mirror of
https://github.com/hwchase17/langchain
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Lance Martin
GitHub
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---
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sidebar_position: 0
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---
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# API chains
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APIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.
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import Example from "@snippets/modules/chains/popular/api.mdx"
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<Example/>
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docs/extras/use_cases/apis.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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"id": "a15e6a18",
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"metadata": {},
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"source": [
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"# Interacting with APIs\n",
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"\n",
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"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/apis.ipynb)\n",
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"\n",
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"## Use case \n",
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"\n",
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"Suppose you want an LLM to interact with external APIs.\n",
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"\n",
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"This can be very useful for retrieving context for the LLM to utilize.\n",
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"\n",
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"And, more generally, it allows us to interact with APIs using natural langugage! \n",
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" \n",
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"\n",
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"## Overview\n",
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"\n",
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"There are two primary ways to interface LLMs with external APIs:\n",
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" \n",
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"* `Functions`: For example, [OpenAI functions](https://platform.openai.com/docs/guides/gpt/function-calling) is one popular means of doing this.\n",
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"* `LLM-generated interface`: Use an LLM with access to API documentation to create an interface.\n",
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"\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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"id": "abbd82f0",
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"metadata": {},
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"source": [
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"## Quickstart \n",
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"\n",
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"Many APIs already are compatible with OpenAI function calling.\n",
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"\n",
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"For example, [Klarna](https://www.klarna.com/international/press/klarna-brings-smoooth-shopping-to-chatgpt/) has a YAML file that describes its API and allows OpenAI to interact with it:\n",
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"\n",
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"```\n",
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"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\n",
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"```\n",
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"\n",
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"Other options include:\n",
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"\n",
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"* [Speak](https://api.speak.com/openapi.yaml) for translation\n",
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"* [XKCD](https://gist.githubusercontent.com/roaldnefs/053e505b2b7a807290908fe9aa3e1f00/raw/0a212622ebfef501163f91e23803552411ed00e4/openapi.yaml) for comics\n",
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"\n",
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"We can supply the specification to `get_openapi_chain` directly in order to query the API with OpenAI 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": null,
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"id": "5a218fcc",
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install langchain openai \n",
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"\n",
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"# Set env var OPENAI_API_KEY or load from a .env file:\n",
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"# import dotenv\n",
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"# dotenv.load_env()"
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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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"id": "30b780e3",
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"metadata": {
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"scrolled": false
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},
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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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"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\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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"{'query': \"What are some options for a men's large blue button down shirt\",\n",
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" 'response': {'products': [{'name': 'Cubavera Four Pocket Guayabera Shirt',\n",
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" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202055522/Clothing/Cubavera-Four-Pocket-Guayabera-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
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" 'price': '$13.50',\n",
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" 'attributes': ['Material:Polyester,Cotton',\n",
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" 'Target Group:Man',\n",
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" 'Color:Red,White,Blue,Black',\n",
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" 'Properties:Pockets',\n",
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" 'Pattern:Solid Color',\n",
