions = self.extract_followup_questions(content) extra_info.followup_questions = followup_questions # Assume last thought is for generating answer - if self.include_token_usage and extra_info.thoughts and chat_completion_response.usage: + if ( + self.include_token_usage + and extra_info.thoughts + and chat_completion_response.usage + ): extra_info.thoughts[-1].update_token_usage(chat_completion_response.usage) chat_app_response = { "message": {"content": content, "role": role}, @@ -86,8 +94,14 @@ async def run_with_streaming( extra_info, chat_coroutine = await self.run_until_final_call( messages, overrides, auth_claims, should_stream=True ) - chat_coroutine = cast(Awaitable[AsyncStream[ChatCompletionChunk]], chat_coroutine) - yield {"delta": {"role": "assistant"}, "context": extra_info, "session_state": session_state} + chat_coroutine = cast( + Awaitable[AsyncStream[ChatCompletionChunk]], chat_coroutine + ) + yield { + "delta": {"role": "assistant"}, + "context": extra_info, + "session_state": session_state, + } followup_questions_started = False followup_content = "" @@ -104,7 +118,9 @@ async def run_with_streaming( } # if event contains << and not >>, it is start of follow-up question, truncate content = completion["delta"].get("content") - content = content or "" # content may either not exist in delta, or explicitly be None + content = ( + content or "" + ) # content may either not exist in delta, or explicitly be None if overrides.get("suggest_followup_questions") and "<<" in content: followup_questions_started = True earlier_content = content[: content.index("<<")] @@ -119,15 +135,26 @@ async def run_with_streaming( else: # Final chunk at end of streaming should contain usage # https://cookbook.openai.com/examples/how_to_stream_completions#4-how-to-get-token-usage-data-for-streamed-chat-completion-response - if event_chunk.usage and extra_info.thoughts and self.include_token_usage: + if ( + event_chunk.usage + and extra_info.thoughts + and self.include_token_usage + ): extra_info.thoughts[-1].update_token_usage(event_chunk.usage) - yield {"delta": {"role": "assistant"}, "context": extra_info, "session_state": session_state} + yield { + "delta": {"role": "assistant"}, + "context": extra_info, + "session_state": session_state, + } if followup_content: _, followup_questions = self.extract_followup_questions(followup_content) yield { "delta": {"role": "assistant"}, - "context": {"context": extra_info, "followup_questions": followup_questions}, + "context": { + "context": extra_info, + "followup_questions": followup_questions, + }, } async def run( @@ -138,7 +165,9 @@ async def run( ) -> dict[str, Any]: overrides = context.get("overrides", {}) auth_claims = context.get("auth_claims", {}) - return await self.run_without_streaming(messages, overrides, auth_claims, session_state) + return await self.run_without_streaming( + messages, overrides, auth_claims, session_state + ) async def run_stream( self, diff --git a/app/backend/approaches/chatreadretrieveread.py b/app/backend/hrchatbot/approaches/chatreadretrieveread.py similarity index 83% rename from app/backend/approaches/chatreadretrieveread.py rename to app/backend/hrchatbot/approaches/chatreadretrieveread.py index ed87976e3b..5135356df4 100644 --- a/app/backend/approaches/chatreadretrieveread.py +++ b/app/backend/hrchatbot/approaches/chatreadretrieveread.py @@ -12,10 +12,10 @@ ChatCompletionToolParam, ) -from approaches.approach import DataPoints, ExtraInfo, ThoughtStep -from approaches.chatapproach import ChatApproach -from approaches.promptmanager import PromptManager -from core.authentication import AuthenticationHelper +from hrchatbot.approaches.approach import DataPoints, ExtraInfo, ThoughtStep +from hrchatbot.approaches.chatapproach import ChatApproach +from hrchatbot.approaches.promptmanager import PromptManager +from hrchatbot.core.authentication import AuthenticationHelper class ChatReadRetrieveReadApproach(ChatApproach): @@ -37,7 +37,9 @@ def __init__( openai_client: AsyncOpenAI, chatgpt_model: str, chatgpt_deployment: Optional[str], # Not needed for non-Azure OpenAI - embedding_deployment: Optional[str], # Not needed for non-Azure OpenAI or for retrieval_mode="text" + embedding_deployment: Optional[ + str + ], # Not needed for non-Azure OpenAI or for retrieval_mode="text" embedding_model: str, embedding_dimensions: int, embedding_field: str, @@ -66,9 +68,15 @@ def __init__( self.query_language = query_language self.query_speller = query_speller self.prompt_manager = prompt_manager - self.query_rewrite_prompt = self.prompt_manager.load_prompt("chat_query_rewrite.prompty") - self.query_rewrite_tools = self.prompt_manager.load_tools("chat_query_rewrite_tools.json") - self.answer_prompt = self.prompt_manager.load_prompt("chat_answer_question.prompty") + self.query_rewrite_prompt = self.prompt_manager.load_prompt( + "chat_query_rewrite.prompty" + ) + self.query_rewrite_tools = self.prompt_manager.load_tools( + "chat_query_rewrite_tools.json" + ) + self.answer_prompt = self.prompt_manager.load_prompt( + "chat_answer_question.prompty" + ) self.reasoning_effort = reasoning_effort self.include_token_usage = True @@ -78,25 +86,38 @@ async def run_until_final_call( overrides: dict[str, Any], auth_claims: dict[str, Any], should_stream: bool = False, - ) -> tuple[ExtraInfo, Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]]]: - use_agentic_retrieval = True if overrides.get("use_agentic_retrieval") else False + ) -> tuple[ + ExtraInfo, + Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]], + ]: + use_agentic_retrieval = ( + True if overrides.get("use_agentic_retrieval") else False + ) original_user_query = messages[-1]["content"] reasoning_model_support = self.GPT_REASONING_MODELS.get(self.chatgpt_model) - if reasoning_model_support and (not reasoning_model_support.streaming and should_stream): + if reasoning_model_support and ( + not reasoning_model_support.streaming and should_stream + ): raise Exception( f"{self.chatgpt_model} does not support streaming. Please use a different model or disable streaming." ) if use_agentic_retrieval: - extra_info = await self.run_agentic_retrieval_approach(messages, overrides, auth_claims) + extra_info = await self.run_agentic_retrieval_approach( + messages, overrides, auth_claims + ) else: - extra_info = await self.run_search_approach(messages, overrides, auth_claims) + extra_info = await self.run_search_approach( + messages, overrides, auth_claims + ) messages = self.prompt_manager.render_prompt( self.answer_prompt, self.get_system_prompt_variables(overrides.get("prompt_template")) | { - "include_follow_up_questions": bool(overrides.get("suggest_followup_questions")), + "include_follow_up_questions": bool( + overrides.get("suggest_followup_questions") + ), "past_messages": messages[:-1], "user_query": original_user_query, "text_sources": extra_info.data_points.text, @@ -104,7 +125,9 @@ async def run_until_final_call( ) chat_coroutine = cast( - Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]], + Union[ + Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]] + ], self.create_chat_completion( self.chatgpt_deployment, self.chatgpt_model, @@ -127,10 +150,17 @@ async def run_until_final_call( return (extra_info, chat_coroutine) async def run_search_approach( - self, messages: list[ChatCompletionMessageParam], overrides: dict[str, Any], auth_claims: dict[str, Any] + self, + messages: list[ChatCompletionMessageParam], + overrides: dict[str, Any], + auth_claims: dict[str, Any], ): use_text_search = overrides.get("retrieval_mode") in ["text", "hybrid", None] - use_vector_search = overrides.get("retrieval_mode") in ["vectors", "hybrid", None] + use_vector_search = overrides.get("retrieval_mode") in [ + "vectors", + "hybrid", + None, + ] use_semantic_ranker = True if overrides.get("semantic_ranker") else False use_semantic_captions = True if overrides.get("semantic_captions") else False use_query_rewriting = True if overrides.get("query_rewriting") else False @@ -144,7 +174,8 @@ async def run_search_approach( raise ValueError("The most recent message content must be a string.") query_messages = self.prompt_manager.render_prompt( - self.query_rewrite_prompt, {"user_query": original_user_query, "past_messages": messages[:-1]} + self.query_rewrite_prompt, + {"user_query": original_user_query, "past_messages": messages[:-1]}, ) tools: list[ChatCompletionToolParam] = self.query_rewrite_tools @@ -190,7 +221,9 @@ async def run_search_approach( ) # STEP 3: Generate a contextual and content specific answer using the search results and chat history - text_sources = self.get_sources_content(results, use_semantic_captions, use_image_citation=False) + text_sources = self.get_sources_content( + results, use_semantic_captions, use_image_citation=False + ) extra_info = ExtraInfo( DataPoints(text=text_sources), @@ -250,7 +283,9 @@ async def run_agentic_retrieval_approach( results_merge_strategy=results_merge_strategy, ) - text_sources = self.get_sources_content(results, use_semantic_captions=False, use_image_citation=False) + text_sources = self.get_sources_content( + results, use_semantic_captions=False, use_image_citation=False + ) extra_info = ExtraInfo( DataPoints(text=text_sources), @@ -270,7 +305,9 @@ async def run_agentic_retrieval_approach( [result.serialize_for_results() for result in results], { "query_plan": ( - [activity.as_dict() for activity in response.activity] if response.activity else None + [activity.as_dict() for activity in response.activity] + if response.activity + else None ), "model": self.agent_model, "deployment": self.agent_deployment, diff --git a/app/backend/approaches/promptmanager.py b/app/backend/hrchatbot/approaches/promptmanager.py similarity index 99% rename from app/backend/approaches/promptmanager.py rename to app/backend/hrchatbot/approaches/promptmanager.py index 82941b4176..b37a537a94 100644 --- a/app/backend/approaches/promptmanager.py +++ b/app/backend/hrchatbot/approaches/promptmanager.py @@ -6,7 +6,6 @@ class PromptManager: - def load_prompt(self, path: str): raise NotImplementedError @@ -18,7 +17,6 @@ def render_prompt(self, prompt, data) -> list[ChatCompletionMessageParam]: class PromptyManager(PromptManager): - PROMPTS_DIRECTORY = pathlib.Path(__file__).parent / "prompts" def load_prompt(self, path: str): diff --git a/app/backend/approaches/prompts/ask_answer_question.prompty b/app/backend/hrchatbot/approaches/prompts/ask_answer_question.prompty similarity index 100% rename from app/backend/approaches/prompts/ask_answer_question.prompty rename to app/backend/hrchatbot/approaches/prompts/ask_answer_question.prompty diff --git a/app/backend/approaches/prompts/ask_answer_question_vision.prompty b/app/backend/hrchatbot/approaches/prompts/ask_answer_question_vision.prompty similarity index 100% rename from app/backend/approaches/prompts/ask_answer_question_vision.prompty rename to app/backend/hrchatbot/approaches/prompts/ask_answer_question_vision.prompty diff --git a/app/backend/approaches/prompts/chat_answer_question.prompty b/app/backend/hrchatbot/approaches/prompts/chat_answer_question.prompty similarity index 100% rename from app/backend/approaches/prompts/chat_answer_question.prompty rename to app/backend/hrchatbot/approaches/prompts/chat_answer_question.prompty diff --git a/app/backend/approaches/prompts/chat_answer_question_vision.prompty b/app/backend/hrchatbot/approaches/prompts/chat_answer_question_vision.prompty similarity index 100% rename from app/backend/approaches/prompts/chat_answer_question_vision.prompty rename to app/backend/hrchatbot/approaches/prompts/chat_answer_question_vision.prompty diff --git a/app/backend/approaches/prompts/chat_query_rewrite.prompty b/app/backend/hrchatbot/approaches/prompts/chat_query_rewrite.prompty similarity index 100% rename from app/backend/approaches/prompts/chat_query_rewrite.prompty rename to app/backend/hrchatbot/approaches/prompts/chat_query_rewrite.prompty diff --git a/app/backend/approaches/prompts/chat_query_rewrite_tools.json b/app/backend/hrchatbot/approaches/prompts/chat_query_rewrite_tools.json similarity index 100% rename from app/backend/approaches/prompts/chat_query_rewrite_tools.json rename to app/backend/hrchatbot/approaches/prompts/chat_query_rewrite_tools.json diff --git a/app/backend/core/__init__.py b/app/backend/hrchatbot/chat_history/__init__.py similarity index 100% rename from app/backend/core/__init__.py rename to app/backend/hrchatbot/chat_history/__init__.py diff --git a/app/backend/chat_history/cosmosdb.py b/app/backend/hrchatbot/chat_history/cosmosdb.py similarity index 83% rename from app/backend/chat_history/cosmosdb.py rename to app/backend/hrchatbot/chat_history/cosmosdb.py index 764278bc76..ecd48dba45 100644 --- a/app/backend/chat_history/cosmosdb.py +++ b/app/backend/hrchatbot/chat_history/cosmosdb.py @@ -6,17 +6,19 @@ from azure.identity.aio import AzureDeveloperCliCredential, ManagedIdentityCredential from quart import Blueprint, current_app, jsonify, make_response, request -from config import ( +from hrchatbot.config import ( CONFIG_CHAT_HISTORY_COSMOS_ENABLED, CONFIG_COSMOS_HISTORY_CLIENT, CONFIG_COSMOS_HISTORY_CONTAINER, CONFIG_COSMOS_HISTORY_VERSION, CONFIG_CREDENTIAL, ) -from decorators import authenticated -from error import error_response +from hrchatbot.decorators import authenticated +from hrchatbot.error import error_response -chat_history_cosmosdb_bp = Blueprint("chat_history_cosmos", __name__, static_folder="static") +chat_history_cosmosdb_bp = Blueprint( + "chat_history_cosmos", __name__, static_folder="static" +) @chat_history_cosmosdb_bp.post("/chat_history") @@ -70,7 +72,9 @@ async def post_chat_history(auth_claims: dict[str, Any]): batch_operations = [("upsert", (session_item,))] + [ ("upsert", (message_pair_item,)) for message_pair_item in message_pair_items ] - await container.execute_item_batch(batch_operations=batch_operations, partition_key=[entra_oid, session_id]) + await container.execute_item_batch( + batch_operations=batch_operations, partition_key=[entra_oid, session_id] + ) return jsonify({}), 201 except Exception as error: return error_response(error, "/chat_history") @@ -96,7 +100,10 @@ async def get_chat_history_sessions(auth_claims: dict[str, Any]): res = container.query_items( query="SELECT c.id, c.entra_oid, c.title, c.timestamp FROM c WHERE c.entra_oid = @entra_oid AND c.type = @type ORDER BY c.timestamp DESC", - parameters=[dict(name="@entra_oid", value=entra_oid), dict(name="@type", value="session")], + parameters=[ + dict(name="@entra_oid", value=entra_oid), + dict(name="@type", value="session"), + ], partition_key=[entra_oid], max_item_count=count, ) @@ -123,7 +130,9 @@ async def get_chat_history_sessions(auth_claims: dict[str, Any]): except StopAsyncIteration: continuation_token = None - return jsonify({"sessions": sessions, "continuation_token": continuation_token}), 200 + return jsonify( + {"sessions": sessions, "continuation_token": continuation_token} + ), 200 except Exception as error: return error_response(error, "/chat_history/sessions") @@ -146,7 +155,10 @@ async def get_chat_history_session(auth_claims: dict[str, Any], session_id: str) try: res = container.query_items( query="SELECT * FROM c WHERE c.session_id = @session_id AND c.type = @type", - parameters=[dict(name="@session_id", value=session_id), dict(name="@type", value="message_pair")], + parameters=[ + dict(name="@session_id", value=session_id), + dict(name="@type", value="message_pair"), + ], partition_key=[entra_oid, session_id], ) @@ -196,7 +208,9 @@ async def delete_chat_history_session(auth_claims: dict[str, Any], session_id: s ids_to_delete.append(item["id"]) batch_operations = [("delete", (id,)) for id in ids_to_delete] - await container.execute_item_batch(batch_operations=batch_operations, partition_key=[entra_oid, session_id]) + await container.execute_item_batch( + batch_operations=batch_operations, partition_key=[entra_oid, session_id] + ) return await make_response("", 204) except Exception as error: return error_response(error, f"/chat_history/sessions/{session_id}") @@ -209,27 +223,38 @@ async def setup_clients(): AZURE_CHAT_HISTORY_DATABASE = os.getenv("AZURE_CHAT_HISTORY_DATABASE") AZURE_CHAT_HISTORY_CONTAINER = os.getenv("AZURE_CHAT_HISTORY_CONTAINER") - azure_credential: Union[AzureDeveloperCliCredential, ManagedIdentityCredential] = current_app.config[ - CONFIG_CREDENTIAL - ] + azure_credential: Union[AzureDeveloperCliCredential, ManagedIdentityCredential] = ( + current_app.config[CONFIG_CREDENTIAL] + ) if USE_CHAT_HISTORY_COSMOS: - current_app.logger.info("USE_CHAT_HISTORY_COSMOS is true, setting up CosmosDB client") + current_app.logger.info( + "USE_CHAT_HISTORY_COSMOS is true, setting up CosmosDB client" + ) if not AZURE_COSMOSDB_ACCOUNT: - raise ValueError("AZURE_COSMOSDB_ACCOUNT must be set when USE_CHAT_HISTORY_COSMOS is true") + raise ValueError( + "AZURE_COSMOSDB_ACCOUNT must be set when USE_CHAT_HISTORY_COSMOS is true" + ) if not AZURE_CHAT_HISTORY_DATABASE: - raise ValueError("AZURE_CHAT_HISTORY_DATABASE