Skip to content

Instantly share code, notes, and snippets.

@codegod100
Created October 26, 2023 06:31
Show Gist options
  • Select an option

  • Save codegod100/6143a6e6e26e6314f7f1ede4189018bc to your computer and use it in GitHub Desktop.

Select an option

Save codegod100/6143a6e6e26e6314f7f1ede4189018bc to your computer and use it in GitHub Desktop.
document look using agent
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.document_loaders import DirectoryLoader
from langchain.chat_models.fireworks import ChatFireworks
from langchain.agents.agent_toolkits import (
create_vectorstore_agent,
VectorStoreToolkit,
VectorStoreInfo,
)
llm = ChatFireworks(model="accounts/fireworks/models/mistral-7b")
path = "/home/vera/agora/garden/journal"
loader = DirectoryLoader(path, glob="**/*.md")
data = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
documents = text_splitter.split_documents(data)
db = FAISS.from_documents(documents, OpenAIEmbeddings())
vectorstore_info = VectorStoreInfo(
name="vera's journal",
description="collection of markdown files containing vera's daily journal",
vectorstore=db,
)
query = "What does vera enjoy doing?"
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)
agent_executor = create_vectorstore_agent(llm=llm, toolkit=toolkit, verbose=True)
resp = agent_executor.run(query)
print(resp)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment