ingestlib 0.3.0

Self-hosted document intelligence for RAG — parse, classify, and split PDF/DOCX/PPTX into searchable, cited, retrieval-ready chunks

ingestlib

Self-hosted document intelligence for RAG pipelines. One library that takes a raw document — PDF, DOCX, PPTX — and produces searchable, cited, retrieval-ready chunks: the territory of LlamaParse / Reducto / Unstructured.io, running on your own stack.

from ingestlib.services import ingest, retrieve

ingest("finance-10k.pdf")            # parse → classify → split → embed → vector store
result = retrieve("what were the total revenues?")
print(result.context)                # ranked chunks, each citing doc · page · section

What it does

Stage	What you get
Parse	Layout-aware markdown per page: tables as HTML (merged cells intact), formulas as LaTeX, charts converted to data tables (estimated values marked ~, printed callouts and growth labels captured), figures extracted as PNG crops with captions and AI descriptions — every block traceable to a bounding box on the page
Classify	Document-type label (invoice, research_paper, …) — open-ended or constrained to your categories, with confidence and alternatives. Works standalone with no OCR
Split	Sections (pages grouped by role: methods, results, …) containing natural chunks — boundaries follow the content, tables never split, each chunk carries a [category › section › heading] breadcrumb in its embedding_text
Ingest	The whole pipeline in one call, every stage persisted to S3, vectors upserted, deduplicated by content checksum
Retrieve	Question → hybrid search (dense embeddings + lexical sparse, merged) → Jina rerank → hits with scores and citations, plus a prompt-ready context block

Engines: PaddleOCR-VL-1.6 (0.9B VLM, runs on your GPU) for layout + recognition, Amazon Nova 2 Lite for judgment (chart reading, review, classification, chunk boundaries), Nova multimodal embeddings, six vector stores (Pinecone, Qdrant, SQLite, Postgres/pgvector, MongoDB, Milvus — all hybrid dense + sparse), S3 for artifacts. ~$0.002/page in LLM spend.

Quickstart

1. Requirements

• Python 3.12+ and uv
• AWS account with Bedrock access (us-east-1): Nova 2 Lite + Nova 2 multimodal embeddings
• Vector database — Pinecone account (serverless, free tier works; the default), a Qdrant server (local docker or Qdrant Cloud), a Postgres with pgvector (RDS/Supabase/Neon or self-hosted), a MongoDB with search (Atlas any tier or 8.2+ self-managed), a Milvus (local docker or Zilliz Cloud) — each just one connection URL — or none at all: the sqlite connector stores vectors in a local file
• Jina AI account for reranking (free tier: 100 RPM)

2. Install

pip install ingestlib          # or: uv add ingestlib

Or work from source:

git clone https://github.com/LangModule/ingestlib.git
cd ingestlib
uv sync

System dependency — LibreOffice (DOCX/PPTX → PDF conversion):

brew install --cask libreoffice          # macOS (binary is `soffice`)
sudo apt install libreoffice-core libreoffice-writer libreoffice-impress   # Linux

3. Start the OCR inference server

Parse runs PaddleOCR-VL-1.6 behind an inference server. First launch downloads ~1.8 GB of weights; later launches load from cache in seconds.

# Apple Silicon (Metal GPU)
uv run python -m mlx_vlm.server --port 8111 --model PaddlePaddle/PaddleOCR-VL-1.6

# NVIDIA (then set paddle_vl.backend: vllm-server in config.yaml)
vllm serve PaddlePaddle/PaddleOCR-VL-1.6 --port 8111

The layout model (PP-DocLayoutV3, ~126 MB) auto-downloads on the first parse.

4. Configure

cp .env.example .env                 # API keys: Jina, plus Pinecone/Qdrant if used (sqlite needs none)
cp config.example.yaml config.yaml   # your AWS profile, bucket name, vector store choice
aws configure --profile your-aws-profile   # Bedrock-enabled credentials

Edit config.yaml: your AWS profile/account, S3 bucket name (globally unique), vector store choice and index/collection names. The S3 bucket and the vector indexes/collection are created automatically on first use — no manual setup.

Config is discovered at call time, never at import: INGESTLIB_CONFIG=/path/to/config.yaml wins, otherwise the working directory and its parents are searched — so installed usage works the same as running inside this repo.

