Vector Indexing¶

After chunking, embed and index your documents for similarity search.

Supported Vector Stores¶

Store	Backend	Notes
Chroma	Local / Server	Default. No extra setup needed.
FAISS	Local	In-memory, fast, no server.
Qdrant	Server	Production-grade, scalable.

Supported Embedding Providers¶

Provider	Models
`huggingface`	`BAAI/bge-base-en-v1.5` (default)
`openai`	`text-embedding-3-small`, `text-embedding-3-large`
`cohere`	`embed-english-v3.0`

Embed via REST API¶

# After finalizing HITL review:
curl -X POST http://localhost:8000/jobs/{job_id}/embed \
  -H "X-API-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "provider": "huggingface",
    "model": "BAAI/bge-base-en-v1.5",
    "vector_db": "chroma",
    "collection_name": "my_docs"
  }'

Search¶

curl -X POST http://localhost:8000/search \
  -H "X-API-Key: your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "job_id": "abc123",
    "query": "What is the revenue for Q3?",
    "top_k": 5
  }'

Index Versions¶

Every embed call creates a new index version so you can roll back:

# List index versions
curl http://localhost:8000/jobs/{job_id} -H "X-API-Key: your-key"

# Search against a specific version
curl -X POST http://localhost:8000/search \
  -d '{"job_id": "abc", "query": "...", "index_version": "v1"}'