mirror of
https://github.com/hwchase17/langchain
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5c3e9c9083
- Description: Added example of running Q&A over structured data using the `Airbyte` loaders and `pandas` - Dependencies: any dependencies required for this change, - Tag maintainer: @hwchase17 - Twitter handle: @pelaseyed
119 lines
2.8 KiB
Plaintext
119 lines
2.8 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Airbyte Question Answering\n",
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"This notebook shows how to do question answering over structured data, in this case using the `AirbyteStripeLoader`.\n",
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"\n",
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"Vectorstores often have a hard time answering questions that requires computing, grouping and filtering structured data so the high level idea is to use a `pandas` dataframe to help with these types of questions. "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. Load data from Stripe using Airbyte. user the `record_handler` paramater to return a JSON from the data loader."
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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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"import pandas as pd\n",
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"\n",
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"from langchain.document_loaders.airbyte import AirbyteStripeLoader\n",
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"from langchain.chat_models.openai import ChatOpenAI\n",
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"from langchain.agents import AgentType, create_pandas_dataframe_agent\n",
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"\n",
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"stream_name = \"customers\"\n",
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"config = {\n",
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" \"client_secret\": os.getenv(\"STRIPE_CLIENT_SECRET\"),\n",
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" \"account_id\": os.getenv(\"STRIPE_ACCOUNT_D\"),\n",
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" \"start_date\": \"2023-01-20T00:00:00Z\",\n",
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"}\n",
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"\n",
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"def handle_record(record: dict, _id: str):\n",
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" return record.data\n",
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"\n",
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"loader = AirbyteStripeLoader(\n",
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" config=config,\n",
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" record_handler=handle_record,\n",
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" stream_name=stream_name,\n",
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")\n",
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"data = loader.load()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. Pass the data to `pandas` dataframe."
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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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"df = pd.DataFrame(data)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"3. Pass the dataframe `df` to the `create_pandas_dataframe_agent` and invoke\n"
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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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"agent = create_pandas_dataframe_agent(\n",
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" ChatOpenAI(temperature=0, model=\"gpt-4\"),\n",
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" df,\n",
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" verbose=True,\n",
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" agent_type=AgentType.OPENAI_FUNCTIONS,\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"4. Run the agent"
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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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"output = agent.run(\"How many rows are there?\")"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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