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##############################################################################
# Utility methods for building LLMs and agent models
#
# @philmui
# Mon May 1 18:34:45 PDT 2023
##############################################################################
import os
import pandas as pd
from langchain.agents import AgentType, load_tools, initialize_agent,\
create_pandas_dataframe_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain import SQLDatabase, SQLDatabaseChain, HuggingFaceHub
OPENAI_LLMS = [
'text-davinci-003',
'text-babbage-001',
'text-curie-001',
'text-ada-001'
]
OPENAI_CHAT_LLMS = [
'gpt-3.5-turbo',
'gpt-4',
]
HUGGINGFACE_LLMS = [
'google/flan-t5-xl',
'databricks/dolly-v2-3b',
'bigscience/bloom-1b7'
]
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
def createLLM(model_name="text-davinci-003", temperature=0):
llm = None
if model_name in OPENAI_LLMS:
llm = OpenAI(model_name=model_name, temperature=temperature)
elif model_name in OPENAI_CHAT_LLMS:
llm = ChatOpenAI(model_name=model_name, temperature=temperature)
elif model_name in HUGGINGFACE_LLMS:
llm = HuggingFaceHub(repo_id=model_name,
model_kwargs={"temperature":1e-10})
return llm
def load_chat_agent(verbose=True):
return createLLM(OPENAI_CHAT_LLMS[0], temperature=0.5)
def load_sales_agent(verbose=True):
'''
Hard-coded agent that gates an internal sales CSV file for demo
'''
chat = createLLM(OPENAI_CHAT_LLMS[0], temperature=0.5)
df = pd.read_csv("data/sales_data.csv")
agent = create_pandas_dataframe_agent(chat, df, verbose=verbose)
return agent
def load_sqlite_agent(model_name="text-davinci-003"):
'''
Hard-coded agent that gates a sqlite DB of digital media for demo
'''
llm = createLLM(OPENAI_LLMS[0])
sqlite_db_path = "./data/Chinook_Sqlite.sqlite"
db = SQLDatabase.from_uri(f"sqlite:///{sqlite_db_path}")
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
return db_chain
from langchain.tools import DuckDuckGoSearchRun, GoogleSearchRun
from langchain.utilities import GoogleSearchAPIWrapper
def load_chained_agent(verbose=True, model_name="text-davinci-003"):
llm = createLLM(model_name)
toolkit = load_tools(["serpapi", "open-meteo-api", "news-api",
"python_repl", "wolfram-alpha"],
llm=llm,
serpapi_api_key=os.getenv('SERPAPI_API_KEY'),
news_api_key=os.getenv('NEWS_API_KEY'),
tmdb_bearer_token=os.getenv('TMDB_BEARER_TOKEN')
)
toolkit += [DuckDuckGoSearchRun()]
agent = initialize_agent(toolkit,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=verbose,
return_intermediate_steps=True)
return agent |