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Add Xorbits agent (#7647)
- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source computing framework that makes it easy to scale data science and machine learning workloads in parallel. Xorbits can leverage multi cores or GPUs to accelerate computation on a single machine, or scale out up to thousands of machines to support processing terabytes of data. - This PR added support for the Xorbits agent, which allows langchain to interact with Xorbits Pandas dataframe and Xorbits Numpy array. - Dependencies: This change requires the Xorbits library to be installed in order to be used. `pip install xorbits` - Request for review: @hinthornw - Twitter handle: https://twitter.com/Xorbitsio
This commit is contained in:
Yifei Song
GitHub
742
docs/extras/modules/agents/toolkits/xorbits.ipynb
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742
docs/extras/modules/agents/toolkits/xorbits.ipynb
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{
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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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"# Xorbits Agent"
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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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"This notebook shows how to use agents to interact with [Xorbits Pandas](https://doc.xorbits.io/en/latest/reference/pandas/index.html) dataframe and [Xorbits Numpy](https://doc.xorbits.io/en/latest/reference/numpy/index.html) ndarray. It is mostly optimized for question answering.\n",
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"\n",
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"**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
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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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"## Pandas examples"
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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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"ExecuteTime": {
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"end_time": "2023-07-13T08:06:33.955439Z",
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"start_time": "2023-07-13T08:06:33.767539500Z"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "05b7c067b1114ce9a8aef4a58a5d5fef",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import xorbits.pandas as pd\n",
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"\n",
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"from langchain.agents import create_xorbits_agent\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"data = pd.read_csv(\"titanic.csv\")\n",
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"agent = create_xorbits_agent(OpenAI(temperature=0), data, verbose=True)"
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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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"ExecuteTime": {
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"end_time": "2023-07-13T08:11:06.622471100Z",
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"start_time": "2023-07-13T08:11:03.183042Z"
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}
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},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows and columns\n",
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"Action: python_repl_ast\n",
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"Action Input: data.shape\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m(891, 12)\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: There are 891 rows and 12 columns.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'There are 891 rows and 12 columns.'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"How many rows and columns are there?\")"
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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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-07-13T08:11:23.189275300Z",
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"start_time": "2023-07-13T08:11:11.029030900Z"
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}
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},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8c63d745a7eb41a484043a5dba357997",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32;1m\u001b[1;3mThought: I need to count the number of people in pclass 1\n",
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"Action: python_repl_ast\n",
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"Action Input: data[data['Pclass'] == 1].shape[0]\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m216\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: There are 216 people in pclass 1.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'There are 216 people in pclass 1.'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"How many people are in pclass 1?\")"
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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": {},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to calculate the mean age\n",
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"Action: python_repl_ast\n",
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"Action Input: data['Age'].mean()\u001b[0m"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "29af2e29f2d64a3397c212812adf0e9b",
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"version_major": 2,
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"version_minor": 0
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The mean age is 29.69911764705882.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'The mean age is 29.69911764705882.'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"whats the mean age?\")"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to group the data by sex and then find the average age for each group\n",
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"Action: python_repl_ast\n",
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"Action Input: data.groupby('Sex')['Age'].mean()\u001b[0m"
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]
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},
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{
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||||
"data": {
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||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c3d28625c35946fd91ebc2a47f8d8c5b",
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||||
"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3mSex\n",
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"female 27.915709\n",
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"male 30.726645\n",
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"Name: Age, dtype: float64\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the average age for each group\n",
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"Final Answer: The average age for female passengers is 27.92 and the average age for male passengers is 30.73.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'The average age for female passengers is 27.92 and the average age for male passengers is 30.73.'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"Group the data by sex and find the average age for each group\")"
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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": 7,
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n"
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]
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},
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{
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"data": {
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||||
"application/vnd.jupyter.widget-view+json": {
