Bookwyrm Logo RAG for Unstructured Data

Free Developer Workshop

Build agents users can trust without manual data prep

Building a reliable RAG pipeline shouldn't mean endless time spent cleaning messy PDFs, wrestling with sub-par chunking, or debugging agent hallucinations. Most developers want to bypass the data prep nightmare and focus on building agents that actually deliver value.

That's exactly what you'll learn to do. Join our FREE developer workshop to see how BookWyrm's APIs handle the complex, unrewarding work for you, giving you clean, structured, and query-ready data instantly.

The result? Better data → better retrieval → better agents.

Agenda

Join our FREE developer Workshop

Tuesday, November 11th @ 4pm CET

What you'll learn to master:

RAG Problem Solved

Low-Quality PDF Text

BookWyrm Endpoint & Focus

High-Fidelity PDF Extraction: See how to extractclean, non-messy text from native and image PDFs, including crucial positional information for advanced retrieval.

RAG Problem Solved

Lost Context in Chunking

BookWyrm Endpoint & Focus

Phrasal Chunking: Learn the technique for splitting text into meaningful, semantically balanced chunks that boost retrieval accuracy—moving beyond arbitrary token splits.

RAG Problem Solved

Agent Hallucination

BookWyrm Endpoint & Focus

Citation for Trust: Discover the Cite endpoint and how to ground every answer with traceable reasoning and sources, making your RAG output reliable and accountable.

RAG Problem Solved

Document Overload

BookWyrm Endpoint & Focus

Summarization: Collapse long or noisy documents into concise, usable summaries for efficient embedding and context injection.

RAG Problem Solved

Pipeline Routing

BookWyrm Endpoint & Focus

Classification: Learn how to auto-classify documents by type and structure for intelligent routing and indexing within your pipeline.

RAG Problem Solved

Live App Experience

BookWyrm Endpoint & Focus

Streaming: See how to provide real-time processing and progress updates, allowing your live applications to show partial results immediately.

BookWyrm Features

Endpoints that enable rapid AI agent deployment

We provide fully managed, production-ready endpoints that solve specific, complex developer problems right out of the box.

Feature endpoint:

Extract from PDF

bookwyrm extract-pdf document.pdf --output extracted.json

The problem:

Low-quality text extraction from native/image PDFs that breaks context and requires manual cleanup.

Core value:

High-fidelity text output: Extracts clean, non-messy text from any PDF. Includes crucial position information (e.g., page and coordinates) for advanced retrieval and UI highlighting.

Feature endpoint:

Citation (Deep Reader)

bookwyrm cite "What is the main theme?" --url https://example.com/chunks.jsonl

The problem:

AI hallucination, lack of trust, and an inability to trace the source of an answer.

Core value:

Hallucination control & traceability: Ask a question against your chunks and get back citations with the original reasoning context. Ground your RAG answers in the source material for total control and trust.

Feature endpoint:

Text Processing (Chunking)

bookwyrm phrasal --file document.txt --format with_offsets --output phrases.jsonl

The problem:

Generic, token-based splitting that breaks semantic meaning, leading to poor retrieval quality.

Core value:

Semantically-balanced phrasing: Splits documents into meaningful, contextually-aware chunks and phrases instead of arbitrary token windows. Allows configurable sizing to fit your retrieval needs.

Feature endpoint:

Summarization

bookwyrm summarize phrases.jsonl --output summary.json

The problem:

Feeding large, noisy blocks of text into agents or models, consuming unnecessary tokens and budget.

Core value:

Concise, embeddable text: Collapse long or noisy text into concise summaries that are easier to embed, search, or feed into agents for efficient context injection.

Feature endpoint:

Categorization

bookwyrm classify --file document.pdf --output results.json

The problem:

Difficulty routing documents correctly for different RAG pipelines, indexing, or processing logic.

Core value:

Intelligent routing: Intelligently classify documents or raw text by format, type, and structure for efficient downstream routing and indexing.

Feature endpoint:

Streaming

bookwyrm cite "AI applications" chunks.jsonl --stream -v

The problem:

Dealing with long processing times in live applications, which degrades user experience.

Core value:

Real-time progress: Real-time processing with progress updates for all major operations, ideal for live applications where you need to surface results quickly.

Each endpoint is standalone, so you can slot BookWyrm into an existing pipeline or stitch multiple pieces together.

These capabilities work together to provide a complete pipeline for document ingestion, processing, and retrieval - the foundation of any RAG system.