llama-index-vector-stores-endee 0.1.1

Vector Database for Fast ANN Searches

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

Parameter	Description	Default
api_token	Endee API token (get one)	None (local config)
index_name	Index name	required
dimension	Vector dimension (must match your embedding model)	required for new indexes
sparse_model	None (dense), "endee_bm25" (BM25), "default" (SPLADE)	None
batch_size	Vectors per upsert	100

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.

sparse_model value	Encoder	Install
"endee_bm25"	BM25 via endee_model	included (core dep)
"default"	SPLADE++ via fastembed	pip install llama-index-vector-stores-endee[splade]

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]}...")

dense_rrf_weight	Effect
1.0	Dense only
0.5	Balanced (default)
0.0	Sparse only

---

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():

Parameter	Description	Default
dense_rrf_weight	Dense (1.0) vs sparse (0.0) balance when sparse_model is set	0.5
include_vectors	False to skip returning embeddings	True

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:

Method	What it does
vector_store.fetch(["node-id-1"])	Fetch vectors by ID → calls Index.get_vector()
vector_store.update_filters([{"id": "...", "filter": {...}}])	Update filter metadata → calls Index.update_filters()
vector_store.delete_vector("node-id-1")	Delete by vector ID → calls Index.delete_vector()
vector_store.delete(ref_doc_id="doc-uuid")	Delete by source document → calls Index.delete_with_filter()
vector_store.clear()	Delete the entire index and all its vectors → calls Client.delete_index()
vector_store.describe()	Index metadata → calls Index.describe()
vector_store.client	Direct access to the Endee SDK Index object

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How LlamaIndex maps to EndeeVectorStore

LlamaIndex call	EndeeVectorStore method	Endee SDK call
VectorStoreIndex.from_documents(docs, ...)	vector_store.add(nodes)	Index.upsert()
index.as_retriever().retrieve("query")	vector_store.query(query)	Index.query()
EndeeVectorStore.from_params(...)	creates or reconnects	Endee.create_index() / Endee.get_index()

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Links

• Endee Quick Start — install, create index, upsert, query
• Endee GitHub — open-source vector database
• LlamaIndex Docs — framework documentation
• Colab Notebook — interactive demo