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" 'Size (Small-Large):S,XL,L,M,XXL']},\n",
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" {'name': 'Polo Ralph Lauren Plaid Short Sleeve Button-down Oxford Shirt',\n",
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" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3207163438/Clothing/Polo-Ralph-Lauren-Plaid-Short-Sleeve-Button-down-Oxford-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
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" 'price': '$52.20',\n",
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" 'attributes': ['Material:Cotton',\n",
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" 'Target Group:Man',\n",
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" 'Color:Red,Blue,Multicolor',\n",
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" 'Size (Small-Large):S,XL,L,M,XXL']},\n",
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" {'name': 'Brixton Bowery Flannel Shirt',\n",
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" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202331096/Clothing/Brixton-Bowery-Flannel-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
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" 'price': '$27.48',\n",
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" 'attributes': ['Material:Cotton',\n",
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" 'Target Group:Man',\n",
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" 'Color:Gray,Blue,Black,Orange',\n",
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" 'Properties:Pockets',\n",
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" 'Pattern:Checkered',\n",
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" 'Size (Small-Large):XL,3XL,4XL,5XL,L,M,XXL']},\n",
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" {'name': 'Vineyard Vines Gingham On-The-Go brrr Classic Fit Shirt Crystal',\n",
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" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201938510/Clothing/Vineyard-Vines-Gingham-On-The-Go-brrr-Classic-Fit-Shirt-Crystal/?utm_source=openai&ref-site=openai_plugin',\n",
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" 'price': '$80.64',\n",
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" 'attributes': ['Material:Cotton',\n",
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" 'Target Group:Man',\n",
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" 'Color:Blue',\n",
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" 'Size (Small-Large):XL,XS,L,M']},\n",
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" {'name': \"Carhartt Men's Loose Fit Midweight Short Sleeve Plaid Shirt\",\n",
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" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201826024/Clothing/Carhartt-Men-s-Loose-Fit-Midweight-Short-Sleeve-Plaid-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
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" 'price': '$17.99',\n",
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" 'attributes': ['Material:Cotton',\n",
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" 'Target Group:Man',\n",
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" 'Color:Red,Brown,Blue,Green',\n",
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" 'Properties:Pockets',\n",
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" 'Pattern:Checkered',\n",
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" 'Size (Small-Large):S,XL,L,M']}]}}"
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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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"from langchain.chains.openai_functions.openapi import get_openapi_chain\n",
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"chain = get_openapi_chain(\"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")\n",
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"chain(\"What are some options for a men's large blue button down shirt\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9162c91c",
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"metadata": {},
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"source": [
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"## Functions \n",
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"\n",
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"We can unpack what is hapening when we use the funtions to calls external APIs.\n",
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"\n",
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"Let's look at the [LangSmith trace](https://smith.langchain.com/public/76a58b85-193f-4eb7-ba40-747f0d5dd56e/r):\n",
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"\n",
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"* See [here](https://github.com/langchain-ai/langchain/blob/7fc07ba5df99b9fa8bef837b0fafa220bc5c932c/libs/langchain/langchain/chains/openai_functions/openapi.py#L279C9-L279C19) that we call the OpenAI LLM with the provided API spec:\n",
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"\n",
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"```\n",
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"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\n",
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"```\n",
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"\n",
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"* The prompt then tells the LLM to use the API spec wiith input question:\n",
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"\n",
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"```\n",
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"Use the provided API's to respond to this user query:\n",
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"What are some options for a men's large blue button down shirt\n",
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"```\n",
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"\n",
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"* The LLM returns the parameters for the function call `productsUsingGET`, which is [specified in the provided API spec](https://www.klarna.com/us/shopping/public/openai/v0/api-docs/):\n",
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"```\n",
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"function_call:\n",
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" name: productsUsingGET\n",
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" arguments: |-\n",
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" {\n",
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" \"params\": {\n",
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" \"countryCode\": \"US\",\n",
