Sifter
Your documents are a dark database.
Open-source document intelligence engine — schema-driven extraction, NL query, MCP server, Python and TypeScript SDKs. Self-hostable under MIT.
Why not RAG?
RAG is built for retrieval — find me chunks similar to this query. It breaks on homogeneous collections like invoices, contracts, or receipts where every document looks alike and the question is an aggregation, not a search.
Sifter's approach: extract structured fields once (client, date, total), store them as typed records, query with real filters and aggregations. The answer is exact and reproducible — because it's a database query, not a similarity search.
Quickstart
git clone https://github.com/sifter-ai/sifter
cd sifter/code
cp server/.env.example server/.env.local # set SIFTER_DEFAULT_API_KEY (required)
docker compose up -dOpen http://localhost:3000 — create a sift, upload documents, query results.
Python SDK
pip install sifter-aifrom sifter import Sifter
s = Sifter(api_key="sk-...")
sift = s.create_sift("Invoices", "client name, date, total amount")
sift.upload("./invoices/")
sift.wait()
for record in sift.records():
print(record["extracted_data"])
# {"client": "Acme Corp", "date": "2024-01-15", "total_amount": 1500.0}
