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llama-stack-mirror/docs/docs/providers/vector_io/inline_sqlite-vec.mdx
Alexey Rybak d23865757f
docs: provider and distro codegen migration (#3531) 
# What does this PR do?

<!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. -->

<!-- If resolving an issue, uncomment and update the line below -->

<!-- Closes #[issue-number] -->

- Updates provider and distro codegen to handle the new format
- Migrates provider and distro files to the new format

## Test Plan

- Manual testing

<!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
2025-09-24 14:01:29 -07:00

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---
description: |
[SQLite-Vec](https://github.com/asg017/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()`.
```{image} ../../../../_static/providers/vector_io/write_time_comparison_sqlite-vec-faiss.png
: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.
```{image} ../../../../_static/providers/vector_io/write_time_sequence_sqlite-vec-faiss.png
: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.
```{image} ../../../../_static/providers/vector_io/read_time_comparison_sqlite-vec-faiss.png
: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:
```python
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},
)
# Using RRF ranker
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,
"ranker": {"type": "rrf", "impact_factor": 60.0},
},
)
# Using weighted ranker
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,
"ranker": {"type": "weighted", "alpha": 0.7}, # 70% vector, 30% keyword
},
)
```
Example with explicit vector search:
```python
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:
```python
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 vector store supports three search modes:
1. **Vector Search** (`mode="vector"`): Uses vector similarity to find relevant chunks
2. **Keyword Search** (`mode="keyword"`): Uses keyword matching to find relevant chunks
3. **Hybrid Search** (`mode="hybrid"`): Combines both vector and keyword scores using a ranker
### Hybrid Search
Hybrid search combines the strengths of both vector and keyword search by:
- Computing vector similarity scores
- Computing keyword match scores
- Using a ranker to combine these scores
Two ranker types are supported:
1. **RRF (Reciprocal Rank Fusion)**:
- Combines ranks from both vector and keyword results
- Uses an impact factor (default: 60.0) to control the weight of higher-ranked results
- Good for balancing between vector and keyword results
- The default impact factor of 60.0 comes from the original RRF paper by Cormack et al. (2009) [^1], which found this value to provide optimal performance across various retrieval tasks
2. **Weighted**:
- Linearly combines normalized vector and keyword scores
- Uses an alpha parameter (0-1) to control the blend:
- alpha=0: Only use keyword scores
- alpha=1: Only use vector scores
- alpha=0.5: Equal weight to both (default)
Example using RAGQueryConfig with different search modes:
```python
from llama_stack.apis.tools import RAGQueryConfig, RRFRanker, WeightedRanker
# Vector search
config = RAGQueryConfig(mode="vector", max_chunks=5)
# Keyword search
config = RAGQueryConfig(mode="keyword", max_chunks=5)
# Hybrid search with custom RRF ranker
config = RAGQueryConfig(
mode="hybrid",
max_chunks=5,
ranker=RRFRanker(impact_factor=50.0), # Custom impact factor
)
# Hybrid search with weighted ranker
config = RAGQueryConfig(
mode="hybrid",
max_chunks=5,
ranker=WeightedRanker(alpha=0.7), # 70% vector, 30% keyword
)
# Hybrid search with default RRF ranker
config = RAGQueryConfig(
mode="hybrid", max_chunks=5
) # Will use RRF with impact_factor=60.0
```
Note: The ranker configuration is only used in hybrid mode. For vector or keyword modes, the ranker parameter is ignored.
## Installation
You can install SQLite-Vec using pip:
```bash
pip install sqlite-vec
```
## Documentation
See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) for more details about sqlite-vec in general.
[^1]: Cormack, G. V., Clarke, C. L., & Buettcher, S. (2009). [Reciprocal rank fusion outperforms condorcet and individual rank learning methods](https://dl.acm.org/doi/10.1145/1571941.1572114). In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 758-759).
sidebar_label: Sqlite-Vec
title: inline::sqlite-vec
---
# inline::sqlite-vec
## Description
[SQLite-Vec](https://github.com/asg017/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()`.
```{image} ../../../../_static/providers/vector_io/write_time_comparison_sqlite-vec-faiss.png
: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.
```{image} ../../../../_static/providers/vector_io/write_time_sequence_sqlite-vec-faiss.png
: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.
```{image} ../../../../_static/providers/vector_io/read_time_comparison_sqlite-vec-faiss.png
: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:
```python
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},
)
# Using RRF ranker
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,
"ranker": {"type": "rrf", "impact_factor": 60.0},
},
)
# Using weighted ranker
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,
"ranker": {"type": "weighted", "alpha": 0.7}, # 70% vector, 30% keyword
},
)
```
Example with explicit vector search:
```python
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:
```python
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 vector store supports three search modes:
1. **Vector Search** (`mode="vector"`): Uses vector similarity to find relevant chunks
2. **Keyword Search** (`mode="keyword"`): Uses keyword matching to find relevant chunks
3. **Hybrid Search** (`mode="hybrid"`): Combines both vector and keyword scores using a ranker
### Hybrid Search
Hybrid search combines the strengths of both vector and keyword search by:
- Computing vector similarity scores
- Computing keyword match scores
- Using a ranker to combine these scores
Two ranker types are supported:
1. **RRF (Reciprocal Rank Fusion)**:
- Combines ranks from both vector and keyword results
- Uses an impact factor (default: 60.0) to control the weight of higher-ranked results
- Good for balancing between vector and keyword results
- The default impact factor of 60.0 comes from the original RRF paper by Cormack et al. (2009) [^1], which found this value to provide optimal performance across various retrieval tasks
2. **Weighted**:
- Linearly combines normalized vector and keyword scores
- Uses an alpha parameter (0-1) to control the blend:
- alpha=0: Only use keyword scores
- alpha=1: Only use vector scores
- alpha=0.5: Equal weight to both (default)
Example using RAGQueryConfig with different search modes:
```python
from llama_stack.apis.tools import RAGQueryConfig, RRFRanker, WeightedRanker
# Vector search
config = RAGQueryConfig(mode="vector", max_chunks=5)
# Keyword search
config = RAGQueryConfig(mode="keyword", max_chunks=5)
# Hybrid search with custom RRF ranker
config = RAGQueryConfig(
mode="hybrid",
max_chunks=5,
ranker=RRFRanker(impact_factor=50.0), # Custom impact factor
)
# Hybrid search with weighted ranker
config = RAGQueryConfig(
mode="hybrid",
max_chunks=5,
ranker=WeightedRanker(alpha=0.7), # 70% vector, 30% keyword
)
# Hybrid search with default RRF ranker
config = RAGQueryConfig(
mode="hybrid", max_chunks=5
) # Will use RRF with impact_factor=60.0
```
Note: The ranker configuration is only used in hybrid mode. For vector or keyword modes, the ranker parameter is ignored.
## Installation
You can install SQLite-Vec using pip:
```bash
pip install sqlite-vec
```
## Documentation
See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) for more details about sqlite-vec in general.
[^1]: Cormack, G. V., Clarke, C. L., & Buettcher, S. (2009). [Reciprocal rank fusion outperforms condorcet and individual rank learning methods](https://dl.acm.org/doi/10.1145/1571941.1572114). In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 758-759).
## Configuration
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `db_path` | `<class 'str'>` | No | | Path to the SQLite database file |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
## Sample Configuration
```yaml
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db
```
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