wehub-resource-sync
5294 lines
189 KiB
Plaintext
5294 lines
189 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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"# RAG + SQL Router with Cleanlab Codex\n",
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"\n",
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"This notebook demonstrates a (Text2SQL + RAG), hybrid agentic workflow"
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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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"## Setup and Configuration"
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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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"id": "guPyzpg83PxX"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"CODEX_API_KEY\"] = \"<your_codex_api_key_here>\""
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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": 2,
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"metadata": {
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"id": "EiGcgg9A3PxY"
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},
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"outputs": [],
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"source": [
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"import nest_asyncio\n",
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"\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 Configuration\n",
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"\n",
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"Configuring the language model (OpenRouter) and embedding model for semantic search capabilities"
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]
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},
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{
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"cell_type": "code",
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},
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"id": "PxQc2SZu3Pxa",
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"outputId": "90a80033-272d-4327-9b16-38829853195c"
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},
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"outputs": [],
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"source": [
|
||
"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.embeddings.huggingface import HuggingFaceEmbedding\n",
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"\n",
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"# Set the LLM and embedding model\n",
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"Settings.llm = OpenRouter(api_key=\"<your_openrouter_api_key_here>\", model=\"qwen/qwen-turbo\")\n",
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"Settings.embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-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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||
"## 🗄️ SQL Database 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": 4,
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||
"metadata": {
|
||
"id": "kglsOm_L6nEG"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sqlalchemy import create_engine\n",
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||
"from llama_index.core import SQLDatabase\n",
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||
"from llama_index.core.query_engine import NLSQLTableQueryEngine\n",
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"\n",
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"# Setup SQLite database\n",
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"db_path = \"city_database.sqlite\"\n",
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"engine = create_engine(f\"sqlite:///{db_path}\")\n",
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"sql_database = SQLDatabase(engine)\n",
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"\n",
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"# Create SQL query engine\n",
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"sql_query_engine = NLSQLTableQueryEngine(sql_database=sql_database, tables=[\"city_stats\"])"
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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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"## 📚 Document Query Engine Setup\n",
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"\n",
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"Creating a vector-based document search 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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
|
||
"from llama_index.vector_stores.milvus import MilvusVectorStore\n",
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"from llama_index.core.node_parser import MarkdownNodeParser\n",
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"from llama_index.readers.docling import DoclingReader\n",
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"from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, PromptTemplate, StorageContext\n",
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"\n",
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"def create_docs_query_engine():\n",
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" # Initialize reader and parser\n",
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" reader = DoclingReader()\n",
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" node_parser = MarkdownNodeParser()\n",
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"\n",
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" # Load documents from directory\n",
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" loader = SimpleDirectoryReader(input_dir=\"data\", file_extractor={\".pdf\": reader, \".docx\": reader})\n",
