wehub-resource-sync
613 lines
20 KiB
Plaintext
613 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Chat with Code - RAG System with Codex Validation\n",
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"\n",
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"This notebook demonstrates a Retrieval-Augmented Generation (RAG) system that allows you to chat with code repositories. The system uses LlamaIndex for orchestration and Milvus for vector search, combined with Cleanlab Codex for response validation.\n",
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"\n",
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"## Features\n",
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"- Clone and parse GitHub repositories\n",
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"- Support for multiple file types (Python, JavaScript, TypeScript, Markdown, Jupyter notebooks)\n",
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"- Vector-based similarity search using Milvus\n",
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"- Custom prompt templates for better responses\n",
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"- Response validation using Cleanlab Codex"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 📦 Dependencies and Imports\n",
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"\n",
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"Setting up all required libraries for the RAG system:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import re\n",
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"import glob\n",
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"import subprocess\n",
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"import nest_asyncio\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, display\n",
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"\n",
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"from llama_index.core import Settings\n",
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"from llama_index.llms.openrouter import OpenRouter\n",
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"from llama_index.core import PromptTemplate\n",
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"from llama_index.core import SimpleDirectoryReader\n",
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"from llama_index.core import VectorStoreIndex\n",
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"from llama_index.core.storage.storage_context import StorageContext\n",
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"from llama_index.core.node_parser import CodeSplitter, MarkdownNodeParser\n",
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"\n",
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"from llama_index.core.indices.vector_store.base import VectorStoreIndex\n",
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"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
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"from llama_index.vector_stores.milvus import MilvusVectorStore"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 🔧 Codex Client Setup\n",
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"\n",
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"Initialize Cleanlab Codex for response validation and quality assurance."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from cleanlab_codex.project import Project\n",
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"from cleanlab_codex.client import Client\n",
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"\n",
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"# Set your Codex API key (from https://codex.cleanlab.ai/account)\n",
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"os.environ[\"CODEX_API_KEY\"] = \"<your_codex_api_key_here>\"\n",
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"\n",
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"# Initialize Codex client and project\n",
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"codex_client = Client()\n",
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"project = codex_client.create_project(name=\"Chat-with-Code\", description=\"Code RAG project with added validation of Codex\")\n",
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"access_key = project.create_access_key(\"test-access-key\")\n",
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"project = Project.from_access_key(access_key)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## ⚙️ Configuration Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Allows nested access to the event loop\n",
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"nest_asyncio.apply()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 🤖 LLM and Embedding Model Configuration\n",
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"\n",
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"Setting up OpenRouter LLM and HuggingFace embedding model for the RAG pipeline."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e4910b8ae3e44ad2a0ab4554db2c18d3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"model_id": "2fb6a9eac3854330a361e60227ce6fd4",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"config_sentence_transformers.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"model_id": "6ad3c56ab0644c8dac1e2ee54017a91d",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"README.md: 0.00B [00:00, ?B/s]"
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"sentence_bert_config.json: 0%| | 0.00/52.0 [00:00<?, ?B/s]"
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"metadata": {},
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"output_type": "display_data"
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"version_minor": 0
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"model_id": "2e26f0cd108a4f6ea8218ecaafacd034",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"metadata": {},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9fcc9d43c120464eb6ac590e0ecc287a",
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"version_major": 2,
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"version_minor": 0
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"metadata": {},
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"data": {
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"model_id": "e951f6b370a546ad974d5792425f8509",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"vocab.txt: 0.00B [00:00, ?B/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"model_id": "f6cfbc9424bf4f648eda54f402a6d3e6",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"tokenizer.json: 0.00B [00:00, ?B/s]"
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "983efcb64d14473b95305f09ac754892",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6f9dbd11585e43b9a789326bf6492018",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Setting up the LLM\n",
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"llm = OpenRouter(api_key=\"<your_openrouter_api_key_here>\", model=\"qwen/qwen3-coder:free\")\n",
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"Settings.llm = llm\n",
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"\n",
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"# Setting up the embedding model\n",
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"Settings.embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-base-en-v1.5\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 🛠️ Utility Functions\n",
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"\n",
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"Core functions for repository handling, document parsing, and index creation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def parse_github_url(url):\n",
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" pattern = r\"https://github\\.com/([^/]+)/([^/]+)\"\n",
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" match = re.match(pattern, url)\n",
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" return match.groups() if match else (None, None)\n",
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"\n",
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"def clone_github_repo(repo_url): \n",
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" try:\n",
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" print('Cloning the repo ...')\n",
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" result = subprocess.run([\"git\", \"clone\", repo_url], check=True, text=True, capture_output=True)\n",
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" except subprocess.CalledProcessError as e:\n",
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" print(f\"Failed to clone repository: {e}\")\n",
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" return None\n",
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"\n",
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"def validate_owner_repo(owner, repo):\n",
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" return bool(owner) and bool(repo)\n",
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"\n",
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"def parse_docs_by_file_types(ext, language, input_dir_path):\n",
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" try:\n",
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" files = glob.glob(f\"{input_dir_path}/**/*{ext}\", recursive=True)\n",
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" \n",
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" if len(files) > 0:\n",
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" loader = SimpleDirectoryReader(\n",
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" input_dir=input_dir_path, required_exts=[ext], recursive=True\n",
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" )\n",
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" docs = loader.load_data()\n",
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"\n",
