diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 3f65d350..bb158471 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -24,6 +24,10 @@ jobs: - 11434:11434 volumes: - ollama:/root/.ollama + + strategy: + matrix: + model: [gemini/gemini-2.0-flash, openai/gpt-4o] # List of models steps: - uses: actions/checkout@v4 @@ -73,4 +77,6 @@ jobs: REGION: ${{ vars.REGION }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} + GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }} + TEST_MODEL: ${{ matrix.model }} # Pass the model as an environment variable run: poetry run pytest diff --git a/.gitignore b/.gitignore index ab174994..c3418c0c 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,5 @@ output logs falkordb-data -weaviate-data \ No newline at end of file +weaviate-data +.deepeval_telemtry.txt diff --git a/graphrag_sdk/fixtures/prompts.py b/graphrag_sdk/fixtures/prompts.py index 929af0af..1f79c5d8 100644 --- a/graphrag_sdk/fixtures/prompts.py +++ b/graphrag_sdk/fixtures/prompts.py @@ -433,12 +433,9 @@ For example, given the question "Which managers own Neo4j stocks?", the OpenCypher statement should look like this: -``` MATCH (m:Manager)-[:OWNS]->(s:Stock) WHERE s.name CONTAINS 'Neo4j' -RETURN m, s -``` -""" +RETURN m, s""" CYPHER_GEN_PROMPT = """ Using the ontology provided, generate an OpenCypher statement to query the graph database returning all relevant entities, relationships, and attributes to answer the question below. @@ -447,7 +444,7 @@ Please think if your answer is a valid Cypher query, and correct it if it is not. Question: {question} -""" +Your generated Cypher: """ CYPHER_GEN_PROMPT_WITH_ERROR = """ @@ -472,9 +469,8 @@ Respect the order of the relationships; the arrows should always point from the "source" to the "target". Last Answer: {last_answer} - Question: {question} -""" +Your generated Cypher: """ GRAPH_QA_SYSTEM = """ You are an assistant that helps to form nice and human understandable answers. diff --git a/graphrag_sdk/graph_metrics.py b/graphrag_sdk/graph_metrics.py new file mode 100644 index 00000000..0d15e338 --- /dev/null +++ b/graphrag_sdk/graph_metrics.py @@ -0,0 +1,635 @@ +""" +This module is based on DeepEval framwork and provides metrics for evaluating Knowledge Graph performance. + +This module provides two metrics: +1. GraphContextualRelevancy - Measures how relevant the retrieved context is to the input query +2. GraphContextualRecall - Measures how well the expected output is supported by the retrieved context +""" + +from typing import Optional +from deepeval.metrics import BaseMetric +from typing import Optional, List, Union +from deepeval.utils import prettify_list +from deepeval.test_case import LLMTestCase +from deepeval.models import DeepEvalBaseLLM +from deepeval.metrics.indicator import metric_progress_indicator +from deepeval.metrics.contextual_relevancy.schema import ContextualRelevancyVerdicts +from deepeval.metrics.contextual_recall.schema import ( + Reason, + Verdicts, + ContextualRecallVerdict,) +from deepeval.test_case import ( + LLMTestCase, + LLMTestCaseParams, + ConversationalTestCase,) +from deepeval.metrics.utils import ( + construct_verbose_logs, + trimAndLoadJson, + check_llm_test_case_params, + initialize_model,) +REQUIRED_PARAMS: List[LLMTestCaseParams] = [ + LLMTestCaseParams.INPUT, + LLMTestCaseParams.ACTUAL_OUTPUT, + LLMTestCaseParams.RETRIEVAL_CONTEXT, + LLMTestCaseParams.EXPECTED_OUTPUT, +] + + + +class GraphContextualRecall(BaseMetric): + """ + Evaluates how well the expected output is supported by the retrieval context. + + A high score indicates that most sentences in the expected output + can be attributed to information in the context. + """ + + def __init__( + self, + threshold: float = 0.5, + model: Optional[Union[str, DeepEvalBaseLLM]] = None, + include_reason: bool = True, + strict_mode: bool = False, + ): + """ + Initialize the GraphContextualRecall metric. + + Args: + threshold: Minimum score to consider the test successful (0.0-1.0) + model: LLM to use for evaluation (string name or DeepEvalBaseLLM instance) + include_reason: Whether to generate a reason for the score + strict_mode: If True, requires perfect score to pass + verbose_mode: If True, includes detailed logs in the output + """ + self.threshold = 1 if strict_mode else threshold + self.model, self.using_native_model = initialize_model(model) + self.evaluation_model = self.model.get_model_name() + self.include_reason = include_reason + self.strict_mode = strict_mode + + def