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" \"q\": \"men's large blue button down shirt\",\n",
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" \"size\": 5,\n",
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" \"min_price\": 0,\n",
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" \"max_price\": 100\n",
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" }\n",
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" }\n",
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" ```\n",
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" \n",
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"\n",
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" \n",
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"* This `Dict` above split and the [API is called here](https://github.com/langchain-ai/langchain/blob/7fc07ba5df99b9fa8bef837b0fafa220bc5c932c/libs/langchain/langchain/chains/openai_functions/openapi.py#L215)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1fe49a0d",
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"metadata": {},
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"source": [
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"## API Chain \n",
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"\n",
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"We can also build our own interface to external APIs using the `APIChain` and provided API documentation."
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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": 8,
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"id": "4ef0c3d0",
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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 APIChain chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mhttps://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&hourly=temperature_2m&temperature_unit=fahrenheit¤t_weather=true\u001b[0m\n",
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|
"\u001b[33;1m\u001b[1;3m{\"latitude\":48.14,\"longitude\":11.58,\"generationtime_ms\":1.0769367218017578,\"utc_offset_seconds\":0,\"timezone\":\"GMT\",\"timezone_abbreviation\":\"GMT\",\"elevation\":521.0,\"current_weather\":{\"temperature\":52.9,\"windspeed\":12.6,\"winddirection\":239.0,\"weathercode\":3,\"is_day\":0,\"time\":\"2023-08-07T22:00\"},\"hourly_units\":{\"time\":\"iso8601\",\"temperature_2m\":\"°F\"},\"hourly\":{\"time\":[\"2023-08-07T00:00\",\"2023-08-07T01:00\",\"2023-08-07T02:00\",\"2023-08-07T03:00\",\"2023-08-07T04:00\",\"2023-08-07T05:00\",\"2023-08-07T06:00\",\"2023-08-07T07:00\",\"2023-08-07T08:00\",\"2023-08-07T09:00\",\"2023-08-07T10:00\",\"2023-08-07T11:00\",\"2023-08-07T12:00\",\"2023-08-07T13:00\",\"2023-08-07T14:00\",\"2023-08-07T15:00\",\"2023-08-07T16:00\",\"2023-08-07T17:00\",\"2023-08-07T18:00\",\"2023-08-07T19:00\",\"2023-08-07T20:00\",\"2023-08-07T21:00\",\"2023-08-07T22:00\",\"2023-08-07T23:00\",\"2023-08-08T00:00\",\"2023-08-08T01:00\",\"2023-08-08T02:00\",\"2023-08-08T03:00\",\"2023-08-08T04:00\",\"2023-08-08T05:00\",\"2023-08-08T06:00\",\"2023-08-08T07:00\",\"2023-08-08T08:00\",\"2023-08-08T09:00\",\"2023-08-08T10:00\",\"2023-08-08T11:00\",\"2023-08-08T12:00\",\"2023-08-08T13:00\",\"2023-08-08T14:00\",\"2023-08-08T15:00\",\"2023-08-08T16:00\",\"2023-08-08T17:00\",\"2023-08-08T18:00\",\"2023-08-08T19:00\",\"2023-08-08T20:00\",\"2023-08-08T21:00\",\"2023-08-08T22:00\",\"2023-08-08T23:00\",\"2023-08-09T00:00\",\"2023-08-09T01:00\",\"2023-08-09T02:00\",\"2023-08-09T03:00\",\"2023-08-09T04:00\",\"2023-08-09T05:00\",\"2023-08-09T06:00\",\"2023-08-09T07:00\",\"2023-08-09T08:00\",\"2023-08-09T09:00\",\"2023-08-09T10:00\",\"2023-08-09T11:00\",\"2023-08-09T12:00\",\"2023-08-09T13:00\",\"2023-08-09T14:00\",\"2023-08-09T15:00\",\"2023-08-09T16:00\",\"2023-08-09T17:00\",\"2023-08-09T18:00\",\"2023-08-09T19:00\",\"2023-08-09T20:00\",\"2023-08-09T21:00\",\"2023-08-09T22:00\",\"2023-08-09T23:00\",\"2023-08-10T00:00\",\"2023-08-10T01:00\",\"2023-08-10T02:00\",\"2023-08-10T03:00\",\"2023-08-10T04:00\",\"2023-08-10T05:00\",\"2023-08-10T06:00\",\"2023-08-10T07:00\",\"2023-08-10T08:00\",\"2023-08-10T09:00\",\"2023-08-10T10:00\",\"2023-08-10T11:00\",\"2023-08-10T12:00\",\"2023-08-10T13:00\",\"2023-08-10T14:00\",\"2023-08-10T15:00\",\"2023-08-10T16:00\",\"2023-08-10T17:00\",\"2023-08-10T18:00\",\"2023-08-10T19:00\",\"2023-08-10T20:00\",\"2023-08-10T21:00\",\"2023-08-10T22:00\",\"2023-08-10T23:00\",\"2023-08-11T00:00\",\"2023-08-11T01:00\",\"2023-08-11T02:00\",\"2023-08-11T03:00\",\"2023-08-11T04:00\",\"2023-08-11T05:00\",\"2023-08-11T06:00\",\"2023-08-11T07:00\",\"2023-08-11T08:00\",\"2023-08-11T09:00\",\"2023-08-11T10:00\",\"2023-08-11T11:00\",\"2023-08-11T12:00\",\"2023-08-11T13:00\",\"2023-08-11T14:00\",\"2023-08-11T15:00\",\"2023-08-11T16:00\",\"2023-08-11T17:00\",\"2023-08-11T18:00\",\"2023-08-11T19:00\",\"2023-08-11T20:00\",\"2023-08-11T21:00\",\"2023-08-11T22:00\",\"2023-08-11T23:00\",\"2023-08-12T00:00\",\"2023-08-12T01:00\",\"2023-08-12T02:00\",\"2023-08-12T03:00\",\"2023-08-12T04:00\",\"2023-08-12T05:00\",\"2023-08-12T06:00\",\"2023-08-12T07:00\",\"2023-08-12T08:00\",\"2023-08-12T09:00\",\"2023-08-12T10:00\",\"2023-08-12T11:00\",\"2023-08-12T12:00\",\"2023-08-12T13:00\",\"2023-08-12T14:00\",\"2023-08-12T15:00\",\"2023-08-12T16:00\",\"2023-08-12T17:00\",\"2023-08-12T18:00\",\"2023-08-12T19:00\",\"2023-08-12T20:00\",\"2023-08-12T21:00\",\"2023-08-12T22:00\",\"2023-08-12T23:00\",\"2023-08-13T00:00\",\"2023-08-13T01:00\",\"2023-08-13T02:00\",\"2023-08-13T03:00\",\"2023-08-13T04:00\",\"2023-08-13T05:00\",\"2023-08-13T06:00\",\"2023-08-13T07:00\",\"2023-08-13T08:00\",\"2023-08-13T09:00\",\"2023-08-13T10:00\",\"2023-08-13T11:00\",\"2023-08-13T12:00\",\"2023-08-13T13:00\",\"2023-08-13T14:00\",\"2023-08-13T15:00\",\"2023-08-13T16:00\",\"2023-08-13T17:00\",\"2023-08-13T18:00\",\"2023-08-13T19:00\",\"2023-08-13T20:00\",\"2023-08-13T21:00\",\"2023-08-13T22:00\",\"2023-08-13T23:00\"],\"temperature_2m\":[53.0,51.2,50.9,50.4,50.7,51.3,51.7,52.9,54.3,56.1,57.4,59.3,59.1,60.7,59.7,58.8,58.8,57.8,56.6,55.3,53.9,52.7,52.9,53.2,52.0,51.8,51.3,50.7,50.8,51.5,53.9,57.7,61.2,63.2,64.7,66.6,67.5,67.0,68.7,68.7,67.9,66.2,64.4,61.4,59.8,58.9,57.9,56.3,55.7,55.3,55.5,55.4,55.7,56.5,57.6,58.8,59.7,59.1,58.9,60.6,59.9,59.8,59.9,61.7,63.2,63.6,62.3,58.9,57.3,57.1,57.0,56.5,56.2,56.0,55.3,54.7,54.4,55.2,57.8,60.7,63.0,65.3,66.9,68.2,70.1,72.1,72.6,71.4,69.7,68.6,66.2,63.6,61.8,60.6,59.6,58.9,58.0,57.1,56.3,56.2,56.7,57.9,59.9,63.7,68.4,72.4,75.0,76.8,78.0,78.7,78.9,78.4,76.9,74.8,72.5,70.1,67.6,65.6,64.4,63.9,63.4,62.7,62.2,62.1,62.5,63.4,65.1,68.0,71.7,74.8,76.8,78.2,79.1,79.6,79.7,79.2,77.6,75.3,73.7,68.6,66.8,65.3,64.2,63.4,62.6,61.7,60.9,60.6,60.9,61.6,63.2,65.9,69.3,72.2,74.4,76.2,77.6,78.8,79.6,79.6,78.4,76.4,74.3,72.3,70.4,68.7,67.6,66.8]}}\u001b[0m\n",
|
||||||
|
"\n",
|
||||||
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"' The current temperature in Munich, Germany is 52.9°F.'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 8,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from langchain.llms import OpenAI\n",
|
||||||
|
"from langchain.chains import APIChain\n",
|
||||||
|
"from langchain.chains.api import open_meteo_docs\n",
|
||||||
|
"llm = OpenAI(temperature=0)\n",
|
||||||
|
"chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)\n",
|
||||||
|
"chain.run('What is the weather like right now in Munich, Germany in degrees Fahrenheit?')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5b179318",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Note that we supply information about the API:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 37,
|
||||||
|
"id": "a9e03cc2",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"'BASE URL: https://api.open-meteo.com/\\n\\nAPI Documentation\\nThe API endpoint /v1/forecast accepts a geographical coordinate, a list of weather variables and responds with a JSON hourly weather forecast for 7 days. Time always starts at 0:00 today and contains 168 hours. All URL parameters are listed below:\\n\\nParameter\\tFormat\\tRequired\\tDefault\\tDescription\\nlatitude, longitude\\tFloating point\\tYes\\t\\tGeographical WGS84 coordinate of the location\\nhourly\\tString array\\tNo\\t\\tA list of weather variables which shou'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 37,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"open_meteo_docs.OPEN_METEO_DOCS[0:500]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "3fab7930",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Under the hood, we do two things:\n",
|
||||||
|
" \n",
|
||||||
|
"* `api_request_chain`: Generate an API URL based on the input question and the api_docs\n",
|
||||||
|
"* `api_answer_chain`: generate a final answer based on the API response\n",
|
||||||
|
"\n",
|
||||||
|
"We can look at the [LangSmith trace](https://smith.langchain.com/public/1e0d18ca-0d76-444c-97df-a939a6a815a7/r) to inspect this:\n",
|
||||||
|
"\n",
|
||||||
|
"* The `api_request_chain` produces the API url from our question and the API documentation:\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"* [Here](https://github.com/langchain-ai/langchain/blob/bbd22b9b761389a5e40fc45b0570e1830aabb707/libs/langchain/langchain/chains/api/base.py#L82) we make the API request with the API url.\n",
|
||||||
|
"* The `api_answer_chain` takes the response from the API and provides us with a natural langugae response:\n",
|
||||||
|
"\n",
|
||||||
|
""
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "2511f446",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Going deeper\n",
|
||||||
|
"\n",
|
||||||
|
"**Test with other APIs**"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "1e1cf418",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import os\n",
|
||||||
|
"os.environ['TMDB_BEARER_TOKEN'] = \"\"\n",
|
||||||
|
"from langchain.chains.api import tmdb_docs\n",
|
||||||
|
"headers = {\"Authorization\": f\"Bearer {os.environ['TMDB_BEARER_TOKEN']}\"}\n",
|
||||||
|
"chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)\n",
|
||||||
|
"chain.run(\"Search for 'Avatar'\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "dd80a717",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import os\n",
|
||||||
|
"from langchain.llms import OpenAI\n",
|
||||||
|
"from langchain.chains.api import podcast_docs\n",
|
||||||
|
"from langchain.chains import APIChain\n",
|
||||||
|
" \n",
|
||||||
|
"listen_api_key = 'xxx' # Get api key here: https://www.listennotes.com/api/pricing/\n",
|
||||||
|
"llm = OpenAI(temperature=0)\n",
|
||||||
|
"headers = {\"X-ListenAPI-Key\": listen_api_key}\n",
|
||||||
|
"chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)\n",
|
||||||
|
"chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "a5939be5",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"**Web requests**\n",
|
||||||
|
"\n",
|
||||||
|
"URL requets are such a common use-case that we have the `LLMRequestsChain`, which makes a HTTP GET request. "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 39,
|
||||||
|
"id": "0b158296",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from langchain.llms import OpenAI\n",
|
||||||
|
"from langchain.prompts import PromptTemplate\n",
|
||||||
|
"from langchain.chains import LLMRequestsChain, LLMChain"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 40,
|
||||||
|
"id": "d49c33e4",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
|
||||||
|
"Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
|
||||||
|
"Use the format\n",
|
||||||
|
"Extracted:<answer or \"not found\">\n",
|
||||||
|
">>> {requests_result} <<<\n",
|
||||||
|
"Extracted:\"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
"PROMPT = PromptTemplate(\n",
|
||||||
|
" input_variables=[\"query\", \"requests_result\"],\n",
|
||||||
|
" template=template,\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 43,
|
||||||
|
"id": "d0fd4aab",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
|
||||||
|
" 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
|
||||||
|
" 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), China (9,706,961 km²)'}"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 43,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))\n",
|
||||||
|
"question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
|
||||||
|
"inputs = {\n",
|
||||||
|
" \"query\": question,\n",
|
||||||
|
" \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),\n",
|
||||||
|
"}\n",
|
||||||
|
"chain(inputs)"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"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": "3.9.16"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@ -1,24 +0,0 @@
|
|||||||
---
|
|
||||||
sidebar_position: 3
|
|
||||||
---
|
|
||||||
|
|
||||||
# Interacting with APIs
|
|
||||||
|
|
||||||
Lots of data and information is stored behind APIs.
|
|
||||||
This page covers all resources available in LangChain for working with APIs.
|
|
||||||
|
|
||||||
## Chains
|
|
||||||
|
|
||||||
If you are just getting started, and you have relatively simple apis, you should get started with chains.
|
|
||||||
Chains are a sequence of predetermined steps, so they are good to get started with as they give you more control and let you
|
|
||||||
understand what is happening better.
|
|
||||||
|
|
||||||
- [API Chain](/docs/use_cases/apis/api.html)
|
|
||||||
|
|
||||||
## Agents
|
|
||||||
|
|
||||||
Agents are more complex, and involve multiple queries to the LLM to understand what to do.
|
|
||||||
The downside of agents are that you have less control. The upside is that they are more powerful,
|
|
||||||
which allows you to use them on larger and more complex schemas.
