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LakeFlow Backend

FastAPI backend and data pipelines for LakeFlow: ingest, staging, processing, embedding, and semantic search.


Overview

  • API: FastAPI app (lakeflow.main:app) — auth, search, embed, pipeline trigger, Qdrant proxy, system.
  • Data Lake: Layered zones under LAKEFLOW_DATA_BASE_PATH: 000_inbox → 100_raw → 200_staging → 300_processed → 400_embeddings → 500_catalog.
  • Vector store: Qdrant (default collection lakeflow_chunks). Embeddings via sentence-transformers (e.g. all-MiniLM-L6-v2).

Requirements

  • Python ≥ 3.10
  • Qdrant (e.g. Docker: docker compose up -d qdrant)
  • See requirements.txt for Python dependencies

Install & run

With Docker (from the LakeFlow repo root where docker-compose.yml is):

docker compose up --build
# API: http://localhost:8011

Local dev (from repo root, go to lakeflow):

cd lakeflow
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
pip install -e .
# Create/copy .env (repo root or lakeflow) with LAKEFLOW_DATA_BASE_PATH, QDRANT_HOST, etc.
python -m uvicorn lakeflow.main:app --reload --port 8011
  • If you get bad interpreter (venv points to wrong Python): remove .venv, run python3 -m venv .venv again then pip install -r requirements.txt and pip install -e ..

  • If you get Address already in use (port 8011 in use): free the port then restart the server — lsof -ti :8011 | xargs kill -9

  • Swagger: http://localhost:8011/docs

  • ReDoc: http://localhost:8011/redoc

  • Embed API: docs/API_EMBED.md — POST /search/embed


Pipeline steps (CLI)

Run from the lakeflow directory (with venv activated and LAKEFLOW_DATA_BASE_PATH set in .env or environment).

Step Command Output
0 – Inbox → Raw python -m lakeflow.scripts.step0_inbox Hash, dedup, catalog
1 – Staging python -m lakeflow.scripts.step1_raw pdf_profile.json, validation.json
2 – Processed python -m lakeflow.scripts.step2_staging clean_text.txt, chunks.json, tables.json
3 – Embeddings python -m lakeflow.scripts.step3_processed_files embeddings.npy, chunks_meta.json
4 – Qdrant python -m lakeflow.scripts.step3_processed_qdrant Points in Qdrant

Or use the Streamlit UI (Pipeline Runner) when LAKEFLOW_MODE=DEV.


Main APIs

  • POST /auth/login – Demo login (e.g. admin / admin123), returns JWT.
  • POST /search/embed – Body {"text": "..."} → vector, embedding, dim.
  • POST /search/semantic – Body {"query": "...", "top_k": 5, "qdrant_url": "...", "collection_name": "..."}.
  • POST /search/qa – RAG-style Q&A (semantic search + LLM). Optional.
  • POST /pipeline/run – Run a pipeline step (auth required).
  • GET/POST /qdrant/ – Qdrant collections and points (proxy).

Design notes

  • Idempotent pipelines; deterministic UUIDs for Qdrant.
  • SQLite without WAL (NAS-friendly).
  • No full-file load for large files; streaming where applicable.

License

Same as the root repository.

Metadata

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