measure( + self, + test_case: LLMTestCase, + _show_indicator: bool = True, + ) -> float: + """ + Measure the contextual recall score. + + Args: + test_case: The test case to evaluate + _show_indicator: Whether to show a progress indicator + + Returns: + The recall score (0.0-1.0) + """ + # Validate test case has required parameters + check_llm_test_case_params(test_case, REQUIRED_PARAMS, self) + + self.evaluation_cost = 0 if self.using_native_model else None + with metric_progress_indicator(self, _show_indicator=_show_indicator): + # Generate verdicts + self.verdicts: List[ContextualRecallVerdict] = ( + self._generate_verdicts( + test_case.expected_output, test_case.retrieval_context, test_case.additional_metadata + ) + ) + + # Calculate the score and generate the reason + self.score = self._calculate_score() + self.reason = self._generate_reason(test_case.expected_output) + self.success = self.score >= self.threshold + # Generate verbose logs + self.verbose_logs = construct_verbose_logs( + self, + steps=[ + f"Verdicts:\n{prettify_list(self.verdicts)}", + f"Score: {self.score}\nReason: {self.reason}", + ], + ) + + return self.score + + def _generate_reason(self, expected_output: str): + """ + Generate a human-readable reason for the metric score. + + Args: + expected_output: The expected output to evaluate + + Returns: + A string explaining the score + """ + if self.include_reason is False: + return None + + # Collect supportive and unsupportive reasons + supportive_reasons = [] + unsupportive_reasons = [] + for verdict in self.verdicts: + if verdict.verdict.lower() == "yes": + supportive_reasons.append(verdict.reason) + else: + unsupportive_reasons.append(verdict.reason) + + # Generate the prompt for the reason + prompt = GraphContextualRecallTemplate.generate_reason( + expected_output=expected_output, + supportive_reasons=supportive_reasons, + unsupportive_reasons=unsupportive_reasons, + score=format(self.score, ".2f"), + ) + + # Generate the reason using the appropriate model + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + return data["reason"] + else: + try: + res: Reason = self.model.generate(prompt, schema=Reason) + return res.reason + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + return data["reason"] + + def _calculate_score(self): + """ + Calculate the recall score based on the verdicts. + + Returns: + A score between 0.0 and 1.0 + """ + number_of_verdicts = len(self.verdicts) + if number_of_verdicts == 0: + return 0 + + justified_sentences = 0 + for verdict in self.verdicts: + if verdict.verdict.lower() == "yes": + justified_sentences += 1 + + score = justified_sentences / number_of_verdicts + + # In strict mode, return 0 if below threshold + return 0 if self.strict_mode and score < self.threshold else score + + def _generate_verdicts( + self, expected_output: str, retrieval_context: List[str], cypher_query: Optional[str] = None + ) -> List[ContextualRecallVerdict]: + """ + Generate verdicts for each sentence in the expected output. + + Args: + expected_output: The expected output to evaluate + retrieval_context: List of retrieval contexts to check against + cypher_query: Optional Cypher query used to generate the context + + Returns: + List of ContextualRecallVerdict for each sentence + """ + prompt = GraphContextualRecallTemplate.generate_verdicts( + expected_output=expected_output, retrieval_context=retrieval_context, cypher_query=cypher_query + ) + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + verdicts = [ + ContextualRecallVerdict(**item) for item in data["verdicts"] + ] + return verdicts + else: + try: + res: Verdicts = self.model.generate(prompt, schema=Verdicts) + verdicts: Verdicts = [item for item in res.verdicts] + return verdicts + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + verdicts = [ + ContextualRecallVerdict(**item) for item in data["verdicts"] + ] + return verdicts + + def is_successful(self) -> bool: + """ + Check if the metric meets the success threshold. + + Returns: + True if the score meets or exceeds the threshold, False otherwise + """ + if self.error is not None: + self.success = False + else: + try: + self.success = self.score >= self.threshold + except: + self.success = False + return self.success + + @property + def __name__(self): + return "Graph Contextual Recall" + +class GraphContextualRecallTemplate: + """ + Template for generating prompts for the GraphContextualRecall metric. + """ + + @staticmethod + def generate_reason( + expected_output, supportive_reasons, unsupportive_reasons, score + ): + """ + Generate a prompt for generating a reason for the recall score. + + Args: + expected_output: The original expected output + supportive_reasons: List of supportive reasons for the score + unsupportive_reasons: List of unsupportive reasons for the score + score: The calculated recall score + + Returns: + A formatted prompt for generating the reason + """ + return f""" +Given the original expected output, a list of supportive reasons, and a list of unsupportive reasons (which is deduced directly from the 'expected output'), and a contextual recall score (closer to 1 the better), summarize a CONCISE reason for the score. +A supportive reason is the reason why a certain sentence in the original expected output can be attributed to the node in the retrieval context. +An unsupportive reason is the reason why a certain sentence in the original expected output cannot be attributed to anything in the retrieval context. +In your reason, you should related supportive/unsupportive reasons to the sentence number in expected output, and info regarding the node number in retrieval context to support your final reason. The first mention of "node(s)" should specify "node(s) in retrieval context)". + +** +IMPORTANT: Please make sure to only return in JSON format, with the 'reason' key providing the reason. +Example JSON: +{{ + "reason": "The score is because ." +}} + +DO NOT mention 'supportive reasons' and 'unsupportive reasons' in your reason, these terms are just here for you to understand the broader scope of things. +If the score is 1, keep it short and say something positive with an upbeat encouraging tone (but don't overdo it otherwise it gets annoying). +** + +Contextual Recall Score: +{score} + +Expected Output: +{expected_output} + +Supportive Reasons: +{supportive_reasons} + +Unsupportive Reasons: +{unsupportive_reasons} + +JSON: +""" + + @staticmethod + def generate_verdicts(expected_output, retrieval_context, cypher_query): + """ + Generate a prompt for generating verdicts for the expected output. + + Args: + expected_output: The original expected output + retrieval_context: List of retrieval contexts to check against + cypher_query: Optional Cypher query used to generate the context + + Returns: + A formatted prompt for generating the verdicts + """ + return f""" +For EACH sentence in the given expected output below, determine whether the sentence can be attributed to the nodes of retrieval contexts (cypher query and its output). Please generate a list of JSON with two keys: `verdict` and `reason`. +The `verdict` key should STRICTLY be either a 'yes' or 'no'. Answer 'yes' if the sentence can be attributed to any parts of the retrieval context (cypher query and its output), else answer 'no'. +The `reason` key should provide a reason why to the verdict. In the reason, you should aim to include the node(s) count in the retrieval context (eg., 1st node, and 2nd node in the retrieval context) that is attributed to said sentence. You should also aim to quote the specific part of the retrieval context to justify your verdict, but keep it extremely concise and cut short the quote with an ellipsis if possible. + + +** +IMPORTANT: Please make sure to only return in JSON format, with the 'verdicts' key as a list of JSON objects, each with two keys: `verdict` and `reason`. + +{{ + "verdicts": [ + {{ + "verdict": "yes", + "reason": "..." + }}, + ... + ] +}} + +Since you are going to generate a verdict for each sentence, the number of 'verdicts' SHOULD BE STRICTLY EQUAL to the number of sentences in of `expected output`. +** + +Expected Output: +{expected_output} + +Cypher Query: +{cypher_query} + +Retrieval Context: +{retrieval_context} + +JSON: +""" + +class GraphContextualRelevancy(BaseMetric): + """ + Evaluates how relevant the retrieval context is to the input query. + + A high score indicates that most of the statements in the context + are relevant to answering the query. + """ + + def __init__( + self, + threshold: float = 0.5, + model: Optional[Union[str, DeepEvalBaseLLM]] = None, + include_reason: bool = True, + strict_mode: bool = False, + ): + """ + Initialize the GraphContextualRelevancy metric. + + Args: + threshold: Minimum score to consider the test successful (0.0-1.0) + model: LLM to use for evaluation (string name or DeepEvalBaseLLM instance) + include_reason: Whether to generate a reason for the score + strict_mode: If True, requires perfect score to pass + verbose_mode: If True, includes detailed logs