OCR-Ingested Custom RAG with Fine-Tuned LLM

Scenario

A team trains domain-specific embedding models and fine-tunes a custom LLM on PAI, deploys the LLM to Bailian as a managed inference endpoint, then builds an end-to-end document intelligence pipeline that ingests scanned PDFs and images via Bailian OCR, embeds extracted text with the custom-trained models, indexes them into OpenSearch for hybrid vector-plus-BM25 retrieval, and generates answers using the fine-tuned LLM — delivering a fully custom RAG system from raw scanned documents to production inference.

How the products combine

1. es · ml-powered-semantic-search-pipeline-b3728a — ML-Powered Semantic Search Pipeline

See _combos/ml-powered-semantic-search-pipeline-b3728a.

2. alinux · full-stack-custom-rag-train-to-production-e68446 — Full-Stack Custom RAG: Train to Production

See _combos/full-stack-custom-rag-train-to-production-e68446.

3. airec · custom-trained-ocr-rag-pipeline-324afe — Custom-Trained OCR RAG Pipeline

See _combos/custom-trained-ocr-rag-pipeline-324afe.

4. alinux · full-custom-rag-custom-llm-custom-embeddings-75fbf5 — Full Custom RAG: Custom LLM + Custom Embeddings

See _combos/full-custom-rag-custom-llm-custom-embeddings-75fbf5.

Typical questions

• OCR documents then answer with custom trained LLM
• scanned PDF RAG with fine-tuned model and custom embeddings
• PAI训练自定义模型加OCR文档处理加生产RAG
• full pipeline from scanned docs to custom LLM answers
• train custom LLM and embeddings then OCR documents for hybrid search
• 从扫描文档到自定义大模型回答的全链路RAG
• OCR ingestion plus custom trained generation pipeline
• end-to-end document intelligence with fine-tuned LLM

Q: How does the end-to-end pipeline process scanned documents and generate answers with a custom fine-tuned LLM? A: This setup delivers a fully custom RAG system by training domain-specific embedding models and a fine-tuned LLM on PAI and deploying the model to Bailian for managed inference. The pipeline ingests scanned PDFs and images via Bailian OCR, embeds the extracted text with the custom models, indexes them into OpenSearch for hybrid vector-plus-BM25 retrieval, and generates answers using the fine-tuned LLM.