RAG AI Agent with Milvus and Cohere

For the RAG AI Agent with Milvus and Cohere, automate the processing of new PDF files in Google Drive, enabling quick insertion into a Milvus vector database. This workflow enhances information retrieval for AI interactions, allowing users to efficiently respond to chat messages with relevant data. By leveraging advanced embeddings and memory management, it streamlines the integration of multilingual content, ensuring high performance and scalability for demanding applications.

7/8/2025
14 nodes
Medium
yj7cf3gcsziargftmanualmediumlangchaingoogledrivetriggergoogle driveextractfromfilesticky noteadvancedfilesstorage
Categories:
Manual TriggeredMedium WorkflowCloud Storage & File Management
Integrations:
LangChainGoogleDriveTriggerGoogle DriveExtractFromFileSticky Note

Target Audience

Target Audience


- Data Scientists: Those looking to enhance their data processing capabilities and leverage AI for document understanding and retrieval.
- Business Analysts: Professionals who need to automate the extraction and analysis of information from documents stored in Google Drive.
- Developers: Individuals interested in implementing AI-driven applications using LangChain and vector databases like Milvus.
- Organizations: Companies aiming to improve their knowledge management systems and customer service with AI agents.

Problem Solved

Problem Solved


This workflow addresses the challenge of efficiently managing and retrieving information from a large number of documents stored in Google Drive. It automates the process of:
- Document Extraction: Automatically extracting content from newly uploaded PDF files.
- Data Ingestion: Inserting extracted data into a vector database (Milvus) for fast retrieval.
- AI Interaction: Enabling users to interact with an AI agent that can respond based on the information stored in the vector database, significantly reducing response times and improving user experience.

Workflow Steps

Workflow Steps


1. Trigger on New Files: The workflow starts when a new file is uploaded to a specific Google Drive folder.
2. Download File: The newly created file is downloaded from Google Drive.
3. Extract Content: The content of the file is extracted (specifically for PDFs).
4. Set Chunks: The extracted content is split into manageable chunks for processing.
5. Generate Embeddings: The chunks are converted into embeddings using Cohere's model, allowing for semantic search.
6. Insert into Milvus: The generated embeddings are inserted into the Milvus vector database for efficient retrieval.
7. Chat Trigger: The workflow can also be triggered by chat messages, allowing users to interact with the RAG agent.
8. Retrieve from Milvus: When a chat message is received, the agent retrieves relevant information from Milvus.
9. AI Response: The retrieved information is processed by the AI language model (OpenAI) to provide a coherent response to the user.

Customization Guide

Customization Guide


- Change Trigger Settings: Users can modify the folder in Google Drive to watch for new files by adjusting the folderToWatch parameter in the Watch New Files node.
- Adjust Embedding Model: Users can select different models for generating embeddings in the Embeddings Cohere node by changing the modelName parameter.
- Modify Chunk Size: The chunk size and overlap can be adjusted in the Set Chunks node to better suit the specific document types being processed.
- Alter Response Model: The AI response model can be changed in the OpenAI 4o node by modifying the model parameter to use different versions or configurations of OpenAI's language models.
- Enhance Data Processing: Additional nodes can be added for further data processing or integration with other services, depending on the business requirements.