mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
2667ddc686
**Description: a description of the change**
Fixed `make docs_build` and related scripts which caused errors. There
are several changes.
First, I made the build of the documentation and the API Reference into
two separate commands. This is because it takes less time to build. The
commands for documents are `make docs_build`, `make docs_clean`, and
`make docs_linkcheck`. The commands for API Reference are `make
api_docs_build`, `api_docs_clean`, and `api_docs_linkcheck`.
It looked like `docs/.local_build.sh` could be used to build the
documentation, so I used that. Since `.local_build.sh` was also building
API Rerefence internally, I removed that process. `.local_build.sh` also
added some Bash options to stop in error or so. Futher more added `cd
"${SCRIPT_DIR}"` at the beginning so that the script will work no matter
which directory it is executed in.
`docs/api_reference/api_reference.rst` is removed, because which is
generated by `docs/api_reference/create_api_rst.py`, and added it to
.gitignore.
Finally, the description of CONTRIBUTING.md was modified.
**Issue: the issue # it fixes (if applicable)**
https://github.com/hwchase17/langchain/issues/6413
**Dependencies: any dependencies required for this change**
`nbdoc` was missing in group docs so it was added. I installed it with
the `poetry add --group docs nbdoc` command. I am concerned if any
modifications are needed to poetry.lock. I would greatly appreciate it
if you could pay close attention to this file during the review.
**Tag maintainer**
- General / Misc / if you don't know who to tag: @baskaryan
If this PR needs any additional changes, I'll be happy to make them!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
387 lines
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387 lines
11 KiB
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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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"id": "g9EmNu5DD9YI"
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},
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"source": [
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"# Custom functions with OpenAI Functions Agent\n",
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"\n",
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"This notebook goes through how to integrate custom functions with OpenAI Functions 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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"id": "LFKylC3CPtTl"
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},
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"source": [
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"Install libraries which are required to run this example notebook\n",
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"\n",
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"`pip install -q openai langchain yfinance`"
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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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"id": "E2DqzmEGDPak"
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},
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"source": [
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"## Define custom functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "SiucthMs6SIK"
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},
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"outputs": [],
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"source": [
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"import yfinance as yf\n",
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"from datetime import datetime, timedelta\n",
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"\n",
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"\n",
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"def get_current_stock_price(ticker):\n",
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" \"\"\"Method to get current stock price\"\"\"\n",
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"\n",
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" ticker_data = yf.Ticker(ticker)\n",
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" recent = ticker_data.history(period=\"1d\")\n",
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" return {\"price\": recent.iloc[0][\"Close\"], \"currency\": ticker_data.info[\"currency\"]}\n",
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"\n",
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"\n",
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"def get_stock_performance(ticker, days):\n",
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" \"\"\"Method to get stock price change in percentage\"\"\"\n",
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"\n",
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" past_date = datetime.today() - timedelta(days=days)\n",
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" ticker_data = yf.Ticker(ticker)\n",
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" history = ticker_data.history(start=past_date)\n",
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" old_price = history.iloc[0][\"Close\"]\n",
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" current_price = history.iloc[-1][\"Close\"]\n",
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" return {\"percent_change\": ((current_price - old_price) / old_price) * 100}"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "vRLINGvQR1rO",
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"outputId": "68230a4b-dda2-4273-b956-7439661e3785"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'price': 334.57000732421875, 'currency': 'USD'}"
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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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"get_current_stock_price(\"MSFT\")"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "57T190q235mD",
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"outputId": "c6ee66ec-0659-4632-85d1-263b08826e68"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'percent_change': 1.014466941163018}"
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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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"get_stock_performance(\"MSFT\", 30)"
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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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"id": "MT8QsdyBDhwg"
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},
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"source": [
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"## Make custom tools"
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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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"id": "NvLOUv-XP3Ap"
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},
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"outputs": [],
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"source": [
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"from typing import Type\n",
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"from pydantic import BaseModel, Field\n",
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"from langchain.tools import BaseTool\n",
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"\n",
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"\n",
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"class CurrentStockPriceInput(BaseModel):\n",
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" \"\"\"Inputs for get_current_stock_price\"\"\"\n",
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"\n",
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" ticker: str = Field(description=\"Ticker symbol of the stock\")\n",
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"\n",
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"\n",
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"class CurrentStockPriceTool(BaseTool):\n",
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" name = \"get_current_stock_price\"\n",
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" description = \"\"\"\n",
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" Useful when you want to get current stock price.\n",
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" You should enter the stock ticker symbol recognized by the yahoo finance\n",
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" \"\"\"\n",
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" args_schema: Type[BaseModel] = CurrentStockPriceInput\n",
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"\n",
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" def _run(self, ticker: str):\n",
