langchain-graph-retriever

import os from langchain_community.vectorstores import Chroma from langchain_community.embeddings import OpenAIEmbeddings from langchain_core.documents import Document from langchain_graph_retriever import GraphRetriever # Set your OpenAI API key. This example uses a mock key if not set in environment. # For actual use, ensure OPENAI_API_KEY is properly configured. os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-YOUR_OPENAI_API_KEY") if not os.environ["OPENAI_API_KEY"].startswith("sk-") or os.environ["OPENAI_API_KEY"] == "sk-YOUR_OPENAI_API_KEY": print("OPENAI_API_KEY not set or is placeholder. Skipping quickstart execution.") print("Please set the OPENAI_API_KEY environment variable for a functional example.") else: try: # 1. Initialize Embeddings (requires 'openai' package) embeddings = OpenAIEmbeddings() # 2. Create a dummy vector store (in-memory Chroma for demonstration, requires 'langchain-community' package) # In a real application, this would be populated with documents and their graph relationships documents = [ Document(page_content="The quick brown fox jumps over the lazy dog.", metadata={"doc_id": "doc1"}), Document(page_content="The dog is lazy and enjoys napping.", metadata={"doc_id": "doc2"}), Document(page_content="A fox is a small omnivorous mammal.", metadata={"doc_id": "doc3"}), Document(page_content="Dogs are domesticated canids.", metadata={"doc_id": "doc4"}) ] vectorstore = Chroma.from_documents(documents, embeddings) # 3. Initialize the GraphRetriever # The GraphRetriever works by taking initial search results from the vector store # and then traversing the graph of related documents. The 'k' and 'depth' parameters # control the initial vector search and subsequent graph traversal depth. retriever = GraphRetriever( vectorstore=vectorstore, k=2, # Number of initial documents to retrieve from the vectorstore depth=1 # How many levels deep to traverse the graph from the initial documents # (requires graph relationships to be defined in your actual vector store/graph DB) ) # 4. Perform a retrieval query = "Tell me about animals." relevant_docs = retriever.get_relevant_documents(query) print(f"\nRetrieved {len(relevant_docs)} documents:") for i, doc in enumerate(relevant_docs): print(f"--- Document {i+1} ---") print(f"Content: {doc.page_content}") print(f"Metadata: {doc.metadata}") except Exception as e: print(f"An error occurred during quickstart execution: {e}")