In this lesson, we build a stack of the GPU's strong GPU that includes Ollama and Langchain. It includes the required libraries, activate the Ollama server, pull the model, and wrap the custom Langchain, it allows us to control the temperature, token limit, and context. We include a retreek-Augmented Generagement Server that includes PDFs or text, chunks to, and move them to sentences in sentences, and worked for supported transformers. We carry a multi-time memory memory for many events, registration tools (Web Search + Question), and then correct the agent showing when to call them.
import os
import sys
import subprocess
import time
import threading
import queue
import json
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
from contextlib import contextmanager
import asyncio
from concurrent.futures import ThreadPoolExecutor
def install_packages():
"""Install required packages for Colab environment"""
packages = [
"langchain",
"langchain-community",
"langchain-core",
"chromadb",
"sentence-transformers",
"faiss-cpu",
"pypdf",
"python-docx",
"requests",
"psutil",
"pyngrok",
"gradio"
]
for package in packages:
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
install_packages()
import requests
import psutil
import threading
from queue import Queue
from langchain.llms.base import LLM
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage
from langchain.memory import ConversationBufferWindowMemory, ConversationSummaryBufferMemory
from langchain.chains import ConversationChain, RetrievalQA
from langchain.prompts import PromptTemplate, ChatPromptTemplate
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS, Chroma
from langchain.agents import AgentType, initialize_agent, Tool
from langchain.tools import DuckDuckGoSearchRunWe imported the necessary Python Equities in Colab with a comuringrency, system calls, and carrying JSON. It explains and uses calculating_paackages () to pull the Langchain, motivation, vector stores, uploads, monitoring, and reliance. We have imported Langchain LLGCHAIN, Memory, Recovery, and agent's tools (including DUCKDUKGO search) to build rag and agent raf.
[Download the full codes with notebook here]
@dataclass
class OllamaConfig:
"""Configuration for Ollama setup"""
model_name: str = "llama2"
base_url: str = "
max_tokens: int = 2048
temperature: float = 0.7
gpu_layers: int = -1
context_window: int = 4096
batch_size: int = 512
threads: int = 4We define Ollamaconfig data so we keep all Ollama Ollama settings in one clean area. We put the name of the model and the Expoint area of API, and the behavior of the generation (max_tokens, temperature, and the center_andow). We control working with GPU_Layers (-1 = Load all in GPU where possible), Batch_Need, and similar fibers.
@dataclass
class OllamaConfig:
"""Configuration for Ollama setup"""
model_name: str = "llama2"
base_url: str = "
max_tokens: int = 2048
temperature: float = 0.7
gpu_layers: int = -1
context_window: int = 4096
batch_size: int = 512
threads: int = 4
We define an OllamaConfig dataclass so we keep all Ollama runtime settings in one clean place. We set the model name and local API endpoint, as well as the generation behavior (max_tokens, temperature, and context_window). We control performance with gpu_layers (‑1 = load all to GPU when possible), batch_size, and threads for parallelism.
class OllamaManager:
"""Advanced Ollama manager for Colab environment"""
def __init__(self, config: OllamaConfig):
self.config = config
self.process = None
self.is_running = False
self.models_cache = {}
self.performance_monitor = PerformanceMonitor()
def install_ollama(self):
"""Install Ollama in Colab environment"""
try:
subprocess.run([
"curl", "-fsSL", " "-o", "/tmp/install.sh"
], check=True)
subprocess.run(["bash", "/tmp/install.sh"], check=True)
print("✅ Ollama installed successfully")
except subprocess.CalledProcessError as e:
print(f"❌ Failed to install Ollama: {e}")
raise
def start_server(self):
"""Start Ollama server with GPU support"""
if self.is_running:
print("Ollama server is already running")
return
try:
env = os.environ.copy()
env["OLLAMA_NUM_PARALLEL"] = str(self.config.threads)
env["OLLAMA_MAX_LOADED_MODELS"] = "3"
self.process = subprocess.Popen(
["ollama", "serve"],
env=env,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
time.sleep(5)
if self.health_check():
self.is_running = True
print("✅ Ollama server started successfully")
self.performance_monitor.start()
else:
raise Exception("Server failed to start properly")
except Exception as e:
print(f"❌ Failed to start Ollama server: {e}")
raise
def health_check(self) -> bool:
"""Check if Ollama server is healthy"""
try:
response = requests.get(f"{self.config.base_url}/api/tags", timeout=10)
return response.status_code == 200
except:
return False
def pull_model(self, model_name: str) -> bool:
"""Pull a model from Ollama registry"""
try:
print(f"🔄 Pulling model: {model_name}")
result = subprocess.run(
["ollama", "pull", model_name],
capture_output=True,
text=True,
timeout=1800
)
if result.returncode == 0:
print(f"✅ Model {model_name} pulled successfully")
self.models_cache[model_name] = True
return True
else:
print(f"❌ Failed to pull model {model_name}: {result.stderr}")
return False
except subprocess.TimeoutExpired:
print(f"❌ Timeout pulling model {model_name}")
return False
except Exception as e:
print(f"❌ Error pulling model {model_name}: {e}")
return False
def list_models(self) -> List[str]:
"""List available local models"""
try:
result = subprocess.run(
["ollama", "list"],
capture_output=True,
text=True
)
models = []
for line in result.stdout.split('n')[1:]:
if line.strip():
model_name = line.split()[0]
models.append(model_name)
return models
except Exception as e:
print(f"❌ Error listing models: {e}")
return []
def stop_server(self):
"""Stop Ollama server"""
if self.process:
self.process.terminate()
self.process.wait()
self.is_running = False
self.performance_monitor.stop()
print("✅ Ollama server stopped")We create a category of Ollamimanager to install, start, monitor, and manage the Ollama server in the Colob area. We put the environmental variables of GPU parallel, run a server in the back, and make sure that by your health check. We pull models in terms, save, a list of available location, and close the server when work is completed, all during the following performance.