must be set when USE_CHAT_HISTORY_COSMOS is true") + raise ValueError( + "AZURE_CHAT_HISTORY_DATABASE must be set when USE_CHAT_HISTORY_COSMOS is true" + ) if not AZURE_CHAT_HISTORY_CONTAINER: - raise ValueError("AZURE_CHAT_HISTORY_CONTAINER must be set when USE_CHAT_HISTORY_COSMOS is true") + raise ValueError( + "AZURE_CHAT_HISTORY_CONTAINER must be set when USE_CHAT_HISTORY_COSMOS is true" + ) cosmos_client = CosmosClient( - url=f"https://{AZURE_COSMOSDB_ACCOUNT}.documents.azure.com:443/", credential=azure_credential + url=f"https://{AZURE_COSMOSDB_ACCOUNT}.documents.azure.com:443/", + credential=azure_credential, ) cosmos_db = cosmos_client.get_database_client(AZURE_CHAT_HISTORY_DATABASE) cosmos_container = cosmos_db.get_container_client(AZURE_CHAT_HISTORY_CONTAINER) current_app.config[CONFIG_COSMOS_HISTORY_CLIENT] = cosmos_client current_app.config[CONFIG_COSMOS_HISTORY_CONTAINER] = cosmos_container - current_app.config[CONFIG_COSMOS_HISTORY_VERSION] = os.environ["AZURE_CHAT_HISTORY_VERSION"] + current_app.config[CONFIG_COSMOS_HISTORY_VERSION] = os.environ[ + "AZURE_CHAT_HISTORY_VERSION" + ] @chat_history_cosmosdb_bp.after_app_serving diff --git a/app/backend/config.py b/app/backend/hrchatbot/config.py similarity index 100% rename from app/backend/config.py rename to app/backend/hrchatbot/config.py diff --git a/app/backend/prepdocslib/__init__.py b/app/backend/hrchatbot/core/__init__.py similarity index 100% rename from app/backend/prepdocslib/__init__.py rename to app/backend/hrchatbot/core/__init__.py diff --git a/app/backend/core/authentication.py b/app/backend/hrchatbot/core/authentication.py similarity index 83% rename from app/backend/core/authentication.py rename to app/backend/hrchatbot/core/authentication.py index 2c9aaf87d4..d8d1bed050 100644 --- a/app/backend/core/authentication.py +++ b/app/backend/hrchatbot/core/authentication.py @@ -63,13 +63,18 @@ def __init__( self.key_url = f"{self.authority}/discovery/v2.0/keys" if self.use_authentication: - field_names = [field.name for field in search_index.fields] if search_index else [] + field_names = ( + [field.name for field in search_index.fields] if search_index else [] + ) self.has_auth_fields = "oids" in field_names and "groups" in field_names self.require_access_control = require_access_control self.enable_global_documents = enable_global_documents self.enable_unauthenticated_access = enable_unauthenticated_access self.confidential_client = ConfidentialClientApplication( - server_app_id, authority=self.authority, client_credential=server_app_secret, token_cache=TokenCache() + server_app_id, + authority=self.authority, + client_credential=server_app_secret, + token_cache=TokenCache(), ) else: self.has_auth_fields = False @@ -121,11 +126,15 @@ def get_token_auth_header(headers: dict) -> str: parts = auth.split() if parts[0].lower() != "bearer": - raise AuthError(error="Authorization header must start with Bearer", status_code=401) + raise AuthError( + error="Authorization header must start with Bearer", status_code=401 + ) elif len(parts) == 1: raise AuthError(error="Token not found", status_code=401) elif len(parts) > 2: - raise AuthError(error="Authorization header must be Bearer token", status_code=401) + raise AuthError( + error="Authorization header must be Bearer token", status_code=401 + ) token = parts[1] return token @@ -138,23 +147,36 @@ def get_token_auth_header(headers: dict) -> str: raise AuthError(error="Authorization header is expected", status_code=401) - def build_security_filters(self, overrides: dict[str, Any], auth_claims: dict[str, Any]): + def build_security_filters( + self, overrides: dict[str, Any], auth_claims: dict[str, Any] + ): # Build different permutations of the oid or groups security filter using OData filters # https://learn.microsoft.com/azure/search/search-security-trimming-for-azure-search # https://learn.microsoft.com/azure/search/search-query-odata-filter - use_oid_security_filter = self.require_access_control or overrides.get("use_oid_security_filter") - use_groups_security_filter = self.require_access_control or overrides.get("use_groups_security_filter") + use_oid_security_filter = self.require_access_control or overrides.get( + "use_oid_security_filter" + ) + use_groups_security_filter = self.require_access_control or overrides.get( + "use_groups_security_filter" + ) - if (use_oid_security_filter or use_groups_security_filter) and not self.has_auth_fields: + if ( + use_oid_security_filter or use_groups_security_filter + ) and not self.has_auth_fields: raise AuthError( - error="oids and groups must be defined in the search index to use authentication", status_code=400 + error="oids and groups must be defined in the search index to use authentication", + status_code=400, ) oid_security_filter = ( - "oids/any(g:search.in(g, '{}'))".format(auth_claims.get("oid", "")) if use_oid_security_filter else None + "oids/any(g:search.in(g, '{}'))".format(auth_claims.get("oid", "")) + if use_oid_security_filter + else None ) groups_security_filter = ( - "groups/any(g:search.in(g, '{}'))".format(", ".join(auth_claims.get("groups", []))) + "groups/any(g:search.in(g, '{}'))".format( + ", ".join(auth_claims.get("groups", [])) + ) if use_groups_security_filter else None ) @@ -180,16 +202,22 @@ def build_security_filters(self, overrides: dict[str, Any], auth_claims: dict[st @staticmethod async def list_groups(graph_resource_access_token: dict) -> list[str]: - headers = {"Authorization": "Bearer " + graph_resource_access_token["access_token"]} + headers = { + "Authorization": "Bearer " + graph_resource_access_token["access_token"] + } groups = [] async with aiohttp.ClientSession(headers=headers) as session: resp_json = None resp_status = None - async with session.get(url="https://graph.microsoft.com/v1.0/me/transitiveMemberOf?$select=id") as resp: + async with session.get( + url="https://graph.microsoft.com/v1.0/me/transitiveMemberOf?$select=id" + ) as resp: resp_json = await resp.json() resp_status = resp.status if resp_status != 200: - raise AuthError(error=json.dumps(resp_json), status_code=resp_status) + raise AuthError( + error=json.dumps(resp_json), status_code=resp_status + ) while resp_status == 200: value = resp_json["value"] @@ -220,8 +248,11 @@ async def get_auth_claims_if_enabled(self, headers: dict) -> dict[str, Any]: # Use the on-behalf-of-flow to acquire another token for use with Microsoft Graph # See https://learn.microsoft.com/entra/identity-platform/v2-oauth2-on-behalf-of-flow for more information - graph_resource_access_token = self.confidential_client.acquire_token_on_behalf_of( - user_assertion=auth_token, scopes=["https://graph.microsoft.com/.default"] + graph_resource_access_token = ( + self.confidential_client.acquire_token_on_behalf_of( + user_assertion=auth_token, + scopes=["https://graph.microsoft.com/.default"], + ) ) if "error" in graph_resource_access_token: raise AuthError(error=str(graph_resource_access_token), status_code=401) @@ -229,7 +260,10 @@ async def get_auth_claims_if_enabled(self, headers: dict) -> dict[str, Any]: # Read the claims from the response. The oid and groups claims are used for security filtering # https://learn.microsoft.com/entra/identity-platform/id-token-claims-reference id_token_claims = graph_resource_access_token["id_token_claims"] - auth_claims = {"oid": id_token_claims["oid"], "groups": id_token_claims.get("groups", [])} + auth_claims = { + "oid": id_token_claims["oid"], + "groups": id_token_claims.get("groups", []), + } # A groups claim may have been omitted either because it was not added in the application manifest for the API application, # or a groups overage claim may have been emitted. @@ -242,10 +276,14 @@ async def get_auth_claims_if_enabled(self, headers: dict) -> dict[str, Any]: ) if missing_groups_claim or has_group_overage_claim: # Read the user's groups from Microsoft Graph - auth_claims["groups"] = await AuthenticationHelper.list_groups(graph_resource_access_token) + auth_claims["groups"] = await AuthenticationHelper.list_groups( + graph_resource_access_token + ) return auth_claims except AuthError as e: - logging.exception("Exception getting authorization information - " + json.dumps(e.error)) + logging.exception( + "Exception getting authorization information - " + json.dumps(e.error) + ) if self.require_access_control and not self.enable_unauthenticated_access: raise return {} @@ -255,9 +293,13 @@ async def get_auth_claims_if_enabled(self, headers: dict) -> dict[str, Any]: raise return {} - async def check_path_auth(self, path: str, auth_claims: dict[str, Any], search_client: SearchClient) -> bool: + async def check_path_auth( + self, path: str, auth_claims: dict[str, Any], search_client: SearchClient + ) -> bool: # Start with the standard security filter for all queries - security_filter = self.build_security_filters(overrides={}, auth_claims=auth_claims) + security_filter = self.build_security_filters( + overrides={}, auth_claims=auth_claims + ) # If there was no security filter or no path, then the path is allowed if not security_filter or len(path) == 0: return True @@ -290,14 +332,19 @@ async def create_pem_format(self, jwks, token): if key["kid"] == unverified_header["kid"]: # Construct the RSA public key public_numbers = rsa.RSAPublicNumbers( - e=int.from_bytes(base64.urlsafe_b64decode(key["e"] + "=="), byteorder="big"), - n=int.from_bytes(base64.urlsafe_b64decode(key["n"] + "=="), byteorder="big"), + e=int.from_bytes( + base64.urlsafe_b64decode(key["e"] + "=="), byteorder="big" + ), + n=int.from_bytes( + base64.urlsafe_b64decode(key["n"] + "=="), byteorder="big" + ), ) public_key = public_numbers.public_key() # Convert to PEM format pem_key = public_key.public_bytes( - encoding=serialization.Encoding.PEM, format=serialization.PublicFormat.SubjectPublicKeyInfo + encoding=serialization.Encoding.PEM, + format=serialization.PublicFormat.SubjectPublicKeyInfo, ) rsa_key = pem_key return rsa_key @@ -319,7 +366,8 @@ async def validate_access_token(self, token: str): resp_status = resp.status if resp_status in [500, 502, 503, 504]: raise AuthError( - error=f"Failed to get keys info: {await resp.text()}", status_code=resp_status + error=f"Failed to get keys info: {await resp.text()}", + status_code=resp_status, ) jwks = await resp.json() @@ -340,7 +388,9 @@ async def validate_access_token(self, token: str): raise AuthError("Unable to find appropriate key", 401) if issuer not in self.valid_issuers: - raise AuthError(f"Issuer {issuer} not in {','.join(self.valid_issuers)}", 401) + raise AuthError( + f"Issuer {issuer} not in {','.join(self.valid_issuers)}", 401 + ) if audience not in self.valid_audiences: raise AuthError( @@ -349,7 +399,9 @@ async def validate_access_token(self, token: str): ) try: - jwt.decode(token, rsa_key, algorithms=["RS256"], audience=audience, issuer=issuer) + jwt.decode( + token, rsa_key, algorithms=["RS256"], audience=audience, issuer=issuer + ) except jwt.ExpiredSignatureError as jwt_expired_exc: raise AuthError("Token is expired", 401) from jwt_expired_exc except (jwt.InvalidAudienceError, jwt.InvalidIssuerError) as jwt_claims_exc: diff --git a/app/backend/core/imageshelper.py b/app/backend/hrchatbot/core/imageshelper.py similarity index 77% rename from app/backend/core/imageshelper.py rename to app/backend/hrchatbot/core/imageshelper.py index 87e8b8970f..80d87a48d8 100644 --- a/app/backend/core/imageshelper.py +++ b/app/backend/hrchatbot/core/imageshelper.py @@ -18,11 +18,15 @@ class ImageURL(TypedDict, total=False): """Specifies the detail level of the image.""" -async def download_blob_as_base64(blob_container_client: ContainerClient, file_path: str) -> Optional[str]: +async def download_blob_as_base64( + blob_container_client: ContainerClient, file_path: str +) -> Optional[str]: base_name, _ = os.path.splitext(file_path) image_filename = base_name + ".png" try: - blob = await blob_container_client.get_blob_client(image_filename).download_blob() + blob = await blob_container_client.get_blob_client( + image_filename + ).download_blob() if not blob.properties: logging.warning(f"No blob exists for {image_filename}") return None @@ -33,7 +37,9 @@ async def download_blob_as_base64(blob_container_client: ContainerClient, file_p return None -async def fetch_image(blob_container_client: ContainerClient, result: Document) -> Optional[str]: +async def fetch_image( + blob_container_client: ContainerClient, result: Document +) -> Optional[str]: if result.sourcepage: img = await download_blob_as_base64(blob_container_client, result.sourcepage) return img diff --git a/app/backend/core/sessionhelper.py b/app/backend/hrchatbot/core/sessionhelper.py similarity index 100% rename from app/backend/core/sessionhelper.py rename to app/backend/hrchatbot/core/sessionhelper.py diff --git a/app/backend/custom_uvicorn_worker.py b/app/backend/hrchatbot/custom_uvicorn_worker.py similarity index 100% rename from app/backend/custom_uvicorn_worker.py rename to app/backend/hrchatbot/custom_uvicorn_worker.py diff --git a/app/backend/decorators.py b/app/backend/hrchatbot/decorators.py similarity index 85% rename from app/backend/decorators.py rename to app/backend/hrchatbot/decorators.py index 6638767435..5ca8b1aaa4 100644 --- a/app/backend/decorators.py +++ b/app/backend/hrchatbot/decorators.py @@ -4,9 +4,9 @@ from quart import abort, current_app, request -from config import CONFIG_AUTH_CLIENT, CONFIG_SEARCH_CLIENT -from core.authentication import AuthError -from error import error_response +from hrchatbot.config import CONFIG_AUTH_CLIENT, CONFIG_SEARCH_CLIENT +from hrchatbot.core.authentication import AuthError +from hrchatbot.error import error_response def authenticated_path(route_fn: Callable[[str, dict[str, Any]], Any]): @@ -22,7 +22,9 @@ async def auth_handler(path=""): authorized = False try: auth_claims = await auth_helper.get_auth_claims_if_enabled(request.headers) - authorized = await auth_helper.check_path_auth(path, auth_claims, search_client) + authorized = await auth_helper.check_path_auth( + path, auth_claims, search_client + ) except AuthError: abort(403) except Exception as error: diff --git a/app/backend/error.py b/app/backend/hrchatbot/error.py similarity index 90% rename from app/backend/error.py rename to app/backend/hrchatbot/error.py index 0a21afe6b7..d0b070d9f3 100644 --- a/app/backend/error.py +++ b/app/backend/hrchatbot/error.py @@ -7,7 +7,9 @@ If you are an administrator of the app, view the full error in the logs. See aka.ms/appservice-logs for more information. Error type: {error_type} """ -ERROR_MESSAGE_FILTER = """Your message contains content that was flagged by the OpenAI content filter.""" +ERROR_MESSAGE_FILTER = ( + """Your message contains content that was flagged by the OpenAI content filter.""" +) ERROR_MESSAGE_LENGTH = """Your message exceeded the context length limit for this OpenAI model. Please shorten your message or change your settings to retrieve fewer search results.""" diff --git a/app/backend/load_azd_env.py b/app/backend/hrchatbot/load_azd_env.py similarity index 65% rename from app/backend/load_azd_env.py rename to app/backend/hrchatbot/load_azd_env.py index 2f2db6aa8f..fed6f32575 100644 --- a/app/backend/load_azd_env.py +++ b/app/backend/hrchatbot/load_azd_env.py @@ -10,7 +10,9 @@ def load_azd_env(): """Get path to current azd env file and load file using python-dotenv""" - result = subprocess.run("azd env list -o json", shell=True, capture_output=True, text=True) + result = subprocess.run( + "azd env list -o json", shell=True, capture_output=True, text=True + ) if result.returncode != 0: raise Exception("Error loading azd env") env_json = json.loads(result.stdout) @@ -22,8 +24,14 @@ def load_azd_env(): raise Exception("No default azd env file found") loading_mode = os.getenv("LOADING_MODE_FOR_AZD_ENV_VARS") or "override" if loading_mode == "no-override": - logger.info("Loading azd env from %s, but not overriding existing environment variables", env_file_path) + logger.info( + "Loading azd env from %s, but not overriding existing environment variables", + env_file_path, + ) load_dotenv(env_file_path, override=False) else: - logger.info("Loading azd env from %s, which may override existing environment variables", env_file_path) + logger.info( + "Loading azd env from %s, which may override existing environment variables", + env_file_path, + ) load_dotenv(env_file_path, override=True) diff --git a/app/backend/hrchatbot/main.py b/app/backend/hrchatbot/main.py new file mode 100644 index 0000000000..96352c2cb1 --- /dev/null +++ b/app/backend/hrchatbot/main.py @@ -0,0 +1,35 @@ +import asyncio +import os +import socket + +from hrchatbot.app import create_app +from hrchatbot.load_azd_env import load_azd_env + + +def force_bind_port(desired_port=8000): + """Force bind to port using SO_REUSEADDR""" + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + try: + s.bind(("localhost", desired_port)) + return desired_port + except OSError: + # Still couldn't bind, find free port + s.bind(("localhost", 0)) + return s.getsockname()[1] + + +RUNNING_ON_AZURE = ( + os.getenv("WEBSITE_HOSTNAME") is not None + or os.getenv("RUNNING_IN_PRODUCTION") is not None +) + +if not RUNNING_ON_AZURE: + load_azd_env() + +app = create_app() + +if __name__ == "__main__": + port = force_bind_port(8000) + url = f"http://localhost:{port}" + asyncio.run(app.run_task(host="localhost", port=port)) diff --git a/app/backend/prepdocs.py b/app/backend/hrchatbot/prepdocs.py similarity index 76% rename from app/backend/prepdocs.py rename to app/backend/hrchatbot/prepdocs.py index f03baac0dc..85bdc81714 100644 --- a/app/backend/prepdocs.py +++ b/app/backend/hrchatbot/prepdocs.py @@ -9,31 +9,31 @@ from azure.identity.aio import AzureDeveloperCliCredential, get_bearer_token_provider from rich.logging import RichHandler -from load_azd_env import load_azd_env -from prepdocslib.blobmanager import BlobManager -from prepdocslib.csvparser import CsvParser -from prepdocslib.embeddings import ( +from hrchatbot.load_azd_env import load_azd_env +from hrchatbot.prepdocslib.blobmanager import BlobManager +from hrchatbot.prepdocslib.csvparser import CsvParser +from hrchatbot.prepdocslib.embeddings import ( AzureOpenAIEmbeddingService, ImageEmbeddings, OpenAIEmbeddingService, ) -from prepdocslib.fileprocessor import FileProcessor -from prepdocslib.filestrategy import FileStrategy -from prepdocslib.htmlparser import LocalHTMLParser -from prepdocslib.integratedvectorizerstrategy import ( +from hrchatbot.prepdocslib.fileprocessor import FileProcessor +from hrchatbot.prepdocslib.filestrategy import FileStrategy +from hrchatbot.prepdocslib.htmlparser import LocalHTMLParser +from hrchatbot.prepdocslib.integratedvectorizerstrategy import ( IntegratedVectorizerStrategy, ) -from prepdocslib.jsonparser import JsonParser -from prepdocslib.listfilestrategy import ( +from hrchatbot.prepdocslib.jsonparser import JsonParser +from hrchatbot.prepdocslib.listfilestrategy import ( ADLSGen2ListFileStrategy, ListFileStrategy, LocalListFileStrategy, ) -from prepdocslib.parser import Parser -from prepdocslib.pdfparser import DocumentAnalysisParser, LocalPdfParser -from prepdocslib.strategy import DocumentAction, SearchInfo, Strategy -from prepdocslib.textparser import TextParser -from prepdocslib.textsplitter import SentenceTextSplitter, SimpleTextSplitter +from hrchatbot.prepdocslib.parser import Parser +from hrchatbot.prepdocslib.pdfparser import DocumentAnalysisParser, LocalPdfParser +from hrchatbot.prepdocslib.strategy import DocumentAction, SearchInfo, Strategy +from hrchatbot.prepdocslib.textparser import TextParser +from hrchatbot.prepdocslib.textsplitter import SentenceTextSplitter, SimpleTextSplitter logger = logging.getLogger("scripts") @@ -61,7 +61,9 @@ async def setup_search_info( azure_credential if search_key is None else AzureKeyCredential(search_key) ) if use_agentic_retrieval and azure_openai_searchagent_model is None: - raise ValueError("Azure OpenAI SearchAgent model must be specified when using agentic retrieval.") + raise ValueError( + "Azure OpenAI SearchAgent model must be specified when using agentic retrieval." + ) return SearchInfo( endpoint=f"https://{search_service}.search.windows.net/", @@ -85,7 +87,9 @@ def setup_blob_manager( search_images: bool, storage_key: Union[str, None] = None, ): - storage_creds: Union[AsyncTokenCredential, str] = azure_credential if storage_key is None else storage_key + storage_creds: Union[AsyncTokenCredential, str] = ( + azure_credential if storage_key is None else storage_key + ) return BlobManager( endpoint=f"https://{storage_account}.blob.core.windows.net", container=storage_container, @@ -108,9 +112,15 @@ def setup_list_file_strategy( list_file_strategy: ListFileStrategy if datalake_storage_account: if datalake_filesystem is None or datalake_path is None: - raise ValueError("DataLake file system and path are required when using Azure Data Lake Gen2") - adls_gen2_creds: Union[AsyncTokenCredential, str] = azure_credential if datalake_key is None else datalake_key - logger.info("Using Data Lake Gen2 Storage Account: %s", datalake_storage_account) + raise ValueError( + "DataLake file system and path are required when using Azure Data Lake Gen2" + ) + adls_gen2_creds: Union[AsyncTokenCredential, str] = ( + azure_credential if datalake_key is None else datalake_key + ) + logger.info( + "Using Data Lake Gen2 Storage Account: %s", datalake_storage_account + ) list_file_strategy = ADLSGen2ListFileStrategy( data_lake_storage_account=datalake_storage_account, data_lake_filesystem=datalake_filesystem, @@ -121,7 +131,9 @@ def setup_list_file_strategy( logger.info("Using local files: %s", local_files) list_file_strategy = LocalListFileStrategy(path_pattern=local_files) else: - raise ValueError("Either local_files or datalake_storage_account must be provided.") + raise ValueError( + "Either local_files or datalake_storage_account must be provided." + ) return list_file_strategy @@ -159,7 +171,9 @@ def setup_embeddings_service( ) else: if openai_key is None: - raise ValueError("OpenAI key is required when using the non-Azure OpenAI API") + raise ValueError( + "OpenAI key is required when using the non-Azure OpenAI API" + ) return OpenAIEmbeddingService( open_ai_model_name=openai_model_name, open_ai_dimensions=openai_dimensions, @@ -185,7 +199,9 @@ def setup_file_processors( # check if Azure Document Intelligence credentials are provided if document_intelligence_service is not None: documentintelligence_creds: Union[AsyncTokenCredential, AzureKeyCredential] = ( - azure_credential if document_intelligence_key is None else AzureKeyCredential(document_intelligence_key) + azure_credential + if document_intelligence_key is None + else AzureKeyCredential(document_intelligence_key) ) doc_int_parser = DocumentAnalysisParser( endpoint=f"https://{document_intelligence_service}.cognitiveservices.azure.com/", @@ -219,9 +235,13 @@ def setup_file_processors( } # These require either a Python package or Document Intelligence if pdf_parser is not None: - file_processors.update({".pdf": FileProcessor(pdf_parser, sentence_text_splitter)}) + file_processors.update( + {".pdf": FileProcessor(pdf_parser, sentence_text_splitter)} + ) if html_parser is not None: - file_processors.update({".html": FileProcessor(html_parser, sentence_text_splitter)}) + file_processors.update( + {".html": FileProcessor(html_parser, sentence_text_splitter)} + ) # These file formats require Document Intelligence if doc_int_parser is not None: file_processors.update( @@ -241,15 +261,21 @@ def setup_file_processors( def setup_image_embeddings_service( - azure_credential: AsyncTokenCredential, vision_endpoint: Union[str, None], search_images: bool + azure_credential: AsyncTokenCredential, + vision_endpoint: Union[str, None], + search_images: bool, ) -> Union[ImageEmbeddings, None]: image_embeddings_service: Optional[ImageEmbeddings] = None if search_images: if vision_endpoint is None: - raise ValueError("A computer vision endpoint is required when GPT-4-vision is enabled.") + raise ValueError( + "A computer vision endpoint is required when GPT-4-vision is enabled." + ) image_embeddings_service = ImageEmbeddings( endpoint=vision_endpoint, - token_provider=get_bearer_token_provider(azure_credential, "https://cognitiveservices.azure.com/.default"), + token_provider=get_bearer_token_provider( + azure_credential, "https://cognitiveservices.azure.com/.default" + ), ) return image_embeddings_service @@ -268,13 +294,18 @@ async def main(strategy: Strategy, setup_index: bool = True): parser.add_argument("files", nargs="?", help="Files to be processed") parser.add_argument( - "--category", help="Value for the category field in the search index for all sections indexed in this run" + "--category", + help="Value for the category field in the search index for all sections indexed in this run", ) parser.add_argument( - "--skipblobs", action="store_true", help="Skip uploading individual pages to Azure Blob Storage" + "--skipblobs", + action="store_true", + help="Skip uploading individual pages to Azure Blob Storage", ) parser.add_argument( - "--disablebatchvectors", action="store_true", help="Don't compute embeddings in batch for the sections" + "--disablebatchvectors", + action="store_true", + help="Don't compute embeddings in batch for the sections", ) parser.add_argument( "--remove", @@ -299,7 +330,9 @@ async def main(strategy: Strategy, setup_index: bool = True): help="Optional. Use this Azure Blob Storage account key instead of the current user identity to login (use az login to set current user for Azure)", ) parser.add_argument( - "--datalakekey", required=False, help="Optional. Use this key when authenticating to Azure Data Lake Gen2" + "--datalakekey", + required=False, + help="Optional. Use this key when authenticating to Azure Data Lake Gen2", ) parser.add_argument( "--documentintelligencekey", @@ -316,7 +349,11 @@ async def main(strategy: Strategy, setup_index: bool = True): args = parser.parse_args() if args.verbose: - logging.basicConfig(format="%(message)s", datefmt="[%X]", handlers=[RichHandler(rich_tracebacks=True)]) + logging.basicConfig( + format="%(message)s", + datefmt="[%X]", + handlers=[RichHandler(rich_tracebacks=True)], + ) # We only set the level to INFO for our logger, # to avoid seeing the noisy INFO level logs from the Azure SDKs logger.setLevel(logging.DEBUG) @@ -327,19 +364,30 @@ async def main(strategy: Strategy, setup_index: bool = True): logger.error("AZURE_PUBLIC_NETWORK_ACCESS is set to Disabled. Exiting.") exit(0) - use_int_vectorization = os.getenv("USE_FEATURE_INT_VECTORIZATION", "").lower() == "true" + use_int_vectorization = ( + os.getenv("USE_FEATURE_INT_VECTORIZATION", "").lower() == "true" + ) use_gptvision = os.getenv("USE_GPT4V", "").lower() == "true" use_acls = os.getenv("AZURE_ENFORCE_ACCESS_CONTROL") is not None dont_use_vectors = os.getenv("USE_VECTORS", "").lower() == "false" use_agentic_retrieval = os.getenv("USE_AGENTIC_RETRIEVAL", "").lower() == "true" - use_content_understanding = os.getenv("USE_MEDIA_DESCRIBER_AZURE_CU", "").lower() == "true" + use_content_understanding = ( + os.getenv("USE_MEDIA_DESCRIBER_AZURE_CU", "").lower() == "true" + ) # Use the current user identity to connect to Azure services. See infra/main.bicep for role assignments. if tenant_id := os.getenv("AZURE_TENANT_ID"): - logger.info("Connecting to Azure services using the azd credential for tenant %s", tenant_id) - azd_credential = AzureDeveloperCliCredential(tenant_id=tenant_id, process_timeout=60) + logger.info( + "Connecting to Azure services using the azd credential for tenant %s", + tenant_id, + ) + azd_credential = AzureDeveloperCliCredential( + tenant_id=tenant_id, process_timeout=60 + ) else: - logger.info("Connecting to Azure services using the azd credential for home tenant") + logger.info( + "Connecting to Azure services using the azd credential for home tenant" + ) azd_credential = AzureDeveloperCliCredential(process_timeout=60) if args.removeall: @@ -356,7 +404,9 @@ async def main(strategy: Strategy, setup_index: bool = True): # Check for incompatibility # if openai host is not azure if openai_host != "azure" and use_agentic_retrieval: - raise Exception("Agentic retrieval requires an Azure OpenAI chat completion service") + raise Exception( + "Agentic retrieval requires an Azure OpenAI chat completion service" + ) search_info = loop.run_until_complete( setup_search_info( @@ -364,9 +414,13 @@ async def main(strategy: Strategy, setup_index: bool = True): index_name=os.environ["AZURE_SEARCH_INDEX"], use_agentic_retrieval=use_agentic_retrieval, agent_name=os.getenv("AZURE_SEARCH_AGENT"), - agent_max_output_tokens=int(os.getenv("AZURE_SEARCH_AGENT_MAX_OUTPUT_TOKENS", 10000)), + agent_max_output_tokens=int( + os.getenv("AZURE_SEARCH_AGENT_MAX_OUTPUT_TOKENS", 10000) + ), azure_openai_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], - azure_openai_searchagent_deployment=os.getenv("AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT"), + azure_openai_searchagent_deployment=os.getenv( + "AZURE_OPENAI_SEARCHAGENT_DEPLOYMENT" + ), azure_openai_searchagent_model=os.getenv("AZURE_OPENAI_SEARCHAGENT_MODEL"), azure_credential=azd_credential, search_key=clean_key_if_exists(args.searchkey), @@ -418,9 +472,12 @@ async def main(strategy: Strategy, setup_index: bool = True): ingestion_strategy: Strategy if use_int_vectorization: - - if not openai_embeddings_service or not isinstance(openai_embeddings_service, AzureOpenAIEmbeddingService): - raise Exception("Integrated vectorization strategy requires an Azure OpenAI embeddings service") + if not openai_embeddings_service or not isinstance( + openai_embeddings_service, AzureOpenAIEmbeddingService + ): + raise Exception( + "Integrated vectorization strategy requires an Azure OpenAI embeddings service" + ) ingestion_strategy = IntegratedVectorizerStrategy( search_info=search_info, @@ -438,13 +495,17 @@ async def main(strategy: Strategy, setup_index: bool = True): else: file_processors = setup_file_processors( azure_credential=azd_credential, - document_intelligence_service=os.getenv("AZURE_DOCUMENTINTELLIGENCE_SERVICE"), + document_intelligence_service=os.getenv( + "AZURE_DOCUMENTINTELLIGENCE_SERVICE" + ), document_intelligence_key=clean_key_if_exists(args.documentintelligencekey), local_pdf_parser=os.getenv("USE_LOCAL_PDF_PARSER") == "true", local_html_parser=os.getenv("USE_LOCAL_HTML_PARSER") == "true", search_images=use_gptvision, use_content_understanding=use_content_understanding, - content_understanding_endpoint=os.getenv("AZURE_CONTENTUNDERSTANDING_ENDPOINT"), + content_understanding_endpoint=os.getenv( + "AZURE_CONTENTUNDERSTANDING_ENDPOINT" + ), ) image_embeddings_service = setup_image_embeddings_service( azure_credential=azd_credential, @@ -462,12 +523,18 @@ async def main(strategy: Strategy, setup_index: bool = True): image_embeddings=image_embeddings_service, search_analyzer_name=os.getenv("AZURE_SEARCH_ANALYZER_NAME"), # Default to the previous field names for backward compatibility - search_field_name_embedding=os.getenv("AZURE_SEARCH_FIELD_NAME_EMBEDDING", "embedding"), + search_field_name_embedding=os.getenv( + "AZURE_SEARCH_FIELD_NAME_EMBEDDING", "embedding" + ), use_acls=use_acls, category=args.category, use_content_understanding=use_content_understanding, - content_understanding_endpoint=os.getenv("AZURE_CONTENTUNDERSTANDING_ENDPOINT"), + content_understanding_endpoint=os.getenv( + "AZURE_CONTENTUNDERSTANDING_ENDPOINT" + ), ) - loop.run_until_complete(main(ingestion_strategy, setup_index=not args.remove and not args.removeall)) + loop.run_until_complete( + main(ingestion_strategy, setup_index=not args.remove and not args.removeall) + ) loop.close() diff --git a/app/backend/hrchatbot/prepdocslib/__init__.py b/app/backend/hrchatbot/prepdocslib/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/app/backend/prepdocslib/blobmanager.py b/app/backend/hrchatbot/prepdocslib/blobmanager.py similarity index 74% rename from app/backend/prepdocslib/blobmanager.py rename to app/backend/hrchatbot/prepdocslib/blobmanager.py index d5c21e0d41..686dd22a52 100644 --- a/app/backend/prepdocslib/blobmanager.py +++ b/app/backend/hrchatbot/prepdocslib/blobmanager.py @@ -46,9 +46,14 @@ def __init__( self.user_delegation_key: Optional[UserDelegationKey] = None async def upload_blob(self, file: File) -> Optional[list[str]]: - async with BlobServiceClient( - account_url=self.endpoint, credential=self.credential, max_single_put_size=4 * 1024 * 1024 - ) as service_client, service_client.get_container_client(self.container) as container_client: + async with ( + BlobServiceClient( + account_url=self.endpoint, + credential=self.credential, + max_single_put_size=4 * 1024 * 1024, + ) as service_client, + service_client.get_container_client(self.container) as container_client, + ): if not await container_client.exists(): await container_client.create_container() @@ -57,14 +62,20 @@ async def upload_blob(self, file: File) -> Optional[list[str]]: with open(file.content.name, "rb") as reopened_file: blob_name = BlobManager.blob_name_from_file_name(file.content.name) logger.info("Uploading blob for whole file -> %s", blob_name) - blob_client = await container_client.upload_blob(blob_name, reopened_file, overwrite=True) + blob_client = await container_client.upload_blob( + blob_name, reopened_file, overwrite=True + ) file.url = blob_client.url if self.store_page_images: if os.path.splitext(file.content.name)[1].lower() == ".pdf": - return await self.upload_pdf_blob_images(service_client, container_client, file) + return await self.upload_pdf_blob_images( + service_client, container_client, file + ) else: - logger.info("File %s is not a PDF, skipping image upload", file.content.name) + logger.info( + "File %s is not a PDF, skipping image upload", file.content.name + ) return None @@ -72,7 +83,10 @@ def get_managedidentity_connectionstring(self): return f"ResourceId=/subscriptions/{self.subscriptionId}/resourceGroups/{self.resourceGroup}/providers/Microsoft.Storage/storageAccounts/{self.account};" async