llama-stack-mirror/docs/source/providers/vector_io/sqlite-vec.md

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SQLite-Vec

SQLite-Vec is an inline vector database provider for Llama Stack. It allows you to store and query vectors directly within an SQLite database. That means you're not limited to storing vectors in memory or in a separate service.

Features

• Lightweight and easy to use
• Fully integrated with Llama Stacks
• Uses disk-based storage for persistence, allowing for larger vector storage

Comparison to Faiss

The choice between Faiss and sqlite-vec should be made based on the needs of your application, as they have different strengths.

Choosing the Right Provider

Scenario	Recommended Tool	Reason
Online Analytical Processing (OLAP)	Faiss	Fast, in-memory searches
Online Transaction Processing (OLTP)	sqlite-vec	Frequent writes and reads
Frequent writes	sqlite-vec	Efficient disk-based storage and incremental indexing
Large datasets	sqlite-vec	Disk-based storage for larger vector storage
Datasets that can fit in memory, frequent reads	Faiss	Optimized for speed, indexing, and GPU acceleration

Empirical Example

Consider the histogram below in which 10,000 randomly generated strings were inserted in batches of 100 into both Faiss and sqlite-vec using client.tool_runtime.rag_tool.insert().

:alt: Comparison of SQLite-Vec and Faiss write times
:width: 400px

You will notice that the average write time for sqlite-vec was 788ms, compared to 47,640ms for Faiss. While the number is jarring, if you look at the distribution, you can see that it is rather uniformly spread across the [1500, 100000] interval.

Looking at each individual write in the order that the documents are inserted you'll see the increase in write speed as Faiss reindexes the vectors after each write.

:alt: Comparison of SQLite-Vec and Faiss write times
:width: 400px

In comparison, the read times for Faiss was on average 10% faster than sqlite-vec. The modes of the two distributions highlight the differences much further where Faiss will likely yield faster read performance.

:alt: Comparison of SQLite-Vec and Faiss read times
:width: 400px

Usage

To use sqlite-vec in your Llama Stack project, follow these steps:

1. Install the necessary dependencies.
2. Configure your Llama Stack project to use SQLite-Vec.
3. Start storing and querying vectors.

The SQLite-vec provider supports three search modes:

1. Vector Search (mode="vector"): Performs pure vector similarity search using the embeddings.
2. Keyword Search (mode="keyword"): Performs full-text search using SQLite's FTS5.
3. Hybrid Search (mode="hybrid"): Combines both vector and keyword search for better results. First performs keyword search to get candidate matches, then applies vector similarity search on those candidates.

Example with hybrid search:

response = await vector_io.query_chunks(
    vector_db_id="my_db",
    query="your query here",
    params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
)

Example with explicit vector search:

response = await vector_io.query_chunks(
    vector_db_id="my_db",
    query="your query here",
    params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
)

Example with keyword search:

response = await vector_io.query_chunks(
    vector_db_id="my_db",
    query="your query here",
    params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
)

Supported Search Modes

The sqlite-vec provider supports both vector-based and keyword-based (full-text) search modes.

When using the RAGTool interface, you can specify the desired search behavior via the mode parameter in RAGQueryConfig. For example:

from llama_stack.apis.tool_runtime.rag import RAGQueryConfig

query_config = RAGQueryConfig(max_chunks=6, mode="vector")

results = client.tool_runtime.rag_tool.query(
    vector_db_ids=[vector_db_id],
    content="what is torchtune",
    query_config=query_config,
)

Installation

You can install SQLite-Vec using pip:

pip install sqlite-vec

Documentation

See sqlite-vec's GitHub repo for more details about sqlite-vec in general.