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" docs = loader.load_data()\n",
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"\n",
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" # Initialize vector store and storage context\n",
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" vector_store = MilvusVectorStore(uri=\"http://localhost:19530\", dim=384, overwrite=True)\n",
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" storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
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"\n",
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" # Create vector index from documents\n",
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" vector_index = VectorStoreIndex.from_documents(\n",
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" docs,\n",
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" show_progress=True,\n",
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" transformations=[node_parser],\n",
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" storage_context=storage_context\n",
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" )\n",
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"\n",
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" # Define custom QA prompt\n",
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" template = (\n",
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" \"You are a meticulous and accurate document analyst. Your task is to answer the user's question based exclusively on the provided context. \"\n",
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" \"Follow these rules strictly:\\n\"\n",
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" \"1. Your entire response must be grounded in the facts provided in the 'Context' section. Do not use any prior knowledge.\\n\"\n",
|
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" \"2. If multiple parts of the context are relevant, synthesize them into a single, coherent answer.\\n\"\n",
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" \"3. If the context does not contain the information needed to answer the question, you must state only: 'The provided context does not contain enough information to answer this question.'\\n\"\n",
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" \"-----------------------------------------\\n\"\n",
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" \"Context: {context_str}\\n\"\n",
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" \"-----------------------------------------\\n\"\n",
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" \"Question: {query_str}\\n\\n\"\n",
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" \"Answer:\"\n",
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" )\n",
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" qa_template = PromptTemplate(template)\n",
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"\n",
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" # Build and return query engine\n",
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" docs_query_engine = vector_index.as_query_engine(text_qa_template=qa_template, similarity_top_k=3)\n",
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" return docs_query_engine"
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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": [
|
||
"## 🛡️ Cleanlab Codex Integration\n",
|
||
"\n",
|
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"Setting up Cleanlab Codex for response validation"
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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": [
|
||
"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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"def create_codex_project():\n",
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" # Initialize Codex client and create a project\n",
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" codex_client = Client()\n",
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" project = codex_client.create_project(name=\"RAG + SQL\")\n",
|
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" access_key = project.create_access_key(\"default key\")\n",
|
||
" project = Project.from_access_key(access_key)\n",
|
||
" return project"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 🚀 Initialize Components\n",
|
||
"\n",
|
||
"Creating the core components:\n",
|
||
"1. **Document Query Engine**\n",
|
||
"2. **Codex Project**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "38419d5305d94590ac0b1dfff427f459",
|
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"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
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"Parsing nodes: 0%| | 0/1 [00:00<?, ?it/s]"
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]
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||
},
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||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "be0e08c223b94b67ada1b33736054fd9",
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"version_major": 2,
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"version_minor": 0
|
||
},
|
||
"text/plain": [
|
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"Generating embeddings: 0%| | 0/43 [00:00<?, ?it/s]"
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]
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||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create the query engine and project\n",
|
||
"docs_query_engine = create_docs_query_engine()\n",
|
||
"project = create_codex_project()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 🔍 Enhanced Document Query Function\n",