def upload_pdf_blob_images( - self, service_client: BlobServiceClient, container_client: ContainerClient, file: File + self, + service_client: BlobServiceClient, + container_client: ContainerClient, + file: File, ) -> list[str]: with open(file.content.name, "rb") as reopened_file: reader = PdfReader(reopened_file) @@ -87,9 +101,13 @@ async def upload_pdf_blob_images( font = ImageFont.truetype("arial.ttf", 20) except OSError: try: - font = ImageFont.truetype("/usr/share/fonts/truetype/freefont/FreeMono.ttf", 20) + font = ImageFont.truetype( + "/usr/share/fonts/truetype/freefont/FreeMono.ttf", 20 + ) except OSError: - logger.info("Unable to find arial.ttf or FreeMono.ttf, using default font") + logger.info( + "Unable to find arial.ttf or FreeMono.ttf, using default font" + ) for i in range(page_count): blob_name = BlobManager.blob_image_name_from_file_page(file.content.name, i) @@ -102,7 +120,9 @@ async def upload_pdf_blob_images( # Create a new image with additional space for text text_height = 40 # Height of the text area - new_img = Image.new("RGB", (original_img.width, original_img.height + text_height), "white") + new_img = Image.new( + "RGB", (original_img.width, original_img.height + text_height), "white" + ) # Paste the original image onto the new image new_img.paste(original_img, (0, text_height)) @@ -120,9 +140,13 @@ async def upload_pdf_blob_images( new_img.save(output, format="PNG") output.seek(0) - blob_client = await container_client.upload_blob(blob_name, output, overwrite=True) + blob_client = await container_client.upload_blob( + blob_name, output, overwrite=True + ) if not self.user_delegation_key: - self.user_delegation_key = await service_client.get_user_delegation_key(start_time, expiry_time) + self.user_delegation_key = await service_client.get_user_delegation_key( + start_time, expiry_time + ) if blob_client.account_name is not None: sas_token = generate_blob_sas( @@ -139,9 +163,12 @@ async def upload_pdf_blob_images( return sas_uris async def remove_blob(self, path: Optional[str] = None): - async with BlobServiceClient( - account_url=self.endpoint, credential=self.credential - ) as service_client, service_client.get_container_client(self.container) as container_client: + async with ( + BlobServiceClient( + account_url=self.endpoint, credential=self.credential + ) as service_client, + service_client.get_container_client(self.container) as container_client, + ): if not await container_client.exists(): return if path is None: @@ -149,13 +176,16 @@ async def remove_blob(self, path: Optional[str] = None): blobs = container_client.list_blob_names() else: prefix = os.path.splitext(os.path.basename(path))[0] - blobs = container_client.list_blob_names(name_starts_with=os.path.splitext(os.path.basename(prefix))[0]) + blobs = container_client.list_blob_names( + name_starts_with=os.path.splitext(os.path.basename(prefix))[0] + ) async for blob_path in blobs: # This still supports PDFs split into individual pages, but we could remove in future to simplify code if ( prefix is not None and ( - not re.match(rf"{prefix}-\d+\.pdf", blob_path) or not re.match(rf"{prefix}-\d+\.png", blob_path) + not re.match(rf"{prefix}-\d+\.pdf", blob_path) + or not re.match(rf"{prefix}-\d+\.png", blob_path) ) ) or (path is not None and blob_path == os.path.basename(path)): continue @@ -165,13 +195,13 @@ async def remove_blob(self, path: Optional[str] = None): @classmethod def sourcepage_from_file_page(cls, filename, page=0) -> str: if os.path.splitext(filename)[1].lower() == ".pdf": - return f"{os.path.basename(filename)}#page={page+1}" + return f"{os.path.basename(filename)}#page={page + 1}" else: return os.path.basename(filename) @classmethod def blob_image_name_from_file_page(cls, filename, page=0) -> str: - return os.path.splitext(os.path.basename(filename))[0] + f"-{page+1}" + ".png" + return os.path.splitext(os.path.basename(filename))[0] + f"-{page + 1}" + ".png" @classmethod def blob_name_from_file_name(cls, filename) -> str: diff --git a/app/backend/prepdocslib/csvparser.py b/app/backend/hrchatbot/prepdocslib/csvparser.py similarity index 100% rename from app/backend/prepdocslib/csvparser.py rename to app/backend/hrchatbot/prepdocslib/csvparser.py diff --git a/app/backend/prepdocslib/embeddings.py b/app/backend/hrchatbot/prepdocslib/embeddings.py similarity index 84% rename from app/backend/prepdocslib/embeddings.py rename to app/backend/hrchatbot/prepdocslib/embeddings.py index df56f39c08..e2e484f0c4 100644 --- a/app/backend/prepdocslib/embeddings.py +++ b/app/backend/hrchatbot/prepdocslib/embeddings.py @@ -52,7 +52,12 @@ class OpenAIEmbeddings(ABC): "text-embedding-3-large": True, } - def __init__(self, open_ai_model_name: str, open_ai_dimensions: int, disable_batch: bool = False): + def __init__( + self, + open_ai_model_name: str, + open_ai_dimensions: int, + disable_batch: bool = False, + ): self.open_ai_model_name = open_ai_model_name self.open_ai_dimensions = open_ai_dimensions self.disable_batch = disable_batch @@ -61,14 +66,18 @@ async def create_client(self) -> AsyncOpenAI: raise NotImplementedError def before_retry_sleep(self, retry_state): - logger.info("Rate limited on the OpenAI embeddings API, sleeping before retrying...") + logger.info( + "Rate limited on the OpenAI embeddings API, sleeping before retrying..." + ) def calculate_token_length(self, text: str): encoding = tiktoken.encoding_for_model(self.open_ai_model_name) return len(encoding.encode(text)) def split_text_into_batches(self, texts: list[str]) -> list[EmbeddingBatch]: - batch_info = OpenAIEmbeddings.SUPPORTED_BATCH_AOAI_MODEL.get(self.open_ai_model_name) + batch_info = OpenAIEmbeddings.SUPPORTED_BATCH_AOAI_MODEL.get( + self.open_ai_model_name + ) if not batch_info: raise NotImplementedError( f"Model {self.open_ai_model_name} is not supported with batch embedding operations" @@ -81,7 +90,10 @@ def split_text_into_batches(self, texts: list[str]) -> list[EmbeddingBatch]: batch_token_length = 0 for text in texts: text_token_length = self.calculate_token_length(text) - if batch_token_length + text_token_length >= batch_token_limit and len(batch) > 0: + if ( + batch_token_length + text_token_length >= batch_token_limit + and len(batch) > 0 + ): batches.append(EmbeddingBatch(batch, batch_token_length)) batch = [] batch_token_length = 0 @@ -98,7 +110,9 @@ def split_text_into_batches(self, texts: list[str]) -> list[EmbeddingBatch]: return batches - async def create_embedding_batch(self, texts: list[str], dimensions_args: ExtraArgs) -> list[list[float]]: + async def create_embedding_batch( + self, texts: list[str], dimensions_args: ExtraArgs + ) -> list[list[float]]: batches = self.split_text_into_batches(texts) embeddings = [] client = await self.create_client() @@ -111,7 +125,9 @@ async def create_embedding_batch(self, texts: list[str], dimensions_args: ExtraA ): with attempt: emb_response = await client.embeddings.create( - model=self.open_ai_model_name, input=batch.texts, **dimensions_args + model=self.open_ai_model_name, + input=batch.texts, + **dimensions_args, ) embeddings.extend([data.embedding for data in emb_response.data]) logger.info( @@ -122,7 +138,9 @@ async def create_embedding_batch(self, texts: list[str], dimensions_args: ExtraA return embeddings - async def create_embedding_single(self, text: str, dimensions_args: ExtraArgs) -> list[float]: + async def create_embedding_single( + self, text: str, dimensions_args: ExtraArgs + ) -> list[float]: client = await self.create_client() async for attempt in AsyncRetrying( retry=retry_if_exception_type(RateLimitError), @@ -134,22 +152,29 @@ async def create_embedding_single(self, text: str, dimensions_args: ExtraArgs) - emb_response = await client.embeddings.create( model=self.open_ai_model_name, input=text, **dimensions_args ) - logger.info("Computed embedding for text section. Character count: %d", len(text)) + logger.info( + "Computed embedding for text section. Character count: %d", + len(text), + ) return emb_response.data[0].embedding async def create_embeddings(self, texts: list[str]) -> list[list[float]]: - dimensions_args: ExtraArgs = ( {"dimensions": self.open_ai_dimensions} if OpenAIEmbeddings.SUPPORTED_DIMENSIONS_MODEL.get(self.open_ai_model_name) else {} ) - if not self.disable_batch and self.open_ai_model_name in OpenAIEmbeddings.SUPPORTED_BATCH_AOAI_MODEL: + if ( + not self.disable_batch + and self.open_ai_model_name in OpenAIEmbeddings.SUPPORTED_BATCH_AOAI_MODEL + ): return await self.create_embedding_batch(texts, dimensions_args) - return [await self.create_embedding_single(text, dimensions_args) for text in texts] + return [ + await self.create_embedding_single(text, dimensions_args) for text in texts + ] class AzureOpenAIEmbeddingService(OpenAIEmbeddings): @@ -176,7 +201,9 @@ def __init__( elif open_ai_custom_url: self.open_ai_endpoint = open_ai_custom_url else: - raise ValueError("Either open_ai_service or open_ai_custom_url must be provided") + raise ValueError( + "Either open_ai_service or open_ai_custom_url must be provided" + ) self.open_ai_deployment = open_ai_deployment self.open_ai_api_version = open_ai_api_version self.credential = credential @@ -253,11 +280,15 @@ async def create_embeddings(self, blob_urls: list[str]) -> list[list[float]]: ): with attempt: body = {"url": blob_url} - async with session.post(url=endpoint, params=params, json=body) as resp: + async with session.post( + url=endpoint, params=params, json=body + ) as resp: resp_json = await resp.json() embeddings.append(resp_json["vector"]) return embeddings def before_retry_sleep(self, retry_state): - logger.info("Rate limited on the Vision embeddings API, sleeping before retrying...") + logger.info( + "Rate limited on the Vision embeddings API, sleeping before retrying..." + ) diff --git a/app/backend/prepdocslib/fileprocessor.py b/app/backend/hrchatbot/prepdocslib/fileprocessor.py similarity index 100% rename from app/backend/prepdocslib/fileprocessor.py rename to app/backend/hrchatbot/prepdocslib/fileprocessor.py diff --git a/app/backend/prepdocslib/filestrategy.py b/app/backend/hrchatbot/prepdocslib/filestrategy.py similarity index 83% rename from app/backend/prepdocslib/filestrategy.py rename to app/backend/hrchatbot/prepdocslib/filestrategy.py index 37f399cf4b..b4790e1f05 100644 --- a/app/backend/prepdocslib/filestrategy.py +++ b/app/backend/hrchatbot/prepdocslib/filestrategy.py @@ -29,9 +29,12 @@ async def parse_file( pages = [page async for page in processor.parser.parse(content=file.content)] logger.info("Splitting '%s' into sections", file.filename()) if image_embeddings: - logger.warning("Each page will be split into smaller chunks of text, but images will be of the entire page.") + logger.warning( + "Each page will be split into smaller chunks of text, but images will be of the entire page." + ) sections = [ - Section(split_page, content=file, category=category) for split_page in processor.splitter.split_pages(pages) + Section(split_page, content=file, category=category) + for split_page in processor.splitter.split_pages(pages) ] return sections @@ -88,12 +91,16 @@ async def setup(self): if self.use_content_understanding: if self.content_understanding_endpoint is None: - raise ValueError("Content Understanding is enabled but no endpoint was provided") + raise ValueError( + "Content Understanding is enabled but no endpoint was provided" + ) if isinstance(self.search_info.credential, AzureKeyCredential): raise ValueError( "AzureKeyCredential is not supported for Content Understanding, use keyless auth instead" ) - cu_manager = ContentUnderstandingDescriber(self.content_understanding_endpoint, self.search_info.credential) + cu_manager = ContentUnderstandingDescriber( + self.content_understanding_endpoint, self.search_info.credential + ) await cu_manager.create_analyzer() async def run(self): @@ -102,13 +109,21 @@ async def run(self): files = self.list_file_strategy.list() async for file in files: try: - sections = await parse_file(file, self.file_processors, self.category, self.image_embeddings) + sections = await parse_file( + file, self.file_processors, self.category, self.image_embeddings + ) if sections: blob_sas_uris = await self.blob_manager.upload_blob(file) blob_image_embeddings: Optional[list[list[float]]] = None if self.image_embeddings and blob_sas_uris: - blob_image_embeddings = await self.image_embeddings.create_embeddings(blob_sas_uris) - await self.search_manager.update_content(sections, blob_image_embeddings, url=file.url) + blob_image_embeddings = ( + await self.image_embeddings.create_embeddings( + blob_sas_uris + ) + ) + await self.search_manager.update_content( + sections, blob_image_embeddings, url=file.url + ) finally: if file: file.close() @@ -152,7 +167,9 @@ def __init__( async def add_file(self, file: File): if self.image_embeddings: - logging.warning("Image embeddings are not currently supported for the user upload feature") + logging.warning( + "Image embeddings are not currently supported for the user upload feature" + ) sections = await parse_file(file, self.file_processors) if sections: await self.search_manager.update_content(sections, url=file.url) diff --git a/app/backend/prepdocslib/htmlparser.py b/app/backend/hrchatbot/prepdocslib/htmlparser.py similarity index 90% rename from app/backend/prepdocslib/htmlparser.py rename to app/backend/hrchatbot/prepdocslib/htmlparser.py index 719045b393..071bcacad1 100644 --- a/app/backend/prepdocslib/htmlparser.py +++ b/app/backend/hrchatbot/prepdocslib/htmlparser.py @@ -39,7 +39,10 @@ async def parse(self, content: IO) -> AsyncGenerator[Page, None]: Returns: Page: The parsed html Page. """ - logger.info("Extracting text from '%s' using local HTML parser (BeautifulSoup)", content.name) + logger.info( + "Extracting text from '%s' using local HTML parser (BeautifulSoup)", + content.name, + ) data = content.read() soup = BeautifulSoup(data, "html.parser") diff --git a/app/backend/prepdocslib/integratedvectorizerstrategy.py b/app/backend/hrchatbot/prepdocslib/integratedvectorizerstrategy.py similarity index 90% rename from app/backend/prepdocslib/integratedvectorizerstrategy.py rename to app/backend/hrchatbot/prepdocslib/integratedvectorizerstrategy.py index 9e89facc4c..92efedcd5b 100644 --- a/app/backend/prepdocslib/integratedvectorizerstrategy.py +++ b/app/backend/hrchatbot/prepdocslib/integratedvectorizerstrategy.py @@ -48,7 +48,6 @@ def __init__( use_acls: bool = False, category: Optional[str] = None, ): - self.list_file_strategy = list_file_strategy self.blob_manager = blob_manager self.document_action = document_action @@ -104,12 +103,21 @@ async def create_embedding_skill(self, index_name: str) -> SearchIndexerSkillset parent_key_field_name="parent_id", source_context="/document/pages/*", mappings=[ - InputFieldMappingEntry(name="content", source="/document/pages/*"), - InputFieldMappingEntry(name="sourcepage", source="/document/metadata_storage_name"), - InputFieldMappingEntry(name="sourcefile", source="/document/metadata_storage_name"), - InputFieldMappingEntry(name="storageUrl", source="/document/metadata_storage_path"), InputFieldMappingEntry( - name=self.search_field_name_embedding, source="/document/pages/*/vector" + name="content", source="/document/pages/*" + ), + InputFieldMappingEntry( + name="sourcepage", source="/document/metadata_storage_name" + ), + InputFieldMappingEntry( + name="sourcefile", source="/document/metadata_storage_name" + ), + InputFieldMappingEntry( + name="storageUrl", source="/document/metadata_storage_path" + ), + InputFieldMappingEntry( + name=self.search_field_name_embedding, + source="/document/pages/*/vector", ), ], ), @@ -154,7 +162,9 @@ async def setup(self): await ds_client.create_or_update_data_source_connection(data_source_connection) - embedding_skillset = await self.create_embedding_skill(self.search_info.index_name) + embedding_skillset = await self.create_embedding_skill( + self.search_info.index_name + ) await ds_client.create_or_update_skillset(embedding_skillset) await ds_client.close() diff --git a/app/backend/prepdocslib/jsonparser.py b/app/backend/hrchatbot/prepdocslib/jsonparser.py similarity index 100% rename from app/backend/prepdocslib/jsonparser.py rename to app/backend/hrchatbot/prepdocslib/jsonparser.py diff --git a/app/backend/prepdocslib/listfilestrategy.py b/app/backend/hrchatbot/prepdocslib/listfilestrategy.py similarity index 78% rename from app/backend/prepdocslib/listfilestrategy.py rename to app/backend/hrchatbot/prepdocslib/listfilestrategy.py index bdceef0754..53d84dbb0f 100644 --- a/app/backend/prepdocslib/listfilestrategy.py +++ b/app/backend/hrchatbot/prepdocslib/listfilestrategy.py @@ -23,7 +23,12 @@ class File: This file might contain access control information about which users or groups can access it """ - def __init__(self, content: IO, acls: Optional[dict[str, list]] = None, url: Optional[str] = None): + def __init__( + self, + content: IO, + acls: Optional[dict[str, list]] = None, + url: Optional[str] = None, + ): self.content = content self.acls = acls or {} self.url = url @@ -36,7 +41,9 @@ def file_extension(self): def filename_to_id(self): filename_ascii = re.sub("[^0-9a-zA-Z_-]", "_", self.filename()) - filename_hash = base64.b16encode(self.filename().encode("utf-8")).decode("ascii") + filename_hash = base64.b16encode(self.filename().encode("utf-8")).decode( + "ascii" + ) acls_hash = "" if self.acls: acls_hash = base64.b16encode(str(self.acls).encode("utf-8")).decode("ascii") @@ -130,21 +137,37 @@ def __init__( self.credential = credential async def list_paths(self) -> AsyncGenerator[str, None]: - async with DataLakeServiceClient( - account_url=f"https://{self.data_lake_storage_account}.dfs.core.windows.net", credential=self.credential - ) as service_client, service_client.get_file_system_client(self.data_lake_filesystem) as filesystem_client: - async for path in filesystem_client.get_paths(path=self.data_lake_path, recursive=True): + async with ( + DataLakeServiceClient( + account_url=f"https://{self.data_lake_storage_account}.dfs.core.windows.net", + credential=self.credential, + ) as service_client, + service_client.get_file_system_client( + self.data_lake_filesystem + ) as filesystem_client, + ): + async for path in filesystem_client.get_paths( + path=self.data_lake_path, recursive=True + ): if path.is_directory: continue yield path.name async def list(self) -> AsyncGenerator[File, None]: - async with DataLakeServiceClient( - account_url=f"https://{self.data_lake_storage_account}.dfs.core.windows.net", credential=self.credential - ) as service_client, service_client.get_file_system_client(self.data_lake_filesystem) as filesystem_client: + async with ( + DataLakeServiceClient( + account_url=f"https://{self.data_lake_storage_account}.dfs.core.windows.net", + credential=self.credential, + ) as service_client, + service_client.get_file_system_client( + self.data_lake_filesystem + ) as filesystem_client, + ): async for path in self.list_paths(): - temp_file_path = os.path.join(tempfile.gettempdir(), os.path.basename(path)) + temp_file_path = os.path.join( + tempfile.gettempdir(), os.path.basename(path) + ) try: async with filesystem_client.get_file_client(path) as file_client: with open(temp_file_path, "wb") as temp_file: @@ -169,10 +192,18 @@ async def list(self) -> AsyncGenerator[File, None]: acls["oids"].append(acl_parts[1]) if acl_parts[0] == "group" and "r" in acl_parts[2]: acls["groups"].append(acl_parts[1]) - yield File(content=open(temp_file_path, "rb"), acls=acls, url=file_client.url) + yield File( + content=open(temp_file_path, "rb"), + acls=acls, + url=file_client.url, + ) except Exception as data_lake_exception: - logger.error(f"\tGot an error while reading {path} -> {data_lake_exception} --> skipping file") + logger.error( + f"\tGot an error while reading {path} -> {data_lake_exception} --> skipping file" + ) try: os.remove(temp_file_path) except Exception as file_delete_exception: - logger.error(f"\tGot an error while deleting {temp_file_path} -> {file_delete_exception}") + logger.error( + f"\tGot an error while deleting {temp_file_path} -> {file_delete_exception}" + ) diff --git a/app/backend/prepdocslib/mediadescriber.py b/app/backend/hrchatbot/prepdocslib/mediadescriber.py similarity index 86% rename from app/backend/prepdocslib/mediadescriber.py rename to app/backend/hrchatbot/prepdocslib/mediadescriber.py index 5aae79232e..9631243727 100644 --- a/app/backend/prepdocslib/mediadescriber.py +++ b/app/backend/hrchatbot/prepdocslib/mediadescriber.py @@ -11,7 +11,6 @@ class MediaDescriber(ABC): - async def describe_image(self, image_bytes) -> str: raise NotImplementedError # pragma: no cover @@ -43,8 +42,11 @@ def __init__(self, endpoint: str, credential: AsyncTokenCredential): self.credential = credential async def poll_api(self, session, poll_url, headers): - - @retry(stop=stop_after_attempt(60), wait=wait_fixed(2), retry=retry_if_exception_type(ValueError)) + @retry( + stop=stop_after_attempt(60), + wait=wait_fixed(2), + retry=retry_if_exception_type(ValueError), + ) async def poll(): async with session.get(poll_url, headers=headers) as response: response.raise_for_status() @@ -60,15 +62,23 @@ async def poll(): async def create_analyzer(self): logger.info("Creating analyzer '%s'...", self.analyzer_schema["analyzerId"]) - token_provider = get_bearer_token_provider(self.credential, "https://cognitiveservices.azure.com/.default") + token_provider = get_bearer_token_provider( + self.credential, "https://cognitiveservices.azure.com/.default" + ) token = await token_provider() - headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"} + headers = { + "Authorization": f"Bearer {token}", + "Content-Type": "application/json", + } params = {"api-version": self.CU_API_VERSION} analyzer_id = self.analyzer_schema["analyzerId"] cu_endpoint = f"{self.endpoint}/contentunderstanding/analyzers/{analyzer_id}" async with aiohttp.ClientSession() as session: async with session.put( - url=cu_endpoint, params=params, headers=headers, json=self.analyzer_schema + url=cu_endpoint, + params=params, + headers=headers, + json=self.analyzer_schema, ) as response: if response.status == 409: logger.info("Analyzer '%s' already exists.", analyzer_id) @@ -86,7 +96,9 @@ async def create_analyzer(self): async def describe_image(self, image_bytes: bytes) -> str: logger.info("Sending image to Azure Content Understanding service...") async with aiohttp.ClientSession() as session: - token = await self.credential.get_token("https://cognitiveservices.azure.com/.default") + token = await self.credential.get_token( + "https://cognitiveservices.azure.com/.default" + ) headers = {"Authorization": "Bearer " + token.token} params = {"api-version": self.CU_API_VERSION} analyzer_name = self.analyzer_schema["analyzerId"] diff --git a/app/backend/prepdocslib/page.py b/app/backend/hrchatbot/prepdocslib/page.py similarity index 100% rename from app/backend/prepdocslib/page.py rename to app/backend/hrchatbot/prepdocslib/page.py diff --git a/app/backend/prepdocslib/parser.py b/app/backend/hrchatbot/prepdocslib/parser.py similarity index 100% rename from app/backend/prepdocslib/parser.py rename to app/backend/hrchatbot/prepdocslib/parser.py diff --git a/app/backend/prepdocslib/pdfparser.py b/app/backend/hrchatbot/prepdocslib/pdfparser.py similarity index 80% rename from app/backend/prepdocslib/pdfparser.py rename to app/backend/hrchatbot/prepdocslib/pdfparser.py index c96980d21c..f058bead0b 100644 --- a/app/backend/prepdocslib/pdfparser.py +++ b/app/backend/hrchatbot/prepdocslib/pdfparser.py @@ -33,7 +33,9 @@ class LocalPdfParser(Parser): """ async def parse(self, content: IO) -> AsyncGenerator[Page, None]: - logger.info("Extracting text from '%s' using local PDF parser (pypdf)", content.name) + logger.info( + "Extracting text from '%s' using local PDF parser (pypdf)", content.name + ) reader = PdfReader(content) pages = reader.pages @@ -65,7 +67,9 @@ def __init__( self.content_understanding_endpoint = content_understanding_endpoint async def parse(self, content: IO) -> AsyncGenerator[Page, None]: - logger.info("Extracting text from '%s' using Azure Document Intelligence", content.name) + logger.info( + "Extracting text from '%s' using Azure Document Intelligence", content.name + ) async with DocumentIntelligenceClient( endpoint=self.endpoint, credential=self.credential @@ -73,17 +77,23 @@ async def parse(self, content: IO) -> AsyncGenerator[Page, None]: file_analyzed = False if self.use_content_understanding: if self.content_understanding_endpoint is None: - raise ValueError("Content Understanding is enabled but no endpoint was provided") + raise ValueError( + "Content Understanding is enabled but no endpoint was provided" + ) if isinstance(self.credential, AzureKeyCredential): raise ValueError( "AzureKeyCredential is not supported for Content Understanding, use keyless auth instead" ) - cu_describer = ContentUnderstandingDescriber(self.content_understanding_endpoint, self.credential) + cu_describer = ContentUnderstandingDescriber( + self.content_understanding_endpoint, self.credential + ) content_bytes = content.read() try: poller = await document_intelligence_client.begin_analyze_document( model_id="prebuilt-layout", - analyze_request=AnalyzeDocumentRequest(bytes_source=content_bytes), + analyze_request=AnalyzeDocumentRequest( + bytes_source=content_bytes + ), output=["figures"], features=["ocrHighResolution"], output_content_format="markdown", @@ -104,7 +114,9 @@ async def parse(self, content: IO) -> AsyncGenerator[Page, None]: if file_analyzed is False: poller = await document_intelligence_client.begin_analyze_document( - model_id=self.model_id, analyze_request=content, content_type="application/octet-stream" + model_id=self.model_id, + analyze_request=content, + content_type="application/octet-stream", ) analyze_result: AnalyzeResult = await poller.result() @@ -113,14 +125,16 @@ async def parse(self, content: IO) -> AsyncGenerator[Page, None]: tables_on_page = [ table for table in (analyze_result.tables or []) - if table.bounding_regions and table.bounding_regions[0].page_number == page.page_number + if table.bounding_regions + and table.bounding_regions[0].page_number == page.page_number ] figures_on_page = [] if self.use_content_understanding: figures_on_page = [ figure for figure in (analyze_result.figures or []) - if figure.bounding_regions and figure.bounding_regions[0].page_number == page.page_number + if figure.bounding_regions + and figure.bounding_regions[0].page_number == page.page_number ] class ObjectType(Enum): @@ -130,7 +144,9 @@ class ObjectType(Enum): page_offset = page.spans[0].offset page_length = page.spans[0].length - mask_chars: list[tuple[ObjectType, Union[int, None]]] = [(ObjectType.NONE, None)] * page_length + mask_chars: list[tuple[ObjectType, Union[int, None]]] = [ + (ObjectType.NONE, None) + ] * page_length # mark all positions of the table spans in the page for table_idx, table in enumerate(tables_on_page): for span in table.spans: @@ -159,16 +175,22 @@ class ObjectType(Enum): if object_idx is None: raise ValueError("Expected object_idx to be set") if mask_char not in added_objects: - page_text += DocumentAnalysisParser.table_to_html(tables_on_page[object_idx]) + page_text += DocumentAnalysisParser.table_to_html( + tables_on_page[object_idx] + ) added_objects.add(mask_char) elif object_type == ObjectType.FIGURE: if cu_describer is None: - raise ValueError("cu_describer should not be None, unable to describe figure") + raise ValueError( + "cu_describer should not be None, unable to describe figure" + ) if object_idx is None: raise ValueError("Expected object_idx to be set") if mask_char not in added_objects: figure_html = await DocumentAnalysisParser.figure_to_html( - doc_for_pymupdf, figures_on_page[object_idx], cu_describer + doc_for_pymupdf, + figures_on_page[object_idx], + cu_describer, ) page_text += figure_html added_objects.add(mask_char) @@ -181,14 +203,19 @@ class ObjectType(Enum): @staticmethod async def figure_to_html( - doc: pymupdf.Document, figure: DocumentFigure, cu_describer: ContentUnderstandingDescriber + doc: pymupdf.Document, + figure: DocumentFigure, + cu_describer: ContentUnderstandingDescriber, ) -> str: figure_title = (figure.caption and figure.caption.content) or "" logger.info("Describing figure %s with title '%s'", figure.id, figure_title) if not figure.bounding_regions: return f"
{figure_title}
" if len(figure.bounding_regions) > 1: - logger.warning("Figure %s has more than one bounding region, using the first one", figure.id) + logger.warning( + "Figure %s has more than one bounding region, using the first one", + figure.id, + ) first_region = figure.bounding_regions[0] # To learn more about bounding regions, see https://aka.ms/bounding-region bounding_box = ( @@ -198,7 +225,9 @@ async def figure_to_html( first_region.polygon[5], # y1 (bottom) ) page_number = first_region["pageNumber"] # 1-indexed - cropped_img = DocumentAnalysisParser.crop_image_from_pdf_page(doc, page_number - 1, bounding_box) + cropped_img = DocumentAnalysisParser.crop_image_from_pdf_page( + doc, page_number - 1, bounding_box + ) figure_description = await cu_describer.describe_image(cropped_img) return f"
{figure_title}
{figure_description}
" @@ -206,13 +235,20 @@ async def figure_to_html( def table_to_html(table: DocumentTable): table_html = "
" rows = [ - sorted([cell for cell in table.cells if cell.row_index == i], key=lambda cell: cell.column_index) + sorted( + [cell for cell in table.cells if cell.row_index == i], + key=lambda cell: cell.column_index, + ) for i in range(table.row_count) ] for row_cells in rows: table_html += "" for cell in row_cells: - tag = "th" if (cell.kind == "columnHeader" or cell.kind == "rowHeader") else "td" + tag = ( + "th" + if (cell.kind == "columnHeader" or cell.kind == "rowHeader") + else "td" + ) cell_spans = "" if cell.column_span is not None and cell.column_span > 1: cell_spans += f" colSpan={cell.column_span}" @@ -225,7 +261,9 @@ def table_to_html(table: DocumentTable): @staticmethod def crop_image_from_pdf_page( - doc: pymupdf.Document, page_number: int, bbox_inches: tuple[float, float, float, float] + doc: pymupdf.Document, + page_number: int, + bbox_inches: tuple[float, float, float, float], ) -> bytes: """ Crops a region from a given page in a PDF and returns it as an image. @@ -243,7 +281,9 @@ def crop_image_from_pdf_page( # and use the matrix to convert between the 2 DPIs page_dpi = 300 page = doc.load_page(page_number) - pix = page.get_pixmap(matrix=pymupdf.Matrix(page_dpi / bbox_dpi, page_dpi / bbox_dpi), clip=rect) + pix = page.get_pixmap( + matrix=pymupdf.Matrix(page_dpi / bbox_dpi, page_dpi / bbox_dpi), clip=rect + ) img = Image.frombytes("RGB", (pix.width, pix.height), pix.samples) bytes_io = io.BytesIO() diff --git a/app/backend/prepdocslib/searchmanager.py b/app/backend/hrchatbot/prepdocslib/searchmanager.py similarity index 75% rename from app/backend/prepdocslib/searchmanager.py rename to app/backend/hrchatbot/prepdocslib/searchmanager.py index e6ca925e24..11ff74123d 100644 --- a/app/backend/prepdocslib/searchmanager.py +++ b/app/backend/hrchatbot/prepdocslib/searchmanager.py @@ -45,7 +45,9 @@ class Section: A section of a page that is stored in a search service. These sections are used as context by Azure OpenAI service """ - def __init__(self, split_page: SplitPage, content: File, category: Optional[str] = None): + def __init__( + self, split_page: SplitPage, content: File, category: Optional[str] = None + ): self.split_page = split_page self.content = content self.category = category @@ -72,15 +74,18 @@ def __init__( self.use_acls = use_acls self.use_int_vectorization = use_int_vectorization self.embeddings = embeddings - self.embedding_dimensions = self.embeddings.open_ai_dimensions if self.embeddings else None + self.embedding_dimensions = ( + self.embeddings.open_ai_dimensions if self.embeddings else None + ) self.field_name_embedding = field_name_embedding self.search_images = search_images async def create_index(self): - logger.info("Checking whether search index %s exists...", self.search_info.index_name) + logger.info( + "Checking whether search index %s exists...", self.search_info.index_name + ) async with self.search_info.create_search_index_client() as search_index_client: - embedding_field = None image_embedding_field = None text_vector_search_profile = None @@ -130,7 +135,13 @@ async def create_index(self): name=f"{self.field_name_embedding}-profile", algorithm_configuration_name=text_vector_algorithm.name, compression_name=text_vector_compression.compression_name, - **({"vectorizer_name": text_vectorizer.vectorizer_name if text_vectorizer else None}), + **( + { + "vectorizer_name": text_vectorizer.vectorizer_name + if text_vectorizer + else None + } + ), ) embedding_field = SearchField( @@ -167,7 +178,9 @@ async def create_index(self): vector_search_profile_name=image_vector_search_profile.name, ) - if self.search_info.index_name not in [name async for name in search_index_client.list_index_names()]: + if self.search_info.index_name not in [ + name async for name in search_index_client.list_index_names() + ]: logger.info("Creating new search index %s", self.search_info.index_name) fields = [ ( @@ -188,7 +201,12 @@ async def create_index(self): type="Edm.String", analyzer_name=self.search_analyzer_name, ), - SimpleField(name="category", type="Edm.String", filterable=True, facetable=True), + SimpleField( + name="category", + type="Edm.String", + filterable=True, + facetable=True, + ), SimpleField( name="sourcepage", type="Edm.String", @@ -212,28 +230,41 @@ async def create_index(self): fields.append( SimpleField( name="oids", - type=SearchFieldDataType.Collection(SearchFieldDataType.String), + type=SearchFieldDataType.Collection( + SearchFieldDataType.String + ), filterable=True, ) ) fields.append( SimpleField( name="groups", - type=SearchFieldDataType.Collection(SearchFieldDataType.String), + type=SearchFieldDataType.Collection( + SearchFieldDataType.String + ), filterable=True, ) ) if self.use_int_vectorization: - logger.info("Including