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    Document Ingestion Pipeline

    The unglamorous half of RAG: parse PDFs and HTML, chunk with overlap, embed, and UPSERT into pgvector. Redis tracks job progress and locks files so two workers never embed the same object. Bad chunks are quarantined instead of silently poisoning retrieval.

    500 – 50Kusers supported
    $180 – $2,000/month infrastructure

    Architecture Diagram

    Interactive — hover over any node to see its role and description.

    UploaderIngest APIS3Raw filesJob QueueRedis / SQSParserOCR optionalEmbedderpgvectorChunksRedisProgressUserAPIStorageQueueBackendAI / MLDatabaseCache

    Use Cases

    Customer knowledge bases that update daily
    Contract and policy libraries
    Ticket + wiki unified search
    Multi-tenant document rooms
    Re-embed when you change models

    Technology Stack

    backend

    PythonLangChain / LlamaIndexCelery

    database

    PostgreSQL + pgvectorRedis

    infrastructure

    S3ECS workersSQS

    ai

    OpenAI embeddingsoptional OCR

    Scalability Roadmap

    Stage 1Low volume users· One worker

    Celery + Redis on one box. pgvector ivfflat.

    Stage 2Daily dumps users· Worker pool

    Parallel parse. GPU optional for OCR. HNSW when vector count grows.

    Stage 3Multi-tenant users· Namespaced indexes

    tenant_id on every chunk. Separate queues for large customers.

    Stage 4Global users· Regional ingest

    Files stay in-region. pgvector replica for retrieval. Redis jobs local.

    Cost Breakdown

    Development Cost

    $8,000 – $20,000 (5–10 weeks)

    Infrastructure Cost

    $180 – $2,000/month (embeddings dominate)

    Maintenance Cost

    $1,000 – $3,000/month for parsers and re-embed campaigns

    Security Considerations

    Malware scan before parse
    ACL copied onto every chunk row
    PII redaction option before embed
    S3 bucket not public; pre-signed uploads only

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