[Download the full codes with notebook here]
class PerformanceMonitor:
"""Monitor system performance and resource usage"""
def __init__(self):
self.monitoring = False
self.stats = {
"cpu_usage": [],
"memory_usage": [],
"gpu_usage": [],
"inference_times": []
}
self.monitor_thread = None
def start(self):
"""Start performance monitoring"""
self.monitoring = True
self.monitor_thread = threading.Thread(target=self._monitor_loop)
self.monitor_thread.daemon = True
self.monitor_thread.start()
def stop(self):
"""Stop performance monitoring"""
self.monitoring = False
if self.monitor_thread:
self.monitor_thread.join()
def _monitor_loop(self):
"""Main monitoring loop"""
while self.monitoring:
try:
cpu_percent = psutil.cpu_percent(interval=1)
memory = psutil.virtual_memory()
self.stats["cpu_usage"].append(cpu_percent)
self.stats["memory_usage"].append(memory.percent)
for key in ["cpu_usage", "memory_usage"]:
if len(self.stats[key]) > 100:
self.stats[key] = self.stats[key][-100:]
time.sleep(5)
except Exception as e:
print(f"Monitoring error: {e}")
def get_stats(self) -> Dict[str, Any]:
"""Get current performance statistics"""
return {
"avg_cpu": sum(self.stats["cpu_usage"][-10:]) / max(len(self.stats["cpu_usage"][-10:]), 1),
"avg_memory": sum(self.stats["memory_usage"][-10:]) / max(len(self.stats["memory_usage"][-10:]), 1),
"total_inferences": len(self.stats["inference_times"]),
"avg_inference_time": sum(self.stats["inference_times"]) / max(len(self.stats["inference_times"]), 1)
}
It describes the Performonmancembonitor phase to track CPU, memory, and measurement periods during the actual time when the Ollama server works. We present the backpple to collect statistics every few seconds, keep the latest metrics, and give between interactive abbreviations. This helps us to look at the program's responsibility and improve the performance during model adoption.
[Download the full codes with notebook here]
class OllamaLLM(LLM):
"""Custom LangChain LLM for Ollama"""
model_name: str = "llama2"
base_url: str = "
temperature: float = 0.7
max_tokens: int = 2048
performance_monitor: Optional[PerformanceMonitor] = None
@property
def _llm_type(self) -> str:
return "ollama"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Make API call to Ollama"""
start_time = time.time()
try:
payload = {
"model": self.model_name,
"prompt": prompt,
"stream": False,
"options": {
"temperature": self.temperature,
"num_predict": self.max_tokens,
"stop": stop or []
}
}
response = requests.post(
f"{self.base_url}/api/generate",
json=payload,
timeout=120
)
response.raise_for_status()
result = response.json()
inference_time = time.time() - start_time
if self.performance_monitor:
self.performance_monitor.stats["inference_times"].append(inference_time)
return result.get("response", "")
except Exception as e:
print(f"❌ Ollama API error: {e}")
return f"Error: {str(e)}"We threaten Ollama API within the Ollamallm Class culture dealing with Langchain's LLM Interface. It explains how the dumps are sent to the Ollama server and record each time running. This allows us to connect the Ollama directly to the chains of Langchain, agents and memory components while they look effective.
