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from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.memory import ConversationSummaryMemory
from langchain.schema import HumanMessage, SystemMessage
from langchain.chains import ConversationalRetrievalChain
from langchain.schema import AIMessage, HumanMessage
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
import openai
import gradio as gr
import os
#os.envrion["OPENAI_API_KEY"] = "sk-..." # Replace with your key
# use the following line to load a directory of PDFs
loader = PyPDFDirectoryLoader("data/")
data = loader.load_and_split()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=0
)
all_splits = text_splitter.split_documents(data)
vectorstore = Chroma.from_documents(
documents=all_splits,
embedding=OpenAIEmbeddings()
)
llm = ChatOpenAI(temperature=1.0, model="gpt-4-1106-preview")
memory = ConversationSummaryMemory(
llm=llm,
memory_key="chat_history",
return_messages=True
)
retriever = vectorstore.as_retriever()
# Initialize the Conversational Retrieval Chain
qa_chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory
)
def predict(message, history):
# Get a response from the Conversational Retrieval Chain
response = qa_chain.run(question=message)
# Extract and return the content of the response
return response # or modify as needed based on the response structure
demo = gr.ChatInterface(predict)
demo.launch(share=True) |