wehub-resource-sync
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550 lines
16 KiB
Plaintext
550 lines
16 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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"<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/workflow/multi_step_query_engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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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"# MultiStep Query Engine\n",
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"\n",
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"The `MultiStepQueryEngine` breaks down a complex query into sequential sub-questions.\n",
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"\n",
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"To answer the query: In which city did the author found his first company, Viaweb?, we need to answer the following sub-questions sequentially:\n",
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"\n",
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"1. Who is the author that founded his first company, Viaweb?\n",
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"2. In which city did Paul Graham found his first company, Viaweb?\n",
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"\n",
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"As an example, the answer from each step (sub-query-1) is used to generate the next step's question (sub-query-2), with steps created sequentially rather than all at once.\n",
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"\n",
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"In this notebook, we will implement the same with [MultiStepQueryEngine](https://docs.llamaindex.ai/en/stable/examples/query_transformations/simpleindexdemo-multistep/) using workflows."
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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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"!pip install -U llama-index"
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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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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
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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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"Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n",
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"\n",
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"```python\n",
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"async def main():\n",
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" <async code>\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" import asyncio\n",
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" asyncio.run(main())\n",
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"```"
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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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"## The Workflow\n",
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"\n",
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"## Designing the Workflow\n",
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"\n",
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"MultiStepQueryEngine consists of some clearly defined steps\n",
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"1. Indexing data, creating an index.\n",
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"2. Create multiple sub-queries to answer the query.\n",
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"3. Synthesize the final response\n",
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"\n",
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"With this in mind, we can create events and workflow steps to follow this process!\n",
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"\n",
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"### The Workflow Event\n",
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"\n",
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"To handle these steps, we need to define `QueryMultiStepEvent`\n",
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"\n",
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"The other steps will use the built-in `StartEvent` and `StopEvent` events."
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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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"## Define Event"
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt_tab to\n",
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"[nltk_data] /Users/ravithejad/Desktop/llamaindex/lib/python3.9/sit\n",
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"[nltk_data] e-packages/llama_index/core/_static/nltk_cache...\n",
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"[nltk_data] Package punkt_tab is already up-to-date!\n"
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]
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}
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],
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"source": [
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"from llama_index.core.workflow import Event\n",
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"from typing import Dict, List, Any\n",
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"from llama_index.core.schema import NodeWithScore\n",
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"\n",
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"\n",
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"class QueryMultiStepEvent(Event):\n",
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" \"\"\"\n",
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" Event containing results of a multi-step query process.\n",
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"\n",
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" Attributes:\n",
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" nodes (List[NodeWithScore]): List of nodes with their associated scores.\n",
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" source_nodes (List[NodeWithScore]): List of source nodes with their scores.\n",
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" final_response_metadata (Dict[str, Any]): Metadata associated with the final response.\n",
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" \"\"\"\n",
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"\n",
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" nodes: List[NodeWithScore]\n",
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" source_nodes: List[NodeWithScore]\n",
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" final_response_metadata: Dict[str, Any]"
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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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"## Define Workflow"
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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 llama_index.core.indices.query.query_transform.base import (\n",
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" StepDecomposeQueryTransform,\n",
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")\n",
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"from llama_index.core.response_synthesizers import (\n",
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" get_response_synthesizer,\n",
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")\n",
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"\n",
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"from llama_index.core.schema import QueryBundle, TextNode\n",
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"\n",
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"from llama_index.core.workflow import (\n",
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" Context,\n",
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" Workflow,\n",
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" StartEvent,\n",
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" StopEvent,\n",
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" step,\n",
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")\n",
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"\n",
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"from llama_index.core import Settings\n",
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"from llama_index.core.llms import LLM\n",
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"\n",
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"from typing import cast\n",
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"from IPython.display import Markdown, display\n",
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"\n",
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"\n",
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"class MultiStepQueryEngineWorkflow(Workflow):\n",
