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import chainlit as cl
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent, create_retriever_tool
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import CacheBackedEmbeddings, OpenAIEmbeddings
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma #, FAISS
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.storage import LocalFileStore
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
import chainlit as cl
from build_langchain_vector_store import chunk_docs, load_gitbook_docs, tiktoken_len
from tiktoken import Encoding, encoding_for_model
import openai
# import os
# openai.api_key = os.getenv("OPENAI_API_KEY")
openai.api_base = 'https://api.openai.com/v1' # default
@cl.on_chat_start
async def init():
msg = cl.Message(content="Building Index...")
await msg.send()
docs_url = "https://docs.pulze.ai/"
embedding_model_name = "text-embedding-ada-002"
langchain_documents = load_gitbook_docs(docs_url)
chunked_langchain_documents = chunk_docs(
langchain_documents,
tokenizer=encoding_for_model(embedding_model_name),
chunk_size=200,
)
embedding_model = OpenAIEmbeddings(model=embedding_model_name)
vector_store = Chroma.from_documents(
chunked_langchain_documents, embedding=embedding_model, persist_directory="langchain-chroma-pulze-docs"
)
read_vector_store = Chroma(
persist_directory="langchain-chroma-pulze-docs", embedding_function=embedding_model
)
msg.content = "Index built!"
await msg.send()
# set up search pulze docs retriever tool
tool = create_retriever_tool(
read_vector_store.as_retriever(),
"search_pulze_docs",
"Searches and returns documents regarding Pulze."
)
tools = [tool]
#set llm and agent
llm = ChatOpenAI(temperature = 0)
agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True)
cl.user_session.set("agent_executor", agent_executor)
@cl.on_message
async def main(message):
chain: Chain = cl.user_session.get("agent_executor")
cb = cl.AsyncLangchainCallbackHandler(
stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
)
cb.answer_reached = True
answer = chain.run({"input": message})
await cl.Message(content=answer["output"]).send() |