How to Build a Budget-Friendly RAG Pipeline for Document Management Using Open-Source Tools

Organizations evaluating AI for document management often face a choice: fast proprietary APIs with unpredictable costs, or open-source stacks that require more engineering upfront but deliver long-term savings.

Reference architecture

• Ingestion: Chunk PDFs and policies with metadata tags for access control.
• Embeddings: Use open-source embedding models or cost-tiered API routing.
• Vector store: Pinecone or Qdrant for filtered retrieval by department or program.
• Orchestration: LangGraph for multi-step retrieval, re-ranking, and citation formatting.
• Model layer: Route simple queries to smaller OSS LLMs; escalate complex tasks to frontier models.

Cost controls that matter

• Cache frequent queries and embedding lookups.
• Set per-user and per-department token budgets.
• Instrument cost per resolved document request.

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