docpipe-sdk 0.5.1

Unified document parsing, structured extraction, vector ingestion, and RAG pipeline SDK

docpipe

Unified document parsing, structured extraction, vector ingestion, and RAG pipeline SDK.

Overview

docpipe connects document parsing (Docling / GLM-OCR), LLM-based structured extraction (LangExtract + LangChain), vector ingestion (pgvector or optional turbovec), and RAG querying into a single composable pipeline.

Four pipelines, composable together:

1. Parse — Unstructured docs → parsed text/markdown
2. Extract — Text → structured entities via LLM
3. Ingest — Chunks → embeddings → your vector store
4. RAG — Questions → grounded answers with citations (six retrieval strategies)

docpipe never stores your data. It connects to your infrastructure and gets out of the way.

Full documentation (install extras, Docker, API reference, RAG strategies, observability, turbovec, plugins): docpipe docs · Marketing site

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Install

pip install docpipe-sdk
# API server + OpenTelemetry (optional)
pip install "docpipe-sdk[server,observability]"

Optional extras (docling, openai, google, pgvector, turbovec, rag, rerank, http, all, …) are listed on the Install guide.

For unreleased commits: pip install git+https://github.com/thesunnysinha/docpipe.git

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Quick start

import docpipe

# Parse
doc = docpipe.parse("invoice.pdf")
print(doc.markdown)

# Extract
schema = docpipe.ExtractionSchema(
    description="Extract invoice line items with amounts",
    model_id="gemini-2.5-flash",
)
results = docpipe.extract(doc.text, schema)

# Ingest + RAG (configure your DB + providers)
config = docpipe.IngestionConfig(
    connection_string="postgresql://user:pass@localhost:5432/mydb",
    table_name="invoices",
    embedding_provider="openai",
    embedding_model="text-embedding-3-small",
)
docpipe.ingest("invoice.pdf", config=config)

rag_config = docpipe.RAGConfig(
    connection_string=config.connection_string,
    table_name=config.table_name,
    embedding_provider="openai",
    embedding_model="text-embedding-3-small",
    llm_provider="openai",
    llm_model="gpt-4o",
    strategy="hyde",
)
result = docpipe.query("What is the total on the invoice?", config=rag_config)
print(result.answer)

CLI: docpipe parse, docpipe ingest, docpipe rag query, docpipe serve — see CLI & API server.

Docker: docker pull ghcr.io/thesunnysinha/docpipe:latest — compose examples and env vars are in the Docker guide and .env.example.

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Learn more

Topic	Where
Install extras & providers	docs
REST API (/ingest, /rag/query, /rag/stream, …)	docs
RAG strategies (naive, hyde, hybrid, auto, …)	docs
Observability (OTEL, Prometheus, JSON logs)	docs · .env.example
turbovec (local file indices)	docs
Custom parsers / extractors	CONTRIBUTING.md
Jingo sidecar integration	Jingo

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License

MIT — see LICENSE.