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https://github.com/meta-llama/llama-stack.git
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Changed the langChain dir name to langchain
Changed the langChain dir name to langchain
This commit is contained in:
Omar Abdelwahab
parent
84c960e9b5
commit
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2 changed files with 521 additions and 0 deletions
288
docs/notebooks/langchain/langchain-llama-stack.py
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288
docs/notebooks/langchain/langchain-llama-stack.py
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import os
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import re
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import html
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import requests
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from bs4 import BeautifulSoup
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from readability import Document as ReadabilityDocument
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from markdownify import markdownify
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from langchain_community.document_loaders import PyPDFLoader, TextLoader
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import tempfile
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from llama_stack_client import LlamaStackClient
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from langchain_core.language_models.llms import LLM
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from typing import Optional, List, Any
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from langchain.chains import LLMChain
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from langchain_core.prompts import PromptTemplate
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from rich.pretty import pprint
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# Global variables
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client = None
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llm = None
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summary_chain = None
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facts_chain = None
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qa_chain = None
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processed_docs = {}
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# Prompt Templates (defined globally)
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summary_template = PromptTemplate(
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input_variables=["document"],
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template="""Create a concise summary of this document in 5-10 sentences:
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{document}
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SUMMARY:"""
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)
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facts_template = PromptTemplate(
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input_variables=["document"],
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template="""Extract the most important facts from this document. List them as bullet points:
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{document}
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KEY FACTS:
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-"""
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)
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qa_template = PromptTemplate(
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input_variables=["document", "question"],
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template="""Based on the following document, answer the question. If the answer isn't in the document, say so.
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DOCUMENT:
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{document}
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QUESTION: {question}
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ANSWER:"""
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)
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class LlamaStackLLM(LLM):
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"""Simple LangChain wrapper for Llama Stack"""
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# Pydantic model fields
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client: Any = None
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model_id: str = "llama3:70b-instruct"
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def __init__(self, client, model_id: str = "llama3:70b-instruct"):
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# Initialize with field values
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super().__init__(client=client, model_id=model_id)
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def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs) -> str:
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"""Make inference call to Llama Stack"""
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response = self.client.inference.chat_completion(
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model_id=self.model_id,
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messages=[{"role": "user", "content": prompt}]
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)
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return response.completion_message.content
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@property
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def _llm_type(self) -> str:
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return "llama_stack"
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def load_document(source: str) -> str:
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is_url = source.startswith(('http://', 'https://'))
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is_pdf = source.lower().endswith('.pdf')
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if is_pdf:
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return load_pdf(source, is_url=is_url)
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elif is_url:
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return load_from_url(source)
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else:
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raise ValueError(f"Unsupported format. Use URLs or PDF files.")
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def load_pdf(source: str, is_url: bool = False) -> str:
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if is_url:
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response = requests.get(source)
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response.raise_for_status()
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with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
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temp_file.write(response.content)
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file_path = temp_file.name
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else:
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file_path = source
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try:
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loader = PyPDFLoader(file_path)
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docs = loader.load()
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return "\\n\\n".join([doc.page_content for doc in docs])
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finally:
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if is_url:
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os.remove(file_path)
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def load_from_url(url: str) -> str:
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headers = {'User-Agent': 'Mozilla/5.0 (compatible; DocumentLoader/1.0)'}
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response = requests.get(url, headers=headers, timeout=15)
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response.raise_for_status()
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doc = ReadabilityDocument(response.text)
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html_main = doc.summary(html_partial=True)
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soup = BeautifulSoup(html_main, "html.parser")
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for tag in soup(["script", "style", "noscript", "header", "footer", "nav", "aside"]):
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tag.decompose()
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md_text = markdownify(str(soup), heading_style="ATX")
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md_text = html.unescape(md_text)
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md_text = re.sub(r"\n{3,}", "\n\n", md_text).strip()
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return md_text
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def process_document(source: str):
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global summary_chain, facts_chain, processed_docs
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print(f"📄 Loading document from: {source}")
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document = load_document(source)
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print(f"✅ Loaded {len(document):,} characters")
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print("\n📝 Generating summary...")
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summary = summary_chain.invoke({"document": document})["text"]
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print("Summary generated")
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print("🔍 Extracting key facts...")
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facts = facts_chain.invoke({"document": document})["text"]
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processed_docs[source] = {
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"document": document,
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"summary": summary,
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"facts": facts
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}
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print(f"\n✅ Processing complete!")
