langchain-endee 0.1.0b3

High Speed Vector Database for Faster and Efficient ANN Searches with LangChain

Endee LangChain Integration

LangChain vector store integration for Endee.

For Endee setup, features, and server docs see docs.endee.io.

---

1. Setup

Install

pip install langchain-endee endee endee-model

Pick an embedding model:

# Option A: Local (no API key)
pip install langchain-huggingface sentence-transformers

# Option B: OpenAI
pip install langchain-openai

For hybrid search with SPLADE (optional):

pip install fastembed

Connect to Endee

Cloud — create a token at app.endee.io:

from langchain_endee import EndeeVectorStore
from langchain_core.documents import Document
from endee import Precision

from langchain_huggingface import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
DIMENSION = 384

# Or OpenAI:
# from langchain_openai import OpenAIEmbeddings
# embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
# DIMENSION = 1536

Local — if running Endee on http://127.0.0.1:8080 (setup guide):

vector_store = EndeeVectorStore(
    embedding=embeddings,
    api_token=None,
    index_name="local_index",
    dimension=DIMENSION,
)

Ingest Documents

Each LangChain Document has page_content (the text to embed) and metadata (key-value pairs for filtering).

documents = [
    Document(
        page_content="Python is a high-level programming language known for readability.",
        metadata={"topic": "programming", "language": "python"},
    ),
    Document(
        page_content="Rust is a systems language focused on safety and speed.",
        metadata={"topic": "programming", "language": "rust"},
    ),
    Document(
        page_content="Machine learning gives systems the ability to learn from data.",
        metadata={"topic": "ai", "field": "ml"},
    ),
    Document(
        page_content="Vector databases store embeddings for fast similarity search.",
        metadata={"topic": "database", "type": "vector"},
    ),
    Document(
        page_content="RAG enhances LLM responses by retrieving relevant documents first.",
        metadata={"topic": "ai", "field": "rag"},
    ),
]

There are three ways to insert:

from_documents() — create index + insert Document objects

vector_store = EndeeVectorStore.from_documents(
    documents=documents,
    embedding=embeddings,
    api_token="your-token",
    index_name="my_index",
    dimension=DIMENSION,
    space_type="cosine",
    precision=Precision.INT16,
    force_recreate=True,
)

from_texts() — create index + insert raw strings

vector_store = EndeeVectorStore.from_texts(
    texts=[
        "Python is a high-level programming language.",
        "Rust is a systems language focused on safety.",
    ],
    metadatas=[
        {"topic": "programming", "language": "python"},
        {"topic": "programming", "language": "rust"},
    ],
    embedding=embeddings,
    api_token="your-token",
    index_name="my_index",
    dimension=DIMENSION,
)

add_texts() — insert into an existing store

new_ids = vector_store.add_texts(
    texts=[
        "Go is designed for scalable services.",
        "TypeScript adds static typing to JavaScript.",
    ],
    metadatas=[
        {"topic": "programming", "language": "go"},
        {"topic": "programming", "language": "typescript"},
    ],
    batch_size=1000,           # vectors per upsert (max 1000)
    embedding_chunk_size=100,  # texts per embedding API call
)
print(f"Inserted IDs: {new_ids}")

Reconnect to an Existing Index

Use from_existing_index() to reconnect without re-ingesting — ideal for production.

vector_store = EndeeVectorStore.from_existing_index(
    index_name="my_index",
    embedding=embeddings,
    api_token="your-token",
)

---

2. Dense Search

similarity_search()

results = vector_store.similarity_search(query="How does RAG work?", k=3)

for doc in results:
    print(f"[{doc.metadata.get('topic')}] {doc.page_content[:70]}")

similarity_search_with_score()

scored = vector_store.similarity_search_with_score(query="neural networks", k=3)

for doc, score in scored:
    print(f"sim={score:.3f}  {doc.page_content[:60]}")

similarity_search_by_vector()

query_vec = embeddings.embed_query("programming language safety")

results = vector_store.similarity_search_by_vector(embedding=query_vec, k=2)

similarity_search_by_vector_with_score()

scored_by_vec = vector_store.similarity_search_by_vector_with_score(
    embedding=query_vec,
    k=3,
    filter=[{"topic": {"$eq": "programming"}}],
)

for doc, score in scored_by_vec:
    print(f"sim={score:.3f}  {doc.page_content[:65]}")

Search tuning

See Endee docs for details on ef, prefilter_cardinality_threshold, and filter_boost_percentage.

results = vector_store.similarity_search(
    query="vector search",
    k=10,
    ef=256,
    filter=[{"topic": {"$eq": "database"}}],
    prefilter_cardinality_threshold=5_000,
    filter_boost_percentage=20,
    include_vectors=False,
)

as_retriever()

retriever = vector_store.as_retriever(search_kwargs={"k": 3})
docs = retriever.invoke("What are vector databases used for?")