|
||
"\n",
|
||
"This function combines traditional RAG with Codex validation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def document_query_tool(query: str):\n",
|
||
" # Query the engine\n",
|
||
" response_obj = docs_query_engine.query(query)\n",
|
||
" initial_response = str(response_obj)\n",
|
||
"\n",
|
||
" # Gather source context\n",
|
||
" context = response_obj.source_nodes\n",
|
||
" context_str = \"\\n\".join([n.node.text for n in context])\n",
|
||
"\n",
|
||
" # Prepare prompt for Codex validation\n",
|
||
" prompt_template = (\n",
|
||
" \"You are a meticulous and accurate document analyst. Your task is to answer the user's question based exclusively on the provided context. \"\n",
|
||
" \"Follow these rules strictly:\\n\"\n",
|
||
" \"1. Your entire response must be grounded in the facts provided in the 'Context' section. Do not use any prior knowledge.\\n\"\n",
|
||
" \"2. If multiple parts of the context are relevant, synthesize them into a single, coherent answer.\\n\"\n",
|
||
" \"3. If the context does not contain the information needed to answer the question, you must state only: 'The provided context does not contain enough information to answer this question.'\\n\"\n",
|
||
" \"-----------------------------------------\\n\"\n",
|
||
" \"Context: {context}\\n\"\n",
|
||
" \"-----------------------------------------\\n\"\n",
|
||
" \"Question: {query}\\n\\n\"\n",
|
||
" \"Answer:\"\n",
|
||
" )\n",
|
||
" user_prompt = prompt_template.format(context=context_str, query=query)\n",
|
||
" messages = [{\"role\": \"user\", \"content\": user_prompt}]\n",
|
||
"\n",
|
||
" # Validate with Codex\n",
|
||
" result = project.validate(\n",
|
||
" messages=messages,\n",
|
||
" query=query,\n",
|
||
" context=context_str,\n",
|
||
" response=initial_response,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Final response selection\n",
|
||
" fallback_response = \"I'm sorry, I couldn't find answer — can I help with else?\"\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",
|
||
" return final_response"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 🛠️ Tool Configuration\n",
|
||
"\n",
|
||
"Creating specialized tools for the workflow system"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"id": "0rchfi4h3Pxc"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core.tools import QueryEngineTool\n",
|
||
"from llama_index.core.tools import FunctionTool\n",
|
||
"\n",
|
||
"# Create tools for SQL and document query engines\n",
|
||
"sql_tool = QueryEngineTool.from_defaults(\n",
|
||
" query_engine=sql_query_engine,\n",
|
||
" name=\"sql_tool\",\n",
|
||
" description=(\n",
|
||
" \"Useful for translating a natural language query into a SQL query over\"\n",
|
||
" \" a table containing: city_stats, containing the population/state of\"\n",
|
||
" \" each city located in the USA.\"\n",
|
||
" ),\n",
|
||
")\n",
|
||
"\n",
|
||
"docs_tool = FunctionTool.from_defaults(\n",
|
||
" document_query_tool,\n",
|
||
" name=\"document_tool\",\n",
|
||
" description=(\n",
|
||
" \"Useful for answering a natural language question by performing a semantic search over \"\n",
|
||
" \"a collection of documents. These documents may contain general knowledge, reports, \"\n",
|
||
" \"or domain-specific content. Returns the most relevant passages or synthesized answers. \"\n",
|
||
" \"If the user query does not relate to US city statistics (population and state), use this document search tool.\"\n",
|
||
" ),\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## ⚡ Workflow System Architecture\n",
|
||
"\n",
|
||
"Building a sophisticated routing workflow"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from typing import Dict, List, Any, Optional\n",
|
||
"from llama_index.core.tools import BaseTool\n",
|
||
"from llama_index.core.llms import ChatMessage\n",
|
||
"from llama_index.core.llms.llm import ToolSelection, LLM\n",
|
||
"from llama_index.core.workflow import (\n",
|
||
" Workflow,\n",
|
||
" Event,\n",
|
||
" StartEvent,\n",
|
||
" StopEvent,\n",
|
||
" step,\n",
|
||
" Context,\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"class InputEvent(Event):\n",
|
||
" \"\"\"Input event.\"\"\"\n",
|
||
"\n",
|
||
"\n",
|
||
"class GatherToolsEvent(Event):\n",
|
||
" \"\"\"Gather Tools Event\"\"\"\n",
|
||
"\n",
|
||
" tool_calls: Any\n",
|
||
"\n",
|
||
"\n",
|
||
"class ToolCallEvent(Event):\n",
|
||
" \"\"\"Tool Call event\"\"\"\n",
|
||
"\n",
|
||
" tool_call: ToolSelection\n",
|
||
"\n",
|
||
"\n",
|
||
"class ToolCallEventResult(Event):\n",
|
||
" \"\"\"Tool call event result.\"\"\"\n",
|
||
"\n",
|
||
" msg: ChatMessage\n",
|
||
"\n",
|
||
"\n",
|
||
"class RouterOutputAgentWorkflow(Workflow):\n",
|
||
" \"\"\"Custom router output agent workflow.\"\"\"\n",
|
||
"\n",
|
||
" def __init__(\n",
|
||
" self,\n",
|
||
" tools: List[BaseTool],\n",
|
||
" timeout: Optional[float] = 10.0,\n",
|
||
" disable_validation: bool = False,\n",
|
||
" verbose: bool = False,\n",
|
||
" llm: Optional[LLM] = None,\n",
|
||
" chat_history: Optional[List[ChatMessage]] = None,\n",
|
||
" ):\n",
|
||
" \"\"\"Constructor.\"\"\"\n",
|
||
" super().__init__(\n",
|
||
" timeout=timeout, disable_validation=disable_validation, verbose=verbose\n",
|
||
" )\n",
|
||
" self.tools: List[BaseTool] = tools\n",
|
||
" self.tools_dict: Optional[Dict[str, BaseTool]] = {\n",
|
||
" tool.metadata.name: tool for tool in self.tools\n",
|
||
" }\n",
|
||
" self.llm: LLM = llm or Settings.llm\n",
|
||
" self.chat_history: List[ChatMessage] = chat_history or []\n",
|
||
"\n",
|
||
" def reset(self) -> None:\n",
|
||
" \"\"\"Resets Chat History\"\"\"\n",
|
||
" self.chat_history = []\n",
|
||
"\n",
|
||
" @step()\n",
|
||
" async def prepare_chat(self, ev: StartEvent) -> InputEvent:\n",
|
||
" message = ev.get(\"message\")\n",
|
||
" if message is None:\n",
|
||
" raise ValueError(\"'message' field is required.\")\n",
|
||
"\n",
|
||
" # Add message to chat history\n",
|
||
" chat_history = self.chat_history\n",
|
||
" chat_history.append(ChatMessage(role=\"user\", content=message))\n",
|
||
" return InputEvent()\n",
|
||
"\n",
|
||
" @step()\n",
|
||
" async def chat(self, ev: InputEvent) -> GatherToolsEvent | StopEvent:\n",
|
||
" \"\"\"Appends msg to chat history, then gets tool calls.\"\"\"\n",
|
||
" try:\n",
|
||
" # Put message into LLM with tools included\n",
|
||
" chat_res = await self.llm.achat_with_tools(\n",
|
||
" self.tools,\n",
|
||
" chat_history=self.chat_history,\n",
|
||
" verbose=self._verbose,\n",
|
||
" allow_parallel_tool_calls=True,\n",
|
||
" )\n",
|
||
" tool_calls = self.llm.get_tool_calls_from_response(\n",
|
||
" chat_res, error_on_no_tool_call=False\n",
|
||
" )\n",
|
||
"\n",
|
||
" ai_message = chat_res.message\n",
|
||
" self.chat_history.append(ai_message)\n",
|
||
" if self._verbose:\n",
|
||
" print(f\"Chat message: {ai_message.content}\")\n",
|
||
"\n",
|
||
" # No tool calls, return chat message.\n",
|
||
" if not tool_calls:\n",
|
||
" return StopEvent(result=ai_message.content)\n",
|
||
"\n",
|
||
" return GatherToolsEvent(tool_calls=tool_calls)\n",
|
||
" except Exception as e:\n",
|
||
" error_msg = f\"Error during chat: {str(e)}\"\n",
|
||
" print(error_msg)\n",
|
||
" return StopEvent(\n",
|
||
" result=\"I'm sorry, I encountered an issue processing your request. Could you try asking in a different way?\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" @step(pass_context=True)\n",
|
||
" async def dispatch_calls(self, ctx: Context, ev: GatherToolsEvent) -> ToolCallEvent:\n",
|
||
" \"\"\"Dispatches calls.\"\"\"\n",
|
||
" tool_calls = ev.tool_calls\n",
|
||
" await ctx.set(\"num_tool_calls\", len(tool_calls))\n",
|
||
"\n",
|
||
" # Trigger tool call events\n",
|
||
" for tool_call in tool_calls:\n",
|
||
" ctx.send_event(ToolCallEvent(tool_call=tool_call))\n",
|
||
"\n",
|
||
" return None\n",
|
||
"\n",
|
||
" @step()\n",
|
||
" async def call_tool(self, ev: ToolCallEvent) -> ToolCallEventResult:\n",
|
||
" \"\"\"Calls tool.\"\"\"\n",
|
||
" tool_call = ev.tool_call\n",
|
||
" # Get tool ID and function call\n",
|
||
" id_ = tool_call.tool_id\n",
|
||
"\n",
|
||
" if self._verbose:\n",
|
||
" print(\n",
|
||
" f\"Calling function {tool_call.tool_name} with msg {tool_call.tool_kwargs}\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Call function and put result into a chat message\n",
|
||
" tool = self.tools_dict[tool_call.tool_name]\n",
|
||
" output = await tool.acall(**tool_call.tool_kwargs)\n",
|
||
" msg = ChatMessage(\n",
|
||
" name=tool_call.tool_name,\n",
|
||
" content=str(output),\n",
|
||
" role=\"tool\",\n",
|
||
" additional_kwargs={\"tool_call_id\": id_, \"name\": tool_call.tool_name},\n",
|
||
" )\n",
|
||
"\n",
|
||
" return ToolCallEventResult(msg=msg)\n",
|
||
"\n",
|
||
" @step(pass_context=True)\n",
|
||
" async def gather(self, ctx: Context, ev: ToolCallEventResult) -> StopEvent | None:\n",
|
||
" \"\"\"Gathers tool calls.\"\"\"\n",
|
||
" # Wait for all tool call events to finish.\n",
|
||
" tool_events = ctx.collect_events(\n",
|
||
" ev, [ToolCallEventResult] * await ctx.get(\"num_tool_calls\")\n",
|
||
" )\n",
|
||
" if not tool_events:\n",
|
||
" return None\n",
|
||
"\n",
|
||
" for tool_event in tool_events:\n",
|
||
" # Append tool call chat messages to history\n",
|
||
" self.chat_history.append(tool_event.msg)\n",
|
||
"\n",
|
||
" # After all tool calls finish, pass input event back, restart agent loop\n",
|
||
" return InputEvent()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 🎯 Workflow Initialization\n",
|
||
"\n",
|
||
"Creating the main workflow instance with:\n",
|
||
"- **Tools**: SQL and Document query engines\n",
|
||
"- **Verbose Mode**: Enabled for detailed logging"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"id": "MgWlr7Xe3Pxd"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Initialize workflow with the given tools\n",
|
||
"wf = RouterOutputAgentWorkflow(tools=[sql_tool, docs_tool], verbose=True, timeout=120)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 📊 Workflow Visualization"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "zgANGaLqFhMk",
|
||
"outputId": "470e5ccf-3472-48a0-93ae-fcb1ee55bc18"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"workflow_all_flows.html\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from llama_index.utils.workflow import draw_all_possible_flows\n",
|
||
"\n",
|
||
"# Draw the workflow diagram\n",
|
||
"draw_all_possible_flows(RouterOutputAgentWorkflow)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 🧪 Testing & Examples"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 402
|
||
},
|
||
"id": "4MXBSJ3-3Pxe",
|
||
"outputId": "00d37a9d-f4dd-4aea-d7d3-1b24f07cb3a6"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Running step prepare_chat\n",
|
||
"Step prepare_chat produced event InputEvent\n",
|
||
"Running step chat\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Chat message: None\n",
|
||
"Step chat produced event GatherToolsEvent\n",
|
||
"Running step dispatch_calls\n",
|
||
"Step dispatch_calls produced no event\n",
|
||
"Running step call_tool\n",
|
||
"Calling function sql_tool with msg {'input': 'What is the population of Houston, Texas?'}\n",
|
||
"Step call_tool produced event ToolCallEventResult\n",
|
||
"Running step gather\n",
|
||
"Step gather produced event InputEvent\n",
|
||
"Running step chat\n",
|
||
"Chat message: The population of Houston, Texas is 2,303,000.\n",
|
||
"Step chat produced event StopEvent\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/markdown": [
|
||
"The population of Houston, Texas is 2,303,000."
