TramAI

tramai-rag

Version: 0.3.1
Status: Stable
Role: Retrieval-Augmented Generation context pipeline.

Purpose

The tramai-rag module provides a comprehensive pipeline for injecting internal knowledge into LLM contexts. It handles document loading, text chunking, embedding generation, and vector retrieval.

Core Concepts

Document Pipeline

  • Loaders: Ingest external data (PDFs, Markdown, Web, DB rows) into raw Document objects.
  • Chunkers: Break large documents into semantically meaningful chunks (e.g. RecursiveCharacterTextChunker) ensuring context limits are respected and overlapping boundaries preserve meaning.

Retrieval Pipeline

  • Embeddings: Interfaces with tramai-embedding to translate text chunks into high-dimensional vectors.
  • Vector Store Integration: Leverages tramai-vectorstore-spi to query databases like Chroma or Postgres pgvector for semantic similarity.
  • Context Injection: Dynamically injects retrieved chunks into an @Operation context right before execution.

Dependencies

// build.gradle.kts
dependencies {
    implementation("dev.tramai:tramai-rag:0.3.1")
    
    // RAG pipelines usually require embedding and a vector store
    implementation("dev.tramai:tramai-embedding:0.3.1")
    implementation("dev.tramai:tramai-vectorstore-chroma:0.3.1") // Or pgvector
}

When to use this module

  • You want the AI to answer questions based on your company's internal PDFs, documentation, or data.
  • You are building a "Chat with your data" feature.

When NOT to use this module

  • The AI only needs its base training knowledge to answer queries.
  • You are not executing semantic search.
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