PDF RAG Pipeline Template

The PDF RAG Pipeline Template provides a pre-configured, production-ready pipeline for data ingestion. Instead of building from scratch, you get a tested configuration that handles the common patterns and edge cases teams encounter. A production-ready pipeline template for ingesting PDF documents, extracting text with OCR fallback, chunking with semantic boundaries, and loading into your vector database. This template has been refined based on real-world deployments across hundreds of IngestIQ users.

Use case: Processing legal contracts and compliance documents. This is a common scenario where the PDF RAG Pipeline Template saves significant development time by providing pre-built handling for the specific data patterns involved. Use case: Ingesting research papers and technical documentation. This is a common scenario where the PDF RAG Pipeline Template saves significant development time by providing pre-built handling for the specific data patterns involved. Use case: Building searchable archives from scanned documents. This is a common scenario where the PDF RAG Pipeline Template saves significant development time by providing pre-built handling for the specific data patterns involved.

This template comes in multiple variations to match your specific needs: Variation 1: Basic text extraction pipeline — suited for different complexity levels and data characteristics. Variation 2: OCR-enhanced pipeline for scanned PDFs — suited for different complexity levels and data characteristics. Variation 3: Table-aware pipeline with structured data extraction — suited for different complexity levels and data characteristics. Choose the variation that best matches your data complexity and processing requirements. You can always upgrade to a more advanced variation as your needs evolve.

Getting started with this template takes minutes, not days. Here is the complete setup process: Step 1: Create a new knowledge base in IngestIQ Step 2: Select the PDF source connector and configure upload settings Step 3: Choose your chunking strategy (semantic recommended for long documents) Step 4: Select your embedding model and target vector database Step 5: Run the pipeline and monitor processing in the dashboard Each step includes validation checks to ensure your pipeline is configured correctly before processing begins.

The PDF RAG Pipeline Template supports extensive customization. Key configuration options include chunking strategy (fixed-size, semantic, or document-structure-aware), embedding model selection (OpenAI, Cohere, or open-source alternatives), target vector database (Pinecone, Qdrant, Milvus, Weaviate, PgVector, or MongoDB Atlas), and metadata extraction rules. All settings can be adjusted through the IngestIQ dashboard or API.

When using this template, start with the default settings and iterate based on retrieval quality. Monitor chunk sizes to ensure they are neither too small (losing context) nor too large (diluting relevance). Use the built-in evaluation tools to measure retrieval accuracy before deploying to production. Set up incremental sync rather than full re-processing to keep your pipeline efficient as data volumes grow.