parent_id field for integrated vectorization support in new index") - fields.append(SearchableField(name="parent_id", type="Edm.String", filterable=True)) + logger.info( + "Including parent_id field for integrated vectorization support in new index" + ) + fields.append( + SearchableField( + name="parent_id", type="Edm.String", filterable=True + ) + ) vectorizers: list[VectorSearchVectorizer] = [] vector_search_profiles = [] vector_algorithms: list[VectorSearchAlgorithmConfiguration] = [] vector_compressions: list[VectorSearchCompression] = [] if embedding_field: - logger.info("Including %s field for text vectors in new index", embedding_field.name) + logger.info( + "Including %s field for text vectors in new index", + embedding_field.name, + ) fields.append(embedding_field) if text_vectorizer is not None: vectorizers.append(text_vectorizer) @@ -242,16 +273,26 @@ async def create_index(self): or text_vector_algorithm is None or text_vector_compression is None ): - raise ValueError("Text vector search profile, algorithm and compression must be set") + raise ValueError( + "Text vector search profile, algorithm and compression must be set" + ) vector_search_profiles.append(text_vector_search_profile) vector_algorithms.append(text_vector_algorithm) vector_compressions.append(text_vector_compression) if image_embedding_field: - logger.info("Including %s field for image vectors in new index", image_embedding_field.name) + logger.info( + "Including %s field for image vectors in new index", + image_embedding_field.name, + ) fields.append(image_embedding_field) - if image_vector_search_profile is None or image_vector_algorithm is None: - raise ValueError("Image search profile and algorithm must be set") + if ( + image_vector_search_profile is None + or image_vector_algorithm is None + ): + raise ValueError( + "Image search profile and algorithm must be set" + ) vector_search_profiles.append(image_vector_search_profile) vector_algorithms.append(image_vector_algorithm) @@ -265,7 +306,9 @@ async def create_index(self): name="default", prioritized_fields=SemanticPrioritizedFields( title_field=SemanticField(field_name="sourcepage"), - content_fields=[SemanticField(field_name="content")], + content_fields=[ + SemanticField(field_name="content") + ], ), ) ], @@ -280,10 +323,19 @@ async def create_index(self): await search_index_client.create_index(index) else: - logger.info("Search index %s already exists", self.search_info.index_name) - existing_index = await search_index_client.get_index(self.search_info.index_name) - if not any(field.name == "storageUrl" for field in existing_index.fields): - logger.info("Adding storageUrl field to index %s", self.search_info.index_name) + logger.info( + "Search index %s already exists", self.search_info.index_name + ) + existing_index = await search_index_client.get_index( + self.search_info.index_name + ) + if not any( + field.name == "storageUrl" for field in existing_index.fields + ): + logger.info( + "Adding storageUrl field to index %s", + self.search_info.index_name, + ) existing_index.fields.append( SimpleField( name="storageUrl", @@ -295,12 +347,17 @@ async def create_index(self): await search_index_client.create_or_update_index(existing_index) if embedding_field and not any( - field.name == self.field_name_embedding for field in existing_index.fields + field.name == self.field_name_embedding + for field in existing_index.fields ): - logger.info("Adding %s field for text embeddings", self.field_name_embedding) + logger.info( + "Adding %s field for text embeddings", self.field_name_embedding + ) existing_index.fields.append(embedding_field) if existing_index.vector_search is None: - raise ValueError("Vector search is not enabled for the existing index") + raise ValueError( + "Vector search is not enabled for the existing index" + ) if text_vectorizer is not None: if existing_index.vector_search.vectorizers is None: existing_index.vector_search.vectorizers = [] @@ -310,56 +367,96 @@ async def create_index(self): or text_vector_algorithm is None or text_vector_compression is None ): - raise ValueError("Text vector search profile, algorithm and compression must be set") + raise ValueError( + "Text vector search profile, algorithm and compression must be set" + ) if existing_index.vector_search.profiles is None: existing_index.vector_search.profiles = [] - existing_index.vector_search.profiles.append(text_vector_search_profile) + existing_index.vector_search.profiles.append( + text_vector_search_profile + ) if existing_index.vector_search.algorithms is None: existing_index.vector_search.algorithms = [] - existing_index.vector_search.algorithms.append(text_vector_algorithm) + existing_index.vector_search.algorithms.append( + text_vector_algorithm + ) if existing_index.vector_search.compressions is None: existing_index.vector_search.compressions = [] - existing_index.vector_search.compressions.append(text_vector_compression) + existing_index.vector_search.compressions.append( + text_vector_compression + ) await search_index_client.create_or_update_index(existing_index) - if image_embedding_field and not any(field.name == "imageEmbedding" for field in existing_index.fields): - logger.info("Adding %s field for image embeddings", image_embedding_field.name) + if image_embedding_field and not any( + field.name == "imageEmbedding" for field in existing_index.fields + ): + logger.info( + "Adding %s field for image embeddings", + image_embedding_field.name, + ) existing_index.fields.append(image_embedding_field) - if image_vector_search_profile is None or image_vector_algorithm is None: - raise ValueError("Image vector search profile and algorithm must be set") + if ( + image_vector_search_profile is None + or image_vector_algorithm is None + ): + raise ValueError( + "Image vector search profile and algorithm must be set" + ) if existing_index.vector_search is None: - raise ValueError("Image vector search is not enabled for the existing index") + raise ValueError( + "Image vector search is not enabled for the existing index" + ) if existing_index.vector_search.profiles is None: existing_index.vector_search.profiles = [] - existing_index.vector_search.profiles.append(image_vector_search_profile) + existing_index.vector_search.profiles.append( + image_vector_search_profile + ) if existing_index.vector_search.algorithms is None: existing_index.vector_search.algorithms = [] - existing_index.vector_search.algorithms.append(image_vector_algorithm) + existing_index.vector_search.algorithms.append( + image_vector_algorithm + ) await search_index_client.create_or_update_index(existing_index) if existing_index.semantic_search: if not existing_index.semantic_search.default_configuration_name: - logger.info("Adding default semantic configuration to index %s", self.search_info.index_name) - existing_index.semantic_search.default_configuration_name = "default" + logger.info( + "Adding default semantic configuration to index %s", + self.search_info.index_name, + ) + existing_index.semantic_search.default_configuration_name = ( + "default" + ) if existing_index.semantic_search.configurations: - existing_semantic_config = existing_index.semantic_search.configurations[0] + existing_semantic_config = ( + existing_index.semantic_search.configurations[0] + ) if ( existing_semantic_config.prioritized_fields and existing_semantic_config.prioritized_fields.title_field - and not existing_semantic_config.prioritized_fields.title_field.field_name == "sourcepage" + and not existing_semantic_config.prioritized_fields.title_field.field_name + == "sourcepage" ): - logger.info("Updating semantic configuration for index %s", self.search_info.index_name) - existing_semantic_config.prioritized_fields.title_field = SemanticField( - field_name="sourcepage" + logger.info( + "Updating semantic configuration for index %s", + self.search_info.index_name, + ) + existing_semantic_config.prioritized_fields.title_field = ( + SemanticField(field_name="sourcepage") ) if existing_index.vector_search is not None and ( existing_index.vector_search.vectorizers is None or len(existing_index.vector_search.vectorizers) == 0 ): - if self.embeddings is not None and isinstance(self.embeddings, AzureOpenAIEmbeddingService): - logger.info("Adding vectorizer to search index %s", self.search_info.index_name) + if self.embeddings is not None and isinstance( + self.embeddings, AzureOpenAIEmbeddingService + ): + logger.info( + "Adding vectorizer to search index %s", + self.search_info.index_name, + ) existing_index.vector_search.vectorizers = [ AzureOpenAIVectorizer( vectorizer_name=f"{self.search_info.index_name}-vectorizer", @@ -384,13 +481,16 @@ async def create_agent(self): if self.search_info.agent_name: logger.info(f"Creating search agent named {self.search_info.agent_name}") - async with self.search_info.create_search_index_client() as search_index_client: + async with ( + self.search_info.create_search_index_client() as search_index_client + ): await search_index_client.create_or_update_agent( agent=KnowledgeAgent( name=self.search_info.agent_name, target_indexes=[ KnowledgeAgentTargetIndex( - index_name=self.search_info.index_name, default_include_reference_source_data=True + index_name=self.search_info.index_name, + default_include_reference_source_data=True, ) ], models=[ @@ -411,10 +511,16 @@ async def create_agent(self): logger.info("Agent %s created successfully", self.search_info.agent_name) async def update_content( - self, sections: list[Section], image_embeddings: Optional[list[list[float]]] = None, url: Optional[str] = None + self, + sections: list[Section], + image_embeddings: Optional[list[list[float]]] = None, + url: Optional[str] = None, ): MAX_BATCH_SIZE = 1000 - section_batches = [sections[i : i + MAX_BATCH_SIZE] for i in range(0, len(sections), MAX_BATCH_SIZE)] + section_batches = [ + sections[i : i + MAX_BATCH_SIZE] + for i in range(0, len(sections), MAX_BATCH_SIZE) + ] async with self.search_info.create_search_client() as search_client: for batch_index, batch in enumerate(section_batches): @@ -452,13 +558,19 @@ async def update_content( document[self.field_name_embedding] = embeddings[i] if image_embeddings: for i, (document, section) in enumerate(zip(documents, batch)): - document["imageEmbedding"] = image_embeddings[section.split_page.page_num] + document["imageEmbedding"] = image_embeddings[ + section.split_page.page_num + ] await search_client.upload_documents(documents) - async def remove_content(self, path: Optional[str] = None, only_oid: Optional[str] = None): + async def remove_content( + self, path: Optional[str] = None, only_oid: Optional[str] = None + ): logger.info( - "Removing sections from '{%s or ''}' from search index '%s'", path, self.search_info.index_name + "Removing sections from '{%s or ''}' from search index '%s'", + path, + self.search_info.index_name, ) async with self.search_info.create_search_client() as search_client: while True: @@ -470,7 +582,10 @@ async def remove_content(self, path: Optional[str] = None, only_oid: Optional[st filter = f"sourcefile eq '{path_for_filter}'" max_results = 1000 result = await search_client.search( - search_text="", filter=filter, top=max_results, include_total_count=True + search_text="", + filter=filter, + top=max_results, + include_total_count=True, ) result_count = await result.get_count() if result_count == 0: diff --git a/app/backend/prepdocslib/strategy.py b/app/backend/hrchatbot/prepdocslib/strategy.py similarity index 93% rename from app/backend/prepdocslib/strategy.py rename to app/backend/hrchatbot/prepdocslib/strategy.py index 05bc72804d..16b0d59811 100644 --- a/app/backend/prepdocslib/strategy.py +++ b/app/backend/hrchatbot/prepdocslib/strategy.py @@ -39,7 +39,11 @@ def __init__( self.azure_openai_endpoint = azure_openai_endpoint def create_search_client(self) -> SearchClient: - return SearchClient(endpoint=self.endpoint, index_name=self.index_name, credential=self.credential) + return SearchClient( + endpoint=self.endpoint, + index_name=self.index_name, + credential=self.credential, + ) def create_search_index_client(self) -> SearchIndexClient: return SearchIndexClient(endpoint=self.endpoint, credential=self.credential) diff --git a/app/backend/prepdocslib/textparser.py b/app/backend/hrchatbot/prepdocslib/textparser.py similarity index 100% rename from app/backend/prepdocslib/textparser.py rename to app/backend/hrchatbot/prepdocslib/textparser.py diff --git a/app/backend/prepdocslib/textsplitter.py b/app/backend/hrchatbot/prepdocslib/textsplitter.py similarity index 84% rename from app/backend/prepdocslib/textsplitter.py rename to app/backend/hrchatbot/prepdocslib/textsplitter.py index 1beedf86a1..62b266e72c 100644 --- a/app/backend/prepdocslib/textsplitter.py +++ b/app/backend/hrchatbot/prepdocslib/textsplitter.py @@ -93,9 +93,13 @@ def __init__(self, max_tokens_per_section: int = 500): self.max_section_length = DEFAULT_SECTION_LENGTH self.sentence_search_limit = 100 self.max_tokens_per_section = max_tokens_per_section - self.section_overlap = int(self.max_section_length * DEFAULT_OVERLAP_PERCENT / 100) + self.section_overlap = int( + self.max_section_length * DEFAULT_OVERLAP_PERCENT / 100 + ) - def split_page_by_max_tokens(self, page_num: int, text: str) -> Generator[SplitPage, None, None]: + def split_page_by_max_tokens( + self, page_num: int, text: str + ) -> Generator[SplitPage, None, None]: """ Recursively splits page by maximum number of tokens to better handle languages with higher token/word ratios. """ @@ -147,7 +151,9 @@ def find_page(offset): length = len(all_text) if length <= self.max_section_length: - yield from self.split_page_by_max_tokens(page_num=find_page(0), text=all_text) + yield from self.split_page_by_max_tokens( + page_num=find_page(0), text=all_text + ) return start = 0 @@ -162,13 +168,18 @@ def find_page(offset): # Try to find the end of the sentence while ( end < length - and (end - start - self.max_section_length) < self.sentence_search_limit + and (end - start - self.max_section_length) + < self.sentence_search_limit and all_text[end] not in self.sentence_endings ): if all_text[end] in self.word_breaks: last_word = end end += 1 - if end < length and all_text[end] not in self.sentence_endings and last_word > 0: + if ( + end < length + and all_text[end] not in self.sentence_endings + and last_word > 0 + ): end = last_word # Fall back to at least keeping a whole word if end < length: end += 1 @@ -177,7 +188,8 @@ def find_page(offset): last_word = -1 while ( start > 0 - and start > end - self.max_section_length - 2 * self.sentence_search_limit + and start + > end - self.max_section_length - 2 * self.sentence_search_limit and all_text[start] not in self.sentence_endings ): if all_text[start] in self.word_breaks: @@ -189,11 +201,14 @@ def find_page(offset): start += 1 section_text = all_text[start:end] - yield from self.split_page_by_max_tokens(page_num=find_page(start), text=section_text) + yield from self.split_page_by_max_tokens( + page_num=find_page(start), text=section_text + ) last_figure_start = section_text.rfind(" 2 * self.sentence_search_limit and last_figure_start > section_text.rfind( - " 2 * self.sentence_search_limit + and last_figure_start > section_text.rfind(" Generator[SplitPage, None, None]: # its too big, so we need to split it for i in range(0, length, self.max_object_length): - yield SplitPage(page_num=i // self.max_object_length, text=all_text[i : i + self.max_object_length]) + yield SplitPage( + page_num=i // self.max_object_length, + text=all_text[i : i + self.max_object_length], + ) return diff --git a/app/backend/main.py b/app/backend/main.py deleted file mode 100644 index 0f2914a483..0000000000 --- a/app/backend/main.py +++ /dev/null @@ -1,12 +0,0 @@ -import os - -from app import create_app -from load_azd_env import load_azd_env - -# WEBSITE_HOSTNAME is always set by App Service, RUNNING_IN_PRODUCTION is set in main.bicep -RUNNING_ON_AZURE = os.getenv("WEBSITE_HOSTNAME") is not None or os.getenv("RUNNING_IN_PRODUCTION") is not None - -if not RUNNING_ON_AZURE: - load_azd_env() - -app = create_app() diff --git a/app/backend/package-lock.json b/app/backend/package-lock.json new file mode 100644 index 0000000000..dfb18f1156 --- /dev/null +++ b/app/backend/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "backend", + "lockfileVersion": 3, + "requires": true, + "packages": {} +} diff --git a/app/backend/pyproject.toml b/app/backend/pyproject.toml new file mode 100644 index 0000000000..81f470ef04 --- /dev/null +++ b/app/backend/pyproject.toml @@ -0,0 +1,112 @@ +[project] +name = "HR-Chatbot" +version = "0.1.0" +description = "HR Chatbot" +readme = "README.md" +requires-python = ">=3.11.8" +dependencies = [ + "azure-identity", + "quart", + "quart-cors", + "openai>=1.3.7", + "tiktoken", + "tenacity", + "azure-ai-documentintelligence==1.0.0b4", + "azure-cognitiveservices-speech", + "azure-cosmos", + "azure-search-documents==11.6.0b12", + "azure-storage-blob", + "azure-storage-file-datalake", + "uvicorn", + "aiohttp", + "azure-monitor-opentelemetry", + "opentelemetry-instrumentation-asgi", + "opentelemetry-instrumentation-httpx", + "opentelemetry-instrumentation-aiohttp-client", + "opentelemetry-instrumentation-openai", + "msal", + "cryptography", + "PyJWT", + "Pillow", + "types-Pillow", + "pypdf", + "PyMuPDF", + "beautifulsoup4", + "types-beautifulsoup4", + "msgraph-sdk", + "python-dotenv", + "prompty", + "rich", + "typing-extensions", + "gunicorn", +] + +[project.optional-dependencies] +dev = [ + "ruff", + "black", + "pytest", + "pytest-asyncio", + "pytest-snapshot", + "coverage", + "playwright", + "pytest-cov", + "pytest-playwright", + "pre-commit", + "mypy==1.14.1", +] + +evals = [ + "dotenv-azd==0.3.0", + "ragas==0.2.13", + "rapidfuzz==3.12.1", + "langchain==0.3.17", + "evaltools @ git+https://github.com/mattgotteiner/ai-rag-chat-evaluator@patch-1", +] + +[tool.setuptools] +packages = ["approaches", "chat_history", "core", "prepdocslib"] + +[tool.hatch.build.targets.wheel] +packages = ["approaches", "chat_history", "core", "prepdocslib"] + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.ruff] +target-version = "py311" +lint.select = ["E", "F", "I", "UP"] +lint.ignore = ["E501", "E701"] # line too long, multiple statements on one line +src = ["app/hrchatbot/backend", "scripts"] + +[tool.ruff.lint.isort] +known-local-folder = ["scripts"] + +[tool.black] +line-length = 120 + +[tool.pytest.ini_options] +addopts = "-ra" +pythonpath = ["app/hrchatbot/backend", "scripts"] + +[tool.coverage.paths] +source = ["scripts", "app"] + +[tool.coverage.report] +show_missing = true + +[tool.mypy] +check_untyped_defs = true +python_version = "3.11" + +[[tool.mypy.overrides]] +module = [ + "msal.