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" parser = (\n",
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" MarkdownNodeParser()\n",
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" if ext == \".md\"\n",
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" else CodeSplitter.from_defaults(language=language)\n",
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" )\n",
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" return parser.get_nodes_from_documents(docs)\n",
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" else:\n",
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" return []\n",
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" except Exception as e:\n",
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" print(f'Exception {e} occurred while parsing docs into nodes of file type {ext}')\n",
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" return []\n",
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"\n",
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"def create_index(nodes):\n",
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" vector_store = MilvusVectorStore(uri=\"http://localhost:19530\", dim=768, overwrite=True)\n",
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" storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
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" index = VectorStoreIndex(\n",
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" nodes,\n",
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" storage_context=storage_context,\n",
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" )\n",
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" return index"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 🔍 Query Engine Setup\n",
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"\n",
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"Main function to set up the complete RAG pipeline for a given GitHub repository."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def setup_query_engine(github_url):\n",
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" owner, repo = parse_github_url(github_url)\n",
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" \n",
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" if validate_owner_repo(owner, repo):\n",
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" # Clone the GitHub repo & save it in a directory\n",
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" # input_dir_path = f\"/teamspace/studios/this_studio/{repo}\"\n",
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" input_dir_path = os.path.join(os.getcwd(), repo)\n",
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"\n",
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" if os.path.exists(input_dir_path):\n",
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" pass\n",
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" else:\n",
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" clone_github_repo(github_url)\n",
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"\n",
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" try:\n",
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" file_types = {\n",
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" \".md\": \"markdown\",\n",
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" \".py\": \"python\",\n",
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" \".ipynb\": \"python\",\n",
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" \".js\": \"javascript\",\n",
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" \".ts\": \"typescript\"\n",
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" }\n",
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"\n",
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" nodes = []\n",
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" for ext, language in file_types.items():\n",
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" nodes += parse_docs_by_file_types(ext, language, input_dir_path)\n",
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"\n",
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" # ====== Create vector store index ======\n",
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" try:\n",
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" index = create_index(nodes)\n",
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" except:\n",
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" index = VectorStoreIndex(nodes=nodes, show_progress=True)\n",
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"\n",
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" # TODO try async index creation for faster emebdding generation & persist it to memory!\n",
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" # index = VectorStoreIndex(docs, use_async=True)\n",
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"\n",
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" # ====== Setup a query engine ======\n",
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" query_engine = index.as_query_engine(similarity_top_k=4)\n",
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" \n",
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" # ====== Customise prompt template ======\n",
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" qa_prompt_tmpl_str = (\n",
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" \"Context information is below.\\n\"\n",
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" \"---------------------\\n\"\n",
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" \"{context_str}\\n\"\n",
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" \"---------------------\\n\"\n",
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" \"Given the context information above, I want you to think step by step to answer the query in a crisp manner. \"\n",
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" \"First, carefully check if the answer can be found in the provided context. \"\n",
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" \"If the answer is available in the context, use that information to respond. \"\n",
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" \"If the answer is not available in the context or the context is insufficient, \"\n",
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" \"you may use your own knowledge to provide a helpful response. \"\n",
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" \"Only say 'I don't know!' if you cannot answer the question using either the context or your general knowledge.\\n\"\n",
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" \"Query: {query_str}\\n\"\n",
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" \"Answer: \"\n",
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" )\n",
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"\n",
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" qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)\n",
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"\n",
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" query_engine.update_prompts(\n",
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" {\"response_synthesizer:text_qa_template\": qa_prompt_tmpl}\n",
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" )\n",
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"\n",
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" if nodes:\n",
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" print(\"Data loaded successfully!!\")\n",
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" print(\"Ready to chat!!\")\n",
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" else:\n",
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" print(\"No data found, check if the repository is not empty!\")\n",
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" \n",
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" return query_engine\n",
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"\n",
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" except Exception as e:\n",
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" print(f\"An error occurred: {e}\")\n",
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" else:\n",
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" print('Invalid github repo, try again!')\n",
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" return None"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 🚀 Usage Example\n",
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"\n",
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"Let's test the system with a sample repository."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cloning the repo ...\n",
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"Data loaded successfully!!\n",
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"Ready to chat!!\n"
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]
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}
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],
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"source": [
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"# Provide url to the repository you want to chat with\n",
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"github_url = \"https://github.com/sitamgithub-MSIT/ClassyText\"\n",
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"\n",
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"query_engine = setup_query_engine(github_url=github_url)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 💬 Basic Query Test\n",
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"\n",
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"Testing the query engine with a simple question."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
|
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"The name of the Zero-shot Text Classification model used in this project is **ModernBERT-large-zeroshot-v2.0**."