RAG Chain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


retriever = vector_store.as_retriever(search_kwargs={"k": 3})
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

prompt = ChatPromptTemplate.from_template(
    "Answer the question based only on the context below.\n\n"
    "Context:\n{context}\n\n"
    "Question: {question}"
)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke("How does vector search work?")
print(answer)

---

3. Hybrid Search

Pass retrieval_mode=RetrievalMode.HYBRID and a sparse_embedding to enable hybrid search. The correct sparse_model is auto-detected.

Sparse Embedding Classes

Class	Model	Install
EndeeModelSparse	Native BM25 (recommended)	included with endee-model
FastEmbedSparse	SPLADE (neural)	pip install fastembed
FastEmbedSparse	Qdrant BM25	pip install fastembed

Create a Hybrid Store

from langchain_endee import EndeeVectorStore, EndeeModelSparse, FastEmbedSparse, RetrievalMode

# Option A: EndeeModelSparse (recommended)
sparse = EndeeModelSparse()

# Option B: FastEmbedSparse with SPLADE
# sparse = FastEmbedSparse()

# Option C: FastEmbedSparse with BM25
# sparse = FastEmbedSparse(model_name="Qdrant/bm25", batch_size=256)

hybrid_store = EndeeVectorStore.from_documents(
    documents=documents,
    embedding=embeddings,
    api_token="your-token",
    index_name="hybrid_index",
    dimension=DIMENSION,
    space_type="cosine",
    retrieval_mode=RetrievalMode.HYBRID,
    sparse_embedding=sparse,
    force_recreate=True,
)

All search methods automatically use both dense and sparse:

results = hybrid_store.similarity_search("vector database semantic search", k=3)

RRF Tuning

See Endee docs for details on Reciprocal Rank Fusion.

results = hybrid_store.similarity_search_with_score(
    query="vector database semantic search",
    k=3,
    rrf_rank_constant=60,
    dense_rrf_weight=0.7,
)

---

4. Filters

Pass filters as a list of dicts (AND logic). See Endee docs for filter operators ($eq, $in, $range).

Search with filters

results = vector_store.similarity_search(
    query="learning from data",
    k=5,
    filter=[{"topic": {"$eq": "ai"}}],
)

results = vector_store.similarity_search(
    query="safe languages",
    k=5,
    filter=[
        {"topic": {"$eq": "programming"}},
        {"language": {"$in": ["python", "rust"]}},
    ],
)

Retriever with filters

retriever = vector_store.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 3, "filter": [{"topic": {"$eq": "ai"}}]},
)
docs = retriever.invoke("machine learning")

get_by_ids()

docs = vector_store.get_by_ids(["id1", "id2"])  # positional-only

update_filters()

Update filter metadata without re-embedding.

vector_store.update_filters([
    {"id": "id1", "filter": {"topic": "updated", "priority": 1}},
])

delete()

# Delete by IDs
vector_store.delete(ids=["id1", "id2"])

# Delete by filter
vector_store.delete(filter=[{"status": {"$eq": "expired"}}])

---

Constructor Parameters

Parameter	Type	Default	Description
embedding	Embeddings	required	LangChain embedding function
index_name	str	required	Name of the Endee index
api_token	str | None	None	From app.endee.io (None for local)
dimension	int | None	None	Vector dimension (required for new indexes)
space_type	str	"cosine"	"cosine", "l2", or "ip"
precision	str	Precision.INT16	See Endee docs
M	int	16	See Endee docs
ef_con	int	128	See Endee docs
retrieval_mode	RetrievalMode	DENSE	DENSE or HYBRID
sparse_embedding	SparseEmbeddings | None	None	Sparse model for hybrid search
max_text_length	int | None	auto-detected	Max text length in tokens
force_recreate	bool	False	Delete and recreate index if exists
validate_index_config	bool	True	Validate dimension/config on connect

Links

• Endee Documentation
• Endee GitHub
• Endee Cloud
• Interactive Demo (Colab)

License

MIT License