{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# RAG + SQL Router with Cleanlab Codex\n",
        "\n",
        "This notebook demonstrates a (Text2SQL + RAG), hybrid agentic workflow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup and Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "guPyzpg83PxX"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"CODEX_API_KEY\"] = \"<your_codex_api_key_here>\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "EiGcgg9A3PxY"
      },
      "outputs": [],
      "source": [
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 🤖 LLM and Embedding Configuration\n",
        "\n",
        "Configuring the language model (OpenRouter) and embedding model for semantic search capabilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "PxQc2SZu3Pxa",
        "outputId": "90a80033-272d-4327-9b16-38829853195c"
      },
      "outputs": [],
      "source": [
        "from llama_index.core import Settings\n",
        "from llama_index.llms.openrouter import OpenRouter\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "\n",
        "# Set the LLM and embedding model\n",
        "Settings.llm = OpenRouter(api_key=\"<your_openrouter_api_key_here>\", model=\"qwen/qwen-turbo\")\n",
        "Settings.embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 🗄️ SQL Database Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "kglsOm_L6nEG"
      },
      "outputs": [],
      "source": [
        "from sqlalchemy import create_engine\n",
        "from llama_index.core import SQLDatabase\n",
        "from llama_index.core.query_engine import NLSQLTableQueryEngine\n",
        "\n",
        "# Setup SQLite database\n",
        "db_path = \"city_database.sqlite\"\n",
        "engine = create_engine(f\"sqlite:///{db_path}\")\n",
        "sql_database = SQLDatabase(engine)\n",
        "\n",
        "# Create SQL query engine\n",
        "sql_query_engine = NLSQLTableQueryEngine(sql_database=sql_database, tables=[\"city_stats\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 📚 Document Query Engine Setup\n",
        "\n",
        "Creating a vector-based document search system"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "from llama_index.vector_stores.milvus import MilvusVectorStore\n",
        "from llama_index.core.node_parser import MarkdownNodeParser\n",
        "from llama_index.readers.docling import DoclingReader\n",
        "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, PromptTemplate, StorageContext\n",
        "\n",
        "def create_docs_query_engine():\n",
        "    # Initialize reader and parser\n",
        "    reader = DoclingReader()\n",
        "    node_parser = MarkdownNodeParser()\n",
        "\n",
        "    # Load documents from directory\n",
        "    loader = SimpleDirectoryReader(input_dir=\"data\", file_extractor={\".pdf\": reader, \".docx\": reader})\n",
        "    docs = loader.load_data()\n",
        "\n",
        "    # Initialize vector store and storage context\n",
        "    vector_store = MilvusVectorStore(uri=\"http://localhost:19530\", dim=384, overwrite=True)\n",
        "    storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
        "\n",
        "    # Create vector index from documents\n",
        "    vector_index = VectorStoreIndex.from_documents(\n",
        "        docs,\n",
        "        show_progress=True,\n",
        "        transformations=[node_parser],\n",
        "        storage_context=storage_context\n",
        "    )\n",
        "\n",
        "    # Define custom QA prompt\n",
        "    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_str}\\n\"\n",
        "        \"-----------------------------------------\\n\"\n",
        "        \"Question: {query_str}\\n\\n\"\n",
        "        \"Answer:\"\n",
        "    )\n",
        "    qa_template = PromptTemplate(template)\n",
        "\n",
        "    # Build and return query engine\n",
        "    docs_query_engine = vector_index.as_query_engine(text_qa_template=qa_template, similarity_top_k=3)\n",
        "    return docs_query_engine"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 🛡️ Cleanlab Codex Integration\n",
        "\n",
        "Setting up Cleanlab Codex for response validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "from cleanlab_codex.project import Project\n",
        "from cleanlab_codex.client import Client\n",
        "\n",
        "def create_codex_project():\n",
        "    # Initialize Codex client and create a project\n",
        "    codex_client = Client()\n",
        "    project = codex_client.create_project(name=\"RAG + SQL\")\n",
        "    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",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Parsing nodes:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be0e08c223b94b67ada1b33736054fd9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating embeddings:   0%|          | 0/43 [00:00<?, ?it/s]"
            ]
          },
          "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))"
      ]
    }
  ],
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