5. Run

from ingestlib.services import ingest, retrieve

r = ingest("report.pdf")
print(r.status, r.category, r.chunks, r.durations)

res = retrieve("what does the report conclude?", top_k=5)
for hit in res.hits:
    print(hit.rerank_score, hit.citation, hit.chunk.heading)

Using the operations directly

Every operation also works standalone:

from ingestlib.operations import parse, classify, split

result = parse("report.pdf")            # ParseResult: pages, regions, figures
print(result.markdown)                  # whole-document markdown
result.save_images("out/")              # extracted figures/charts as PNGs

label = classify("report.pdf")          # no OCR needed — native text + embedded images
chunks = split(result, category=label.category)
for c in chunks.chunks:
    print(c.token_estimate, c.embedding_text.splitlines()[0])

Persistence and vector access are explicit too:

from ingestlib.storage import artifacts, PineconeStore

doc_id = artifacts.save_parse(result)   # S3: source, result.json, page PNGs, crops
artifacts.list_documents()              # registry: filename, pages, category, chunks

Architecture

src/ingestlib/
├── services/       ingest · retrieve          — the product
├── operations/     parse · classify · split   — the tools (each standalone)
├── storage/        artifacts (S3) · base (VectorStore contract) · 6 connectors
│                   (pinecone · qdrant · sqlite · pgvector · mongodb · milvus)
├── foundations/    llm (Bedrock Nova, Jina) · ocr (PaddleOCR-VL)
├── utils/          logger · files
└── config.py       config.yaml + .env → typed configs

Strict downward dependencies. The VectorStore contract means backends drop in as connectors — all six ship hybrid search: Pinecone (dense + hosted sparse model, merged client-side), Qdrant (dense + BM25 with server-side IDF and RRF fusion; local docker or cloud), SQLite (sqlite-vec KNN + built-in FTS5 BM25 with porter stemming, RRF fusion — one local file, no server, no keys), Postgres/pgvector (HNSW cosine + built-in full-text over a generated weighted tsvector, RRF fusion — the extension and table bootstrap automatically), MongoDB (Atlas Vector Search + Atlas Search true BM25, RRF fusion — Atlas any tier or self-managed 8.2+; both search indexes bootstrap automatically), and Milvus (dense ANN + server-computed BM25 sparse, fused server-side with RRF — local docker or Zilliz Cloud). Pick one with vector_store: pinecone | qdrant | sqlite | pgvector | mongodb | milvus in config.yaml. Connection secrets sit in .env together (sqlite needs none) — only the selected connector ever builds a client.

Logging

INGESTLIB_LOG_LEVEL=INFO           # DEBUG | INFO | WARNING | ERROR (default INFO)
INGESTLIB_LOG_THIRD_PARTY=1        # also show paddlex/httpx/botocore chatter
INGESTLIB_LOG_COLOR=0              # disable colored output

Testing

Tests hit real APIs, never mocks. Pure logic runs always; server-hitting suites are opt-in via env gates. The sqlite connector's full suite runs ungated in make test — there is no server, so in-process IS the real thing.

make test                  # fast suite (~230 tests, ~90s; e2e groups skip)
make test-parse            # parse e2e            (needs VL server + Bedrock)
make test-classify         # classify e2e         (needs Bedrock)
make test-split            # split e2e            (needs Bedrock)
make test-s3               # artifact store e2e   (needs AWS)
make test-pinecone         # vector connector e2e (needs Pinecone + Bedrock)
make test-qdrant           # vector connector e2e (needs a Qdrant server + Bedrock)
make test-sqlite           # vector connector suite (no gate — nothing to need)
make test-pgvector         # vector connector e2e (needs Postgres at PGVECTOR_URL)
make test-mongodb          # vector connector e2e (needs MongoDB at MONGODB_URL)
make test-milvus           # vector connector e2e (needs Milvus at MILVUS_URL)
make test-services         # full product e2e     (needs the entire stack)
make test-all              # everything
make eval                  # retrieval quality eval (see below)

Fixture PDFs live in tests/data/pdf/ — 14 real documents (research papers, earnings decks, insurance forms, timetables, 10-Ks).

Retrieval quality

Beyond pass/fail tests, evals/ measures retrieval quality: 22 ground-truth questions over the fixture corpus, run through the real retrieve() flow under dense/hybrid × rerank on/off, scored by hit@k and MRR. Measured so far (consistent across all six connectors): with reranking, every answer lands in the top 3 hits (hit@3 = 1.00); hit@1 ranges 0.86–1.00 across runs. Each run saves a timestamped snapshot to evals/results/, so quality changes are visible over time.

Disk footprint

Component	Size	Location
Python deps	~3 GB	.venv/
PaddleOCR-VL-1.6 weights	~1.8 GB	~/.cache/huggingface/hub/
PP-DocLayoutV3	~126 MB	~/.paddlex/official_models/
LibreOffice	~600 MB	system

Scope

English documents; PDF / DOCX / PPTX input. Images, charts, and tables inside documents are fully extracted and interpreted; direct image files and handwriting are out of scope by design.

Roadmap

• Hover-highlight review UI (bbox provenance already shipped for it)
• Extract: schema-driven field extraction with source provenance

License

See LICENSE.