Spaces:
Sleeping
Sleeping
import os | |
from langchain.text_splitter import RecursiveCharacterTextSplitter | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.vectorstores.chroma import Chroma | |
import os | |
import shutil | |
from langchain.vectorstores.chroma import Chroma | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.agents.agent_toolkits import create_retriever_tool | |
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent | |
from langchain.agents import load_tools | |
from langchain_openai import ChatOpenAI | |
def init_config(loader): | |
# We use the loader created above to load the document | |
documents = loader.load() | |
# We split the document into several chunks as mentioned above | |
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50) | |
texts = text_splitter.split_documents(documents) | |
CHROMA_PATH = "../data/plant_chroma" | |
if os.path.exists(CHROMA_PATH): | |
db = Chroma(persist_directory=CHROMA_PATH,embedding_function=OpenAIEmbeddings()) | |
else: | |
db = Chroma.from_documents( | |
texts, OpenAIEmbeddings(), persist_directory=CHROMA_PATH | |
) | |
db.persist() | |
print(f"Saved {len(texts)} chunks to {CHROMA_PATH}.") | |
retriever = db.as_retriever() | |
# This is the prompt to create a RAG agent for us | |
retriever_name = "plant_os_pdf" | |
retriever_desc = """The purpose of this tool is to answer questions about the blue indigo false plant and its maintenance.""" | |
rag_tool = create_retriever_tool( | |
retriever, | |
retriever_name, | |
retriever_desc | |
) | |
search_tool = load_tools(['serpapi']) | |
tools = [rag_tool, search_tool[0]] | |
llm = ChatOpenAI(model_name="gpt-4") | |
RAG_executor = create_conversational_retrieval_agent(llm=llm, tools=tools, verbose=True) # setting verbose=True to output the thought process of the agent | |
return RAG_executor | |
def answer_question(agent, question): | |
user_query = {"input": question} | |
result = agent(user_query) | |
return result['output'] | |