|
|
||||||
|
|
||||||
- [OpenAPI Agent](/docs/integrations/toolkits/openapi.html)
|
|
||||||
@ -1,123 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "dd7ec7af",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# HTTP request chain\n",
|
|
||||||
"\n",
|
|
||||||
"Using the request library to get HTML results from a URL and then an LLM to parse results"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "dd8eae75",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from langchain.llms import OpenAI\n",
|
|
||||||
"from langchain.chains import LLMRequestsChain, LLMChain"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"id": "65bf324e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from langchain.prompts import PromptTemplate\n",
|
|
||||||
"\n",
|
|
||||||
"template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
|
|
||||||
"Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
|
|
||||||
"Use the format\n",
|
|
||||||
"Extracted:<answer or \"not found\">\n",
|
|
||||||
">>> {requests_result} <<<\n",
|
|
||||||
"Extracted:\"\"\"\n",
|
|
||||||
"\n",
|
|
||||||
"PROMPT = PromptTemplate(\n",
|
|
||||||
" input_variables=[\"query\", \"requests_result\"],\n",
|
|
||||||
" template=template,\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "f36ae0d8",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "b5d22d9d",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
|
|
||||||
"inputs = {\n",
|
|
||||||
" \"query\": question,\n",
|
|
||||||
" \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),\n",
|
|
||||||
"}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"id": "2ea81168",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
|
|
||||||
" 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
|
|
||||||
" 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 5,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain(inputs)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "db8f2b6d",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"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": "3.11.3"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,583 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "9fcaa37f",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# OpenAPI chain\n",
|
|
||||||
"\n",
|
|
||||||
"This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "efa6909f",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from langchain.tools import OpenAPISpec, APIOperation\n",
|
|
||||||
"from langchain.chains import OpenAPIEndpointChain\n",
|
|
||||||
"from langchain.requests import Requests\n",
|
|
||||||
"from langchain.llms import OpenAI"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "71e38c6c",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Load the spec\n",
|
|
||||||
"\n",
|
|
||||||
"Load a wrapper of the spec (so we can work with it more easily). You can load from a url or from a local file."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"id": "0831271b",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"spec = OpenAPISpec.from_url(\n",
|
|
||||||
" \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "189dd506",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# Alternative loading from file\n",
|
|
||||||
"# spec = OpenAPISpec.from_file(\"openai_openapi.yaml\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "f7093582",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Select the Operation\n",
|
|
||||||
"\n",
|
|
||||||
"In order to provide a focused on modular chain, we create a chain specifically only for one of the endpoints. Here we get an API operation from a specified endpoint and method."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "157494b9",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"operation = APIOperation.from_openapi_spec(spec, \"/public/openai/v0/products\", \"get\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "e3ab1c5c",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Construct the chain\n",
|
|
||||||
"\n",
|
|
||||||
"We can now construct a chain to interact with it. In order to construct such a chain, we will pass in:\n",
|
|
||||||
"\n",
|
|
||||||
"1. The operation endpoint\n",
|
|
||||||
"2. A requests wrapper (can be used to handle authentication, etc)\n",
|
|
||||||
"3. The LLM to use to interact with it"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"id": "788a7cef",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"llm = OpenAI() # Load a Language Model"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 6,
|
|
||||||
"id": "c5f27406",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = OpenAPIEndpointChain.from_api_operation(\n",
|
|
||||||
" operation,\n",
|
|
||||||
" llm,\n",
|
|
||||||
" requests=Requests(),\n",
|
|
||||||
" verbose=True,\n",
|
|
||||||
" return_intermediate_steps=True, # Return request and response text\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "23652053",
|
|
||||||
"metadata": {
|
|
||||||
"scrolled": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
|
|
||||||
"Prompt after formatting:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
|
|
||||||
"\n",
|
|
||||||
"API_SCHEMA: ```typescript\n",
|
|
||||||
"/* API for fetching Klarna product information */\n",
|
|
||||||
"type productsUsingGET = (_: {\n",
|
|
||||||
"/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */\n",
|
|
||||||
"\t\tq: string,\n",
|
|
||||||
"/* number of products returned */\n",
|
|
||||||
"\t\tsize?: number,\n",
|
|
||||||
"/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
|
|
||||||
"\t\tmin_price?: number,\n",
|
|
||||||
"/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
|
|
||||||
"\t\tmax_price?: number,\n",
|
|
||||||
"}) => any;\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"USER_INSTRUCTIONS: \"whats the most expensive shirt?\"\n",
|
|
||||||
"\n",
|
|
||||||
"Your arguments must be plain json provided in a markdown block:\n",
|
|
||||||
"\n",
|
|
||||||
"ARGS: ```json\n",
|
|
||||||
"{valid json conforming to API_SCHEMA}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Example\n",
|
|
||||||
"-----\n",
|
|
||||||
"\n",
|
|
||||||
"ARGS: ```json\n",
|
|
||||||
"{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
|
|
||||||
"You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
|
|
||||||
"\n",
|
|
||||||
"If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
|
|
||||||
"\n",
|
|
||||||
"Message: ```text\n",
|
|
||||||
"Concise response requesting the additional information that would make calling the function successful.\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Begin\n",
|
|
||||||
"-----\n",
|
|
||||||
"ARGS:\n",
|
|
||||||
