Interacting with APIs

Обновлено 24 мая 2024

Suppose you want an LLM to interact with external APIs.

This can be very useful for retrieving context for the LLM to utilize.

And, more generally, it allows us to interact with APIs using natural langugage!

Overview

There are two primary ways to interface LLMs with external APIs:

• Functions: For example, OpenAI functions is one popular means of doing this.
• LLM-generated interface: Use an LLM with access to API documentation to create an interface.

Quickstart

Many APIs are already compatible with OpenAI function calling.

For example, Klarna has a YAML file that describes its API and allows OpenAI to interact with it:

https://www.klarna.com/us/shopping/public/openai/v0/api-docs/

Other options include:

• Speak for translation
• XKCD for comics

We can supply the specification to get_openapi_chain directly in order to query the API with OpenAI functions:

pip install gigachain langchain-openai 

# Set env var OPENAI_API_KEY or load from a .env file:
# import dotenv
# dotenv.load_dotenv()

from langchain.chains.openai_functions.openapi import get_openapi_chain

chain = get_openapi_chain(
    "https://www.klarna.com/us/shopping/public/openai/v0/api-docs/"
)
chain("What are some options for a men's large blue button down shirt")

    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.

    {'query': "What are some options for a men's large blue button down shirt",
 'response': {'products': [{'name': 'Cubavera Four Pocket Guayabera Shirt',
    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202055522/Clothing/Cubavera-Four-Pocket-Guayabera-Shirt/?utm_source=openai&ref-site=openai_plugin',
    'price': '$13.50',
    'attributes': ['Material:Polyester,Cotton',
     'Target Group:Man',
     'Color:Red,White,Blue,Black',
     'Properties:Pockets',
     'Pattern:Solid Color',
     'Size (Small-Large):S,XL,L,M,XXL']},
   {'name': 'Polo Ralph Lauren Plaid Short Sleeve Button-down Oxford Shirt',
    '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',
    'price': '$52.20',
    'attributes': ['Material:Cotton',
     'Target Group:Man',
     'Color:Red,Blue,Multicolor',
     'Size (Small-Large):S,XL,L,M,XXL']},
   {'name': 'Brixton Bowery Flannel Shirt',
    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202331096/Clothing/Brixton-Bowery-Flannel-Shirt/?utm_source=openai&ref-site=openai_plugin',
    'price': '$27.48',
    'attributes': ['Material:Cotton',
     'Target Group:Man',
     'Color:Gray,Blue,Black,Orange',
     'Properties:Pockets',
     'Pattern:Checkered',
     'Size (Small-Large):XL,3XL,4XL,5XL,L,M,XXL']},
   {'name': 'Vineyard Vines Gingham On-The-Go brrr Classic Fit Shirt Crystal',
    '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',
    'price': '$80.64',
    'attributes': ['Material:Cotton',
     'Target Group:Man',
     'Color:Blue',
     'Size (Small-Large):XL,XS,L,M']},
   {'name': "Carhartt Men's Loose Fit Midweight Short Sleeve Plaid Shirt",
    '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',
    'price': '$17.99',
    'attributes': ['Material:Cotton',
     'Target Group:Man',
     'Color:Red,Brown,Blue,Green',
     'Properties:Pockets',
     'Pattern:Checkered',
     'Size (Small-Large):S,XL,L,M']}]}}

Functions

We can unpack what is happening when we use the functions to call external APIs.

Let's look at the LangSmith trace:

• See here that we call the OpenAI LLM with the provided API spec:

https://www.klarna.com/us/shopping/public/openai/v0/api-docs/

• The prompt then tells the LLM to use the API spec with input question:

Use the provided APIs to respond to this user query:
What are some options for a men's large blue button down shirt

• The LLM returns the parameters for the function call productsUsingGET, which is specified in the provided API spec:

function_call:
  name: productsUsingGET
  arguments: |-
    {
      "params": {
        "countryCode": "US",
        "q": "men's large blue button down shirt",
        "size": 5,
        "min_price": 0,
        "max_price": 100
      }
    }

* 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).

API Chain

We can also build our own interface to external APIs using the APIChain and provided API documentation.

from langchain.chains import APIChain
from langchain.chains.api import open_meteo_docs
from langchain_openai import OpenAI

llm = OpenAI(temperature=0)
chain = APIChain.from_llm_and_api_docs(
    llm,
    open_meteo_docs.OPEN_METEO_DOCS,
    verbose=True,
    limit_to_domains=["https://api.open-meteo.com/"],
)
chain.run(
    "What is the weather like right now in Munich, Germany in degrees Fahrenheit?"
)

    
    
