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docs/notebooks/langchain/README.md

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+# LangChain + Llama Stack Document Processing
+
+1. **`langchain-llama-stack.py`** - Interactive CLI version
+---
+
+## 📋 Prerequisites
+
+### System Requirements
+- Python 3.12+
+- Llama Stack server running on `http://localhost:8321/`
+- Ollama or compatible model server
+
+### Environment Setup
+```bash
+# Create and activate virtual environment
+python3.12 -m venv llama-env-py312
+source llama-env-py312/bin/activate
+
+# Install dependencies
+pip install llama-stack-client langchain langchain-core langchain-community beautifulsoup4 markdownify readability-lxml requests langchain_openai
+```
+
+---
+
+## 🚀 Quick Start
+
+### Start Llama Stack Server
+Before running either version, ensure your Llama Stack server is running:
+```bash
+# Start Llama Stack server (example)
+llama stack run your-config --port 8321
+```
+
+---
+
+## 📖 Option 1: Interactive CLI Version (`langchain-llama-stack.py`)
+
+### Features
+- ✅ Interactive command-line interface
+- ✅ Document loading from URLs and PDFs
+- ✅ AI-powered summarization and fact extraction
+- ✅ Question-answering based on document content
+- ✅ Session-based document storage
+
+### How to Run
+```bash
+
+# Run the interactive CLI
+cd /docs/notebooks/langchain/
+python langchain-llama-stack.py
+```
+
+### Usage Commands
+Once running, you can use these interactive commands:
+
+```
+🎯 Interactive Document Processing Demo
+Commands:
+  load <url_or_path>  - Process a document
+  ask <question>      - Ask about the document
+  summary            - Show document summary
+  facts              - Show extracted facts
+  help               - Show commands
+  quit               - Exit demo
+```
+
+### Example Session
+```
+> load https://en.wikipedia.org/wiki/Artificial_intelligence
+📄 Loading document from: https://en.wikipedia.org/wiki/Artificial_intelligence
+✅ Loaded 45,832 characters
+📝 Generating summary...
+🔍 Extracting key facts...
+✅ Processing complete!
+
+> summary
+📝 Summary:
+Artificial intelligence (AI) is the simulation of human intelligence...
+
+> ask What are the main types of AI?
+💬 Q: What are the main types of AI?
+📝 A: Based on the document, the main types of AI include...
+
+> facts
+🔍 Key Facts:
+- AI was founded as an academic discipline in 1956
+- Machine learning is a subset of AI...
+
+> quit
+👋 Thanks for exploring LangChain chains!
+```
+
+
+#### Using curl:
+```bash
+# Check service status
+curl http://localhost:8000/
+
+# Process a document
+curl -X POST http://localhost:8000/process \
+     -H 'Content-Type: application/json' \
+     -d '{"source": "https://en.wikipedia.org/wiki/Machine_learning"}'
+
+# Ask a question
+curl -X POST http://localhost:8000/ask \
+     -H 'Content-Type: application/json' \
+     -d '{"question": "What is machine learning?"}'
+
+# Get summary
+curl http://localhost:8000/summary
+
+# Get facts
+curl http://localhost:8000/facts
+
+# List all processed documents
+curl http://localhost:8000/docs
+```
+
+#### Using Python requests:
+```python
+import requests
+
+# Process a document
+response = requests.post(
+    "http://localhost:8000/process",
+    json={"source": "https://en.wikipedia.org/wiki/Deep_learning"},
+)
+print(response.json())
+
+# Ask a question
+response = requests.post(
+    "http://localhost:8000/ask", json={"question": "What are neural networks?"}
+)
+print(response.json())
+
+# Get facts
+response = requests.get("http://localhost:8000/facts")
+print(response.json())
+```
+
+---
+
+## 🔧 Configuration
+
+### Model Configuration
+Both versions use these models by default:
+- **Model ID**: `llama3.2:3b`
+- **Llama Stack URL**: `http://localhost:8321/`
+
+To change the model, edit the `model_id` parameter in the respective files.
+
+### Supported Document Types
+- ✅ **URLs**: Any web page (extracted using readability)
+- ✅ **PDF files**: Local or remote PDF documents
+- ❌ Plain text files (can be added if needed)
+
+---
+
+## 🛠️ Troubleshooting
+
+### Common Issues
+
+#### 1. Connection Refused to Llama Stack
+**Error**: `Connection refused to http://localhost:8321/`
+**Solution**:
+- Ensure Llama Stack server is running
+- Check if port 8321 is correct
+- Verify network connectivity
+
+#### 2. Model Not Found
+**Error**: `Model not found: llama3.2:3b`
+**Solution**:
+- Check available models: `curl http://localhost:8321/models/list`
+- Update `model_id` in the code to match available models
+
+
+#### 4. Missing Dependencies
+### Debug Mode
+To enable verbose logging, add this to the beginning of either file:
+```python
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
+```
+
+---
+
+## 📊 Performance Notes
+
+### CLI Version
+- **Pros**: Simple to use, interactive, good for testing
+- **Cons**: Single-threaded, session-based only
+- **Best for**: Development, testing, manual document analysis
+---
+
+## 🛑 Stopping Services
+
+### CLI Version
+- Press `Ctrl+C` or type `quit` in the interactive prompt
+---
+
+## 📝 Examples
+
+### CLI Workflow
+1. Start: `python langchain-llama-stack.py`
+2. Load document: `load https://arxiv.org/pdf/2103.00020.pdf`
+3. Get summary: `summary`
+4. Ask questions: `ask What are the main contributions?`
+5. Exit: `quit`
+
+---
+
+## 🤝 Contributing
+
+To extend functionality:
+1. Add new prompt templates for different analysis types
+2. Support additional document formats
+3. Add caching for processed documents
+4. Implement user authentication for API version
+
+---
+
+## 📜 License
+
+This project is for educational and research purposes.

docs/notebooks/langchain/langchain-llama-stack.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import html
+import os
+import re
+import tempfile
+from typing import Any, List, Optional
+
+import requests
+from bs4 import BeautifulSoup
+from langchain.chains import LLMChain
+from langchain_community.document_loaders import PyPDFLoader, TextLoader
+
+from langchain_core.language_models.llms import LLM
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+
+from llama_stack_client import LlamaStackClient
+from markdownify import markdownify
+from readability import Document as ReadabilityDocument
+from rich.pretty import pprint
+
+# Global variables
+client = None
+llm = None
+summary_chain = None
+facts_chain = None
+qa_chain = None
+processed_docs = {}
+
+# Prompt Templates (defined globally)
+summary_template = PromptTemplate(
+    input_variables=["document"],
+    template="""Create a concise summary of this document in 5-10 sentences:
+
+{document}
+
+SUMMARY:""",
+)
+
+facts_template = PromptTemplate(
+    input_variables=["document"],
+    template="""Extract the most important facts from this document. List them as bullet points:
+
+{document}
+
+KEY FACTS:
+-""",
+)
+
+qa_template = PromptTemplate(
+    input_variables=["document", "question"],
+    template="""Based on the following document, answer the question. If the answer isn't in the document, say so.
+
+DOCUMENT:
+{document}
+
+QUESTION: {question}
+
+ANSWER:""",
+)
+
+
+def load_document(source: str) -> str:
+    is_url = source.startswith(("http://", "https://"))
+    is_pdf = source.lower().endswith(".pdf")
+    if is_pdf:
+        return load_pdf(source, is_url=is_url)
+    elif is_url:
+        return load_from_url(source)
+    else:
+        raise ValueError(f"Unsupported format. Use URLs or PDF files.")
+
+
+def load_pdf(source: str, is_url: bool = False) -> str:
+    if is_url:
+        response = requests.get(source)
+        response.raise_for_status()
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+            temp_file.write(response.content)
+            file_path = temp_file.name
+    else:
+        file_path = source
+    try:
+        loader = PyPDFLoader(file_path)
+        docs = loader.load()
+        return "\\n\\n".join([doc.page_content for doc in docs])
+    finally:
+        if is_url:
+            os.remove(file_path)
+
+
+def load_from_url(url: str) -> str:
+    headers = {"User-Agent": "Mozilla/5.0 (compatible; DocumentLoader/1.0)"}
+    response = requests.get(url, headers=headers, timeout=15)
+    response.raise_for_status()
+    doc = ReadabilityDocument(response.text)
+    html_main = doc.summary(html_partial=True)
+    soup = BeautifulSoup(html_main, "html.parser")
+    for tag in soup(
+        ["script", "style", "noscript", "header", "footer", "nav", "aside"]
+    ):
+        tag.decompose()
+    md_text = markdownify(str(soup), heading_style="ATX")
+    md_text = html.unescape(md_text)
+    md_text = re.sub(r"\n{3,}", "\n\n", md_text).strip()
+    return md_text
+
+
+def process_document(source: str):
+    global summary_chain, facts_chain, processed_docs
+
+    print(f"📄 Loading document from: {source}")
+    document = load_document(source)
+    print(f"✅ Loaded {len(document):,} characters")
+    print("\n📝 Generating summary...")
+    summary = summary_chain.invoke({"document": document})["text"]
+    print("Summary generated")
+    print("🔍 Extracting key facts...")
+    facts = facts_chain.invoke({"document": document})["text"]
+    processed_docs[source] = {"document": document, "summary": summary, "facts": facts}
+    print(f"\n✅ Processing complete!")