"\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3m{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}\u001b[0m\n",
|
|
||||||
"\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new APIResponderChain chain...\u001b[0m\n",
|
|
||||||
"Prompt after formatting:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3mYou are a helpful AI assistant trained to answer user queries from API responses.\n",
|
|
||||||
"You attempted to call an API, which resulted in:\n",
|
|
||||||
"API_RESPONSE: {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}\n",
|
|
||||||
"\n",
|
|
||||||
"USER_COMMENT: \"whats the most expensive shirt?\"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:\n",
|
|
||||||
"Response: ```json\n",
|
|
||||||
"{\"response\": \"Human-understandable synthesis of the API_RESPONSE\"}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Otherwise respond with the following markdown json block:\n",
|
|
||||||
"Response Error: ```json\n",
|
|
||||||
"{\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response.\n",
|
|
||||||
"\n",
|
|
||||||
"Begin:\n",
|
|
||||||
"---\n",
|
|
||||||
"\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\u001b[33;1m\u001b[1;3mThe most expensive shirt in the API response is the Burberry Check Poplin Shirt, which costs $360.00.\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"output = chain(\"whats the most expensive shirt?\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 8,
|
|
||||||
"id": "c000295e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'request_args': '{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}',\n",
|
|
||||||
" 'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}'}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 8,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# View intermediate steps\n",
|
|
||||||
"output[\"intermediate_steps\"]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "092bdb4d",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Return raw response\n",
|
|
||||||
"\n",
|
|
||||||
"We can also run this chain without synthesizing the response. This will have the effect of just returning the raw API output."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 10,
|
|
||||||
"id": "4dff3849",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = OpenAPIEndpointChain.from_api_operation(\n",
|
|
||||||
" operation,\n",
|
|
||||||
" llm,\n",
|
|
||||||
" requests=Requests(),\n",
|
|
||||||
" verbose=True,\n",
|
|
||||||
" return_intermediate_steps=True, # Return request and response text\n",
|
|
||||||
" raw_response=True, # Return raw response\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 11,
|
|
||||||
"id": "762499a9",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
|
|
||||||
"Prompt after formatting:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
|
|
||||||
"\n",
|
|
||||||
"API_SCHEMA: ```typescript\n",
|
|
||||||
"/* API for fetching Klarna product information */\n",
|
|
||||||
"type productsUsingGET = (_: {\n",
|
|
||||||
"/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */\n",
|
|
||||||
"\t\tq: string,\n",
|
|
||||||
"/* number of products returned */\n",
|
|
||||||
"\t\tsize?: number,\n",
|
|
||||||
"/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
|
|
||||||
"\t\tmin_price?: number,\n",
|
|
||||||
"/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
|
|
||||||
"\t\tmax_price?: number,\n",
|
|
||||||
"}) => any;\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"USER_INSTRUCTIONS: \"whats the most expensive shirt?\"\n",
|
|
||||||
"\n",
|
|
||||||
"Your arguments must be plain json provided in a markdown block:\n",
|
|
||||||
"\n",
|
|
||||||
"ARGS: ```json\n",
|
|
||||||
"{valid json conforming to API_SCHEMA}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Example\n",
|
|
||||||
"-----\n",
|
|
||||||
"\n",
|
|
||||||
"ARGS: ```json\n",
|
|
||||||
"{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
|
|
||||||
"You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
|
|
||||||
"\n",
|
|
||||||
"If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
|
|
||||||
"\n",
|
|
||||||
"Message: ```text\n",
|
|
||||||
"Concise response requesting the additional information that would make calling the function successful.\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Begin\n",
|
|
||||||
"-----\n",
|
|
||||||
"ARGS:\n",
|
|
||||||
"\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3m{\"q\": \"shirt\", \"max_price\": null}\u001b[0m\n",
|
|
||||||
"\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"output = chain(\"whats the most expensive shirt?\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 12,
|
|
||||||
"id": "4afc021a",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'instructions': 'whats the most expensive shirt?',\n",
|
|
||||||
" 'output': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}',\n",
|
|
||||||
" 'intermediate_steps': {'request_args': '{\"q\": \"shirt\", \"max_price\": null}',\n",
|
|
||||||
" 'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}'}}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 12,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"output"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "8d7924e4",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Example POST message\n",
|
|
||||||
"\n",
|
|
||||||
"For this demo, we will interact with the speak API."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 9,
|
|
||||||
"id": "c56b1a04",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
|
|
||||||
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"spec = OpenAPISpec.from_url(\"https://api.speak.com/openapi.yaml\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 10,
|
|
||||||
"id": "177d8275",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"operation = APIOperation.from_openapi_spec(\n",
|
|
||||||
" spec, \"/v1/public/openai/explain-task\", \"post\"\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 11,
|
|
||||||
"id": "835c5ddc",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"llm = OpenAI()\n",
|
|
||||||
"chain = OpenAPIEndpointChain.from_api_operation(\n",
|
|
||||||
" operation, llm, requests=Requests(), verbose=True, return_intermediate_steps=True\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 12,
|
|
||||||
"id": "59855d60",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
|
|
||||||
"Prompt after formatting:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
|
|
||||||
"\n",
|
|
||||||
"API_SCHEMA: ```typescript\n",
|
|
||||||
"type explainTask = (_: {\n",
|
|
||||||
"/* Description of the task that the user wants to accomplish or do. For example, \"tell the waiter they messed up my order\" or \"compliment someone on their shirt\" */\n",