class RAGSystem:
"""Retrieval-Augmented Generation system"""
def __init__(self, llm: OllamaLLM, embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2"):
self.llm = llm
self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
self.vector_store = None
self.qa_chain = None
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
def add_documents(self, file_paths: List[str]):
"""Add documents to the vector store"""
documents = []
for file_path in file_paths:
try:
if file_path.endswith('.pdf'):
loader = PyPDFLoader(file_path)
else:
loader = TextLoader(file_path)
docs = loader.load()
documents.extend(docs)
except Exception as e:
print(f"❌ Error loading {file_path}: {e}")
if documents:
splits = self.text_splitter.split_documents(documents)
if self.vector_store is None:
self.vector_store = FAISS.from_documents(splits, self.embeddings)
else:
self.vector_store.add_documents(splits)
self.qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=self.vector_store.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True
)
print(f"✅ Added {len(splits)} document chunks to vector store")
def query(self, question: str) -> Dict[str, Any]:
"""Query the RAG system"""
if not self.qa_chain:
return {"answer": "No documents loaded. Please add documents first."}
try:
result = self.qa_chain({"query": question})
return {
"answer": result["result"],
"sources": [doc.metadata for doc in result.get("source_documents", [])]
}
except Exception as e:
return {"answer": f"Error: {str(e)}"}We use chat or parameter to manage a long-term memory, which makes both buffer-based history and Sundend-based chat Chat per session. Then, in Oollamalangchainstem, we bring all parts together, server, llm, RAG, memory, tools, and one united interface. We prepare the Ollama installation program, drag-up models, forming agents with tools such as web search and RAG, and disclose the chat, skills of exchange documents.
class ConversationManager:
"""Manage conversation history and memory"""
def __init__(self, llm: OllamaLLM, memory_type: str = "buffer"):
self.llm = llm
self.conversations = {}
self.memory_type = memory_type
def get_conversation(self, session_id: str) -> ConversationChain:
"""Get or create conversation for session"""
if session_id not in self.conversations:
if self.memory_type == "buffer":
memory = ConversationBufferWindowMemory(k=10)
elif self.memory_type == "summary":
memory = ConversationSummaryBufferMemory(
llm=self.llm,
max_token_limit=1000
)
else:
memory = ConversationBufferWindowMemory(k=10)
self.conversations[session_id] = ConversationChain(
llm=self.llm,
memory=memory,
verbose=True
)
return self.conversations[session_id]
def chat(self, session_id: str, message: str) -> str:
"""Chat with specific session"""
conversation = self.get_conversation(session_id)
return conversation.predict(input=message)
def clear_session(self, session_id: str):
"""Clear conversation history for session"""
if session_id in self.conversations:
del self.conversations[session_id]
class OllamaLangChainSystem:
"""Main system integrating all components"""
def __init__(self, config: OllamaConfig):
self.config = config
self.manager = OllamaManager(config)
self.llm = None
self.rag_system = None
self.conversation_manager = None
self.tools = []
self.agent = None
def setup(self):
"""Complete system setup"""
print("🚀 Setting up Ollama + LangChain system...")
self.manager.install_ollama()
self.manager.start_server()
if not self.manager.pull_model(self.config.model_name):
print("❌ Failed to pull default model")
return False
self.llm = OllamaLLM(
model_name=self.config.model_name,
base_url=self.config.base_url,
temperature=self.config.temperature,
max_tokens=self.config.max_tokens,
performance_monitor=self.manager.performance_monitor
)
self.rag_system = RAGSystem(self.llm)
self.conversation_manager = ConversationManager(self.llm)
self._setup_tools()
print("✅ System setup complete!")
return True
def _setup_tools(self):
"""Setup tools for the agent"""
search = DuckDuckGoSearchRun()
self.tools = [
Tool(
name="Search",
func=search.run,
description="Search the internet for current information"
),
Tool(
name="RAG_Query",
func=lambda q: self.rag_system.query(q)["answer"],
description="Query loaded documents using RAG"
)
]
self.agent = initialize_agent(
tools=self.tools,
llm=self.llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
def chat(self, message: str, session_id: str = "default") -> str:
"""Simple chat interface"""
return self.conversation_manager.chat(session_id, message)
def rag_chat(self, question: str) -> Dict[str, Any]:
"""RAG-based chat"""
return self.rag_system.query(question)
def agent_chat(self, message: str) -> str:
"""Agent-based chat with tools"""
return self.agent.run(message)
def switch_model(self, model_name: str) -> bool:
"""Switch to different model"""
if self.manager.pull_model(model_name):
self.llm.model_name = model_name
print(f"✅ Switched to model: {model_name}")
return True
return False
def load_documents(self, file_paths: List[str]):
"""Load documents into RAG system"""
self.rag_system.add_documents(file_paths)
def get_performance_stats(self) -> Dict[str, Any]:
"""Get system performance statistics"""
return self.manager.performance_monitor.get_stats()
def cleanup(self):
"""Clean up resources"""
self.manager.stop_server()
print("✅ System cleanup complete")We use chat or parameter to keep different chat times, individually in its form of memory, can be buffer-based or summary or Sudriphy-based, to enable us to maintain. On Oollamalangchainsystem, we include everything: We enter everything: We're pulling the desired model, fold the rog system, start chat tools, and register outside tools such as web search.