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"model_id": "c72aab63b20d47599f4f9806f6887a69",
|
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32;1m\u001b[1;3mThought: I need to filter the dataframe to get the desired result\n",
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"Action: python_repl_ast\n",
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"Action Input: data[(data['Age'] > 30) & (data['Fare'] > 30) & (data['Fare'] < 50) & ((data['Pclass'] == 1) | (data['Pclass'] == 2))].shape[0]\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m20\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 20\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'20'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\n",
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" \"Show the number of people whose age is greater than 30 and fare is between 30 and 50 , and pclass is either 1 or 2\"\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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"## Numpy examples"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "fa8baf315a0c41c89392edc4a24b76f5",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0.00/100 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import xorbits.numpy as np\n",
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"\n",
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"from langchain.agents import create_xorbits_agent\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"arr = np.array([1, 2, 3, 4, 5, 6])\n",
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"agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to find out the shape of the array\n",
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"Action: python_repl_ast\n",
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"Action Input: data.shape\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m(6,)\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The shape of the array is (6,).\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'The shape of the array is (6,).'"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"Give the shape of the array \")"
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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": 14,
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to access the 2nd element of the array\n",
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"Action: python_repl_ast\n",
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"Action Input: data[1]\u001b[0m"
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]
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},
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{
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"data": {
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||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "64efcc74f81f404eb0a7d3f0326cd8b3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Observation: \u001b[36;1m\u001b[1;3m2\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 2\u001b[0m\n",
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||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'2'"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"Give the 2nd element of the array \")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mThought: I need to reshape the array and then transpose it\n",
|
||||
"Action: python_repl_ast\n",
|
||||
"Action Input: np.reshape(data, (2,3)).T\u001b[0m"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "fce51acf6fb347c0b400da67c6750534",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0.00/100 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m[[1 4]\n",
|
||||
" [2 5]\n",
|
||||
" [3 6]]\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The reshaped and transposed array is [[1 4], [2 5], [3 6]].\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The reshaped and transposed array is [[1 4], [2 5], [3 6]].'"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\n",
|
||||
" \"Reshape the array into a 2-dimensional array with 2 rows and 3 columns, and then transpose it\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mThought: I need to reshape the array and then sum it\n",
|
||||
"Action: python_repl_ast\n",
|
||||
"Action Input: np.sum(np.reshape(data, (3,2)), axis=0)\u001b[0m"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "27fd4a0bbf694936bc41a6991064dec2",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0.00/100 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m[ 9 12]\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The sum of the array along the first axis is [9, 12].\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The sum of the array along the first axis is [9, 12].'"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\n",
|
||||
" \"Reshape the array into a 2-dimensional array with 3 rows and 2 columns and sum the array along the first axis\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "a591b6d7913f45cba98d2f3b71a5120a",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0.00/100 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
|
||||
"agent = create_xorbits_agent(OpenAI(temperature=0), arr, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mThought: I need to use the numpy covariance function\n",
|
||||
"Action: python_repl_ast\n",
|
||||
"Action Input: np.cov(data)\u001b[0m"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5fe40f83cfae48d0919c147627b5839f",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0.00/100 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m[[1. 1. 1.]\n",
|
||||
" [1. 1. 1.]\n",
|
||||
" [1. 1. 1.]]\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The covariance matrix is [[1. 1. 1.], [1. 1. 1.], [1. 1. 1.]].'"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"calculate the covariance matrix\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mThought: I need to use the SVD function\n",
|
||||
"Action: python_repl_ast\n",
|
||||
"Action Input: U, S, V = np.linalg.svd(data)\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now have the U matrix\n",
|
||||
"Final Answer: U = [[-0.70710678 -0.70710678]\n",
|
||||
" [-0.70710678 0.70710678]]\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'U = [[-0.70710678 -0.70710678]\\n [-0.70710678 0.70710678]]'"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"compute the U of Singular Value Decomposition of the matrix\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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",
|
||||
"version": "3.9.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -19,6 +19,7 @@ from langchain.agents.agent_toolkits import (
|
||||
create_sql_agent,
|
||||
create_vectorstore_agent,
|
||||
create_vectorstore_router_agent,
|
||||
create_xorbits_agent,
|
||||
)
|
||||
from langchain.agents.agent_types import AgentType
|
||||
from langchain.agents.conversational.base import ConversationalAgent
|
||||
@@ -74,4 +75,5 @@ __all__ = [
|
||||
"load_huggingface_tool",
|
||||
"load_tools",
|
||||
"tool",
|
||||
"create_xorbits_agent",
|
||||
]
|
||||
|
||||
@@ -35,6 +35,7 @@ from langchain.agents.agent_toolkits.vectorstore.toolkit import (
|
||||
VectorStoreRouterToolkit,
|
||||
VectorStoreToolkit,
|
||||
)
|
||||
from langchain.agents.agent_toolkits.xorbits.base import create_xorbits_agent
|
||||
from langchain.agents.agent_toolkits.zapier.toolkit import ZapierToolkit
|
||||
|
||||
__all__ = [
|
||||
@@ -66,4 +67,5 @@ __all__ = [
|
||||
"PlayWrightBrowserToolkit",
|
||||
"AzureCognitiveServicesToolkit",
|
||||
"O365Toolkit",
|
||||
"create_xorbits_agent",
|
||||
]
|
||||
|
||||
1
langchain/agents/agent_toolkits/xorbits/__init__.py
Normal file
1
langchain/agents/agent_toolkits/xorbits/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
"""Xorbits toolkit."""
|
||||
90
langchain/agents/agent_toolkits/xorbits/base.py
Normal file
90
langchain/agents/agent_toolkits/xorbits/base.py
Normal file
@@ -0,0 +1,90 @@
|
||||
"""Agent for working with xorbits objects."""
