gistfile1.txt

from langchain.utilities import GoogleSearchAPIWrapper
from langchain.agents import Tool
from langchain.tools.file_management.write import WriteFileTool
from langchain.tools.file_management.read import ReadFileTool
from dotenv import load_dotenv
import os

load_dotenv()

os.environ["GOOGLE_CSE_ID"] = os.getenv("GOOGLE_CSE_ID")
os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

search = GoogleSearchAPIWrapper()
tools = [
    Tool(
        name="search",
        func=search.run,
        description="useful for when you need to answer questions about current events. "
                    "You should ask targeted questions."
    ),
    WriteFileTool(),
    ReadFileTool()
]


from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings

# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vector store as empty
import faiss

embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})

from langchain.experimental import AutoGPT
from langchain.chat_models import ChatOpenAI

agent = AutoGPT.from_llm_and_tools(
    ai_name="Gus",
    ai_role="Research Agent",
    tools=tools,
    llm=ChatOpenAI(temperature=0.618),
    memory=vectorstore.as_retriever()
)
# Set verbose to be true
agent.chain.verbose = True

agent.run(["Write a textbook example of the Abstract Factory Pattern in Typescript utilizing types, interfaces, and classes."])