in the output + """ + self.threshold = 1 if strict_mode else threshold + self.model, self.using_native_model = initialize_model(model) + self.evaluation_model = self.model.get_model_name() + self.include_reason = include_reason + self.strict_mode = strict_mode + + def measure( + self, + test_case: Union[LLMTestCase, ConversationalTestCase], + _show_indicator: bool = True, + ) -> float: + """ + Measure the contextual relevancy score. + + Args: + test_case: The test case to evaluate + _show_indicator: Whether to show a progress indicator + + Returns: + The relevancy score (0.0-1.0) + """ + # Convert conversational test case to standard test case if needed + if isinstance(test_case, ConversationalTestCase): + test_case = test_case.turns[0] + + # Validate test case has required parameters + check_llm_test_case_params(test_case, REQUIRED_PARAMS, self) + + # Initialize cost tracking for native models + self.evaluation_cost = 0 if self.using_native_model else None + with metric_progress_indicator(self, _show_indicator=_show_indicator): + # Generate verdicts for each context + self.verdicts_list: List[ContextualRelevancyVerdicts] = [ + (self._generate_verdicts(test_case.input, context, test_case.additional_metadata)) + for context in test_case.retrieval_context + ] + # Calculate the score and generate the reason + self.score = self._calculate_score() + self.reason = self._generate_reason(test_case.input) + self.success = self.score >= self.threshold + # Generate verbose logs + self.verbose_logs = construct_verbose_logs( + self, + steps=[ + f"Verdicts:\n{prettify_list(self.verdicts_list)}", + f"Score: {self.score}\nReason: {self.reason}", + ], + ) + + return self.score + + def _generate_reason(self, input: str): + """ + Generate a human-readable reason for the metric score. + + Args: + input_query: The original input query + + Returns: + A string explaining the score + """ + if self.include_reason is False: + return None + + # Collect irrelevant and relevant statements + irrelevancies = [] + relevant_statements = [] + for verdicts in self.verdicts_list: + for verdict in verdicts.verdicts: + if verdict.verdict.lower() == "no": + irrelevancies.append(verdict.reason) + else: + relevant_statements.append(verdict.statement) + + # Generate the prompt for the reason + prompt: dict = GraphContextualRelevancyTemplate.generate_reason( + input=input, + irrelevancies=irrelevancies, + relevant_statements=relevant_statements, + score=format(self.score, ".2f"), + ) + + # Generate the reason using the appropriate model + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + return data["reason"] + else: + try: + res: Reason = self.model.generate(prompt, schema=Reason) + return res.reason + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + return data["reason"] + + def _calculate_score(self): + """ + Calculate the relevancy score based on the verdicts. + + Returns: + A score between 0.0 and 1.0 + """ + total_verdicts = 0 + relevant_statements = 0 + for verdicts in self.verdicts_list: + for verdict in verdicts.verdicts: + total_verdicts += 1 + if verdict.verdict.lower() == "yes": + relevant_statements += 1 + + if total_verdicts == 0: + return 0 + + score = relevant_statements / total_verdicts + + # In strict mode, return 0 if below threshold + return 0 if self.strict_mode and score < self.threshold else score + + def _generate_verdicts( + self, input: str, context: str, cypher_query: Optional[str] = None + ) -> ContextualRelevancyVerdicts: + """ + Generate verdicts for a single context. + + Args: + input_query: The original input query + context: The retrieval context to evaluate + cypher_query: Optional Cypher query used to generate the context + + Returns: + ContextualRelevancyVerdicts containing verdicts for each statement + """ + prompt = GraphContextualRelevancyTemplate.generate_verdicts( + input=input, context=context, cypher_query=cypher_query + ) + if self.using_native_model: + res, cost = self.model.generate(prompt) + self.evaluation_cost += cost + data = trimAndLoadJson(res, self) + return ContextualRelevancyVerdicts(**data) + else: + try: + res = self.model.generate( + prompt, schema=ContextualRelevancyVerdicts + ) + return res + except TypeError: + res = self.model.generate(prompt) + data = trimAndLoadJson(res, self) + return ContextualRelevancyVerdicts(**data) + + def is_successful(self) -> bool: + """ + Check if the metric meets the success threshold. + + Returns: + True if the score meets or exceeds the threshold, False otherwise + """ + if