+    print(f"📊 Document: {len(document):,} chars")
+    print(f"📝 Summary: {summary[:100]}...")
+    print(f"🔍 Facts: {facts[:1000]}...")
+    return processed_docs[source]
+
+
+def ask_question(question: str, source: str = None):
+    """Answer questions about processed documents"""
+    global qa_chain, processed_docs
+
+    if not processed_docs:
+        return "No documents processed yet. Use process_document() first."
+    if source and source in processed_docs:
+        doc_data = processed_docs[source]
+    else:
+        # Use the most recent document
+        doc_data = list(processed_docs.values())[-1]
+    answer = qa_chain.invoke({"document": doc_data["document"], "question": question})[
+        "text"
+    ]
+    return answer
+
+
+def interactive_demo():
+    print("\n🎯 Interactive Document Processing Demo")
+    print("Commands:")
+    print("  load <url_or_path>  - Process a document")
+    print("  ask <question>      - Ask about the document")
+    print("  summary            - Show document summary")
+    print("  facts              - Show extracted facts")
+    print("  help               - Show commands")
+    print("  quit               - Exit demo")
+
+    while True:
+        try:
+            command = input("\n> ").strip()
+            if command.lower() in ["quit", "exit"]:
+                print("👋 Thanks for exploring LangChain chains!")
+                break
+            elif command.lower() == "help":
+                print("\nCommands:")
+                print("  load <url_or_path>  - Process a document")
+                print("  ask <question>      - Ask about the document")
+                print("  summary            - Show document summary")
+                print("  facts              - Show extracted facts")
+            elif command.startswith("load "):
+                source = command[5:].strip()
+                if source:
+                    try:
+                        process_document(source)
+                    except Exception as e:
+                        print(f"❌ Error processing document: {e}")
+                else:
+                    print("❓ Please provide a URL or file path")
+            elif command.startswith("ask "):
+                question = command[4:].strip()
+                if question:
+                    try:
+                        answer = ask_question(question)
+                        print(f"\n💬 Q: {question}")
+                        print(f"📝 A: {answer}")
+                    except Exception as e:
+                        print(f"❌ Error: {e}")
+                else:
+                    print("❓ Please provide a question")
+            elif command.lower() == "summary":
+                if processed_docs:
+                    latest_doc = list(processed_docs.values())[-1]
+                    print(f"\n📝 Summary:\n{latest_doc['summary']}")
+                else:
+                    print("❓ No documents processed yet")
+            elif command.lower() == "facts":
+                if processed_docs:
+                    latest_doc = list(processed_docs.values())[-1]
+                    print(f"\n🔍 Key Facts:\n{latest_doc['facts']}")
+                else:
+                    print("❓ No documents processed yet")
+            else:
+                print("❓ Unknown command. Type 'help' for options")
+        except (EOFError, KeyboardInterrupt):
+            print("\n👋 Goodbye!")
+            break
+
+
+def main():
+    global client, llm, summary_chain, facts_chain, qa_chain, processed_docs
+
+    print("🚀 Starting LangChain + Llama Stack Document Processing Demo")
+
+    client = LlamaStackClient(
+        base_url="http://localhost:8321/",
+    )
+    llm = ChatOpenAI(model="ollama/llama3:70b-instruct", base_url="http://localhost:8321/v1/openai/v1")
+
+    # Test the wrapper
+    test_response = llm.invoke("Can you help me with the document processing?")
+    print(f"✅ LangChain wrapper working!")
+    print(f"Response: {test_response.content[:100]}...")
+
+    print("Available models:")
+    for m in client.models.list():
+        print(f"- {m.identifier}")
+
+    print("----")
+    print("Available shields (safety models):")
+    for s in client.shields.list():
+        print(s.identifier)
+    print("----")
+
+    model_id = "ollama/llama3:70b-instruct"
+    # Create chains by combining our LLM with prompt templates
+    summary_chain = LLMChain(llm=llm, prompt=summary_template)
+    facts_chain = LLMChain(llm=llm, prompt=facts_template)
+    qa_chain = LLMChain(llm=llm, prompt=qa_template)
+
+    # Initialize storage for processed documents
+    processed_docs = {}
+
+    print("✅ Created 3 prompt templates:")
+    print("  • Summary: Condenses documents into key points")
+    print("  • Facts: Extracts important information as bullets")
+    print("  • Q&A: Answers questions based on document content")
+
+    # Test template formatting
+    test_prompt = summary_template.format(
+        document="This is a sample document about AI..."
+    )
+    print(f"\n📝 Example prompt: {len(test_prompt)} characters")
+
+    # Start the interactive demo
+    interactive_demo()
+
+
+if __name__ == "__main__":
+    main()