Custom-Trained OCR RAG Pipeline

Scenario

Use this combination when your engineering team must transform unstructured scanned documents into a production-ready conversational search system. It bridges domain-specific AI training, automated OCR extraction, hybrid retrieval, and LLM-powered answer generation into a single automated pipeline.

Integration steps

1. Ingest raw documents to OSS: Upload scanned PDFs/images using the CLI: aliyun oss cp ./scanned_docs/ oss://rag-pipeline-bucket/raw/ --recursive.
2. Extract text via Bailian OCR: Call the DashScope API: POST https://dashscope.aliyuncs.com/api/v1/services/ocr/document_parse with payload {"model": "doc-parser-v2", "input": {"file_url": "oss://rag-pipeline-bucket/raw/doc1.pdf"}}. Parse the JSON response to extract structured text blocks.
3. Chunk & embed with PAI models: Deploy your fine-tuned embedding model on PAI-EAS. Send chunks via POST https://<pai-eas-endpoint>/v1/embeddings with {"model": "custom-domain-embed", "input": ["chunk_1_text", ...]}.
4. Index into Elasticsearch: Define a hybrid mapping: PUT /rag-index/_mapping {"properties": {"content": {"type": "text", "analyzer": "standard"}, "embedding": {"type": "dense_vector", "dims": 768, "index": true, "similarity": "cosine"}}}. Bulk index documents using POST /_bulk.
5. Configure OpenSearch RAG Pipeline: Create an ML pipeline: PUT /_plugins/_ml/pipelines/rag-processor {"processors": [{"neural_sparse": {"field": "embedding"}}, {"text_expansion": {"field": "content", "model_id": "fine-tuned-llm-id"}}]}.
6. Execute hybrid retrieval & generation: Query with POST /rag-index/_search using a bool query combining match (BM25) and knn (vector). Route top-5 hits to OpenSearch’s ML connector to generate the final answer via your PAI-fine-tuned LLM.

Architecture

Data flows unidirectionally from OSS (storage) to Bailian (OCR/text extraction). Extracted chunks are vectorized by PAI-hosted custom models and indexed into Elasticsearch, which maintains both BM25 keyword and dense vector fields. OpenSearch sits at the query layer, executing hybrid retrieval, applying reranking logic, and invoking the fine-tuned LLM to synthesize natural language answers from the retrieved context.

Prerequisites

• Active Alibaba Cloud account with OSS, Elasticsearch, OpenSearch, PAI, and Bailian services provisioned
• Pre-trained domain embedding model and fine-tuned LLM deployed on PAI-EAS
• Valid DashScope API key for Bailian OCR access
• Python 3.9+ environment with requests, elasticsearch, and opensearch-py SDKs installed

Common pitfalls

• Dimension mismatch: PAI model outputs 1024-dim vectors while ES mapping expects 768, causing indexing failures. Always verify dims in the mapping before bulk ingestion.
• OCR payload limits: Bailian rejects PDFs >20MB or >50 pages. Implement client-side page splitting before API submission.
• Hybrid score imbalance: Default BM25 weights drown out vector results. Use explicit weight parameters in the bool query to balance keyword and semantic matches.
• OpenSearch pipeline OOM: Large chunk batches exceed JVM heap limits during neural processing. Set pipeline.batch_size: 100 and monitor _nodes/stats memory usage.

Typical questions

• train custom embeddings then OCR documents for RAG
• PAI model training plus scanned document RAG pipeline
• custom embedding OCR hybrid search production
• train domain models and process scanned docs for semantic search
• full stack OCR RAG with custom trained embeddings
• PAI训练自定义嵌入加OCR文档处理RAG流水线
• 从模型训练到OCR文档混合检索生产部署
• 自定义嵌入训练加扫描文档RAG系统

Q: How do I build a custom-trained OCR RAG pipeline that processes scanned documents and deploys to production? A: You build this pipeline by training domain-specific embedding models on PAI and ingesting scanned PDFs or images via Bailian OCR. The extracted text is embedded with your custom models, indexed into Elasticsearch for hybrid BM25 and vector retrieval, and deployed to production with OpenSearch handling the final answer generation.