|
|
||||||
" task_description?: string,\n",
|
|
||||||
"/* The foreign language that the user is learning and asking about. The value can be inferred from question - for example, if the user asks \"how do i ask a girl out in mexico city\", the value should be \"Spanish\" because of Mexico City. Always use the full name of the language (e.g. Spanish, French). */\n",
|
|
||||||
" learning_language?: string,\n",
|
|
||||||
"/* The user's native language. Infer this value from the language the user asked their question in. Always use the full name of the language (e.g. Spanish, French). */\n",
|
|
||||||
" native_language?: string,\n",
|
|
||||||
"/* A description of any additional context in the user's question that could affect the explanation - e.g. setting, scenario, situation, tone, speaking style and formality, usage notes, or any other qualifiers. */\n",
|
|
||||||
" additional_context?: string,\n",
|
|
||||||
"/* Full text of the user's question. */\n",
|
|
||||||
" full_query?: string,\n",
|
|
||||||
"}) => any;\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"USER_INSTRUCTIONS: \"How would ask for more tea in Delhi?\"\n",
|
|
||||||
"\n",
|
|
||||||
"Your arguments must be plain json provided in a markdown block:\n",
|
|
||||||
"\n",
|
|
||||||
"ARGS: ```json\n",
|
|
||||||
"{valid json conforming to API_SCHEMA}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Example\n",
|
|
||||||
"-----\n",
|
|
||||||
"\n",
|
|
||||||
"ARGS: ```json\n",
|
|
||||||
"{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
|
|
||||||
"You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
|
|
||||||
"\n",
|
|
||||||
"If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
|
|
||||||
"\n",
|
|
||||||
"Message: ```text\n",
|
|
||||||
"Concise response requesting the additional information that would make calling the function successful.\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Begin\n",
|
|
||||||
"-----\n",
|
|
||||||
"ARGS:\n",
|
|
||||||
"\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3m{\"task_description\": \"ask for more tea\", \"learning_language\": \"Hindi\", \"native_language\": \"English\", \"full_query\": \"How would I ask for more tea in Delhi?\"}\u001b[0m\n",
|
|
||||||
"\u001b[36;1m\u001b[1;3m{\"explanation\":\"<what-to-say language=\\\"Hindi\\\" context=\\\"None\\\">\\nऔर चाय लाओ। (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\\"None\\\">\\n1. \\\"चाय थोड़ी ज्यादा मिल सकती है?\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\\"Hindi\\\">\\n<context>At home during breakfast.</context>\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new APIResponderChain chain...\u001b[0m\n",
|
|
||||||
"Prompt after formatting:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3mYou are a helpful AI assistant trained to answer user queries from API responses.\n",
|
|
||||||
"You attempted to call an API, which resulted in:\n",
|
|
||||||
"API_RESPONSE: {\"explanation\":\"<what-to-say language=\\\"Hindi\\\" context=\\\"None\\\">\\nऔर चाय लाओ। (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\\"None\\\">\\n1. \\\"चाय थोड़ी ज्यादा मिल सकती है?\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\\"Hindi\\\">\\n<context>At home during breakfast.</context>\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}\n",
|
|
||||||
"\n",
|
|
||||||
"USER_COMMENT: \"How would ask for more tea in Delhi?\"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:\n",
|
|
||||||
"Response: ```json\n",
|
|
||||||
"{\"response\": \"Concise response to USER_COMMENT based on API_RESPONSE.\"}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"Otherwise respond with the following markdown json block:\n",
|
|
||||||
"Response Error: ```json\n",
|
|
||||||
"{\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}\n",
|
|
||||||
"```\n",
|
|
||||||
"\n",
|
|
||||||
"You MUST respond as a markdown json code block.\n",
|
|
||||||
"\n",
|
|
||||||
"Begin:\n",
|
|
||||||
"---\n",
|
|
||||||
"\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\u001b[33;1m\u001b[1;3mIn Delhi you can ask for more tea by saying 'Chai thodi zyada mil sakti hai?'\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"output = chain(\"How would ask for more tea in Delhi?\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 13,
|
|
||||||
"id": "91bddb18",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"['{\"task_description\": \"ask for more tea\", \"learning_language\": \"Hindi\", \"native_language\": \"English\", \"full_query\": \"How would I ask for more tea in Delhi?\"}',\n",
|
|
||||||
" '{\"explanation\":\"<what-to-say language=\\\\\"Hindi\\\\\" context=\\\\\"None\\\\\">\\\\nऔर चाय लाओ। (Aur chai lao.) \\\\n</what-to-say>\\\\n\\\\n<alternatives context=\\\\\"None\\\\\">\\\\n1. \\\\\"चाय थोड़ी ज्यादा मिल सकती है?\\\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\\\n2. \\\\\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\\\n3. \\\\\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\\\n</alternatives>\\\\n\\\\n<usage-notes>\\\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\\\n</usage-notes>\\\\n\\\\n<example-convo language=\\\\\"Hindi\\\\\">\\\\n<context>At home during breakfast.</context>\\\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\\\n</example-convo>\\\\n\\\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}']"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 13,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# Show the API chain's intermediate steps\n",
|
|
||||||
"output[\"intermediate_steps\"]"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"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": "3.11.3"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
@ -1,249 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "e734b314",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"# OpenAPI calls with OpenAI functions\n",
|
|
||||||
"\n",
|
|
||||||
"In this notebook we'll show how to create a chain that automatically makes calls to an API based only on an OpenAPI spec. Under the hood, we're parsing the OpenAPI spec into a JSON schema that the OpenAI functions API can handle. This allows ChatGPT to automatically select and populate the relevant API call to make for any user input. Using the output of ChatGPT we then make the actual API call, and return the result."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "555661b5",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from langchain.chains.openai_functions.openapi import get_openapi_chain"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "a95f510a",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Query Klarna"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "08e19b64",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = get_openapi_chain(\n",
