Insert and retrieve documents

How it works

1. 1.Trigger the workflow manually, via HTTP request, or through chat interface
2. 2.Split documents into manageable text chunks using recursive character splitting
3. 3.Generate vector embeddings for all text segments using OpenAI
4. 4.Store vector embeddings and document metadata in Milvus database
5. 5.Retrieve relevant documents based on semantic similarity queries
6. 6.Extract and format relevant information for user delivery

Use cases

• Build an intelligent document search engine for enterprise knowledge bases
• Automate customer and employee support by answering questions from indexed documents
• Analyze contracts, reports, and PDFs to extract critical business information
• Extend Q&A capabilities across X platform for public-facing document queries

Requirements

• OpenAI API key for embedding generation and language model access
• Deployed and running Milvus instance for vector storage and retrieval
• X API credentials if social media integration is required
• Pre-processed documents in text or structured format for initial indexing

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.