    > Entering new APIChain chain...
    https://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&hourly=temperature_2m&temperature_unit=fahrenheit&current_weather=true
    {"latitude":48.14,"longitude":11.58,"generationtime_ms":0.1710653305053711,"utc_offset_seconds":0,"timezone":"GMT","timezone_abbreviation":"GMT","elevation":521.0,"current_weather_units":{"time":"iso8601","interval":"seconds","temperature":"°F","windspeed":"km/h","winddirection":"°","is_day":"","weathercode":"wmo code"},"current_weather":{"time":"2023-11-01T21:30","interval":900,"temperature":46.5,"windspeed":7.7,"winddirection":259,"is_day":0,"weathercode":3},"hourly_units":{"time":"iso8601","temperature_2m":"°F"},"hourly":{"time":["2023-11-01T00:00","2023-11-01T01:00","2023-11-01T02:00","2023-11-01T03:00","2023-11-01T04:00","2023-11-01T05:00","2023-11-01T06:00","2023-11-01T07:00","2023-11-01T08:00","2023-11-01T09:00","2023-11-01T10:00","2023-11-01T11:00","2023-11-01T12:00","2023-11-01T13:00","2023-11-01T14:00","2023-11-01T15:00","2023-11-01T16:00","2023-11-01T17:00","2023-11-01T18:00","2023-11-01T19:00","2023-11-01T20:00","2023-11-01T21:00","2023-11-01T22:00","2023-11-01T23:00","2023-11-02T00:00","2023-11-02T01:00","2023-11-02T02:00","2023-11-02T03:00","2023-11-02T04:00","2023-11-02T05:00","2023-11-02T06:00","2023-11-02T07:00","2023-11-02T08:00","2023-11-02T09:00","2023-11-02T10:00","2023-11-02T11:00","2023-11-02T12:00","2023-11-02T13:00","2023-11-02T14:00","2023-11-02T15:00","2023-11-02T16:00","2023-11-02T17:00","2023-11-02T18:00","2023-11-02T19:00","2023-11-02T20:00","2023-11-02T21:00","2023-11-02T22:00","2023-11-02T23:00","2023-11-03T00:00","2023-11-03T01:00","2023-11-03T02:00","2023-11-03T03:00","2023-11-03T04:00","2023-11-03T05:00","2023-11-03T06:00","2023-11-03T07:00","2023-11-03T08:00","2023-11-03T09:00","2023-11-03T10:00","2023-11-03T11:00","2023-11-03T12:00","2023-11-03T13:00","2023-11-03T14:00","2023-11-03T15:00","2023-11-03T16:00","2023-11-03T17:00","2023-11-03T18:00","2023-11-03T19:00","2023-11-03T20:00","2023-11-03T21:00","2023-11-03T22:00","2023-11-03T23:00","2023-11-04T00:00","2023-11-04T01:00","2023-11-04T02:00","2023-11-04T03:00","2023-11-04T04:00","2023-11-04T05:00","2023-11-04T06:00","2023-11-04T07:00","2023-11-04T08:00","2023-11-04T09:00","2023-11-04T10:00","2023-11-04T11:00","2023-11-04T12:00","2023-11-04T13:00","2023-11-04T14:00","2023-11-04T15:00","2023-11-04T16:00","2023-11-04T17:00","2023-11-04T18:00","2023-11-04T19:00","2023-11-04T20:00","2023-11-04T21:00","2023-11-04T22:00","2023-11-04T23:00","2023-11-05T00:00","2023-11-05T01:00","2023-11-05T02:00","2023-11-05T03:00","2023-11-05T04:00","2023-11-05T05:00","2023-11-05T06:00","2023-11-05T07:00","2023-11-05T08:00","2023-11-05T09:00","2023-11-05T10:00","2023-11-05T11:00","2023-11-05T12:00","2023-11-05T13:00","2023-11-05T14:00","2023-11-05T15:00","2023-11-05T16:00","2023-11-05T17:00","2023-11-05T18:00","2023-11-05T19:00","2023-11-05T20:00","2023-11-05T21:00","2023-11-05T22:00","2023-11-05T23:00","2023-11-06T00:00","2023-11-06T01:00","2023-11-06T02:00","2023-11-06T03:00","2023-11-06T04:00","2023-11-06T05:00","2023-11-06T06:00","2023-11-06T07:00","2023-11-06T08:00","2023-11-06T09:00","2023-11-06T10:00","2023-11-06T11:00","2023-11-06T12:00","2023-11-06T13:00","2023-11-06T14:00","2023-11-06T15:00","2023-11-06T16:00","2023-11-06T17:00","2023-11-06T18:00","2023-11-06T19:00","2023-11-06T20:00","2023-11-06T21:00","2023-11-06T22:00","2023-11-06T23:00","2023-11-07T00:00","2023-11-07T01:00","2023-11-07T02:00","2023-11-07T03:00","2023-11-07T04:00","2023-11-07T05:00","2023-11-07T06:00","2023-11-07T07:00","2023-11-07T08:00","2023-11-07T09:00","2023-11-07T10:00","2023-11-07T11:00","2023-11-07T12:00","2023-11-07T13:00","2023-11-07T14:00","2023-11-07T15:00","2023-11-07T16:00","2023-11-07T17:00","2023-11-07T18:00","2023-11-07T19:00","2023-11-07T20:00","2023-11-07T21:00","2023-11-07T22:00","2023-11-07T23:00"],"temperature_2m":[47.9,46.9,47.1,46.6,45.8,45.2,43.4,43.5,46.8,51.5,55.0,56.3,58.1,57.9,57.0,56.6,54.4,52.1,49.1,48.3,47.7,46.9,46.2,45.8,44.4,42.4,41.7,41.7,42.0,42.7,43.6,44.3,45.9,48.0,49.1,50.7,52.2,52.6,51.9,50.3,48.1,47.4,47.1,46.9,46.2,45.7,45.6,45.6,45.7,45.3,45.1,44.2,43.6,43.2,42.8,41.6,41.0,42.1,42.4,42.3,42.7,43.9,44.2,43.6,41.9,40.4,39.0,40.8,40.2,40.1,39.6,38.8,38.2,36.9,35.8,36.4,37.3,38.5,38.9,39.0,41.8,45.4,48.7,50.8,51.7,52.1,51.3,49.8,48.6,47.8,47.0,46.3,45.9,45.6,45.7,46.1,46.3,46.4,46.3,46.3,45.8,45.4,45.5,47.1,49.3,51.2,52.4,53.1,53.5,53.4,53.0,52.4,51.6,50.5,49.6,49.0,48.6,48.1,47.6,47.0,46.4,46.0,45.5,45.1,44.4,43.7,43.9,45.6,48.1,50.3,51.7,52.8,53.5,52.7,51.5,50.2,48.8,47.4,46.2,45.5,45.0,44.6,44.3,44.2,43.9,43.4,43.0,42.6,42.3,42.0,42.2,43.0,44.3,45.5,46.8,48.1,48.9,49.0,48.7,48.1,47.4,46.5,45.7,45.1,44.5,44.3,44.5,45.1]}}
    
