diff --git a/pyproject.toml b/pyproject.toml index 382187ee9..d59bfb706 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -115,6 +115,8 @@ dependencies = [ "apify-client>=1.8.1", "pylint>=3.3.4", "ruff>=0.9.7", + "langchain-graph-retriever==0.6.1", + "graph-retriever==0.6.1", ] [dependency-groups] @@ -158,6 +160,7 @@ dev = [ "hypothesis>=6.123.17", "locust>=2.32.9", "pytest-rerunfailures>=15.0", + "faker>=37.0.0", ] [tool.uv.sources] diff --git a/src/backend/base/langflow/components/vectorstores/__init__.py b/src/backend/base/langflow/components/vectorstores/__init__.py index 6fccd25bc..2a548c876 100644 --- a/src/backend/base/langflow/components/vectorstores/__init__.py +++ b/src/backend/base/langflow/components/vectorstores/__init__.py @@ -7,6 +7,7 @@ from .clickhouse import ClickhouseVectorStoreComponent from .couchbase import CouchbaseVectorStoreComponent from .elasticsearch import ElasticsearchVectorStoreComponent from .faiss import FaissVectorStoreComponent +from .graph_rag import GraphRAGComponent from .hcd import HCDVectorStoreComponent from .milvus import MilvusVectorStoreComponent from .mongodb_atlas import MongoVectorStoreComponent @@ -32,6 +33,7 @@ __all__ = [ "CouchbaseVectorStoreComponent", "ElasticsearchVectorStoreComponent", "FaissVectorStoreComponent", + "GraphRAGComponent", "HCDVectorStoreComponent", "MilvusVectorStoreComponent", "MongoVectorStoreComponent", diff --git a/src/backend/base/langflow/components/vectorstores/graph_rag.py b/src/backend/base/langflow/components/vectorstores/graph_rag.py new file mode 100644 index 000000000..2fdcf24c4 --- /dev/null +++ b/src/backend/base/langflow/components/vectorstores/graph_rag.py @@ -0,0 +1,141 @@ +import inspect +from abc import ABC + +import graph_retriever.strategies as strategies_module +from langchain_graph_retriever import GraphRetriever + +from langflow.base.vectorstores.model import LCVectorStoreComponent +from langflow.helpers import docs_to_data +from langflow.inputs import DropdownInput, HandleInput, MultilineInput, NestedDictInput, StrInput +from langflow.schema import Data + + +def traversal_strategies() -> list[str]: + """Retrieves a list of class names from the strategies_module. + + This function uses the `inspect` module to get all the class members + from the `strategies_module` and returns their names as a list of strings. + + Returns: + list[str]: A list of strategy class names. + """ + classes = inspect.getmembers(strategies_module, inspect.isclass) + return [name for name, cls in classes if ABC not in cls.__bases__] + + +class GraphRAGComponent(LCVectorStoreComponent): + """GraphRAGComponent is a component for performing Graph RAG traversal in a vector store. + + Attributes: + display_name (str): The display name of the component. + description (str): A brief description of the component. + name (str): The name of the component. + icon (str): The icon representing the component. + inputs (list): A list of input configurations for the component. + + Methods: + _build_search_args(): + Builds the arguments required for the search operation. + search_documents() -> list[Data]: + Searches for documents using the specified strategy, edge definition, and query. + _edge_definition_from_input() -> tuple: + Processes the edge definition input and returns it as a tuple. + """ + + display_name: str = "Graph RAG" + description: str = "Graph RAG traversal for vector store." + name = "Graph RAG" + icon: str = "AstraDB" + + inputs = [ + HandleInput( + name="embedding_model", + display_name="Embedding Model", + input_types=["Embeddings"], + info="Specify the Embedding Model. Not required for Astra Vectorize collections.", + required=False, + ), + HandleInput( + name="vector_store", + display_name="Vector Store Connection", + input_types=["VectorStore"], + info="Connection to Vector Store.", + ), + StrInput( + name="edge_definition", + display_name="Edge Definition", + info="Edge definition for the graph traversal.", + ), + DropdownInput( + name="strategy", + display_name="Traversal Strategies", + options=traversal_strategies(), + ), + MultilineInput( + name="search_query", + display_name="Search