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" def combine_queries(\n",
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" self,\n",
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" query_bundle: QueryBundle,\n",
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" prev_reasoning: str,\n",
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" index_summary: str,\n",
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" llm: LLM,\n",
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" ) -> QueryBundle:\n",
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" \"\"\"Combine queries using StepDecomposeQueryTransform.\"\"\"\n",
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" transform_metadata = {\n",
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" \"prev_reasoning\": prev_reasoning,\n",
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" \"index_summary\": index_summary,\n",
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" }\n",
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" return StepDecomposeQueryTransform(llm=llm)(\n",
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" query_bundle, metadata=transform_metadata\n",
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" )\n",
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"\n",
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" def default_stop_fn(self, stop_dict: Dict) -> bool:\n",
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" \"\"\"Stop function for multi-step query combiner.\"\"\"\n",
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" query_bundle = cast(QueryBundle, stop_dict.get(\"query_bundle\"))\n",
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" if query_bundle is None:\n",
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" raise ValueError(\"Response must be provided to stop function.\")\n",
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"\n",
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" return \"none\" in query_bundle.query_str.lower()\n",
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"\n",
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" @step\n",
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" async def query_multistep(\n",
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" self, ctx: Context, ev: StartEvent\n",
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" ) -> QueryMultiStepEvent:\n",
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" \"\"\"Execute multi-step query process.\"\"\"\n",
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" prev_reasoning = \"\"\n",
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" cur_response = None\n",
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" should_stop = False\n",
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" cur_steps = 0\n",
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"\n",
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" # use response\n",
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" final_response_metadata: Dict[str, Any] = {\"sub_qa\": []}\n",
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"\n",
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" text_chunks = []\n",
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" source_nodes = []\n",
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"\n",
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" query = ev.get(\"query\")\n",
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" await ctx.store.set(\"query\", ev.get(\"query\"))\n",
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"\n",
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" llm = Settings.llm\n",
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" stop_fn = self.default_stop_fn\n",
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"\n",
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" num_steps = ev.get(\"num_steps\")\n",
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" query_engine = ev.get(\"query_engine\")\n",
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" index_summary = ev.get(\"index_summary\")\n",
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"\n",
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" while not should_stop:\n",
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" if num_steps is not None and cur_steps >= num_steps:\n",
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" should_stop = True\n",
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" break\n",
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" elif should_stop:\n",
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" break\n",
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"\n",
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" updated_query_bundle = self.combine_queries(\n",
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" QueryBundle(query_str=query),\n",
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" prev_reasoning,\n",
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" index_summary,\n",
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" llm,\n",
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" )\n",
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"\n",
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" print(\n",
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" f\"Created query for the step - {cur_steps} is: {updated_query_bundle}\"\n",
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" )\n",
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"\n",
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" stop_dict = {\"query_bundle\": updated_query_bundle}\n",
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" if stop_fn(stop_dict):\n",
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" should_stop = True\n",
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" break\n",
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"\n",
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" cur_response = query_engine.query(updated_query_bundle)\n",
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"\n",
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" # append to response builder\n",
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" cur_qa_text = (\n",
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" f\"\\nQuestion: {updated_query_bundle.query_str}\\n\"\n",
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" f\"Answer: {cur_response!s}\"\n",
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" )\n",
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" text_chunks.append(cur_qa_text)\n",
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" for source_node in cur_response.source_nodes:\n",
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" source_nodes.append(source_node)\n",
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" # update metadata\n",
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" final_response_metadata[\"sub_qa\"].append(\n",
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" (updated_query_bundle.query_str, cur_response)\n",
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" )\n",
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"\n",
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" prev_reasoning += (\n",
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" f\"- {updated_query_bundle.query_str}\\n\" f\"- {cur_response!s}\\n\"\n",
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" )\n",
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" cur_steps += 1\n",
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"\n",
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" nodes = [\n",
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" NodeWithScore(node=TextNode(text=text_chunk))\n",
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" for text_chunk in text_chunks\n",
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" ]\n",
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" return QueryMultiStepEvent(\n",
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" nodes=nodes,\n",
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" source_nodes=source_nodes,\n",
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" final_response_metadata=final_response_metadata,\n",
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" )\n",
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"\n",
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" @step\n",
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" async def synthesize(\n",
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" self, ctx: Context, ev: QueryMultiStepEvent\n",
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" ) -> StopEvent:\n",
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" \"\"\"Synthesize the response.\"\"\"\n",
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" response_synthesizer = get_response_synthesizer()\n",
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" query = await ctx.store.get(\"query\", default=None)\n",
