Document AI RAG with Semantic Recommendations

Scenario

Use this integration when you need to transform unstructured enterprise documents (PDFs, scanned invoices, technical manuals) into a searchable, AI-ready knowledge base that powers both context-grounded RAG Q&A and personalized content discovery. It bridges Bailian’s high-accuracy document extraction with OpenSearch semantic embeddings, Elasticsearch hybrid indexing, and AIRec’s behavior-driven recommendation engine.

Integration steps

1. Store raw documents in OSS: Upload PDFs and scanned images to an Alibaba Cloud OSS bucket. Configure IAM policies to grant Bailian and OpenSearch read access.
2. Extract structured content via Bailian: Call the Document AI extraction API using qvq-max for complex layout parsing:

``bash curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/document-process/extract \ -H "Authorization: Bearer $BAILIAN_API_KEY" \ -d '{"model": "qvq-max", "input": {"url": "https://docs.example.com/manual.pdf"}}' ``

3. Generate semantic embeddings: Pipe the extracted JSON/text into OpenSearch’s embedding pipeline. Use text-embedding-v3 to convert document chunks into 1024-dim vectors.
4. Index in Elasticsearch for hybrid search: Create an ES index with a dense_vector field for semantic retrieval and keyword/text fields for exact matching. Ingest OpenSearch-generated vectors alongside Bailian-extracted metadata.
5. Configure AIRec for personalized discovery: Sync document IDs and vector metadata to AIRec. Map frontend user events (clicks, dwell time) to trigger semantic similarity recommendations alongside collaborative filtering.
6. Assemble the RAG retrieval pipeline: Query ES with hybrid vector+keyword search, then rerank top-50 results using Bailian’s reranking API before passing context to your LLM.

Architecture

Raw files reside in OSS and are fetched by Bailian’s qvq-max model for layout-aware text and table extraction. Extracted chunks flow into OpenSearch for vectorization, then into Elasticsearch for low-latency hybrid indexing. The same semantic vectors feed AIRec’s recommendation engine, which correlates user behavior with document embeddings. Finally, the RAG pipeline queries ES, reranks results, and grounds LLM responses with contextually relevant, personalized document snippets.

Prerequisites

• Active Alibaba Cloud account with OSS, Elasticsearch, OpenSearch, AIRec, and Bailian instances provisioned
• BAILIAN_API_KEY and cross-service IAM roles configured
• Pre-configured OpenSearch embedding model (text-embedding-v3 or compatible)
• AIRec instance with event tracking SDK integrated into your application frontend
• Elasticsearch cluster with dense_vector mapping and hybrid search plugins enabled

Common pitfalls

• Embedding dimension mismatch: OpenSearch and ES must use identical vector dimensions (e.g., 1024); mismatched configs cause index mapping failures during ingestion.
• Chunk boundary fragmentation: Bailian’s layout parser may split tables across chunks, degrading retrieval accuracy. Use chunk_overlap: 150 and preserve structural boundaries during preprocessing.
• AIRec cold-start latency: Recommendations require ~24 hours of user interaction data to stabilize. Seed with semantic similarity fallbacks until behavioral signals accumulate.
• Rate limiting on extraction: qvq-max enforces strict concurrency limits. Implement exponential backoff and batch OSS URLs in parallel to avoid 429 errors.

Typical questions

• extract documents and build RAG with recommendations
• PDF to RAG pipeline with personalized search
• document extraction plus semantic recommendation platform
• 从PDF提取构建RAG并接入智能推荐
• 文档抽取加向量检索加AIRec推荐一体化
• build knowledge base from scanned docs with recommendations
• OCR extract then RAG then recommend
• unstructured documents to retrieval and recommendation system

Q: How do I extract documents and build a RAG pipeline with semantic recommendations? A: You can build a complete document-to-discovery platform by combining Bailian's document understanding, OpenSearch semantic embeddings, and AIRec-powered personalized recommendations. This workflow extracts text and structured data from unstructured documents like PDFs and scanned images to create a RAG knowledge base and retrieval pipeline. The solution integrates these components through predefined combination skills that index semantic embeddings in Elasticsearch and layer on personalized recommendation capabilities.