self.error is not None: + self.success = False + else: + try: + self.success = self.score >= self.threshold + except: + self.success = False + return self.success + + @property + def __name__(self): + return "Graph Contextual Relevancy" + +class GraphContextualRelevancyTemplate: + """ + Template for generating prompts for the GraphContextualRelevancy metric. + """ + @staticmethod + def generate_reason( + input: str, + irrelevancies: List[str], + relevant_statements: List[str], + score: float, + ): + """ + Generate a prompt for generating a reason for the relevancy score. + + Args: + input: The original input query + irrelevancies: List of irrelevant statements in the context + relevant_statements: List of relevant statements in the context + score: The calculated relevancy score + + Returns: + A formatted prompt for generating the reason + """ + return f"""Based on the given input, reasons for why the retrieval context is irrelevant to the input, the statements in the retrieval context that is actually relevant to the retrieval context, and the contextual relevancy score (the closer to 1 the better), please generate a CONCISE reason for the score. +In your reason, you should quote data provided in the reasons for irrelevancy and relevant statements to support your point. + +** +IMPORTANT: Please make sure to only return in JSON format, with the 'reason' key providing the reason. +Example JSON: +{{ + "reason": "The score is because ." +}} + +If the score is 1, keep it short and say something positive with an upbeat encouraging tone (but don't overdo it otherwise it gets annoying). +** + + +Contextual Relevancy Score: +{score} + +Input: +{input} + +Reasons for why the retrieval context is irrelevant to the input: +{irrelevancies} + +Statement in the retrieval context that is relevant to the input: +{relevant_statements} + +JSON: +""" + + @staticmethod + def generate_verdicts(input: str, context: str, cypher_query: Optional[str] = None): + """ + Generate a prompt for generating verdicts for the context. + + Args: + input: The original input query + context: The retrieval context to evaluate + cypher_query: Optional Cypher query used to generate the context + + Returns: + A formatted prompt for generating the verdicts + """ + return f"""Based on the input and context (cypher and query output), please generate a JSON object to indicate whether each statement found in the context is relevant to the provided input. The JSON will be a list of 'verdicts', with 2 mandatory fields: 'verdict' and 'statement', and 1 optional field: 'reason'. +You should first extract statements found in the context, which are high level information found in the context, before deciding on a verdict and optionally a reason for each statement. +The 'verdict' key should STRICTLY be either 'yes' or 'no', and states whether the statement is relevant to the input. +Provide a 'reason' ONLY IF verdict is no. You MUST quote the irrelevant parts of the statement to back up your reason. + +** +IMPORTANT: Please make sure to only return in JSON format. +Example Context: "Einstein won the Nobel Prize for his discovery of the photoelectric effect. He won the Nobel Prize in 1968. There was a cat." +Example Input: "What were some of Einstein's achievements?" + +Example: +{{ + "verdicts": [ + {{ + "verdict": "yes", + "statement": "Einstein won the Nobel Prize for his discovery of the photoelectric effect in 1968", + }}, + {{ + "verdict": "no", + "statement": "There was a cat.", + "reason": "The retrieval context contained the information 'There was a cat' when it has nothing to do with Einstein's achievements." + }} + ] +}} +** + +Input: +{input} + +Cypher Query: +{cypher_query} + +Context: +{context} + +JSON: +""" diff --git a/graphrag_sdk/models/litellm.py b/graphrag_sdk/models/litellm.py index 723f78fb..324bbf06 100644 --- a/graphrag_sdk/models/litellm.py +++ b/graphrag_sdk/models/litellm.py @@ -73,7 +73,7 @@ def check_valid_key(self, model: str): model=model, messages=messages, max_tokens=10 ) return True - except: + except Exception as e: return False def check_and_pull_model(self) -> None: diff --git a/poetry.lock b/poetry.lock index 46b98ebe..d3eeed57 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4,7 +4,7 @@ name = "aiohappyeyeballs" version = "2.4.4" description = "Happy Eyeballs for asyncio" -optional = true +optional = false python-versions = ">=3.8" files = [ {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"}, @@ -15,7 +15,7 @@ files = [ name = "aiohttp" version = "3.11.11" description = "Async http client/server framework (asyncio)" -optional = true +optional = false python-versions = ">=3.9" files = [ {file = "aiohttp-3.11.11-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a60804bff28662cbcf340a4d61598891f12eea3a66af48ecfdc975ceec21e3c8"}, @@ -113,7 +113,7 @@ speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"] name = "aiosignal" version = "1.3.2" description = "aiosignal: a list of registered asynchronous callbacks" -optional = true +optional = false python-versions = ">=3.9" files = [ {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"}, @@ -138,7 +138,7 @@ files = [ name = "annotated-types" version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" -optional = true +optional = false python-versions = ">=3.8" files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, @@ -285,13 +285,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""} [[package]] name = "async-timeout" -version = "5.0.1" +version = "4.0.3" description = "Timeout context manager for asyncio programs" optional = false -python-versions = ">=3.8" +python-versions = ">=3.7" files = [ - {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"}, - {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"}, + {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"}, + {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"}, ] [[package]] @@ -597,7 +597,7 @@ files = [ name = "click" version = "8.1.8" description = "Composable command line interface toolkit" -optional = true +optional = false python-versions = ">=3.7" files = [ {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"}, @@ -635,6 +635,21 @@ traitlets = ">=4" [package.extras] test = ["pytest"] +[[package]] +name = "dataclasses-json" +version = "0.6.7" +description = "Easily serialize dataclasses to and from JSON." +optional = false +python-versions = "<4.0,>=3.7" +files = [ + {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = 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newline at end of file diff --git a/tests/test_kg_gemini.py b/tests/test_kg_gemini.py deleted file mode 100644 index 03c59cc3..00000000 --- a/tests/test_kg_gemini.py +++ /dev/null @@ -1,105 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.relation import Relation -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.gemini import GeminiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig -from google.generativeai import GenerationConfig as GoogleGenerationConfig - - -load_dotenv() -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - - -class TestKGGemini(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - - cls.graph_name = "IMDB_gemini" - - model = GeminiGenerativeModel(model_name="gemini-1.5-flash-001", generation_config=GoogleGenerationConfig(temperature=0)) - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - ) - - def test_kg_creation(self): - file_path = "tests/data/madoff.txt" - - sources = [Source(file_path)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 10, "The number of actors found should be greater than 10" - - def test_kg_delete(self): - self.kg.delete() - - db = FalkorDB() - graphs = db.list_graphs() - self.assertNotIn(self.graph_name, graphs) diff --git a/tests/test_kg_litellm_openai.py b/tests/test_kg_litellm_openai.py deleted file mode 100644 index 77b2ef5f..00000000 --- a/tests/test_kg_litellm_openai.py +++ /dev/null @@ -1,97 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source_FromRawText -from graphrag_sdk.relation import Relation -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.litellm import LiteModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig - -load_dotenv() - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -class TestKGLiteLLM(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - cls.graph_name = "IMDB_openai" - model = LiteModel(model_name="gpt-4o", generation_config=GenerativeModelConfig(temperature=0)) - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - ) - - def test_kg_creation(self): - - file_path = "tests/data/madoff.txt" - with open(file_path) as f: - string = f.read() - - sources = [Source_FromRawText(string)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 10, "The number of actors found should be greater than 10" \ No newline at end of file diff --git a/tests/test_kg_ollama.py b/tests/test_kg_ollama.py deleted file mode 100644 index 06ec03ee..00000000 --- a/tests/test_kg_ollama.py +++ /dev/null @@ -1,109 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.relation