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" final_response = await response_synthesizer.asynthesize(\n",
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" query=query,\n",
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" nodes=ev.nodes,\n",
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" additional_source_nodes=ev.source_nodes,\n",
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" )\n",
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" final_response.metadata = ev.final_response_metadata\n",
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"\n",
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" return StopEvent(result=final_response)"
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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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"## Download Data"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--2024-08-26 14:16:04-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
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"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8002::154, 2606:50c0:8001::154, ...\n",
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"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 75042 (73K) [text/plain]\n",
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"Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
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"\n",
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"data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.01s \n",
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"\n",
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"2024-08-26 14:16:04 (6.91 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
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"\n"
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]
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}
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],
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"source": [
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"!mkdir -p 'data/paul_graham/'\n",
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"!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
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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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"## Load data"
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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 llama_index.core import SimpleDirectoryReader\n",
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"\n",
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"documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()"
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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 LLM"
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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 llama_index.llms.openai import OpenAI\n",
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"\n",
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"llm = OpenAI(model=\"gpt-4\")\n",
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"\n",
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"Settings.llm = llm"
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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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"## Create Index and QueryEngine"
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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 llama_index.core import VectorStoreIndex\n",
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"\n",
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"index = VectorStoreIndex.from_documents(\n",
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" documents=documents,\n",
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")\n",
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"\n",
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"query_engine = index.as_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": [
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"## Run the Workflow!"
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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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"w = MultiStepQueryEngineWorkflow(timeout=200)"
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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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"### Set the parameters"
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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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"# Sets maximum number of steps taken to answer the query.\n",
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"num_steps = 3\n",
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"\n",
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"# Set summary of the index, useful to create modified query at each step.\n",
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"index_summary = \"Used to answer questions about the author\""
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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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"### Test with a query"
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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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"query = \"In which city did the author found his first company, Viaweb?\""
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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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"### Result"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Created query for the step - 0 is: Who is the author who founded Viaweb?\n",
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"Created query for the step - 1 is: In which city did Paul Graham found his first company, Viaweb?\n",
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"Created query for the step - 2 is: None\n"
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]
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||
},
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||
{
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"data": {
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||
"text/markdown": [
|
||
"> Question: In which city did the author found his first company, Viaweb?"
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||
],
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||
"text/plain": [
|
||
"<IPython.core.display.Markdown object>"
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||
]
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||
},
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||
"metadata": {},
|
||
"output_type": "display_data"
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||
},
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||
{
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"data": {
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"text/markdown": [
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"Answer: The author founded his first company, Viaweb, in Cambridge."
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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": [
|
||
"result = await w.run(\n",
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" query=query,\n",
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" query_engine=query_engine,\n",
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" index_summary=index_summary,\n",
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" num_steps=num_steps,\n",
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")\n",
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"\n",
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"# If created query in a step is None, the process will be stopped.\n",
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"\n",
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||
"display(\n",
|
||
" Markdown(\"> Question: {}\".format(query)),\n",
|
||
" Markdown(\"Answer: {}\".format(result)),\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Display step-queries created"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/markdown": [
|
||
"[('Who is the author who founded Viaweb?', 'The author who founded Viaweb is Paul Graham.'), ('In which city did Paul Graham found his first company, Viaweb?', 'Paul Graham founded his first company, Viaweb, in Cambridge.')]"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Markdown object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sub_qa = result.metadata[\"sub_qa\"]\n",
|
||
"tuples = [(t[0], t[1].response) for t in sub_qa]\n",
|
||
"display(Markdown(f\"{tuples}\"))"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "llama-index-caVs7DDe-py3.10",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|