    > Finished chain.

    ' The current temperature in Munich, Germany is 46.5°F.'

Note that we supply information about the API:

open_meteo_docs.OPEN_METEO_DOCS[0:500]

    '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'

Under the hood, we do two things:

• api_request_chain: Generate an API URL based on the input question and the api_docs
• api_answer_chain: generate a final answer based on the API response

We can look at the LangSmith trace to inspect this:

• The api_request_chain produces the API url from our question and the API documentation:

• Here we make the API request with the API url.
• The api_answer_chain takes the response from the API and provides us with a natural language response:

Going deeper

Test with other APIs

import os

os.environ["TMDB_BEARER_TOKEN"] = ""
from langchain.chains.api import tmdb_docs

headers = {"Authorization": f"Bearer {os.environ['TMDB_BEARER_TOKEN']}"}
chain = APIChain.from_llm_and_api_docs(
    llm,
    tmdb_docs.TMDB_DOCS,
    headers=headers,
    verbose=True,
    limit_to_domains=["https://api.themoviedb.org/"],
)
chain.run("Search for 'Avatar'")

import os

from langchain.chains import APIChain
from langchain.chains.api import podcast_docs
from langchain_openai import OpenAI

listen_api_key = "xxx"  # Get api key here: https://www.listennotes.com/api/pricing/
llm = OpenAI(temperature=0)
headers = {"X-ListenAPI-Key": listen_api_key}
chain = APIChain.from_llm_and_api_docs(
    llm,
    podcast_docs.PODCAST_DOCS,
    headers=headers,
    verbose=True,
    limit_to_domains=["https://listen-api.listennotes.com/"],
)
chain.run(
    "Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results"
)

Web requests

URL requets are such a common use-case that we have the LLMRequestsChain, which makes a HTTP GET request.

from langchain.chains import LLMChain, LLMRequestsChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI

template = """Between >>> and <<< are the raw search result text from google.
Extract the answer to the question '{query}' or say "not found" if the information is not contained.
Use the format
Extracted:<answer or "not found">
>>> {requests_result} <<<
Extracted:"""

PROMPT = PromptTemplate(
    input_variables=["query", "requests_result"],
    template=template,
)

chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))
question = "What are the Three (3) biggest countries, and their respective sizes?"
inputs = {
    "query": question,
    "url": "https://www.google.com/search?q=" + question.replace(" ", "+"),
}
chain(inputs)

    {'query': 'What are the Three (3) biggest countries, and their respective sizes?',
 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',
 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), China (9,706,961 km²)'}