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19
docs/docs/concepts/file_operations_vector_stores.md → docs/docs/concepts/file_operations_vector_stores.mdx
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@ -85,15 +85,17 @@ The following vector store providers support file operations:
- **FAISS**: Fast in-memory vector similarity search
- **SQLite-vec**: Disk-based storage with hybrid search capabilities
- **Milvus**: High-performance vector database with advanced indexing
### Remote Providers (Hosted)
- **ChromaDB**: Vector database with metadata filtering
- **Qdrant**: Vector similarity search with payload filtering
- **Weaviate**: Vector database with GraphQL interface
- **Postgres (PGVector)**: Vector extensions for PostgreSQL
### Both Inline & Remote Providers
- **Milvus**: High-performance vector database with advanced indexing
- **Qdrant**: Vector similarity search with payload filtering
## File Processing Pipeline
### 1. File Upload
@ -172,11 +174,6 @@ You can customize chunking when attaching files:
```python
from llama_stack.apis.vector_io import VectorStoreChunkingStrategy
# Custom chunking strategy
chunking_strategy = VectorStoreChunkingStrategy(
type="custom", max_chunk_size_tokens=1000, chunk_overlap_tokens=200
)
# Attach file with custom chunking
file_attach_response = await client.vector_stores.files.create(
vector_store_id=vector_store.id,
@ -333,7 +330,7 @@ results = await client.vector_stores.search(
### Hybrid Search
[SQLite-vec](../providers/vector_io/inline_sqlite-vec.md), [pgvector](../providers/vector_io/remote_pgvector.md), and [Milvus](../providers/vector_io/inline_milvus.md) support combining vector and keyword search.
[SQLite-vec](../providers/vector_io/inline_sqlite-vec.mdx), [pgvector](../providers/vector_io/remote_pgvector.mdx), and [Milvus](../providers/vector_io/inline_milvus.mdx) support combining vector and keyword search.
## Performance Considerations
@ -418,6 +415,6 @@ async def analyze_document(vector_store_id, file_id):
## Next Steps
- Explore the [Files API documentation](../apis/files.md) for detailed API reference
- Check [Vector Store Providers](../providers/vector_io/index.md) for specific implementation details
- Review [Getting Started](../getting_started/index.md) for quick setup instructions
- Explore the [Files API documentation](../../providers/files/files.mdx) for detailed API reference
- Check [Vector Store Providers](../providers/vector_io/index.mdx) for specific implementation details
- Review [Getting Started](../getting_started/quickstart.mdx) for quick setup instructions

5
docs/docs/providers/files/openai_file_operations_support.md
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@ -2,7 +2,7 @@
## Overview
This document provides a comprehensive overview of file operations and Vector Store API support across all available vector store providers in Llama Stack. As of release 0.2.14, the following providers support full file operations integration.
This document provides a comprehensive overview of file operations and Vector Store API support across all available vector store providers in Llama Stack. As of release 0.2.24, the following providers support full file operations integration.
## Supported Providers
@ -76,7 +76,6 @@ All supported providers offer the following file operations capabilities:
- **Default**: 800 tokens with 400 token overlap
- **Custom**: Configurable chunk sizes and overlap
- **Semantic**: Intelligent boundary detection
- **Static**: Fixed-size chunks with overlap
## Provider-Specific Features
@ -285,7 +284,7 @@ Planned improvements for file operations support:
## Support and Resources
- **Documentation**: [File Operations and Vector Store Integration](../concepts/file_operations_vector_stores.md)
- **Documentation**: [File Operations and Vector Store Integration](../../concepts/file_operations_vector_stores.mdx)
- **API Reference**: [Files API](files_api.md)
- **Provider Docs**: [Vector Store Providers](../vector_io/index.md)
- **Examples**: [Getting Started](../getting_started/index.md)