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" price_response = get_current_stock_price(ticker)\n",
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" return price_response\n",
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"\n",
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" def _arun(self, ticker: str):\n",
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" raise NotImplementedError(\"get_current_stock_price does not support async\")\n",
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"\n",
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"\n",
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"class StockPercentChangeInput(BaseModel):\n",
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" \"\"\"Inputs for get_stock_performance\"\"\"\n",
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"\n",
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" ticker: str = Field(description=\"Ticker symbol of the stock\")\n",
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" days: int = Field(description=\"Timedelta days to get past date from current date\")\n",
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"\n",
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"\n",
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"class StockPerformanceTool(BaseTool):\n",
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" name = \"get_stock_performance\"\n",
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" description = \"\"\"\n",
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" Useful when you want to check performance of the stock.\n",
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" You should enter the stock ticker symbol recognized by the yahoo finance.\n",
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" You should enter days as number of days from today from which performance needs to be check.\n",
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" output will be the change in the stock price represented as a percentage.\n",
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" \"\"\"\n",
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" args_schema: Type[BaseModel] = StockPercentChangeInput\n",
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"\n",
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" def _run(self, ticker: str, days: int):\n",
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" response = get_stock_performance(ticker, days)\n",
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" return response\n",
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"\n",
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" def _arun(self, ticker: str):\n",
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" raise NotImplementedError(\"get_stock_performance does not support async\")"
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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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"id": "PVKoqeCyFKHF"
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},
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"source": [
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"## Create 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": 6,
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"metadata": {
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"id": "yY7qNB7vSQGh"
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},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentType\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.agents import initialize_agent\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0613\", temperature=0)\n",
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"\n",
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"tools = [CurrentStockPriceTool(), StockPerformanceTool()]\n",
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"\n",
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"agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, 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": 7,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 321
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},
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"id": "4X96xmgwRkcC",
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"outputId": "a91b13ef-9643-4f60-d067-c4341e0b285e"
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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;3m\n",
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"Invoking: `get_current_stock_price` with `{'ticker': 'MSFT'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 334.57000732421875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 180}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 40.163963297187905}\u001b[0m\u001b[32;1m\u001b[1;3mThe current price of Microsoft stock is $334.57 USD. \n",
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"\n",
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"Over the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.\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 current price of Microsoft stock is $334.57 USD. \\n\\nOver the past 6 months, Microsoft stock has performed well with a 40.16% increase in its price.'"
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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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" \"What is the current price of Microsoft stock? How it has performed over past 6 months?\"\n",
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")"
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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": 9,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 285
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},
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"id": "nkZ_vmAcT7Al",
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"outputId": "092ebc55-4d28-4a4b-aa2a-98ae47ceec20"
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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;3m\n",
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"Invoking: `get_current_stock_price` with `{'ticker': 'GOOGL'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 118.33000183105469, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `get_current_stock_price` with `{'ticker': 'META'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m{'price': 287.04998779296875, 'currency': 'USD'}\u001b[0m\u001b[32;1m\u001b[1;3mThe recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.\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 recent stock price of Google (GOOGL) is $118.33 USD and the recent stock price of Meta (META) is $287.05 USD.'"
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]
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},
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"execution_count": 9,
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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 me recent stock prices of Google and Meta?\")"
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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": 11,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 466
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},
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"id": "jLU-HjMq7n1o",
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"outputId": "a42194dd-26ed-4b5a-d4a2-1038420045c4"
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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;3m\n",
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"Invoking: `get_stock_performance` with `{'ticker': 'MSFT', 'days': 90}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 18.043096235165596}\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `get_stock_performance` with `{'ticker': 'GOOGL', 'days': 90}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[33;1m\u001b[1;3m{'percent_change': 17.286155760642853}\u001b[0m\u001b[32;1m\u001b[1;3mIn the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\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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"\"In the past 3 months, Microsoft (MSFT) has performed better than Google (GOOGL). Microsoft's stock price has increased by 18.04% while Google's stock price has increased by 17.29%.\""
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]
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},
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"execution_count": 11,
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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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" \"In the past 3 months, which stock between Microsoft and Google has performed the best?\"\n",
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")"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
|