langchain-endee 1.1.0b2

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.

Sections: Setup | Dense | Hybrid | Multi-Field | Filters | RAG Chain

---

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

Create a Collection

Collections are created with fields= — the same pattern as the Python client. Each field has a name, type, and params.

from langchain_endee import EndeeVectorStore, RetrievalMode
from langchain_core.documents import Document

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

# Dense-only collection (single vector field)
vector_store = EndeeVectorStore(
    embedding=embeddings,
    api_token="your-token",       # from app.endee.io (None for local)
    collection_name="my_collection",
    fields=[
        {
            "name": "dense",
            "type": "vector",
            "params": {
                "dimension": DIMENSION,
                "space_type": "cosine",
                "precision": "int8",
            },
        },
    ],
    force_recreate=True,
)

Endee Local (Docker)

Run Endee locally — no token needed. See GitHub for setup.

docker run -p 8000:8080 -v endee-data:/data endee-oss:latest

vector_store = EndeeVectorStore(
    embedding=embeddings,
    collection_name="local_collection",
    fields=[
        {"name": "dense", "type": "vector",
         "params": {"dimension": DIMENSION, "space_type": "cosine", "precision": "int8"}},
    ],
    base_url="http://localhost:8000/api/v2",
)

Ingest Documents

documents = [
    Document(
        page_content="Python is a high-level programming language known for readability.",
        metadata={"topic": "programming", "language": "python"},
    ),
    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"},
    ),
]

add_texts() — insert into an existing store

ids = vector_store.add_texts(
    texts=[doc.page_content for doc in documents],
    metadatas=[doc.metadata for doc in documents],
)

from_texts() / from_documents() — create + insert in one call

store = EndeeVectorStore.from_texts(
    texts=["Python is great.", "Rust is fast."],
    metadatas=[{"lang": "python"}, {"lang": "rust"}],
    embedding=embeddings,
    api_token="your-token",
    collection_name="my_collection",
    dimension=DIMENSION,
    force_recreate=True,
)

Reconnect to an Existing Collection

vector_store = EndeeVectorStore.from_existing_collection(
    collection_name="my_collection",
    embedding=embeddings,
    api_token="your-token",
)

---

2. Dense Search

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

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

# similarity_search_by_object
query_vec = embeddings.embed_query("programming language safety")
results = vector_store.similarity_search_by_object(embedding=query_vec, k=2)

# Search tuning
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,
)

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

---

3. Hybrid Search

Create a collection with both vector and sparse fields:

from langchain_endee import EndeeModelSparse

sparse = EndeeModelSparse()  # Native BM25

hybrid_store = EndeeVectorStore(
    embedding=embeddings,
    api_token="your-token",
    collection_name="hybrid_collection",
    fields=[
        {"name": "dense", "type": "vector",
         "params": {"dimension": DIMENSION, "space_type": "cosine", "precision": "int8"}},
        {"name": "sparse", "type": "sparse", "sparse_model": "default"},
    ],
    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

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

---

4. Multi-Field & Multi-Vector

Multiple Dense Fields

Use fields= with multiple vector entries, then add_objects() and multi_field_search_with_rerank():

store = EndeeVectorStore(
    embedding=embeddings,
    api_token="your-token",
    collection_name="multi_field",
    fields=[
        {"name": "title",   "type": "vector",
         "params": {"dimension": 384, "space_type": "cosine", "precision": "int8"}},
        {"name": "content", "type": "vector",
         "params": {"dimension": 768, "space_type": "cosine", "precision": "int8"}},
        {"name": "keywords","type": "sparse", "sparse_model": "default"},
    ],
    dense_field_name="title",   # primary field for similarity_search()
    force_recreate=True,
)

# Upsert with per-field data
store.add_objects([{
    "id": "doc1",
    "meta": {"text": "...", "metadata": {...}},
    "filter": {"topic": "ai"},
    "fields": {
        "title":   title_vec,
        "content": content_vec,
        "keywords": {"indices": [10, 42], "values": [0.9, 0.4]},
    },
}])

# Search + fuse with weighted RRF
results = store.multi_field_search_with_rerank(
    fields={
        "title":   {"query": title_vec,   "limit": 20},
        "content": {"query": content_vec, "limit": 20},
    },
    limit=10,
    field_weights={"title": 0.4, "content": 0.6},
)

Multi-Vector (ColBERT-style)

A multi_vector field stores N vectors per object (one per token/chunk):

store = EndeeVectorStore(
    embedding=embeddings,
    api_token="your-token",
    collection_name="colbert_collection",
    fields=[
        {"name": "dense",   "type": "vector",
         "params": {"dimension": 384, "space_type": "cosine", "precision": "int8"}},
        {"name": "colbert", "type": "multi_vector",
         "params": {"dimension": 128, "space_type": "cosine",
                    "precision": "float16", "pooling": "mean"}},
    ],
    force_recreate=True,
)

# Upsert: colbert field gets a list of vectors
store.add_objects([{
    "id": "doc1",
    "meta": {"text": "..."},
    "filter": {"topic": "ai"},
    "fields": {
        "dense":   [0.1, 0.2, ...],                  # 1 vector
        "colbert": [[0.1, ...], [0.2, ...], ...],     # N vectors
    },
}])

# Search: query is also a list of vectors
raw = store.multi_field_search(
    fields={"colbert": {"query": [[q1], [q2], [q3]], "limit": 10}},
)

# Or fuse dense + ColBERT
results = store.multi_field_search_with_rerank(
    fields={
        "dense":   {"query": dense_vec,  "limit": 10},
        "colbert": {"query": token_vecs, "limit": 10},
    },
    limit=5,
    field_weights={"dense": 0.5, "colbert": 0.5},
)

Manual Rerank

from langchain_endee import rerank

raw = store.multi_field_search(fields={...})
fused = rerank(raw, limit=10, field_weights={"title": 0.3, "content": 0.7}, rrf_k=60)

---

5. Filters

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

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

# Multiple filters (AND)
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_kwargs={"k": 3, "filter": [{"topic": {"$eq": "ai"}}]},
)

# get_by_ids / update_filters / delete
docs = vector_store.get_by_ids(["id1", "id2"])

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

vector_store.delete(ids=["id1", "id2"])
vector_store.delete(filter=[{"status": {"$eq": "expired"}}])

---

6. 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)

---

Field Types

Type	Shape per object	Use case
vector	[float, ...]	Standard single-embedding (sentence-transformers, OpenAI)
sparse	{indices: [int], values: [float]}	BM25 / SPLADE keyword matching
multi_vector	[[float, ...], ...]	Token-level (ColBERT), chunk-level embeddings

Constructor Parameters

Parameter	Type	Default	Description
embedding	Embeddings	required	LangChain embedding function
collection_name	str	required	Name of the Endee collection
fields	list[dict]	None	Field definitions (same as Python client)
api_token	str | None	None	From app.endee.io (None for local)
base_url	str | None	None	API base URL (e.g. http://localhost:8000/api/v2)
retrieval_mode	RetrievalMode	DENSE	DENSE or HYBRID
sparse_embedding	SparseEmbeddings | None	None	Sparse model for hybrid search
dense_field_name	str	"dense"	Primary dense field for similarity_search()
sparse_field_name	str	"sparse"	Sparse field for hybrid search
force_recreate	bool	False	Delete and recreate collection if exists
validate_collection_config	bool	True	Validate dimension/config on connect

Links

• Endee Documentation
• Endee GitHub
• Endee Server

License

MIT License