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"response = query_engine.query(\"What is the name of the Zero-shot Text Classification model used in this project?\")\n",
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"display(Markdown(str(response)))"
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]
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},
|
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{
|
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"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
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"## ✅ Codex-Enhanced Query System\n",
|
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"\n",
|
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"Enhanced query function that includes Cleanlab Codex validation for improved response quality and reliability."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fallback_response = \"I'm sorry, I couldn't find an answer for that — can I help with something else?\"\n",
|
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"\n",
|
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"\n",
|
|
"def codex_validated_query(query_engine, user_query):\n",
|
|
" # Step 1: Get response from your RAG pipeline\n",
|
|
" response_obj = query_engine.query(user_query)\n",
|
|
" initial_response = str(response_obj)\n",
|
|
"\n",
|
|
" # Step 2: Convert to message format\n",
|
|
" context = response_obj.source_nodes\n",
|
|
" context_str = \"\\n\".join([n.node.text for n in context])\n",
|
|
"\n",
|
|
" prompt_template = (\n",
|
|
" \"Context information is below.\\n\"\n",
|
|
" \"---------------------\\n\"\n",
|
|
" \"{context}\\n\"\n",
|
|
" \"---------------------\\n\"\n",
|
|
" \"Given the context information above, I want you to think step by step to answer the query in a crisp manner. \"\n",
|
|
" \"First, carefully check if the answer can be found in the provided context. \"\n",
|
|
" \"If the answer is available in the context, use that information to respond. \"\n",
|
|
" \"If the answer is not available in the context or the context is insufficient, \"\n",
|
|
" \"you may use your own knowledge to provide a helpful response. \"\n",
|
|
" \"Only say 'I don't know!' if you cannot answer the question using either the context or your general knowledge.\\n\"\n",
|
|
" \"Query: {query}\\n\"\n",
|
|
" \"Answer: \"\n",
|
|
" )\n",
|
|
" user_prompt = prompt_template.format(context=context_str, query=user_query)\n",
|
|
" messages = [{\n",
|
|
" \"role\": \"user\",\n",
|
|
" \"content\": user_prompt,\n",
|
|
" }]\n",
|
|
"\n",
|
|
" # Step 3: Validate with Codex\n",
|
|
" result = project.validate(\n",
|
|
" messages=messages,\n",
|
|
" query=user_query,\n",
|
|
" context=context_str,\n",
|
|
" response=initial_response,\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Step 4: Return Codex-evaluated final response\n",
|
|
" final_response = (\n",
|
|
" result.expert_answer\n",
|
|
" if result.expert_answer and result.escalated_to_sme\n",
|
|
" else fallback_response if result.should_guardrail\n",
|
|
" else initial_response\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Step 5: Return both final response and full validation info\n",
|
|
" return {\n",
|
|
" \"final_response\": final_response,\n",
|
|
" \"validation_results\": result.model_dump()\n",
|
|
" }"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 🧪 Testing Codex-Validated Responses\n",
|
|
"\n",
|
|
"Compare the validated response with detailed validation metrics."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Final Answer:\n",
|
|
" The name of the Zero-shot Text Classification model used in this project is **ModernBERT-large-zeroshot-v2.0**.\n",
|
|
"\n",
|
|
"Validation Results:\n",
|
|
" deterministic_guardrails_results: {}\n",
|
|
" escalated_to_sme: False\n",
|
|
" eval_scores: {'trustworthiness': {'score': 0.99999998338089, 'triggered': False, 'triggered_escalation': False, 'triggered_guardrail': False, 'failed': False, 'log': None}, 'context_sufficiency': {'score': 0.99751243781125, 'triggered': False, 'triggered_escalation': False, 'triggered_guardrail': False, 'failed': False, 'log': None}, 'response_helpfulness': {'score': 0.9975124377834605, 'triggered': False, 'triggered_escalation': False, 'triggered_guardrail': False, 'failed': False, 'log': None}, 'query_ease': {'score': 0.7938874203515002, 'triggered': False, 'triggered_escalation': False, 'triggered_guardrail': False, 'failed': False, 'log': None}, 'response_groundedness': {'score': 0.9975124378111279, 'triggered': False, 'triggered_escalation': False, 'triggered_guardrail': False, 'failed': False, 'log': None}}\n",
|
|
" expert_answer: None\n",
|
|
" is_bad_response: False\n",
|
|
" should_guardrail: False\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output = codex_validated_query(query_engine, \"What is the name of the Zero-shot Text Classification model used in this project?\")\n",
|
|
"\n",
|
|
"print(\"Final Answer:\\n\", output[\"final_response\"])\n",
|
|
"print(\"\\nValidation Results:\")\n",
|
|
"for k, v in output[\"validation_results\"].items():\n",
|
|
" print(f\" {k}: {v}\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"language_info": {
|
|
"name": "python"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|