Endee + LlamaIndex Integration

llama-index-vector-stores-endee connects LlamaIndex to the Endee vector database — so you can use LlamaIndex's retrievers, query engines, and filters backed by Endee.

New to Endee? See the Endee Quick Start or the GitHub repo.

New to LlamaIndex? See the LlamaIndex docs.

1. Setup

Installation

pip install llama-index-vector-stores-endee

This installs endee, endee_model, llama-index, and pydantic as core dependencies.

Connecting to Endee

With an API token (Endee Cloud)

Get a token from the Endee Quick Start.

import os
from llama_index.core import Document, StorageContext, VectorStoreIndex, Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index_endee import EndeeVectorStore

Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")

# EndeeVectorStore.from_params() → creates a new index or reconnects to an existing one
vector_store = EndeeVectorStore.from_params(
    api_token=os.getenv("ENDEE_API_TOKEN"),
    index_name="my_index",
    dimension=384,
)

Without a token (local Endee server)

Set up a local server using the Endee GitHub repo.

# No api_token needed for local Endee
vector_store = EndeeVectorStore.from_params(
    index_name="my_index",
    dimension=384,
)

Reconnecting to an existing index

If the index already exists, from_params reconnects — no data loss. You don't need to pass dimension:

vector_store = EndeeVectorStore.from_params(
    api_token="your-token",
    index_name="my_existing_index",
)
# VectorStoreIndex.from_vector_store() → loads existing index for querying
index = VectorStoreIndex.from_vector_store(vector_store)

from_params Parameters

For Endee-specific index parameters (space_type, precision, M, ef_con), see the Endee docs.

2. Dense Search

The default mode when sparse_model is not set.

import os
from llama_index.core import Document, StorageContext, VectorStoreIndex, Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index_endee import EndeeVectorStore

Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")

# EndeeVectorStore.from_params() → creates or reconnects to index
vector_store = EndeeVectorStore.from_params(
    api_token=os.getenv("ENDEE_API_TOKEN"),
    index_name="dense_demo",
    dimension=384,
)

# VectorStoreIndex.from_documents() → chunks, embeds, and calls vector_store.add()
documents = [
    Document(text="Endee is a vector database for AI search.", metadata={"category": "database"}),
    Document(text="LlamaIndex is a data framework for LLM apps.", metadata={"category": "ai"}),
]
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# index.as_retriever().retrieve() → calls vector_store.query()
results = index.as_retriever(similarity_top_k=3).retrieve("Tell me about vector databases")
for node in results:
    print(f"{node.get_score():.4f} | {node.text}")

Loading many documents

from llama_index.core import SimpleDirectoryReader

# Load all files from a directory (PDF, TXT, CSV, etc.)
documents = SimpleDirectoryReader("./data").load_data()

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
print(f"Indexed {len(documents)} documents")

Documents are automatically chunked, embedded, and upserted in batches (default batch_size=100).

3. Dense + Sparse Search

Set sparse_model to enable dense + sparse search.

import os
from llama_index.core import Document, StorageContext, VectorStoreIndex, Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index_endee import EndeeVectorStore

Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")

# EndeeVectorStore.from_params(sparse_model=...) → creates sparse-enabled index
# BM25 sparse search (works out of the box)
vector_store = EndeeVectorStore.from_params(
    api_token=os.getenv("ENDEE_API_TOKEN"),
    index_name="bm25_demo",
    dimension=384,
    sparse_model="endee_bm25",
)

# SPLADE sparse search (requires fastembed)
# vector_store = EndeeVectorStore.from_params(
#     api_token=os.getenv("ENDEE_API_TOKEN"),
#     index_name="splade_demo",
#     dimension=384,
#     sparse_model="default",
# )

# VectorStoreIndex.from_documents() → chunks, embeds, computes sparse vectors, and calls vector_store.add()
documents = [
    Document(text="Endee is a vector database for AI search.", metadata={"category": "database"}),
    Document(text="LlamaIndex is a data framework for LLM apps.", metadata={"category": "ai"}),
]
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# index.as_retriever().retrieve() → calls vector_store.query() with both dense and sparse vectors
results = index.as_retriever(similarity_top_k=3).retrieve("Tell me about vector databases")
for node in results:
    print(f"{node.get_score():.4f} | {node.text}")

Control dense vs sparse balance

Use vector_store_kwargs to pass dense_rrf_weight to vector_store.query():

for weight, label in [(1.0, "dense-only"), (0.5, "balanced"), (0.0, "sparse-only")]:
    retriever = index.as_retriever(
        similarity_top_k=3,
        vector_store_kwargs={"dense_rrf_weight": weight},  # passed to vector_store.query()
    )
    results = retriever.retrieve("privacy vector search")
    print(f"  {label}: {results[0].get_score():.4f} | {results[0].text[:60]}...")

4. Metadata Filtering

Pass filters to as_retriever() — they are converted and forwarded to vector_store.query().

from llama_index.core.vector_stores.types import MetadataFilters, MetadataFilter, FilterOperator

# EQ — exact match
filters = MetadataFilters(
    filters=[MetadataFilter(key="category", value="ai", operator=FilterOperator.EQ)]
)
results = index.as_retriever(similarity_top_k=2, filters=filters).retrieve("machine learning")

# IN — match any in list
filters = MetadataFilters(
    filters=[MetadataFilter(key="category", value=["ai", "database"], operator=FilterOperator.IN)]
)
results = index.as_retriever(similarity_top_k=3, filters=filters).retrieve("vector search")

# Multiple filters (AND logic)
filters = MetadataFilters(filters=[
    MetadataFilter(key="category", value="database", operator=FilterOperator.EQ),
    MetadataFilter(key="type", value="vector", operator=FilterOperator.EQ),
])

Supported operators: EQ and IN.

Additional Features

Query Tuning

Pass tuning parameters via vector_store_kwargs — they are forwarded to vector_store.query():

For Endee-specific query parameters (ef, prefilter_cardinality_threshold, filter_boost_percentage, rrf_rank_constant), see the Endee docs.

Vector Operations

These methods call the Endee SDK directly, bypassing LlamaIndex's query engine:

How LlamaIndex maps to EndeeVectorStore

Links

• Endee Quick Start — install, create index, upsert, query
• Endee GitHub — open-source vector database
• LlamaIndex Docs — framework documentation
• Integration Tutorial — step-by-step walkthrough with diagrams
• Colab Notebook — interactive demo