RAG AI Agent with Milvus and Cohere
This workflow integrates a vector database and a multilingual embedding model to achieve intelligent document processing and a question-answering system. It can automatically monitor and process PDF files in Google Drive, extract text, and generate vectors, supporting efficient semantic retrieval and intelligent responses. Users can quickly access a vast amount of document information, enhancing the management and query efficiency of multilingual content. It is suitable for scenarios such as enterprise knowledge bases, customer service robots, and automatic indexing and querying in specialized fields.
Workflow Name
RAG AI Agent with Milvus and Cohere
Key Features and Highlights
This workflow implements a Retrieval-Augmented Generation (RAG) AI agent based on the Milvus vector database and Cohere’s multilingual embedding model. It can automatically process PDF files uploaded to a designated Google Drive folder, extract their content, and convert it into vector representations for storage. This enables efficient semantic search and intelligent Q&A. Highlights include:
- Automatic monitoring of new files in Google Drive with real-time download and processing
- Generation of text vectors using Cohere’s multilingual embedding model, supporting cross-lingual semantic understanding
- Storage and retrieval of large-scale vectors via Milvus cloud vector database, ensuring high-performance and scalable vector search
- Integration of OpenAI GPT-4o large language model to generate contextually relevant intelligent responses based on retrieved information
- Memory buffer window supporting continuous contextual conversations, enhancing interaction experience
Core Problems Addressed
- Semantic indexing and rapid retrieval of massive document information
- Overcoming barriers in multilingual document processing by supporting unified vector representations across languages
- Improving AI Q&A accuracy and knowledge coverage by combining retrieval and generation
- Automating document update workflows to avoid manual intervention and achieve intelligent data synchronization
Application Scenarios
- Building enterprise internal knowledge bases and intelligent Q&A systems
- Multilingual document management and fast content retrieval
- Knowledge enhancement for customer service bots and intelligent assistants
- Automated indexing and querying of professional documents in legal, medical, and other specialized fields
- Intelligent search and teaching assistance for educational and training materials
Main Workflow Steps
- Monitor newly uploaded PDF files in a specified Google Drive folder via Google Drive trigger
- Automatically download new files and extract text content
- Use a text chunker to segment content appropriately
- Generate vector embeddings of text chunks using Cohere’s multilingual model
- Insert vector data into Milvus vector database for storage
- Upon receiving chat messages, the RAG agent queries Milvus to retrieve relevant vector information
- Combine OpenAI GPT-4o model with historical conversation memory to generate context-aware responses
- Return intelligent answers to enable natural language interaction
Involved Systems or Services
- Google Drive (file storage and trigger)
- Milvus (cloud vector database)
- Cohere (multilingual text embedding service)
- OpenAI GPT-4o (large language model generation)
- n8n (workflow automation platform)
Target Users and Value Proposition
- Enterprise IT and data teams: Quickly build intelligent knowledge bases and Q&A systems
- AI developers and data scientists: Achieve high-quality multilingual vector retrieval and generation
- Customer service and support teams: Enhance customer interaction efficiency and reduce manual costs
- Professionals in specialized fields: Conveniently manage and query vast document repositories
- Innovative businesses seeking automated intelligent Q&A powered by cloud services
By integrating leading vector database technology with large language models, this workflow delivers an efficient, intelligent, and multilingual document Q&A solution that significantly enhances the value extraction and application scope of document information.