Query", + tool_mode=True, + ), + NestedDictInput( + name="graphrag_strategy_kwargs", + display_name="Strategy Parameters", + info=( + "Optional dictionary of additional parameters for the retrieval strategy. " + "Please see https://datastax.github.io/graph-rag/reference/graph_retriever/strategies/ for details." + ), + advanced=True, + ), + ] + + def search_documents(self) -> list[Data]: + """Searches for documents using the graph retriever based on the selected strategy, edge definition, and query. + + Returns: + list[Data]: A list of retrieved documents. + + Raises: + AttributeError: If there is an issue with attribute access. + TypeError: If there is a type mismatch. + ValueError: If there is a value error. + """ + additional_params = self.graphrag_strategy_kwargs or {} + + # Invoke the graph retriever based on the selected strategy, edge definition, and query + strategy_class = getattr(strategies_module, self.strategy) + retriever = GraphRetriever( + store=self.vector_store, + edges=[self._evaluate_edge_definition_input()], + strategy=strategy_class(**additional_params), + ) + + return docs_to_data(retriever.invoke(self.search_query)) + + def _edge_definition_from_input(self) -> tuple: + """Generates the edge definition from the input data. + + Returns: + tuple: A tuple representing the edge definition. + """ + values = self.edge_definition.split(",") + values = [value.strip() for value in values] + + return tuple(values) + + def _evaluate_edge_definition_input(self) -> tuple: + from graph_retriever.edges.metadata import Id + + """Evaluates the edge definition, converting any function calls from strings. + + Args: + edge_definition (tuple): The edge definition to evaluate. + + Returns: + tuple: The evaluated edge definition. + """ + evaluated_values = [] + for value in self._edge_definition_from_input(): + if value == "Id()": + evaluated_values.append(Id()) # Evaluate Id() as a function call + else: + evaluated_values.append(value) + return tuple(evaluated_values) diff --git a/src/backend/tests/unit/components/vectorstores/test_graph_rag_component.py b/src/backend/tests/unit/components/vectorstores/test_graph_rag_component.py new file mode 100644 index 000000000..4b065e6e4 --- /dev/null +++ b/src/backend/tests/unit/components/vectorstores/test_graph_rag_component.py @@ -0,0 +1,164 @@ +import random + +import pytest +from faker import Faker +from langchain_community.embeddings.fake import DeterministicFakeEmbedding +from langchain_core.documents import Document +from langchain_core.vectorstores.in_memory import InMemoryVectorStore +from langflow.components.vectorstores.graph_rag import GraphRAGComponent + +from tests.base import ComponentTestBaseWithoutClient + + +class TestGraphRAGComponent(ComponentTestBaseWithoutClient): + """Test suite for the GraphRAGComponent class, focusing on graph traversal and retrieval functionality. + + Fixtures: + component_class: Returns the GraphRAGComponent class to be tested. + animals: Provides a list of Document objects representing various animals with metadata. + embedding: Provides a FakeEmbeddings instance with a specified size. + vector_store: Initializes an InMemoryVectorStore with the provided animals and embedding. + file_names_mapping: Returns an empty list since this component doesn't have version-specific files. + default_kwargs: Returns an empty dictionary since this component doesn't have any default arguments. + + Test Cases: + test_graphrag: Tests the search_documents method of the GraphRAGComponent class by setting attributes and + verifying the number of results returned. + """ + + @pytest.fixture + def component_class(self): + """Return the component class to test.""" + return GraphRAGComponent + + @pytest.fixture + def animals(self, n: int = 20, match_prob: float = 0.3) -> list[Document]: + """Animals dataset for testing. + + Generate a list of animal-related document objects with random metadata. + + Parameters: + n (int): Number of documents to generate. + match_prob (float): Probability of sharing metadata across documents. + + Returns: + List[Document]: A list of generated