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https://github.com/Shubhamsaboo/awesome-llm-apps.git
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local version of the same
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Madhu
279
rag_tutorials/llm_app_hybrid_RAG_claude/local_main.py
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279
rag_tutorials/llm_app_hybrid_RAG_claude/local_main.py
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import os
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import logging
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import chainlit as cl
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from raglite import RAGLiteConfig, insert_document, hybrid_search, retrieve_chunks, rerank_chunks, rag
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from rerankers import Reranker
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from typing import List, Tuple
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from pathlib import Path
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from chainlit.input_widget import TextInput
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import time
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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my_config = None
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# System prompt for local LLM
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RAG_SYSTEM_PROMPT = """
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You are a friendly and knowledgeable assistant that provides complete and insightful answers.
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Answer the user's question using only the context below.
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When responding, you MUST NOT reference the existence of the context, directly or indirectly.
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Instead, you MUST treat the context as if its contents are entirely part of your working memory.
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""".strip()
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def initialize_config(settings: dict) -> RAGLiteConfig:
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"""Initialize RAGLite configuration with local models."""
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try:
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return RAGLiteConfig(
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db_url=settings["DBUrl"],
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llm=f"llama-cpp-python/{settings['LLMPath']}",
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embedder=f"llama-cpp-python/{settings['EmbedderPath']}",
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embedder_normalize=True,
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chunk_max_size=2000,
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embedder_sentence_window_size=2,
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reranker=(
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("en", Reranker("ms-marco-MiniLM-L-12-v2", model_type="flashrank")),
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("other", Reranker("ms-marco-MultiBERT-L-12", model_type="flashrank")),
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)
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)
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except KeyError as e:
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raise ValueError(f"Missing required setting: {e}")
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def process_document(file_path: str) -> None:
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"""Process and embed a document into the database."""
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logger.info(f"Starting to process document: {file_path}")
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try:
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import time
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start_time = time.time()
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# Inserting document into PostgreSQL database
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insert_document(Path(file_path), config=my_config)
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processing_time = time.time() - start_time
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logger.info(f"Document processed and embedded in {processing_time:.2f} seconds")
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except Exception as e:
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logger.error(f"Error processing document: {str(e)}")
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raise
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def perform_search(query: str) -> List[dict]:
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"""Perform hybrid search and reranking on the query."""
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logger.info(f"Performing hybrid search for: {query}")
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try:
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chunk_ids, scores = hybrid_search(query, num_results=10, config=my_config)
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logger.debug(f"Found {len(chunk_ids)} chunks with scores: {scores}")
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if not chunk_ids:
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logger.info("No relevant chunks found in database")
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return []
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chunks = retrieve_chunks(chunk_ids, config=my_config)
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reranked_chunks = rerank_chunks(query, chunks, config=my_config)
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return reranked_chunks
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except Exception as e:
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logger.error(f"Search error: {str(e)}")
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return []
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@cl.on_settings_update
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async def handle_settings_update(settings: dict):
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"""Handle settings updates when user submits the form."""
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try:
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def validate_path(path: str, path_type: str) -> bool:
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if not path:
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raise ValueError(f"{path_type} path is required")
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return True
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def validate_db_url(url: str) -> bool:
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if not url:
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raise ValueError("Database URL is required")
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if not url.startswith(('sqlite://', 'postgresql://')):
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raise ValueError("Invalid database URL format")
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return True
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# Validate settings
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validate_path(settings["LLMPath"], "LLM")
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validate_path(settings["EmbedderPath"], "Embedder")
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validate_db_url(settings["DBUrl"])
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global my_config
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my_config = initialize_config(settings)
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cl.user_session.set("settings", settings)
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await cl.Message(content="✅ Successfully configured with local models!").send()
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# Ask for file upload
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files = await cl.AskFileMessage(
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content="Please upload one or more PDF documents to begin!",
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accept=["application/pdf"],
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max_size_mb=20,
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timeout=300,
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max_files=5
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).send()
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if files:
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success = False
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# Process uploaded files
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for file in files:
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logger.info(f"Starting to process file: {file.name}")
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# Create new message for each file
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await cl.Message(f"Processing {file.name}...").send()
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try:
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logger.info(f"Embedding document: {file.path}")
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process_document(file_path=file.path)
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success = True
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await cl.Message(f"✅ Successfully processed: {file.name}").send()
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logger.info(f"Successfully processed and embedded: {file.name}")
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except Exception as proc_error:
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error_msg = f"Failed to process {file.name}: {str(proc_error)}"
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logger.error(error_msg)
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await cl.Message(f"❌ {error_msg}").send()
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continue
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if success:
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# Send completion message
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await cl.Message(
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content="✅ Documents are ready! You can now ask questions about them."
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).send()
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# Store session state
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cl.user_session.set("documents_loaded", True)
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# Reset the chat interface
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await cl.Message(content="Ask your first question:").send()
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# Clear any existing message elements
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cl.user_session.set("message_elements", [])
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else:
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await cl.Message(
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content="❌ No documents were successfully processed. Please try uploading again."
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).send()
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except Exception as e:
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error_msg = f"❌ Error with provided settings: {str(e)}"
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logger.error(error_msg)
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await cl.Message(content=error_msg).send()
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@cl.on_chat_start
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async def start() -> None:
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"""Initialize chat and request model paths."""
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try:
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logger.info("Chat session started")
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cl.user_session.set("chat_history", [])
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await cl.Message(
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content="""# 🤖 Local LLM-Powered Hybrid Search-RAG Assistant
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Welcome! To get started:
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Enter your model paths and database URL below on the ChatSettings widget
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"""
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).send()
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await cl.ChatSettings([
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TextInput(
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id="LLMPath",
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label="LLM Path",
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initial="",
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placeholder="e.g., bartowski/Llama-3.2-3B-Instruct-GGUF/*Q4_K_M.gguf@4096"
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),
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TextInput(
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id="EmbedderPath",
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label="Embedder Path",
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initial="",
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placeholder="e.g., lm-kit/bge-m3-gguf/*F16.gguf@1024"
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),
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TextInput(
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id="DBUrl",
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label="Database URL",
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initial="",
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placeholder="sqlite:///raglite.sqlite or postgresql://user:pass@host:port/db"
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),
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]).send()
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except Exception as e:
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logger.error(f"Error in chat start: {str(e)}")
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await cl.Message(content=f"Error initializing chat: {str(e)}").send()
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@cl.on_message
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async def message_handler(message: cl.Message) -> None:
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"""Handle user queries using local RAG."""
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try:
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if not cl.user_session.get("documents_loaded"):
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await cl.Message(content="❌ Please upload and process documents first!").send()
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return
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if not my_config:
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await cl.Message(content="❌ Please configure your model paths first!").send()
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return
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msg = cl.Message(content="")
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await msg.send()
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query = message.content.strip()
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if not query:
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await cl.Message(content="❌ Please enter a valid question.").send()
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return
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logger.info(f"Processing query: {query}")
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try:
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reranked_chunks = perform_search(query)
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if not reranked_chunks:
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await cl.Message(content="No relevant information found in the documents.").send()
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return
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chat_history = cl.user_session.get("chat_history", [])
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formatted_messages = []
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for user_msg, assistant_msg in chat_history:
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formatted_messages.append({"role": "user", "content": user_msg})
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formatted_messages.append({"role": "assistant", "content": assistant_msg})
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response_stream = rag(
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prompt=query,
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system_prompt=RAG_SYSTEM_PROMPT,
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search=hybrid_search,
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messages=formatted_messages,
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max_contexts=5,
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config=my_config
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)
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full_response = ""
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for chunk in response_stream:
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if chunk:
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full_response += chunk
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await msg.stream_token(chunk)
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if not full_response:
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await cl.Message(content="❌ No response generated. Please try rephrasing your question.").send()
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return
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chat_history.append((query, full_response))
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cl.user_session.set("chat_history", chat_history)
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except Exception as e:
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logger.error(f"RAG error: {str(e)}")
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await cl.Message(content=f"❌ Error generating response: {str(e)}").send()
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except Exception as e:
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error_msg = f"Error processing your question: {str(e)}"
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logger.error(error_msg)
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await cl.Message(content=f"❌ {error_msg}").send()
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@cl.on_chat_end
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async def on_chat_end():
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"""Clean up session data when chat ends."""
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try:
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cl.user_session.clear()
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logger.info("Chat session ended, cleared session data")
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except Exception as e:
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logger.error(f"Error clearing session data: {e}")
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if __name__ == "__main__":
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cl.run()
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