Code Repository RAG Template

The Code Repository RAG 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. Template for indexing code repositories, documentation, and developer resources to build AI coding assistants that understand your codebase. This template has been refined based on real-world deployments across hundreds of IngestIQ users.

Use case: Building codebase-aware AI assistants. This is a common scenario where the Code Repository RAG Template saves significant development time by providing pre-built handling for the specific data patterns involved. Use case: Creating searchable code documentation. This is a common scenario where the Code Repository RAG Template saves significant development time by providing pre-built handling for the specific data patterns involved. Use case: Onboarding new developers with AI-powered code exploration. This is a common scenario where the Code Repository RAG 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: Single repository pipeline — suited for different complexity levels and data characteristics. Variation 2: Multi-repo organization pipeline — suited for different complexity levels and data characteristics. Variation 3: Code + documentation combined pipeline — 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: Connect your code repositories Step 2: Configure file type filters (code, docs, configs) Step 3: Set up code-aware chunking (function-level splitting) Step 4: Choose a code-optimized embedding model Step 5: Test with representative developer queries Each step includes validation checks to ensure your pipeline is configured correctly before processing begins.

The Code Repository RAG 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.