QdrantVectorStore Class — langchain Architecture

Source Code

libs/partners/qdrant/langchain_qdrant/qdrant.py lines 36–1295

class QdrantVectorStore(VectorStore):
    """Qdrant vector store integration.

    Setup:
        Install `langchain-qdrant` package.

        ```bash
        pip install -qU langchain-qdrant
        ```

    Key init args — indexing params:
        collection_name:
            Name of the collection.
        embedding:
            Embedding function to use.
        sparse_embedding:
            Optional sparse embedding function to use.

    Key init args — client params:
        client:
            Qdrant client to use.
        retrieval_mode:
            Retrieval mode to use.

    Instantiate:
        ```python
        from langchain_qdrant import QdrantVectorStore
        from qdrant_client import QdrantClient
        from qdrant_client.http.models import Distance, VectorParams
        from langchain_openai import OpenAIEmbeddings

        client = QdrantClient(":memory:")

        client.create_collection(
            collection_name="demo_collection",
            vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
        )

        vector_store = QdrantVectorStore(
            client=client,
            collection_name="demo_collection",
            embedding=OpenAIEmbeddings(),
        )
        ```

    Add Documents:
        ```python
        from langchain_core.documents import Document
        from uuid import uuid4

        document_1 = Document(page_content="foo", metadata={"baz": "bar"})
        document_2 = Document(page_content="thud", metadata={"bar": "baz"})
        document_3 = Document(page_content="i will be deleted :(")

        documents = [document_1, document_2, document_3]
        ids = [str(uuid4()) for _ in range(len(documents))]
        vector_store.add_documents(documents=documents, ids=ids)
        ```

    Delete Documents:
        ```python
        vector_store.delete(ids=[ids[-1]])
        ```

    Search:
        ```python
        results = vector_store.similarity_search(
            query="thud",
            k=1,
        )
        for doc in results:
            print(f"* {doc.page_content} [{doc.metadata}]")
        ```

        ```python
        *thud[
            {
                "bar": "baz",
                "_id": "0d706099-6dd9-412a-9df6-a71043e020de",
                "_collection_name": "demo_collection",
            }