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import os |
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import pandas as pd |
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from langchain.agents import AgentType, load_tools, initialize_agent,\ |
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create_pandas_dataframe_agent |
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from langchain import SQLDatabase, SQLDatabaseChain, HuggingFaceHub |
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from langchain.chat_models import ChatOpenAI |
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from langchain.llms import OpenAI |
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from langchain.chains import RetrievalQA |
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from langchain.document_loaders import DirectoryLoader, TextLoader |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.vectorstores import Chroma |
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from langchain.text_splitter import CharacterTextSplitter |
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OPENAI_LLMS = [ |
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'text-davinci-003', |
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'text-babbage-001', |
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'text-curie-001', |
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'text-ada-001' |
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] |
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OPENAI_CHAT_LLMS = [ |
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'gpt-3.5-turbo', |
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'gpt-4', |
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] |
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HUGGINGFACE_LLMS = [ |
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'google/flan-t5-xl', |
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'databricks/dolly-v2-3b', |
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'bigscience/bloom-1b7' |
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] |
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HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") |
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def createLLM(model_name="text-davinci-003", temperature=0): |
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llm = None |
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if model_name in OPENAI_LLMS: |
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llm = OpenAI(model_name=model_name, temperature=temperature) |
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elif model_name in OPENAI_CHAT_LLMS: |
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llm = ChatOpenAI(model_name=model_name, temperature=temperature) |
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elif model_name in HUGGINGFACE_LLMS: |
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llm = HuggingFaceHub(repo_id=model_name, |
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model_kwargs={"temperature":1e-10}) |
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return llm |
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def load_chat_agent(verbose=True): |
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return createLLM(OPENAI_CHAT_LLMS[0], temperature=0.5) |
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import os |
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import chromadb |
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from chromadb.config import Settings |
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DB_DIR = "./db" |
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def load_book_agent(verbose=True): |
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retriever = None |
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embeddings = OpenAIEmbeddings(openai_api_key = os.environ['OPENAI_API_KEY']) |
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if not os.path.exists(DB_DIR): |
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loader = DirectoryLoader(path="./data/", glob="**/*.txt") |
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docs = loader.load() |
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=20) |
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text_chunks = text_splitter.split_documents(documents=docs) |
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docsearch = Chroma.from_documents(text_chunks, embeddings, |
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persist_directory="./db") |
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retriever = docsearch.as_retriever() |
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else: |
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vectordb = Chroma(persist_directory=DB_DIR, |
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embedding_function=embeddings) |
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retriever = vectordb.as_retriever() |
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qa = RetrievalQA.from_chain_type(llm = OpenAI(temperature=0.9), |
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chain_type="stuff", |
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retriever=retriever, |
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return_source_documents=True |
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) |
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return qa |
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def load_sales_agent(verbose=True): |
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''' |
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Hard-coded agent that gates an internal sales CSV file for demo |
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''' |
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chat = createLLM(model_name='text-davinci-003') |
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df = pd.read_csv("data/sales_data.csv") |
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agent = create_pandas_dataframe_agent(chat, df, verbose=verbose) |
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return agent |
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def load_sqlite_agent(model_name="text-davinci-003"): |
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''' |
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Hard-coded agent that gates a sqlite DB of digital media for demo |
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''' |
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llm = createLLM(OPENAI_LLMS[0]) |
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sqlite_db_path = "./data/Chinook_Sqlite.sqlite" |
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db = SQLDatabase.from_uri(f"sqlite:///{sqlite_db_path}") |
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db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True) |
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return db_chain |
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from langchain.tools import DuckDuckGoSearchRun, GoogleSearchRun |
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from langchain.utilities import GoogleSearchAPIWrapper |
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def load_chained_agent(verbose=True, model_name="text-davinci-003"): |
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llm = createLLM(model_name) |
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toolkit = load_tools(["serpapi", "open-meteo-api", "news-api", |
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"python_repl", "wolfram-alpha"], |
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llm=llm, |
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serpapi_api_key=os.getenv('SERPAPI_API_KEY'), |
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news_api_key=os.getenv('NEWS_API_KEY'), |
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tmdb_bearer_token=os.getenv('TMDB_BEARER_TOKEN') |
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) |
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toolkit += [DuckDuckGoSearchRun()] |
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agent = initialize_agent(toolkit, |
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llm, |
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agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, |
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verbose=verbose, |
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return_intermediate_steps=True) |
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return agent |
