> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# FAISS

> Using FAISS as a vector database provider

## Overview

FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. It supports local file-based storage with HNSW, IVF\_FLAT, and FLAT index types, plus quantization options.

**Provider Class:** `FaissProvider`\
**Config Class:** `FaissConfig`

## Install

<Note>
  Install the FAISS optional dependency group:

  ```bash theme={null}
  uv pip install "upsonic[faiss]"
  ```
</Note>

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import FaissProvider, FaissConfig, HNSWIndexConfig

# Setup embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# Create FAISS configuration
config = FaissConfig(
    collection_name="my_collection",
    vector_size=1536,
    db_path="./faiss_db",
    index=HNSWIndexConfig(m=16, ef_construction=200),
    normalize_vectors=True
)
vectordb = FaissProvider(config)

# Create knowledge base
kb = KnowledgeBase(
    sources="document.pdf",
    embedding_provider=embedding,
    vectordb=vectordb
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="Search the documents",
    context=[kb]
)
result = agent.do(task)
```

## Parameters

### Base Parameters (from BaseVectorDBConfig)

| Parameter | Type | Description | Default | Required |
| - | - | - | - | - |
| `collection_name` | `str` | Name of the collection | `"default_collection"` | No |
| `vector_size` | `int` | Dimension of vectors | - | **Yes** |
| `distance_metric` | `DistanceMetric` | Similarity metric (`COSINE`, `EUCLIDEAN`, `DOT_PRODUCT`) | `COSINE` | No |
| `recreate_if_exists` | `bool` | Recreate collection if it exists | `False` | No |
| `default_top_k` | `int` | Default number of results | `10` | No |
| `default_similarity_threshold` | `Optional[float]` | Minimum similarity score (0.0-1.0) | `None` | No |
| `dense_search_enabled` | `bool` | Enable dense vector search | `True` | No |
| `full_text_search_enabled` | `bool` | Enable full-text search | `True` | No |
| `hybrid_search_enabled` | `bool` | Enable hybrid search | `True` | No |
| `default_hybrid_alpha` | `float` | Default alpha for hybrid search (0.0-1.0) | `0.5` | No |
| `default_fusion_method` | `Literal['rrf', 'weighted']` | Default fusion method for hybrid search | `'weighted'` | No |
| `provider_name` | `Optional[str]` | Provider name | `None` | No |
| `provider_description` | `Optional[str]` | Provider description | `None` | No |
| `provider_id` | `Optional[str]` | Provider ID | `None` | No |
| `default_metadata` | `Optional[Dict[str, Any]]` | Default metadata for all records | `None` | No |
| `indexed_fields` | `Optional[List[Union[str, Dict[str, Any]]]]` | Fields to index for filtering | `None` | No |

### FAISS-Specific Parameters

| Parameter | Type | Description | Default | Required |
| - | - | - | - | - |
| `db_path` | `Optional[str]` | Path for persistent storage (required except in-memory) | `None` | No |
| `index` | `IndexConfig` | Index type configuration (`HNSWIndexConfig`, `IVFIndexConfig`, `FlatIndexConfig`) | `HNSWIndexConfig()` | No |
| `normalize_vectors` | `bool` | Auto-normalize vectors for cosine similarity (must be `True` for `COSINE` metric) | `True` | No |
| `quantization_type` | `Optional[Literal['scalar', 'product']]` | Quantization method for compression | `None` | No |
| `quantization_bits` | `int` | Bits for quantization | `8` | No |


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