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Markdown object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from IPython.display import display, Markdown\n",
|
||
"\n",
|
||
"result = await wf.run(message=\"What is the population of Houston, Texas?\")\n",
|
||
"display(Markdown(result))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Running step prepare_chat\n",
|
||
"Step prepare_chat produced event InputEvent\n",
|
||
"Running step chat\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Chat message: None\n",
|
||
"Step chat produced event GatherToolsEvent\n",
|
||
"Running step dispatch_calls\n",
|
||
"Step dispatch_calls produced no event\n",
|
||
"Running step call_tool\n",
|
||
"Calling function document_tool with msg {'query': 'What is the weather in California?'}\n",
|
||
"Step call_tool produced event ToolCallEventResult\n",
|
||
"Running step gather\n",
|
||
"Step gather produced event InputEvent\n",
|
||
"Running step chat\n",
|
||
"Chat message: The provided context does not have enough information to answer the question about the weather in California. Let me know if you'd like help finding current weather information!\n",
|
||
"Step chat produced event StopEvent\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/markdown": [
|
||
"The provided context does not have enough information to answer the question about the weather in California. Let me know if you'd like help finding current weather information!"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Markdown object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"result = await wf.run(message=\"What is the weather in California?\")\n",
|
||
"display(Markdown(result))"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"accelerator": "GPU",
|
||
"colab": {
|
||
"gpuType": "T4",
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "llamacloud-demo",
|
||
"language": "python",
|
||
"name": "llamacloud-demo"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.8"
|
||
},
|
||
"widgets": {
|
||
"application/vnd.jupyter.widget-state+json": {
|
||
"005d81f91db947298f56a2873a8d5142": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "1.5.0",
|
||
"model_name": "HTMLModel",
|
||
"state": {
|
||
"_dom_classes": [],
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "1.5.0",
|
||
"_model_name": "HTMLModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/controls",
|
||
"_view_module_version": "1.5.0",
|
||
"_view_name": "HTMLView",
|
||
"description": "",
|
||
"description_tooltip": null,
|
||
"layout": "IPY_MODEL_3ccd368e196b44d1a627d8dfaa9908f4",
|
||
"placeholder": "",
|
||
"style": "IPY_MODEL_cfbbd2e00e4a4110bf1a627260e6ebcf",
|
||
"value": "vocab.txt: 100%"
|
||
}
|
||
},
|
||
"021632a727284e10bdffbb5a9bd4851d": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "1.5.0",
|
||
"model_name": "DescriptionStyleModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "1.5.0",
|
||
"_model_name": "DescriptionStyleModel",
|
||
"_view_count": null,
|
||
"_view_module": "@jupyter-widgets/base",
|
||
"_view_module_version": "1.2.0",
|
||
"_view_name": "StyleView",
|
||
"description_width": ""
|
||
}
|
||
},
|
||
"0444f40ca27b4102a9d0c251367e746e": {
|
||
"model_module": "@jupyter-widgets/controls",
|
||
"model_module_version": "1.5.0",
|
||
"model_name": "DescriptionStyleModel",
|
||
"state": {
|
||
"_model_module": "@jupyter-widgets/controls",
|
||
"_model_module_version": "1.5.0",
|
||
"_model_name": "DescriptionStyleModel",
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