ollama_langchain_tutorial_marktechpost.py

# -*- coding: utf-8 -*-
"""ollama_langchain_tutorial_Marktechpost.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1NcQL_jwAowtjqvXcjTrRg-M_Bx9iJd4R
"""

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 DuckDuckGoSearchRun

@dataclass
class OllamaConfig:
    """Configuration for Ollama setup"""
    model_name: str = "llama2"
    base_url: str = "http://localhost:11434"
    max_tokens: int = 2048
    temperature: float = 0.7
    gpu_layers: int = -1
    context_window: int = 4096
    batch_size: int = 512
    threads: int = 4

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", "https://ollama.com/install.sh", "-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")

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)
        }

class OllamaLLM(LLM):
    """Custom LangChain LLM for Ollama"""

    model_name: str = "llama2"
    base_url: str = "http://localhost:11434"
    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)}"

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)}"}

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")

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 link