|
|
||||||
" \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "3959f866",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'products': [{'name': \"Tommy Hilfiger Men's Short Sleeve Button-Down Shirt\",\n",
|
|
||||||
" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3204878580/Clothing/Tommy-Hilfiger-Men-s-Short-Sleeve-Button-Down-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
|
|
||||||
" 'price': '$26.78',\n",
|
|
||||||
" 'attributes': ['Material:Linen,Cotton',\n",
|
|
||||||
" 'Target Group:Man',\n",
|
|
||||||
" 'Color:Gray,Pink,White,Blue,Beige,Black,Turquoise',\n",
|
|
||||||
" 'Size:S,XL,M,XXL']},\n",
|
|
||||||
" {'name': \"Van Heusen Men's Long Sleeve Button-Down Shirt\",\n",
|
|
||||||
" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201809514/Clothing/Van-Heusen-Men-s-Long-Sleeve-Button-Down-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
|
|
||||||
" 'price': '$18.89',\n",
|
|
||||||
" 'attributes': ['Material:Cotton',\n",
|
|
||||||
" 'Target Group:Man',\n",
|
|
||||||
" 'Color:Red,Gray,White,Blue',\n",
|
|
||||||
" 'Size:XL,XXL']},\n",
|
|
||||||
" {'name': 'Brixton Bowery Flannel Shirt',\n",
|
|
||||||
" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202331096/Clothing/Brixton-Bowery-Flannel-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
|
|
||||||
" 'price': '$34.48',\n",
|
|
||||||
" 'attributes': ['Material:Cotton',\n",
|
|
||||||
" 'Target Group:Man',\n",
|
|
||||||
" 'Color:Gray,Blue,Black,Orange',\n",
|
|
||||||
" 'Size:XL,3XL,4XL,5XL,L,M,XXL']},\n",
|
|
||||||
" {'name': 'Cubavera Four Pocket Guayabera Shirt',\n",
|
|
||||||
" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202055522/Clothing/Cubavera-Four-Pocket-Guayabera-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
|
|
||||||
" 'price': '$23.22',\n",
|
|
||||||
" 'attributes': ['Material:Polyester,Cotton',\n",
|
|
||||||
" 'Target Group:Man',\n",
|
|
||||||
" 'Color:Red,White,Blue,Black',\n",
|
|
||||||
" 'Size:S,XL,L,M,XXL']},\n",
|
|
||||||
" {'name': 'Theory Sylvain Shirt - Eclipse',\n",
|
|
||||||
" 'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202028254/Clothing/Theory-Sylvain-Shirt-Eclipse/?utm_source=openai&ref-site=openai_plugin',\n",
|
|
||||||
" 'price': '$86.01',\n",
|
|
||||||
" 'attributes': ['Material:Polyester,Cotton',\n",
|
|
||||||
" 'Target Group:Man',\n",
|
|
||||||
" 'Color:Blue',\n",
|
|
||||||
" 'Size:S,XL,XS,L,M,XXL']}]}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 3,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain.run(\"What are some options for a men's large blue button down shirt\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "6f648c77",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Query a translation service\n",
|
|
||||||
"\n",
|
|
||||||
"Additionally, see the request payload by setting `verbose=True`"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "bf6cd695",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = get_openapi_chain(\"https://api.speak.com/openapi.yaml\", verbose=True)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 10,
|
|
||||||
"id": "1ba51609",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
|
||||||
"Prompt after formatting:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3mHuman: Use the provided API's to respond to this user query:\n",
|
|
||||||
"\n",
|
|
||||||
"How would you say no thanks in Russian\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
|
||||||
"Calling endpoint \u001b[32;1m\u001b[1;3mtranslate\u001b[0m with arguments:\n",
|
|
||||||
"\u001b[32;1m\u001b[1;3m{\n",
|
|
||||||
" \"json\": {\n",
|
|
||||||
" \"phrase_to_translate\": \"no thanks\",\n",
|
|
||||||
" \"learning_language\": \"russian\",\n",
|
|
||||||
" \"native_language\": \"english\",\n",
|
|
||||||
" \"additional_context\": \"\",\n",
|
|
||||||
" \"full_query\": \"How would you say no thanks in Russian\"\n",
|
|
||||||
" }\n",
|
|
||||||
"}\u001b[0m\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n",
|
|
||||||
"\n",
|
|
||||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'explanation': '<translation language=\"Russian\">\\nНет, спасибо. (Net, spasibo)\\n</translation>\\n\\n<alternatives>\\n1. \"Нет, я в порядке\" *(Neutral/Formal - Can be used in professional settings or formal situations.)*\\n2. \"Нет, спасибо, я откажусь\" *(Formal - Can be used in polite settings, such as a fancy dinner with colleagues or acquaintances.)*\\n3. \"Не надо\" *(Informal - Can be used in informal situations, such as declining an offer from a friend.)*\\n</alternatives>\\n\\n<example-convo language=\"Russian\">\\n<context>Max is being offered a cigarette at a party.</context>\\n* Sasha: \"Хочешь покурить?\"\\n* Max: \"Нет, спасибо. Я бросил.\"\\n* Sasha: \"Окей, понятно.\"\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=noczaa460do8yqs8xjun6zdm})*',\n",
|
|
||||||
" 'extra_response_instructions': 'Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.'}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 10,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain.run(\"How would you say no thanks in Russian\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "4923a291",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Query XKCD"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "a9198f62",
|
|
||||||
"metadata": {
|
|
||||||
"scrolled": true
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"chain = get_openapi_chain(\n",
|
|
||||||
" \"https://gist.githubusercontent.com/roaldnefs/053e505b2b7a807290908fe9aa3e1f00/raw/0a212622ebfef501163f91e23803552411ed00e4/openapi.yaml\"\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "3110c398",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'month': '6',\n",
|
|
||||||
" 'num': 2793,\n",
|
|
||||||
" 'link': '',\n",
|
|
||||||
" 'year': '2023',\n",
|
|
||||||
" 'news': '',\n",
|
|
||||||
" 'safe_title': 'Garden Path Sentence',\n",
|
|
||||||
" 'transcript': '',\n",
|
|
||||||
" 'alt': 'Arboretum Owner Denied Standing in Garden Path Suit on Grounds Grounds Appealing Appealing',\n",
|
|
||||||
" 'img': 'https://imgs.xkcd.com/comics/garden_path_sentence.png',\n",
|
|
||||||
" 'title': 'Garden Path Sentence',\n",
|
|
||||||
" 'day': '23'}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 7,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"chain.run(\"What's today's comic?\")"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "venv",
|
|
||||||
"language": "python",
|
|
||||||
"name": "venv"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.11.3"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user