import Relation -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.ollama import OllamaGenerativeModel -from graphrag_sdk.models.openai import OpenAiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig - -load_dotenv() - - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -class TestKGOllama(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - - cls.graph_name = "IMDB_ollama" - - model_ollama = OllamaGenerativeModel(model_name="llama3:8b", generation_config=GenerativeModelConfig(temperature=0)) - model_openai = OpenAiGenerativeModel(model_name="gpt-3.5-turbo") - - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig(extract_data=model_openai, cypher_generation=model_ollama, qa=model_ollama), - ) - - def test_kg_creation(self): - - file_path = "tests/data/madoff.txt" - - sources = [Source(file_path)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 5, "The number of actors found should be greater than 5" - - def test_kg_delete(self): - - self.kg.delete() - - db = FalkorDB() - graphs = db.list_graphs() - self.assertNotIn(self.graph_name, graphs) diff --git a/tests/test_kg_openai.py b/tests/test_kg_openai.py deleted file mode 100644 index 23ed1871..00000000 --- a/tests/test_kg_openai.py +++ /dev/null @@ -1,102 +0,0 @@ -import re -import logging -import unittest -from falkordb import FalkorDB -from dotenv import load_dotenv -from graphrag_sdk.entity import Entity -from graphrag_sdk.source import Source -from graphrag_sdk.relation import Relation -from graphrag_sdk.ontology import Ontology -from graphrag_sdk.attribute import Attribute, AttributeType -from graphrag_sdk.models.openai import OpenAiGenerativeModel -from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig - -load_dotenv() - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger(__name__) - -class TestKGOpenAI(unittest.TestCase): - """ - Test Knowledge Graph - """ - - @classmethod - def setUpClass(cls): - - cls.ontology = Ontology([], []) - - cls.ontology.add_entity( - Entity( - label="Actor", - attributes=[ - Attribute( - name="name", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_entity( - Entity( - label="Movie", - attributes=[ - Attribute( - name="title", - attr_type=AttributeType.STRING, - unique=True, - required=True, - ), - ], - ) - ) - cls.ontology.add_relation( - Relation( - label="ACTED_IN", - source="Actor", - target="Movie", - attributes=[ - Attribute( - name="role", - attr_type=AttributeType.STRING, - unique=False, - required=False, - ), - ], - ) - ) - cls.graph_name = "IMDB_openai" - model = OpenAiGenerativeModel(model_name="gpt-3.5-turbo-0125", generation_config=GenerativeModelConfig(temperature=0)) - cls.kg = KnowledgeGraph( - name=cls.graph_name, - ontology=cls.ontology, - model_config=KnowledgeGraphModelConfig.with_model(model), - ) - - def test_kg_creation(self): - - file_path = "tests/data/madoff.txt" - - sources = [Source(file_path)] - - self.kg.process_sources(sources) - - chat = self.kg.chat_session() - answer = chat.send_message("How many actors acted in a movie?") - answer = answer['response'] - - logger.info(f"Answer: {answer}") - - actors_count = re.findall(r'\d+', answer) - num_actors = 0 if len(actors_count) == 0 else int(actors_count[0]) - - assert num_actors > 10, "The number of actors found should be greater than 10" - - def test_kg_delete(self): - self.kg.delete() - - db = FalkorDB() - graphs = db.list_graphs() - self.assertNotIn(self.graph_name, graphs) diff --git a/tests/test_rag.py b/tests/test_rag.py new file mode 100644 index 00000000..866f4644 --- /dev/null +++ b/tests/test_rag.py @@ -0,0 +1,207 @@ +import os +import pytest +import logging +import numpy as np +from dotenv import load_dotenv +from graphrag_sdk.entity import Entity +from deepeval.test_case import LLMTestCase +from graphrag_sdk.relation import Relation +from graphrag_sdk.ontology import Ontology +from graphrag_sdk.models.litellm import LiteModel +from graphrag_sdk.source import Source_FromRawText +from graphrag_sdk.attribute import Attribute, AttributeType +# Custom metrics of DeepEval +from graphrag_sdk.graph_metrics import GraphContextualRelevancy, GraphContextualRecall +from graphrag_sdk import KnowledgeGraph, KnowledgeGraphModelConfig, GenerativeModelConfig + + +load_dotenv() +TEST_FILE = "tests/data/madoff.txt" + +# Test queries and expected results +BENCHMARK = [ + { + "query": "How many actors acted in a movie?", + "expected": "Over than 10 actors acted in a movie." + }, + { + "query": "Which actors acted in a movie?", + "expected": "Joseph