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print(f"📊 Document: {len(document):,} chars")
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print(f"📝 Summary: {summary[:100]}...")
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print(f"🔍 Facts: {facts[:1000]}...")
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return processed_docs[source]
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def ask_question(question: str, source: str = None):
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"""Answer questions about processed documents"""
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global qa_chain, processed_docs
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if not processed_docs:
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return "No documents processed yet. Use process_document() first."
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if source and source in processed_docs:
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doc_data = processed_docs[source]
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else:
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# Use the most recent document
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doc_data = list(processed_docs.values())[-1]
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answer = qa_chain.invoke({
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"document": doc_data["document"],
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"question": question
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})["text"]
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return answer
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def interactive_demo():
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print("\n🎯 Interactive Document Processing Demo")
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print("Commands:")
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print(" load <url_or_path> - Process a document")
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print(" ask <question> - Ask about the document")
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print(" summary - Show document summary")
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print(" facts - Show extracted facts")
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print(" help - Show commands")
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print(" quit - Exit demo")
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while True:
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try:
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command = input("\n> ").strip()
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if command.lower() in ['quit', 'exit']:
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print("👋 Thanks for exploring LangChain chains!")
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break
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elif command.lower() == 'help':
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print("\nCommands:")
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print(" load <url_or_path> - Process a document")
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print(" ask <question> - Ask about the document")
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print(" summary - Show document summary")
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print(" facts - Show extracted facts")
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elif command.startswith('load '):
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source = command[5:].strip()
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if source:
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try:
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process_document(source)
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except Exception as e:
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print(f"❌ Error processing document: {e}")
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else:
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print("❓ Please provide a URL or file path")
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elif command.startswith('ask '):
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question = command[4:].strip()
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if question:
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try:
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answer = ask_question(question)
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print(f"\n💬 Q: {question}")
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print(f"📝 A: {answer}")
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except Exception as e:
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print(f"❌ Error: {e}")
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else:
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print("❓ Please provide a question")
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elif command.lower() == 'summary':
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if processed_docs:
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latest_doc = list(processed_docs.values())[-1]
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print(f"\n📝 Summary:\n{latest_doc['summary']}")
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else:
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print("❓ No documents processed yet")
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elif command.lower() == 'facts':
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if processed_docs:
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latest_doc = list(processed_docs.values())[-1]
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print(f"\n🔍 Key Facts:\n{latest_doc['facts']}")
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else:
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print("❓ No documents processed yet")
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else:
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print("❓ Unknown command. Type 'help' for options")
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except (EOFError, KeyboardInterrupt):
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print("\n👋 Goodbye!")
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break
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def main():
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global client, llm, summary_chain, facts_chain, qa_chain, processed_docs
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print("🚀 Starting LangChain + Llama Stack Document Processing Demo")
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client = LlamaStackClient(
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base_url="http://localhost:8321/",
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)
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# Initialize the LangChain-compatible LLM
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llm = LlamaStackLLM(client)
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# Test the wrapper
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test_response = llm.invoke("Can you help me with the document processing?")
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print(f"✅ LangChain wrapper working!")
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print(f"Response: {test_response[:100]}...")
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print("Available models:")
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for m in client.models.list():
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print(f"- {m.identifier}")
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print("----")
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print("Available shields (safety models):")
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for s in client.shields.list():
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print(s.identifier)
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print("----")
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# model_id = "llama3.2:3b"
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model_id = "ollama/llama3:70b-instruct"
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response = client.inference.chat_completion(
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model_id=model_id,
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messages=[
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{"role": "system", "content": "You are a friendly assistant."},
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{"role": "user", "content": "Write a two-sentence poem about llama."},
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],
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)
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print(response.completion_message.content)
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# Create chains by combining our LLM with prompt templates
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summary_chain = LLMChain(llm=llm, prompt=summary_template)
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facts_chain = LLMChain(llm=llm, prompt=facts_template)
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qa_chain = LLMChain(llm=llm, prompt=qa_template)
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# Initialize storage for processed documents
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processed_docs = {}
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print("✅ Created 3 prompt templates:")
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print(" • Summary: Condenses documents into key points")
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print(" • Facts: Extracts important information as bullets")
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print(" • Q&A: Answers questions based on document content")
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# Test template formatting
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test_prompt = summary_template.format(document="This is a sample document about AI...")
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print(f"\n📝 Example prompt: {len(test_prompt)} characters")
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# Start the interactive demo
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interactive_demo()
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if __name__ == "__main__":
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main()
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