def main():
"""Main function demonstrating the system"""
config = OllamaConfig(
model_name="llama2",
temperature=0.7,
max_tokens=2048
)
system = OllamaLangChainSystem(config)
try:
if not system.setup():
return
print("n🗣️ Testing basic chat:")
response = system.chat("Hello! How are you?")
print(f"Response: {response}")
print("n🔄 Testing model switching:")
models = system.manager.list_models()
print(f"Available models: {models}")
print("n🤖 Testing agent:")
agent_response = system.agent_chat("What's the current weather like?")
print(f"Agent Response: {agent_response}")
print("n📊 Performance Statistics:")
stats = system.get_performance_stats()
print(json.dumps(stats, indent=2))
except KeyboardInterrupt:
print("n⏹️ Interrupted by user")
except Exception as e:
print(f"❌ Error: {e}")
finally:
system.cleanup()
def create_gradio_interface(system: OllamaLangChainSystem):
"""Create a Gradio interface for easy interaction"""
try:
import gradio as gr
def chat_interface(message, history, mode):
if mode == "Basic Chat":
response = system.chat(message)
elif mode == "RAG Chat":
result = system.rag_chat(message)
response = result["answer"]
elif mode == "Agent Chat":
response = system.agent_chat(message)
else:
response = "Unknown mode"
history.append((message, response))
return "", history
def upload_docs(files):
if files:
file_paths = [f.name for f in files]
system.load_documents(file_paths)
return f"Loaded {len(file_paths)} documents into RAG system"
return "No files uploaded"
def get_stats():
stats = system.get_performance_stats()
return json.dumps(stats, indent=2)
with gr.Blocks(title="Ollama + LangChain System") as demo:
gr.Markdown("# 🦙 Ollama + LangChain Advanced System")
with gr.Tab("Chat"):
chatbot = gr.Chatbot()
mode = gr.Dropdown(
["Basic Chat", "RAG Chat", "Agent Chat"],
value="Basic Chat",
label="Chat Mode"
)
msg = gr.Textbox(label="Message")
clear = gr.Button("Clear")
msg.submit(chat_interface, [msg, chatbot, mode], [msg, chatbot])
clear.click(lambda: ([], ""), outputs=[chatbot, msg])
with gr.Tab("Document Upload"):
file_upload = gr.File(file_count="multiple", label="Upload Documents")
upload_btn = gr.Button("Upload to RAG System")
upload_status = gr.Textbox(label="Status")
upload_btn.click(upload_docs, file_upload, upload_status)
with gr.Tab("Performance"):
stats_btn = gr.Button("Get Performance Stats")
stats_output = gr.Textbox(label="Performance Statistics")
stats_btn.click(get_stats, outputs=stats_output)
return demo
except ImportError:
print("Gradio not installed. Skipping interface creation.")
return None
if __name__ == "__main__":
print("🚀 Ollama + LangChain System for Google Colab")
print("=" * 50)
main()
# Or create a system instance for interactive use
# config = OllamaConfig(model_name="llama2")
# system = OllamaLangChainSystem(config)
# system.setup()
# # Create Gradio interface
# demo = create_gradio_interface(system)
# if demo:
# demo.launch(share=True) # share=True for public linkWe threaten everything in great work to work full, setup, testing, Agent Tools, Agent Tools, Listing of model, and operating statistics. Then, in Decrage_Gradio_ininterface (), create a Gradio app that is easily used for conversing tabs, loading documents in the RAG program, monitoring performance. Finally, we are very expensive () on __ Main__ block directly to Colob, or open the gradio ui for effective tests and community sharing.
In conclusion, we have a variable playground: We change Ollama models, discuss the buffered memory or SUMMARY, and we look at basic statistics within Colob restrictions. This code is a modular, it allows us to extend the toolbar, measurement options, temperature, high tokens, comurringy) in the Ollamaconfig, or sync the RAG pipe in large corpora corpora or models. We start the Gradio app with share = TrueS to work together or embed these parts in our projects. We now have a template in the rapid of the speedy test of the llm.
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