A new guide walks through assembling a GPU-enabled local large language model setup that merges Ollama and LangChain into a single workflow. It explains how to install required Python packages, launch an Ollama inference server, fetch and cache a model, then wrap it in a custom LangChain LLM adapter with controls for generation temperature, maximum token count and context window. A retrieval-augmented generation component processes PDF or text inputs, breaks them into chunks, runs embeddings via Sentence-Transformers and returns answers grounded in the source material. The stack also maintains multi-session chat memory, registers tools for web search and RAG queries, and spins up an agent that decides when to call each tool.
The Colab notebook begins with imports for concurrency utilities, shell commands and JSON handling. An install_packages() function invokes pip installs for packages such as ollama, langchain, sentence-transformers, chromadb, gradio and psutil. After that, the notebook pulls in LangChain’s LLM, memory, retrieval and agent modules. A DuckDuckGo search tool is loaded alongside RAG components to form an extensible question-answering pipeline.
A dataclass named OllamaConfig gathers runtime settings in one place. It holds fields like model_name='llama2', api_url='http://127.0.0.1:11434', max_tokens=512, temperature=0.7 and context_window=2048. Performance options include gpu_layers=-1 (load all layers on GPU), batch_size=8 and threads=4 for parallel inference.
The OllamaManager class takes charge of server lifecycle management inside Colab. It sets environment variables such as OLLAMA_GPU_LAYERS, OLLAMA_BATCH_SIZE and OLLAMA_THREADS, then fires up subprocess.Popen(['ollama', 'serve', '--port', '11434']). A health-check endpoint confirms readiness. When a model is requested the class fetches it from the local cache or pulls it on demand, lists available models, and shuts down the server cleanly at the end of a session. Performance stats are captured at each step.
A PerformanceMonitor class tracks CPU and memory use via the psutil library, as well as inference durations using Python’s time module. It launches a background thread that polls every two seconds, storing the last 100 samples in a deque. Average metrics can be queried to reveal system load during inference calls.
Integration of the Ollama API happens through a custom OllamaLLM adapter that implements LangChain’s LLM interface. Its generate() method posts a JSON payload with prompt text, temperature, max_tokens and context_window to api_url + '/api/generate'. The adapter wraps each request with timing logic and reports inference latency back to the performance monitor. This makes Ollama work seamlessly within LangChain chains, agents and memory modules.
Chat state management comes courtesy of a ConversationManager module that provides two memory modes: an unbounded buffer using ConversationBufferMemory and a summary mode driven by ConversationSummaryMemory. A get_memory(session_id) method returns the appropriate memory object for each session, preserving or condensing context as needed.
All pieces converge in an OllamaLangChainSystem class. Its constructor calls install_packages(), instantiates OllamaManager, pulls the specified model, wraps it in OllamaLLM, sets up a RAG pipeline using SentenceTransformerEmbeddings('all-MiniLM-L6-v2') and a Chroma vector store, then loads document tools like PyPDFLoader. External utilities such as the DuckDuckGo search tool register with the system so agents can pick them when needed.
Tools register under names like web_search_tool and rag_query_tool. A LangChain AgentExecutor built from a ZeroShotAgent instructs the agent on how to choose between a web lookup or a vector-store query. The agent examines the user’s prompt and invokes the appropriate tool before returning a final answer.
A main() function illustrates a full demo: start the server, run example chat turns, call agent methods, list installed models and display performance summaries. The create_gradio_interface() function uses gradio.Blocks() to build a UI with tabs for live chat, document upload to the RAG pipeline and real-time performance graphs. The script executes main() under Python’s if __name__ == '__main__': guard or can launch the Gradio app with share=True on port 7860 for interactive use.
The codebase stays modular so new tools can plug in easily. Inference parameters defined in OllamaConfig remain adjustable, the RAG pipeline can scale to larger corpora or alternate embedders, and model-switching can occur at runtime. This template serves as a foundation for rapid local LLM experimentation on GPU-equipped notebooks.