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain.agents.agent import AgentExecutor
|
||||
from langchain.agents.agent_toolkits.xorbits.prompt import (
|
||||
NP_PREFIX,
|
||||
NP_SUFFIX,
|
||||
PD_PREFIX,
|
||||
PD_SUFFIX,
|
||||
)
|
||||
from langchain.agents.mrkl.base import ZeroShotAgent
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.tools.python.tool import PythonAstREPLTool
|
||||
|
||||
|
||||
def create_xorbits_agent(
|
||||
llm: BaseLLM,
|
||||
data: Any,
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
prefix: str = "",
|
||||
suffix: str = "",
|
||||
input_variables: Optional[List[str]] = None,
|
||||
verbose: bool = False,
|
||||
return_intermediate_steps: bool = False,
|
||||
max_iterations: Optional[int] = 15,
|
||||
max_execution_time: Optional[float] = None,
|
||||
early_stopping_method: str = "force",
|
||||
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
**kwargs: Dict[str, Any],
|
||||
) -> AgentExecutor:
|
||||
"""Construct a xorbits agent from an LLM and dataframe."""
|
||||
try:
|
||||
from xorbits import numpy as np
|
||||
from xorbits import pandas as pd
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Xorbits package not installed, please install with `pip install xorbits`"
|
||||
)
|
||||
|
||||
if not isinstance(data, (pd.DataFrame, np.ndarray)):
|
||||
raise ValueError(
|
||||
f"Expected Xorbits DataFrame or ndarray object, got {type(data)}"
|
||||
)
|
||||
if input_variables is None:
|
||||
input_variables = ["data", "input", "agent_scratchpad"]
|
||||
tools = [PythonAstREPLTool(locals={"data": data})]
|
||||
prompt, partial_input = None, None
|
||||
|
||||
if isinstance(data, pd.DataFrame):
|
||||
prompt = ZeroShotAgent.create_prompt(
|
||||
tools,
|
||||
prefix=PD_PREFIX if prefix == "" else prefix,
|
||||
suffix=PD_SUFFIX if suffix == "" else suffix,
|
||||
input_variables=input_variables,
|
||||
)
|
||||
partial_input = str(data.head())
|
||||
else:
|
||||
prompt = ZeroShotAgent.create_prompt(
|
||||
tools,
|
||||
prefix=NP_PREFIX if prefix == "" else prefix,
|
||||
suffix=NP_SUFFIX if suffix == "" else suffix,
|
||||
input_variables=input_variables,
|
||||
)
|
||||
partial_input = str(data[: len(data) // 2])
|
||||
partial_prompt = prompt.partial(data=partial_input)
|
||||
llm_chain = LLMChain(
|
||||
llm=llm,
|
||||
prompt=partial_prompt,
|
||||
callback_manager=callback_manager,
|
||||
)
|
||||
tool_names = [tool.name for tool in tools]
|
||||
agent = ZeroShotAgent(
|
||||
llm_chain=llm_chain,
|
||||
allowed_tools=tool_names,
|
||||
callback_manager=callback_manager,
|
||||
**kwargs,
|
||||
)
|
||||
return AgentExecutor.from_agent_and_tools(
|
||||
agent=agent,
|
||||
tools=tools,
|
||||
callback_manager=callback_manager,
|
||||
verbose=verbose,
|
||||
return_intermediate_steps=return_intermediate_steps,
|
||||
max_iterations=max_iterations,
|
||||
max_execution_time=max_execution_time,
|
||||
early_stopping_method=early_stopping_method,
|
||||
**(agent_executor_kwargs or {}),
|
||||
)
|
||||
33
langchain/agents/agent_toolkits/xorbits/prompt.py
Normal file
33
langchain/agents/agent_toolkits/xorbits/prompt.py
Normal file
@@ -0,0 +1,33 @@
|
||||
PD_PREFIX = """
|
||||
You are working with Xorbits dataframe object in Python.
|
||||
Before importing Numpy or Pandas in the current script,
|
||||
remember to import the xorbits version of the library instead.
|
||||
To import the xorbits version of Numpy, replace the original import statement
|
||||
`import pandas as pd` with `import xorbits.pandas as pd`.
|
||||
The name of the input is `data`.
|
||||
You should use the tools below to answer the question posed of you:"""
|
||||
|
||||
PD_SUFFIX = """
|
||||
This is the result of `print(data)`:
|
||||
{data}
|
||||
|
||||
Begin!
|
||||
Question: {input}
|
||||
{agent_scratchpad}"""
|
||||
|
||||
NP_PREFIX = """
|
||||
You are working with Xorbits ndarray object in Python.
|
||||
Before importing Numpy in the current script,
|
||||
remember to import the xorbits version of the library instead.
|
||||
To import the xorbits version of Numpy, replace the original import statement
|
||||
`import numpy as np` with `import xorbits.numpy as np`.
|
||||
The name of the input is `data`.
|
||||
You should use the tools below to answer the question posed of you:"""
|
||||
|
||||
NP_SUFFIX = """
|
||||
This is the result of `print(data)`:
|
||||
{data}
|
||||
|
||||
Begin!
|
||||
Question: {input}
|
||||
{agent_scratchpad}"""
|
||||
@@ -36,6 +36,7 @@ _EXPECTED = [
|
||||
"load_huggingface_tool",
|
||||
"load_tools",
|
||||
"tool",
|
||||
"create_xorbits_agent",
|
||||
]
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user