langchain-qdrant

import os from qdrant_client import QdrantClient from qdrant_client.http.models import Distance, VectorParams from langchain_qdrant import QdrantVectorStore from langchain_openai import OpenAIEmbeddings # Requires `pip install langchain-openai` from langchain_core.documents import Document # Set your OpenAI API key for embeddings (replace with your preferred embedding model) os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-...") # 1. Initialize an in-memory Qdrant client client = QdrantClient(":memory:") # 2. Define collection parameters (e.g., for OpenAI embeddings) collection_name = "my_langchain_collection" embedding_dimension = 1536 # For text-embedding-ada-002 or text-embedding-3-small client.create_collection( collection_name=collection_name, vectors_config=VectorParams(size=embedding_dimension, distance=Distance.COSINE), ) # 3. Initialize the embedding model embeddings = OpenAIEmbeddings() # 4. Create a QdrantVectorStore instance vector_store = QdrantVectorStore( client=client, collection_name=collection_name, embedding=embeddings, ) # 5. Add documents to the vector store documents = [ Document(page_content="The quick brown fox jumps over the lazy dog.", metadata={"source": "sentence 1"}), Document(page_content="LangChain provides many integrations with vector stores.", metadata={"source": "sentence 2"}), Document(page_content="Qdrant is an open-source vector database.", metadata={"source": "sentence 3"}), ] vector_store.add_documents(documents) # 6. Perform a similarity search query = "What is Qdrant?" found_docs = vector_store.similarity_search(query, k=1) print(f"Query: {query}") for doc in found_docs: print(f"- Content: {doc.page_content}, Metadata: {doc.metadata}")