vectordb-langchain 0.1.0

LangChain VectorStore integration for VectorDB

vectordb-langchain

LangChain VectorStore integration for VectorDB.

Installation

pip install vectordb-langchain

Quick start

from vectordb_langchain import VectorDBVectorStore

store = VectorDBVectorStore(
    api_key="sk_live_your_key",
    base_url="http://localhost:8888",
    collection="my_docs",
    embedding_model="text-embedding-3-small",
)

# Insert texts with metadata
ids = store.add_texts(
    texts=["LangChain is great", "VectorDB is fast"],
    metadatas=[{"source": "docs"}, {"source": "docs"}],
)

# Search
docs = store.similarity_search("What is LangChain?", k=2)

Constructor parameters

Parameter	Type	Default	Description
api_key	str	required	Bearer token (sk_live_...)
base_url	str	required	VectorDB API URL
collection	str	"default"	Collection name
embedding_model	str | None	None	Server-side model name
embeddings	Embeddings | None	None	LangChain client-side embeddings
k	int	4	Default result count
timeout	float	30.0	HTTP timeout (seconds)

Embedding modes

Server-side (default)

Pass embedding_model — raw text is sent to VectorDB, which embeds it internally.

store = VectorDBVectorStore(
    api_key="sk_live_...",
    base_url="http://localhost:8888",
    embedding_model="text-embedding-3-small",
)

Client-side

Pass a LangChain Embeddings object — vectors are computed locally and raw floats are sent to VectorDB.

from langchain_openai import OpenAIEmbeddings

store = VectorDBVectorStore(
    api_key="sk_live_...",
    base_url="http://localhost:8888",
    embeddings=OpenAIEmbeddings(),
)

Methods

add_texts

ids: List[str] = store.add_texts(
    texts=["doc1", "doc2"],
    metadatas=[{"source": "a"}, {"source": "b"}],
)

similarity_search

docs: List[Document] = store.similarity_search(
    query="your question",
    k=4,
    filter={"source": "wiki"},   # optional metadata filter
)

similarity_search_with_score

results: List[Tuple[Document, float]] = store.similarity_search_with_score(
    query="your question",
    k=4,
)
for doc, score in results:
    print(score, doc.page_content)

similarity_search_by_vector

docs = store.similarity_search_by_vector(
    embedding=[0.1, 0.2, ...],
    k=4,
)

from_texts (class method)

store = VectorDBVectorStore.from_texts(
    texts=["doc1", "doc2"],
    api_key="sk_live_...",
    base_url="http://localhost:8888",
    embedding_model="text-embedding-3-small",
)

Using as a LangChain retriever

retriever = store.as_retriever(search_kwargs={"k": 4})

# Inside a RAG chain
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever)
answer = qa.invoke({"query": "What is VectorDB?"})

Error handling

from vectordb_langchain import VectorDBError

try:
    store.add_texts(["hello"])
except VectorDBError as e:
    print(e.status_code, e.detail)   # e.g. 401, "Invalid or missing API key."

Running tests

pip install "vectordb-langchain[dev]"
pytest tests/ -v --cov=vectordb_langchain

Publishing to PyPI

pip install hatch
hatch build
hatch publish

License

MIT