Document objects. + """ + # Initialize Faker for generating random text + fake = Faker() + random.seed(42) + fake.seed_instance(42) + + # Define possible attributes for animals + animal_types = ["mammal", "bird", "reptile", "insect"] + habitats = ["savanna", "marine", "wetlands", "forest", "desert"] + diets = ["carnivorous", "herbivorous", "omnivorous"] + origins = ["north america", "south america", "africa", "asia", "australia"] + + shared_metadata = {} # Common metadata that may be shared across documents + + def update_metadata(meta: dict) -> dict: + """Modify metadata based on predefined conditions and probability.""" + if random.random() < match_prob: # noqa: S311 + meta.update(shared_metadata) # Apply shared metadata + elif meta["type"] == "mammal": + meta["habitat"] = random.choice(habitats) # noqa: S311 + elif meta["type"] == "reptile": + meta["diet"] = random.choice(diets) # noqa: S311 + elif meta["type"] == "insect": + meta["origin"] = random.choice(origins) # noqa: S311 + return meta + + # Generate and return a list of documents + return [ + Document( + id=fake.uuid4(), + page_content=fake.sentence(), + metadata=update_metadata( + { + "type": random.choice(animal_types), # noqa: S311 + "number_of_legs": random.choice([0, 2, 4, 6, 8]), # noqa: S311 + "keywords": fake.words(random.randint(2, 5)), # noqa: S311 + # Add optional tags with 30% probability + **( + { + "tags": [ + {"a": random.randint(1, 10), "b": random.randint(1, 10)} # noqa: S311 + for _ in range(random.randint(1, 2)) # noqa: S311 + ] + } + if random.random() < 0.3 # noqa: S311 + else {} + ), + # Add nested metadata with 20% probability + **({"nested": {"a": random.randint(1, 10)}} if random.random() < 0.2 else {}), # noqa: S311 + } + ), + ) + for _ in range(n) + ] + + @pytest.fixture + def embedding(self): + return DeterministicFakeEmbedding(size=8) + + @pytest.fixture + def vector_store(self, animals: list[Document], embedding: DeterministicFakeEmbedding) -> InMemoryVectorStore: + """Return an empty list since this component doesn't have version-specific files.""" + store = InMemoryVectorStore(embedding=embedding) + store.add_documents(animals) + return store + + @pytest.fixture + def file_names_mapping(self): + """Return an empty list since this component doesn't have version-specific files.""" + + @pytest.fixture + def default_kwargs(self): + """Return an empty dictionary since this component doesn't have any default arguments.""" + return {"k": 10, "start_k": 3, "max_depth": 2} + + def test_graphrag( + self, + component_class: GraphRAGComponent, + embedding: DeterministicFakeEmbedding, + vector_store: InMemoryVectorStore, + default_kwargs, + ): + """Test GraphRAGComponent's document search functionality. + + This test verifies that the component correctly retrieves documents using the + provided embedding model, vector store, and search query. + + Args: + component_class (GraphRAGComponent): The component class to test. + embedding (FakeEmbeddings): The embedding model for the component. + vector_store (InMemoryVectorStore): The vector store used in retrieval. + default_kwargs (dict): Default keyword arguments for the retrieval strategy. + + Returns: + None: The test asserts that 10 search results are returned. + """ + component = component_class() + + component.set_attributes( + { + "embedding_model": embedding, + "vector_store": vector_store, + "edge_definition": "type, type", + "strategy": "Eager", + "search_query": "information environment technology", + "graphrag_strategy_kwargs": default_kwargs, + } + ) + + results = component.search_documents() + + # Quantity of documents + assert len(results) == 10 + + # Ensures all the k-start_k documents returned via traversal have the same metadata as the + # ones returned via the similarity search + assert list({doc.data["type"] for doc in results if doc.data["_depth"] == 0}) == list( + {doc.data["type"] for doc in results if doc.data["_depth"] >= 1} + ) diff --git a/uv.lock b/uv.lock index 3ab533424..510bf9282 100644 --- a/uv.lock +++ b/uv.lock @@ -58,11 +58,11 @@ wheels = [ 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