Scotto, Melony Feliciano, and Donna Pastorello acted in a movie." + }, + { + "query": "What is the role of Joseph Scotto in a movie?", + "expected": "Joseph Scotto played the role of Bernie Madoff in a movie." + }, + { + "query": "Did Donna Pastorello act in a movie?", + "expected": "Yes, Donna Pastorello acted in a movie as Eleanor Squillari." + }, + { + "query": "Who played the role of Mark Madoff in a movie?", + "expected": "Alex Olson played the role of Mark Madoff in a movie." + }, + { + "query": "Did Melony Feliciano have a named role in a movie?", + "expected": "No, Melony Feliciano acted as a background extra in a movie." + },] + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger(__name__) + + +@pytest.fixture +def movie_actor_ontology(): + """ + Create and return an ontology for movies and actors with the ACTED_IN relationship. + """ + ontology = Ontology() + + # Add Actor entity with name attribute + ontology.add_entity( + Entity( + label="Actor", + attributes=[ + Attribute( + name="name", + attr_type=AttributeType.STRING, + unique=True, + required=True, + ), + ], + ) + ) + + # Add Movie entity with title attribute + ontology.add_entity( + Entity( + label="Movie", + attributes=[ + Attribute( + name="title", + attr_type=AttributeType.STRING, + unique=True, + required=True, + ), + ], + ) + ) + + # Add ACTED_IN relation between Actor and Movie with role attribute + ontology.add_relation( + Relation( + label="ACTED_IN", + source="Actor", + target="Movie", + attributes=[ + Attribute( + name="role", + attr_type=AttributeType.STRING, + unique=False, + required=True, + ), + ], + ) + ) + + return ontology + + +@pytest.fixture +def knowledge_graph_setup(movie_actor_ontology): + """ + Sets up the knowledge graph using the provided ontology. + """ + # Get the model name from the environment variable, with a default + model_name = os.getenv("TEST_MODEL", "gemini/gemini-2.0-flash") + graph_name = model_name.split("/")[0] + + # Configure the model with zero temperature for deterministic outputs + model = LiteModel( + model_name=model_name, + generation_config=GenerativeModelConfig(temperature=0) + ) + + # Initialize the knowledge graph with the model and ontology + kg = KnowledgeGraph( + name=graph_name, + ontology=movie_actor_ontology, + model_config=KnowledgeGraphModelConfig.with_model(model), + ) + + # URL for a movie on IMDB + with open(TEST_FILE) as f: + string = f.read() + + sources = [Source_FromRawText(string)] + + # Process the source to populate the knowledge graph + kg.process_sources(sources) + + return kg + + +@pytest.fixture +def delete_kg(): + """ + Returns a function that deletes a given knowledge graph. + """ + def cleanup(kg): + logger.info("Cleaning up test graph...") + kg.delete() + + return cleanup + + +class TestGraphRAGPipeline: + """ + Test Knowledge Graph using LiteLLM for query processing and evaluation. + Tests the ability of the graph to answer questions based on processed sources. + """ + + def test_movie_actor_queries(self, knowledge_graph_setup, delete_kg): + """ + Test the knowledge graph's ability to answer questions about actors and movies. + """ + kg = knowledge_graph_setup + + # Configure evaluation metrics + relevancy_metric = GraphContextualRelevancy(threshold=0.5) + recall_metric = GraphContextualRecall(threshold=0.5) + scores = [] + + # Run each test case and evaluate metrics + for case in BENCHMARK: + chat = kg.chat_session() + answer = chat.send_message(case["query"]) + + # Create a test case for evaluation + test_case = LLMTestCase( + input=case["query"], + actual_output=answer["response"], + retrieval_context=[answer["context"]], + context=[answer["context"]], + name="knowledge_graph_test", + expected_output=case["expected"], + additional_metadata=answer["cypher"], + ) + + # Measure metrics + relevancy_score = relevancy_metric.measure(test_case) + recall_score = recall_metric.measure(test_case) + + # Calculate and store average score + combined_score = (relevancy_score + recall_score) / 2 + scores.append(combined_score) + + # Log results for debugging + logger.info(f"Query: {case['query']}") + logger.info(f"Response: {answer['response']}") + logger.info(f"Combined Score: {combined_score}") + + # Clean up by deleting the graph + delete_kg(kg) + + # Verify that the average score meets the threshold + average_score = np.mean(scores) + logger.info(f"Average Score: {average_score}") + assert average_score >= 0.5, f"Average score {average_score} is below threshold of 0.5" \ No newline at end of file