Creating a GPU-Optimized Ollama LangChain Workflow with RAG Agents and Multi-Session Chat Monitoring
Learn how to build a GPU-accelerated Ollama LangChain workflow with integrated RAG agents and multi-session chat memory management, including performance monitoring for efficient local LLM deployment.
Setting Up a GPU-Accelerated Ollama and LangChain Integration
This tutorial guides you through building a local Large Language Model (LLM) stack that leverages GPU acceleration by integrating Ollama with LangChain. The process begins with installing necessary libraries, launching the Ollama server, pulling models, and wrapping them into a customized LangChain LLM. This setup allows fine control over parameters such as temperature, token limits, and context window.
Incorporating Retrieval-Augmented Generation (RAG)
A Retrieval-Augmented Generation layer is added to ingest documents like PDFs or plain text. These documents are chunked and embedded using Sentence-Transformers, enabling the system to provide grounded and contextually relevant answers. This enhances response accuracy by retrieving pertinent information during generation.
Managing Multi-Session Chat Memory and Tool Integration
The system supports multi-session chat with memory management using buffer or summary-based conversational states. Tools such as web search (via DuckDuckGo) and RAG querying are registered, and an agent is initialized to intelligently decide when to invoke these tools during interactions.
OllamaManager: Server Control and Model Handling
The OllamaManager class automates installing Ollama, starting the GPU-enabled server, pulling and caching models, listing available local models, and stopping the server gracefully. Health checks ensure the server’s availability.
Performance Monitoring
A dedicated PerformanceMonitor runs in a background thread, collecting CPU and memory usage stats as well as inference times, helping optimize system resource usage during workload.
Custom Ollama LLM Wrapper
The OllamaLLM class wraps the Ollama API to provide a LangChain-compatible interface. It handles prompt submission and tracks inference duration for performance insights.
RAG System Implementation
The RAGSystem class loads documents, splits them into chunks, embeds them, and maintains a vector store for similarity search. It supports querying to retrieve contextually relevant answers along with source document metadata.
Conversation Management
ConversationManager maintains session-specific chat histories using either buffer or summary memory, enabling personalized and context-aware dialogues across multiple sessions.
Unified OllamaLangChainSystem
Bringing all components together, OllamaLangChainSystem installs Ollama, starts the server, manages model pulling, initializes the custom LLM, RAG system, conversation manager, and registers external tools. It exposes methods for chatting, RAG querying, agent-based interactions, model switching, document loading, performance stats retrieval, and cleanup.
Demonstration and Interface
The main function demonstrates system setup, basic chat, model switching, agent querying, and performance statistics. A Gradio interface is also provided for user-friendly interaction, including chat tabs, document uploads for RAG, and performance monitoring.
This modular and extensible workflow serves as a robust template for local, GPU-accelerated LLM experimentation with advanced features such as RAG and multi-session context management.
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