*", + "msgraph.*", + "kiota_abstractions.*", + "kiota.*", + "azure.cognitiveservices.*", + "azure.cognitiveservices.speech.*", + "pymupdf.*", +] +ignore_missing_imports = true diff --git a/app/backend/requirements.in b/app/backend/requirements.in deleted file mode 100644 index ac889f2b9d..0000000000 --- a/app/backend/requirements.in +++ /dev/null @@ -1,33 +0,0 @@ -azure-identity -quart -quart-cors -openai>=1.3.7 -tiktoken -tenacity -azure-ai-documentintelligence==1.0.0b4 -azure-cognitiveservices-speech -azure-cosmos -azure-search-documents==11.6.0b12 -azure-storage-blob -azure-storage-file-datalake -uvicorn -aiohttp -azure-monitor-opentelemetry -opentelemetry-instrumentation-asgi -opentelemetry-instrumentation-httpx -opentelemetry-instrumentation-aiohttp-client -opentelemetry-instrumentation-openai -msal -cryptography -PyJWT -Pillow -types-Pillow -pypdf -PyMuPDF -beautifulsoup4 -types-beautifulsoup4 -msgraph-sdk -python-dotenv -prompty -rich -typing-extensions diff --git a/app/backend/requirements.txt b/app/backend/requirements.txt deleted file mode 100644 index 4029e3338f..0000000000 --- a/app/backend/requirements.txt +++ /dev/null @@ -1,459 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile requirements.in -o requirements.txt --python-version 3.9 -aiofiles==24.1.0 - # via - # prompty - # quart -aiohappyeyeballs==2.6.1 - # via aiohttp -aiohttp==3.12.14 - # via - # -r requirements.in - # microsoft-kiota-authentication-azure -aiosignal==1.4.0 - # via aiohttp -annotated-types==0.7.0 - # via pydantic -anyio==4.4.0 - # via - # httpx - # openai -asgiref==3.8.1 - # via opentelemetry-instrumentation-asgi -async-timeout==5.0.1 - # via aiohttp -attrs==25.3.0 - # via aiohttp -azure-ai-documentintelligence==1.0.0b4 - # via -r requirements.in -azure-cognitiveservices-speech==1.40.0 - # via -r requirements.in -azure-common==1.1.28 - # via azure-search-documents -azure-core==1.30.2 - # via - # azure-ai-documentintelligence - # azure-core-tracing-opentelemetry - # azure-cosmos - # azure-identity - # azure-monitor-opentelemetry - # azure-monitor-opentelemetry-exporter - # azure-search-documents - # azure-storage-blob - # azure-storage-file-datalake - # microsoft-kiota-authentication-azure - # msrest -azure-core-tracing-opentelemetry==1.0.0b11 - # via azure-monitor-opentelemetry -azure-cosmos==4.9.0 - # via -r requirements.in -azure-identity==1.17.1 - # via - # -r requirements.in - # msgraph-sdk -azure-monitor-opentelemetry==1.6.1 - # via -r requirements.in -azure-monitor-opentelemetry-exporter==1.0.0b32 - # via azure-monitor-opentelemetry -azure-search-documents==11.6.0b12 - # via -r requirements.in -azure-storage-blob==12.22.0 - # via - # -r requirements.in - # azure-storage-file-datalake -azure-storage-file-datalake==12.16.0 - # via -r requirements.in -beautifulsoup4==4.12.3 - # via -r requirements.in -blinker==1.8.2 - # via - # flask - # quart -certifi==2024.7.4 - # via - # httpcore - # httpx - # msrest - # requests -cffi==1.17.0 - # via cryptography -charset-normalizer==3.3.2 - # via requests -click==8.1.7 - # via - # flask - # prompty - # quart - # uvicorn -cryptography==44.0.1 - # via - # -r requirements.in - # azure-identity - # azure-storage-blob - # msal - # pyjwt -deprecated==1.2.14 - # via - # opentelemetry-api - # opentelemetry-semantic-conventions -distro==1.9.0 - # via openai -exceptiongroup==1.3.0 - # via - # anyio - # hypercorn - # taskgroup -fixedint==0.1.6 - # via azure-monitor-opentelemetry-exporter -flask==3.0.3 - # via quart -frozenlist==1.4.1 - # via - # aiohttp - # aiosignal -h11==0.16.0 - # via - # httpcore - # hypercorn - # uvicorn - # wsproto -h2==4.1.0 - # via - # httpx - # hypercorn -hpack==4.0.0 - # via h2 -httpcore==1.0.9 - # via httpx -httpx[http2]==0.27.0 - # via - # microsoft-kiota-http - # msgraph-core - # openai -hypercorn==0.17.3 - # via quart -hyperframe==6.0.1 - # via h2 -idna==3.10 - # via - # anyio - # httpx - # requests - # yarl -importlib-metadata==8.0.0 - # via - # flask - # opentelemetry-api - # quart -isodate==0.6.1 - # via - # azure-ai-documentintelligence - # azure-search-documents - # azure-storage-blob - # azure-storage-file-datalake - # msrest -itsdangerous==2.2.0 - # via - # flask - # quart -jinja2==3.1.6 - # via - # flask - # prompty - # quart -jiter==0.8.2 - # via openai -markdown-it-py==3.0.0 - # via rich -markupsafe==2.1.5 - # via - # jinja2 - # quart - # werkzeug -mdurl==0.1.2 - # via markdown-it-py -microsoft-kiota-abstractions==1.9.3 - # via - # microsoft-kiota-authentication-azure - # microsoft-kiota-http - # microsoft-kiota-serialization-form - # microsoft-kiota-serialization-json - # microsoft-kiota-serialization-multipart - # microsoft-kiota-serialization-text - # msgraph-core -microsoft-kiota-authentication-azure==1.9.3 - # via msgraph-core -microsoft-kiota-http==1.9.3 - # via msgraph-core -microsoft-kiota-serialization-form==1.9.3 - # via msgraph-sdk -microsoft-kiota-serialization-json==1.9.3 - # via msgraph-sdk -microsoft-kiota-serialization-multipart==1.9.3 - # via msgraph-sdk -microsoft-kiota-serialization-text==1.9.3 - # via msgraph-sdk -msal==1.30.0 - # via - # -r requirements.in - # azure-identity - # msal-extensions -msal-extensions==1.3.1 - # via azure-identity -msgraph-core==1.3.3 - # via msgraph-sdk -msgraph-sdk==1.26.0 - # via -r requirements.in -msrest==0.7.1 - # via azure-monitor-opentelemetry-exporter -multidict==6.0.5 - # via - # aiohttp - # yarl -oauthlib==3.2.2 - # via requests-oauthlib -openai==1.63.0 - # via -r requirements.in -opentelemetry-api==1.31.1 - # via - # azure-core-tracing-opentelemetry - # azure-monitor-opentelemetry-exporter - # microsoft-kiota-abstractions - # microsoft-kiota-authentication-azure - # microsoft-kiota-http - # opentelemetry-instrumentation - # opentelemetry-instrumentation-aiohttp-client - # opentelemetry-instrumentation-asgi - # opentelemetry-instrumentation-dbapi - # opentelemetry-instrumentation-django - # opentelemetry-instrumentation-fastapi - # opentelemetry-instrumentation-flask - # opentelemetry-instrumentation-httpx - # opentelemetry-instrumentation-openai - # opentelemetry-instrumentation-psycopg2 - # opentelemetry-instrumentation-requests - # opentelemetry-instrumentation-urllib - # opentelemetry-instrumentation-urllib3 - # opentelemetry-instrumentation-wsgi - # opentelemetry-sdk - # opentelemetry-semantic-conventions -opentelemetry-instrumentation==0.52b1 - # via - # opentelemetry-instrumentation-aiohttp-client - # opentelemetry-instrumentation-asgi - # opentelemetry-instrumentation-dbapi - # opentelemetry-instrumentation-django - # opentelemetry-instrumentation-fastapi - # opentelemetry-instrumentation-flask - # opentelemetry-instrumentation-httpx - # opentelemetry-instrumentation-openai - # opentelemetry-instrumentation-psycopg2 - # opentelemetry-instrumentation-requests - # opentelemetry-instrumentation-urllib - # opentelemetry-instrumentation-urllib3 - # opentelemetry-instrumentation-wsgi -opentelemetry-instrumentation-aiohttp-client==0.52b1 - # via -r requirements.in -opentelemetry-instrumentation-asgi==0.52b1 - # via - # -r requirements.in - # opentelemetry-instrumentation-fastapi -opentelemetry-instrumentation-dbapi==0.52b1 - # via opentelemetry-instrumentation-psycopg2 -opentelemetry-instrumentation-django==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-fastapi==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-flask==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-httpx==0.52b1 - # via -r requirements.in -opentelemetry-instrumentation-openai==0.39.0 - # via -r requirements.in -opentelemetry-instrumentation-psycopg2==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-requests==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-urllib==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-urllib3==0.52b1 - # via azure-monitor-opentelemetry -opentelemetry-instrumentation-wsgi==0.52b1 - # via - # opentelemetry-instrumentation-django - # opentelemetry-instrumentation-flask -opentelemetry-resource-detector-azure==0.1.5 - # via azure-monitor-opentelemetry -opentelemetry-sdk==1.31.1 - # via - # azure-monitor-opentelemetry - # azure-monitor-opentelemetry-exporter - # microsoft-kiota-abstractions - # microsoft-kiota-authentication-azure - # microsoft-kiota-http - # opentelemetry-resource-detector-azure -opentelemetry-semantic-conventions==0.52b1 - # via - # opentelemetry-instrumentation - # opentelemetry-instrumentation-aiohttp-client - # opentelemetry-instrumentation-asgi - # opentelemetry-instrumentation-dbapi - # opentelemetry-instrumentation-django - # opentelemetry-instrumentation-fastapi - # opentelemetry-instrumentation-flask - # opentelemetry-instrumentation-httpx - # opentelemetry-instrumentation-openai - # opentelemetry-instrumentation-requests - # opentelemetry-instrumentation-urllib - # opentelemetry-instrumentation-urllib3 - # opentelemetry-instrumentation-wsgi - # opentelemetry-sdk -opentelemetry-semantic-conventions-ai==0.4.3 - # via opentelemetry-instrumentation-openai -opentelemetry-util-http==0.52b1 - # via - # opentelemetry-instrumentation-aiohttp-client - # opentelemetry-instrumentation-asgi - # opentelemetry-instrumentation-django - # opentelemetry-instrumentation-fastapi - # opentelemetry-instrumentation-flask - # opentelemetry-instrumentation-httpx - # opentelemetry-instrumentation-requests - # opentelemetry-instrumentation-urllib - # opentelemetry-instrumentation-urllib3 - # opentelemetry-instrumentation-wsgi -packaging==24.1 - # via - # opentelemetry-instrumentation - # opentelemetry-instrumentation-flask -pillow==10.4.0 - # via -r requirements.in -priority==2.0.0 - # via hypercorn -prompty==0.1.50 - # via -r requirements.in -propcache==0.2.0 - # via - # aiohttp - # yarl -psutil==5.9.8 - # via azure-monitor-opentelemetry-exporter -pycparser==2.22 - # via cffi -pydantic==2.8.2 - # via openai -pydantic-core==2.20.1 - # via pydantic -pygments==2.18.0 - # via rich -pyjwt[crypto]==2.10.1 - # via - # -r requirements.in - # msal -pymupdf==1.26.0 - # via -r requirements.in -pypdf==4.3.1 - # via -r requirements.in -python-dotenv==1.1.1 - # via - # -r requirements.in - # prompty -pyyaml==6.0.2 - # via prompty -quart==0.20.0 - # via - # -r requirements.in - # quart-cors -quart-cors==0.7.0 - # via -r requirements.in -regex==2024.11.6 - # via tiktoken -requests==2.32.4 - # via - # azure-core - # msal - # msrest - # requests-oauthlib - # tiktoken -requests-oauthlib==2.0.0 - # via msrest -rich==13.9.4 - # via -r requirements.in -six==1.16.0 - # via - # azure-core - # isodate -sniffio==1.3.1 - # via - # anyio - # httpx - # openai -soupsieve==2.7 - # via beautifulsoup4 -std-uritemplate==2.0.5 - # via microsoft-kiota-abstractions -taskgroup==0.2.2 - # via hypercorn -tenacity==9.0.0 - # via -r requirements.in -tiktoken==0.8.0 - # via - # -r requirements.in - # opentelemetry-instrumentation-openai -tomli==2.2.1 - # via hypercorn -tqdm==4.66.5 - # via openai -types-beautifulsoup4==4.12.0.20240511 - # via -r requirements.in -types-html5lib==1.1.11.20241018 - # via types-beautifulsoup4 -types-pillow==10.2.0.20240822 - # via -r requirements.in -typing-extensions==4.13.2 - # via - # -r requirements.in - # aiosignal - # anyio - # asgiref - # azure-ai-documentintelligence - # azure-core - # azure-cosmos - # azure-identity - # azure-search-documents - # azure-storage-blob - # azure-storage-file-datalake - # exceptiongroup - # hypercorn - # openai - # opentelemetry-sdk - # pydantic - # pydantic-core - # pypdf - # quart - # quart-cors - # rich - # taskgroup - # uvicorn -urllib3==2.5.0 - # via requests -uvicorn==0.30.6 - # via -r requirements.in -werkzeug==3.0.6 - # via - # flask - # quart -wrapt==1.16.0 - # via - # deprecated - # opentelemetry-instrumentation - # opentelemetry-instrumentation-aiohttp-client - # opentelemetry-instrumentation-dbapi - # opentelemetry-instrumentation-httpx - # opentelemetry-instrumentation-urllib3 -wsproto==1.2.0 - # via hypercorn -yarl==1.17.2 - # via aiohttp -zipp==3.21.0 - # via importlib-metadata diff --git a/app/backend/uv.lock b/app/backend/uv.lock new file mode 100644 index 0000000000..8726ad4492 --- /dev/null +++ b/app/backend/uv.lock @@ -0,0 +1,4397 @@ +version = 1 +requires-python = ">=3.11.8" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version >= '3.12.4' and python_full_version < '3.13'", + "python_full_version >= '3.12' and python_full_version < '3.12.4'", + "python_full_version < '3.12'", +] + +[[package]] +name = "aiofiles" 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b/app/start.ps1 @@ -19,26 +19,11 @@ if ($LASTEXITCODE -ne 0) { } -Write-Host 'Creating python virtual environment ".venv"' -$pythonCmd = Get-Command python -ErrorAction SilentlyContinue -if (-not $pythonCmd) { - # fallback to python3 if python not found - $pythonCmd = Get-Command python3 -ErrorAction SilentlyContinue -} -Start-Process -FilePath ($pythonCmd).Source -ArgumentList "-m venv .venv" -Wait -NoNewWindow - -Write-Host "" -Write-Host "Restoring backend python packages" +Write-Host 'Setting up backend with uv sync' Write-Host "" -$directory = Get-Location -$venvPythonPath = "$directory/.venv/scripts/python.exe" -if (Test-Path -Path "/usr") { - # fallback to Linux venv path - $venvPythonPath = "$directory/.venv/bin/python" -} - -Start-Process -FilePath $venvPythonPath -ArgumentList "-m pip install -r backend/requirements.txt" -Wait -NoNewWindow +Set-Location ./backend +uv sync if ($LASTEXITCODE -ne 0) { Write-Host "Failed to restore backend python packages" exit $LASTEXITCODE @@ -47,7 +32,7 @@ if ($LASTEXITCODE -ne 0) { Write-Host "" Write-Host "Restoring frontend npm packages" Write-Host "" -Set-Location ./frontend +Set-Location ../frontend npm install if ($LASTEXITCODE -ne 0) { Write-Host "Failed to restore frontend npm packages" @@ -70,7 +55,7 @@ Set-Location ../backend $port = 50505 $hostname = "localhost" -Start-Process -FilePath $venvPythonPath -ArgumentList "-m quart --app main:app run --port $port --host $hostname --reload" -Wait -NoNewWindow +uv run quart --app hrchatbot.main:app run --port $port --host $hostname --reload if ($LASTEXITCODE -ne 0) { Write-Host "Failed to start backend" diff --git a/app/start.sh b/app/start.sh index 68fbec173c..47a570ca21 100755 --- a/app/start.sh +++ b/app/start.sh @@ -4,15 +4,11 @@ # so that the script generates virtual environments always in the same path. cd "${0%/*}" || exit 1 -cd ../ -echo 'Creating python virtual environment ".venv"' -python3 -m venv .venv - -echo "" -echo "Restoring backend python packages" +echo 'Setting up backend with uv sync' echo "" -./.venv/bin/python -m pip install -r app/backend/requirements.txt +cd backend +uv sync out=$? if [ $out -ne 0 ]; then echo "Failed to restore backend python packages" @@ -23,7 +19,7 @@ echo "" echo "Restoring frontend npm packages" echo "" -cd app/frontend +cd ../frontend npm install out=$? if [ $out -ne 0 ]; then @@ -48,9 +44,7 @@ echo "" cd ../backend -port=50505 -host=localhost -../../.venv/bin/python -m quart --app main:app run --port "$port" --host "$host" --reload +uv run python -m hrchatbot.main out=$? if [ $out -ne 0 ]; then echo "Failed to start backend" diff --git a/azure.yaml b/azure.yaml index f77bfb5828..d9440ffeee 100644 --- a/azure.yaml +++ b/azure.yaml @@ -10,6 +10,7 @@ services: # Please check docs/azure_container_apps.md for more information on how to deploy to Azure Container Apps host: containerapp docker: + path: ./Dockerfile remoteBuild: true # Please check docs/azure_app_service.md for more information on how to deploy to Azure App Service # host: appservice @@ -142,14 +143,3 @@ hooks: run: ./scripts/auth_init.sh interactive: true continueOnError: false - postprovision: - windows: - shell: pwsh - run: ./scripts/auth_update.ps1;./scripts/prepdocs.ps1 - interactive: true - continueOnError: false - posix: - shell: sh - run: ./scripts/auth_update.sh;./scripts/prepdocs.sh - interactive: true - continueOnError: false diff --git a/data/Benefit_Options.pdf b/data/Benefit_Options.pdf deleted file mode 100644 index 6a4c07dc94..0000000000 Binary files a/data/Benefit_Options.pdf and /dev/null differ diff --git a/data/Contoso_Electronics_Company_Overview.md b/data/Contoso_Electronics_Company_Overview.md deleted file mode 100644 index 033d7dd84a..0000000000 --- a/data/Contoso_Electronics_Company_Overview.md +++ /dev/null @@ -1,48 +0,0 @@ -# Contoso Electronics - -*Disclaimer: This content is generated by AI and may not accurately represent factual information about any real entity. Use this information with caution and verify details from reliable sources.* - -## History - -Contoso Electronics, a pioneering force in the tech industry, was founded in 1985 by visionary entrepreneurs with a passion for innovation. Over the years, the company has played a pivotal role in shaping the landscape of consumer electronics. - -| Year | Milestone | -|------|-----------| -| 1985 | Company founded with a focus on cutting-edge technology | -| 1990 | Launched the first-ever handheld personal computer | -| 2000 | Introduced groundbreaking advancements in AI and robotics | -| 2015 | Expansion into sustainable and eco-friendly product lines | - -## Company Overview - -At Contoso Electronics, we take pride in fostering a dynamic and inclusive workplace. Our dedicated team of experts collaborates to create innovative solutions that empower and connect people globally. - -### Core Values - -- **Innovation:** Constantly pushing the boundaries of technology. -- **Diversity:** Embracing different perspectives for creative excellence. -- **Sustainability:** Committed to eco-friendly practices in our products. - -## Vacation Perks - -We believe in work-life balance and understand the importance of well-deserved breaks. Our vacation perks are designed to help our employees recharge and return with renewed enthusiasm. - -| Vacation Tier | Duration | Additional Benefits | -|---------------|----------|---------------------| -| Standard | 2 weeks | Health and wellness stipend | -| Senior | 4 weeks | Travel vouchers for a dream destination | -| Executive | 6 weeks | Luxury resort getaway with family | - -## Employee Recognition - -Recognizing the hard work and dedication of our employees is at the core of our culture. Here are some ways we celebrate achievements: - -- Monthly "Innovator of the Month" awards -- Annual gala with awards for outstanding contributions -- Team-building retreats for high-performing departments - -## Join Us! - -Contoso Electronics is always on the lookout for talented individuals who share our passion for innovation. If you're ready to be part of a dynamic team shaping the future of technology, check out our [careers page](http://www.contoso.com) for exciting opportunities. - -[Learn more about Contoso Electronics!](http://www.contoso.com) diff --git a/data/GPT4V_Examples/Financial Market Analysis Report 2023.pdf b/data/GPT4V_Examples/Financial Market Analysis Report 2023.pdf deleted file mode 100644 index eef17aad75..0000000000 Binary files a/data/GPT4V_Examples/Financial Market Analysis Report 2023.pdf and /dev/null differ diff --git a/data/Gebaeudesach-AVB-Zuehlke-2024-07.pdf b/data/Gebaeudesach-AVB-Zuehlke-2024-07.pdf new file mode 100644 index 0000000000..a1ea0ecd6c Binary files /dev/null and b/data/Gebaeudesach-AVB-Zuehlke-2024-07.pdf differ diff --git a/data/Json_Examples/2189.json b/data/Json_Examples/2189.json deleted file mode 100644 index d7066c9fbd..0000000000 --- a/data/Json_Examples/2189.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "AreaPath": "SmartHotel360", - "AssignedTo": null, - "Categories": null, - "ChangedDate": "2023-12-13T23:08:38.69Z", - "ClosedDate": null, - "CreatedDate": "2023-12-13T23:08:38.69Z", - "Description": "As a customer, I would like to reserve a conference room such that:

1. It should display available date and time slots
2. Give an option to reserve a conference room for X hours
3. One can reserve a conference room for max 4 hours per day
", - "Id": 2189, - "State": "New", - "StateChangeDate": "2023-12-13T23:08:38.69Z", - "Tags": "Reservation", - "Title": "As a customer, I would like to reserve a conference room" -} diff --git a/data/Json_Examples/2190.json b/data/Json_Examples/2190.json deleted file mode 100644 index 5a45f1158f..0000000000 --- a/data/Json_Examples/2190.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "AreaPath": "SmartHotel360", - "AssignedTo": null, - "Categories": null, - "ChangedDate": "2023-12-13T23:08:38.997Z", - "ClosedDate": null, - "CreatedDate": "2023-12-13T23:08:38.997Z", - "Description": "

Enter the guest's name to whom you would\nlike to send a confirmation, display the company, contact, source\nand agent associated\nwith the reservation.

", - "Id": 2190, - "State": "New", - "StateChangeDate": "2023-12-13T23:08:38.997Z", - "Tags": "Notification", - "Title": "As a reservation agent, I would like to send confirmations to guest" -} diff --git a/data/Json_Examples/2191.json b/data/Json_Examples/2191.json deleted file mode 100644 index 455e4c9a24..0000000000 --- a/data/Json_Examples/2191.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "AreaPath": "SmartHotel360", - "AssignedTo": null, - "Categories": null, - "ChangedDate": "2023-12-13T23:08:39.17Z", - "ClosedDate": null, - "CreatedDate": "2023-12-13T23:08:39.17Z", - "Description": "

If you have not picked up\nyour vehicle you can remove or cancel your reservation by clicking here.


1. Car reserved should have an option to cancel the request
2. Car driver should receive a notification about cancellation
", - "Id": 2191, - "State": "New", - "StateChangeDate": "2023-12-13T23:08:39.17Z", - "Tags": "Reservation", - "Title": "As a customer, I should be able to remove a car reservation " -} diff --git a/data/Json_Examples/2192.json b/data/Json_Examples/2192.json deleted file mode 100644 index d2e489f317..0000000000 --- a/data/Json_Examples/2192.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "AreaPath": "SmartHotel360", - "AssignedTo": null, - "Categories": null, - "ChangedDate": "2023-12-13T23:08:39.383Z", - "ClosedDate": null, - "CreatedDate": "2023-12-13T23:08:39.383Z", - "Description": "As a courtesy, grant an\nextra hour or two to leave the room, especially if it isn't booked\nfor the upcoming evening. But customer must call the front desk\nin advance and request a late checkout.


1. Late Check-in time should be displayed
2. Request should be sent to front-desk 
3. Any extra charge should be displayed
", - "Id": 2192, - "State": "New", - "StateChangeDate": "2023-12-13T23:08:39.383Z", - "Tags": "Front-desk; Members; Reservation", - "Title": "As a customer, I should be able to request hotel for late Check-out" -} diff --git a/data/Json_Examples/query.json b/data/Json_Examples/query.json deleted file mode 100644 index ceb9dc1a6f..0000000000 --- a/data/Json_Examples/query.json +++ /dev/null @@ -1,244 +0,0 @@ -[ - { - "fields": { - "System.Id": 2348, - "System.State": "New", - "System.Title": "Provide related items or frequently bought together section when people browse or search", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2348, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2348" - }, - { - "fields": { - "System.Id": 2349, - "System.State": "New", - "System.Title": "As tester, I need to test the website on all the relevant broswers and devices and be sure that it can handle our load.", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2349, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2349" - }, - { - "fields": { - "System.Id": 2350, - "System.State": "New", - "System.Title": "As a customer, I should be able to put items to shopping cart", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2350, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2350" - }, - { - "fields": { - "System.Id": 2351, - "System.State": "New", - "System.Title": "As a customer, I should be able to print my purchase order", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2351, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2351" - }, - { - "fields": { - "System.Id": 2352, - "System.State": "New", - "System.Title": "As a customer, I would like to have a sort capabaility by price and customer ratings", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2352, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2352" - }, - { - "fields": { - "System.Id": 2353, - "System.State": "New", - "System.Title": "Recommended products must be based on customer purchase pattern history", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2353, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2353" - }, - { - "fields": { - "System.Id": 2354, - "System.State": "New", - "System.Title": "As a customer, I would like to save my addresses so that I can easily select the address for delivery", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2354, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2354" - }, - { - "fields": { - "System.Id": 2355, - "System.State": "New", - "System.Title": "As marketer, I want to run an A|B test on alternative Web Sites using Application Insights.", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2355, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2355" - }, - { - "fields": { - "System.AssignedTo": { - "_links": { - "avatar": { - "href": "https://dev.azure.com/codebytes/_apis/GraphProfile/MemberAvatars/aad.ZDlhOGEyZjktMGZmZS03YjY4LTlkYjctNjk1ZWZiNGY2Nzg0" - } - }, - "descriptor": "aad.ZDlhOGEyZjktMGZmZS03YjY4LTlkYjctNjk1ZWZiNGY2Nzg0", - "displayName": "Chris Ayers", - "id": "cd8258ec-ad87-4c0d-9026-e5e343447185", - "imageUrl": "https://dev.azure.com/codebytes/_apis/GraphProfile/MemberAvatars/aad.ZDlhOGEyZjktMGZmZS03YjY4LTlkYjctNjk1ZWZiNGY2Nzg0", - "uniqueName": "chrisayers@microsoft.com", - "url": "https://spsprodeus27.vssps.visualstudio.com/A6b854e9d-a8be-405d-a4cc-5eb8e7027155/_apis/Identities/cd8258ec-ad87-4c0d-9026-e5e343447185" - }, - "System.Id": 2356, - "System.State": "Done", - "System.Title": "Provide customers the ability to track status of the package", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2356, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2356" - }, - { - "fields": { - "System.AssignedTo": { - "_links": { - "avatar": { - "href": "https://dev.azure.com/codebytes/_apis/GraphProfile/MemberAvatars/aad.ZDlhOGEyZjktMGZmZS03YjY4LTlkYjctNjk1ZWZiNGY2Nzg0" - } - }, - "descriptor": "aad.ZDlhOGEyZjktMGZmZS03YjY4LTlkYjctNjk1ZWZiNGY2Nzg0", - "displayName": "Chris Ayers", - "id": "cd8258ec-ad87-4c0d-9026-e5e343447185", - "imageUrl": "https://dev.azure.com/codebytes/_apis/GraphProfile/MemberAvatars/aad.ZDlhOGEyZjktMGZmZS03YjY4LTlkYjctNjk1ZWZiNGY2Nzg0", - "uniqueName": "chrisayers@microsoft.com", - "url": "https://spsprodeus27.vssps.visualstudio.com/A6b854e9d-a8be-405d-a4cc-5eb8e7027155/_apis/Identities/cd8258ec-ad87-4c0d-9026-e5e343447185" - }, - "System.Id": 2357, - "System.State": "Done", - "System.Title": "As a customer, I would like to have the ability to send my items as gift", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2357, - "relations": null, - "rev": 2, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2357" - }, - { - "fields": { - "System.Id": 2358, - "System.State": "Committed", - "System.Title": "As a customer, I would like to store my credit card details securely", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2358, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2358" - }, - { - "fields": { - "System.Id": 2359, - "System.State": "Committed", - "System.Title": "As a customer, I should be able to select different shipping option", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2359, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2359" - }, - { - "fields": { - "System.Id": 2360, - "System.State": "Committed", - "System.Title": "As developer, I want to use Azure Machine Learning to provide a recommendations engine behind the website.", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2360, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2360" - }, - { - "fields": { - "System.Id": 2361, - "System.State": "Committed", - "System.Title": "Provide tentative duration for shipping.", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2361, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2361" - }, - { - "fields": { - "System.Id": 2362, - "System.State": "Approved", - "System.Title": "Notify the user about any changes made to the order", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2362, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2362" - }, - { - "fields": { - "System.Id": 2363, - "System.State": "Approved", - "System.Title": "As a admin, I should be able to update prices on ad-hoc condition", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2363, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2363" - }, - { - "fields": { - "System.Id": 2364, - "System.State": "Approved", - "System.Title": "As a customer, I would like to provide my feedback on items that I have purchased", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2364, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2364" - }, - { - "fields": { - "System.Id": 2365, - "System.State": "Approved", - "System.Title": "As a customer, I would like to have a wishlist where I can add items for future purchase", - "System.WorkItemType": "Product Backlog Item" - }, - "id": 2365, - "relations": null, - "rev": 1, - "url": "https://dev.azure.com/codebytes/_apis/wit/workItems/2365" - } -] diff --git a/data/Northwind_Health_Plus_Benefits_Details.pdf b/data/Northwind_Health_Plus_Benefits_Details.pdf deleted file mode 100644 index 97579a4fb5..0000000000 Binary files a/data/Northwind_Health_Plus_Benefits_Details.pdf and /dev/null differ diff --git a/data/Northwind_Standard_Benefits_Details.pdf b/data/Northwind_Standard_Benefits_Details.pdf deleted file mode 100644 index 7d50ff8c02..0000000000 Binary files a/data/Northwind_Standard_Benefits_Details.pdf and /dev/null differ diff --git a/data/PerksPlus.pdf b/data/PerksPlus.pdf deleted file mode 100644 index 2e167a2a6a..0000000000 Binary files a/data/PerksPlus.pdf and /dev/null differ diff --git a/data/Reise-AVB-Zuehlke-2024-03.pdf b/data/Reise-AVB-Zuehlke-2024-03.pdf new file mode 100644 index 0000000000..a243e1d684 Binary files /dev/null and b/data/Reise-AVB-Zuehlke-2024-03.pdf differ diff --git a/data/Wertsachen-AVB-Zuehlke-2024-09.pdf b/data/Wertsachen-AVB-Zuehlke-2024-09.pdf new file mode 100644 index 0000000000..7afca43e7f Binary files /dev/null and b/data/Wertsachen-AVB-Zuehlke-2024-09.pdf differ diff --git a/data/employee_handbook.pdf b/data/employee_handbook.pdf deleted file mode 100644 index 878f36f7dd..0000000000 Binary files a/data/employee_handbook.pdf and /dev/null differ diff --git a/data/role_library.pdf b/data/role_library.pdf deleted file mode 100644 index ff70c65651..0000000000 Binary files a/data/role_library.pdf and /dev/null differ diff --git a/evals/requirements.txt b/evals/requirements.txt deleted file mode 100644 index ccd1642830..0000000000 --- a/evals/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -dotenv-azd==0.3.0 -rich -ragas==0.2.13 -rapidfuzz==3.12.1 -langchain==0.3.17 -git+https://github.com/mattgotteiner/ai-rag-chat-evaluator@patch-1 diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index b2738baecd..0000000000 --- a/pyproject.toml +++ /dev/null @@ -1,37 +0,0 @@ -[tool.ruff] -target-version = "py39" -lint.select = ["E", "F", "I", "UP"] -lint.ignore = ["E501", "E701"] # line too long, multiple statements on one line -src = ["app/backend", "scripts"] - -[tool.ruff.lint.isort] -known-local-folder = ["scripts"] - -[tool.black] -line-length = 120 - -[tool.pytest.ini_options] -addopts = "-ra" -pythonpath = ["app/backend", "scripts"] - -[tool.coverage.paths] -source = ["scripts", "app"] - -[tool.coverage.report] -show_missing = true - -[tool.mypy] -check_untyped_defs = true -python_version = 3.9 - -[[tool.mypy.overrides]] -module = [ - "msal.*", - "msgraph.*", - "kiota_abstractions.*", - "kiota.*", - "azure.cognitiveservices.*", - "azure.cognitiveservices.speech.*", - "pymupdf.*", -] -ignore_missing_imports = true diff --git a/requirements-dev.txt b/requirements-dev.txt deleted file mode 100644 index c035649f27..0000000000 --- a/requirements-dev.txt +++ /dev/null @@ -1,14 +0,0 @@ --r app/backend/requirements.txt -ruff -black -pytest -pytest-asyncio -pytest-snapshot -coverage -playwright -pytest-cov -pytest-playwright -pytest-snapshot -pre-commit -pip-tools -mypy==1.14.1