openapi_chain_nfl.py

import os

from langchain.tools import OpenAPISpec, APIOperation
from langchain.chains import OpenAPIEndpointChain
from langchain.requests import Requests
from langchain.llms import OpenAI

spec = OpenAPISpec.from_file("nfl/specs/content.yaml")

import os
import nfl_helpers

client_key = os.environ.get("NFL_CLIENT_KEY")
client_secret = os.environ.get("NFL_CLIENT_SECRET")
token = nfl_helpers.get_token(client_key=client_key, client_secret=client_secret)

# %%
token

import os, yaml

from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec

with open("nfl/specs/content.yaml") as f:
    content_api_spec = yaml.load(f, Loader=yaml.Loader)
content_api_spec = reduce_openapi_spec(content_api_spec)

# %%
from langchain.agents.agent_toolkits.openapi import planner

llm = OpenAI(model_name="gpt-4", temperature=0.25)

content_agent = planner.create_openapi_agent(
    api_spec=content_api_spec,
    requests_wrapper=Requests(headers={"Authorization": f"Bearer {token}"}),
    llm=llm,
)

with open("nfl/specs/football-v2.yaml") as f:
    football_api_spec = yaml.load(f, Loader=yaml.Loader)
football_api_spec = reduce_openapi_spec(football_api_spec)
football_agent = planner.create_openapi_agent(
    api_spec=football_api_spec,
    requests_wrapper=Requests(headers={"Authorization": f"Bearer {token}"}),
    llm=llm,
)

# %%
with open("nfl/specs/team-content.yaml") as f:
    team_content_spec = yaml.load(f, Loader=yaml.Loader)
team_content_spec = reduce_openapi_spec(team_content_spec)
team_content_agent = planner.create_openapi_agent(
    api_spec=team_content_spec,
    requests_wrapper=Requests(headers={"Authorization": f"Bearer {token}"}),
    llm=llm,
)

# %%
# Import things that are needed generically
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.tools import BaseTool
from langchain.llms import OpenAI
from langchain import LLMMathChain, SerpAPIWrapper

# %%
tools = [
    Tool(
        name="Content",
        func=content_agent.run,
        description="useful for getting info about articles and promos. Input should be a fully formed question.",
    ),
    Tool(
        name="Football",
        func=football_agent.run,
        description="useful for football data, like persons, teams, and stats. Input should be a fully formed question.",
    ),
    Tool(
        name="Team content",
        func=team_content_agent.run,
        description="useful for team-produced content. This includes articles, authors, coaches, cheerleaders, events, photo galleries, staff members, videos, annd video channels.",
    ),
]

# %%
# Construct the agent. We will use the default agent type here.
# See documentation for a full list of options.
agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

# %%
agent.run("Give me 10 players playing this season (2023)")