File size: 2,939 Bytes
3ec9224 5a1fc91 5be8df6 91137eb 6a32a13 6387af9 5a1fc91 1e253bd 69b7fda 72f27c8 6803339 5a1fc91 5be8df6 5a1fc91 69b7fda 5a1fc91 69b7fda 5a1fc91 4ce7fc5 5a1fc91 69b7fda 5a1fc91 69b7fda 5a1fc91 69b7fda 5a1fc91 69b7fda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import gradio as gr
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain import hub
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
import os
os.environ['USER_AGENT'] = 'myagent'
os.environ['OPENAI_API_KEY'] = os.environ.get("OPENAI_API_KEY")
rag_chain = None
def process_url(url):
try:
loader = WebBaseLoader(web_paths=[url])
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True)
all_splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 2})
prompt = hub.pull("rlm/rag-prompt")
llm = ChatOpenAI(model="gpt-4")
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
global rag_chain
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
return "Successfully processed the URL. You can now ask questions."
except Exception as e:
return f"Error processing URL: {e}"
def chat_with_rag_chain(message, history):
global rag_chain
if rag_chain:
try:
response = rag_chain.invoke(message)
return response
except Exception as e:
return f"Error invoking RAG chain: {e}"
else:
return "Please enter a URL first and process it."
with gr.Blocks() as demo:
gr.Markdown("# RAG Chain URL Processor and Chat Interface")
with gr.Tab("URL Processor"):
url_input = gr.Textbox(label="Enter URL", placeholder="https://example.com")
process_button = gr.Button("Process URL")
url_output = gr.Textbox(label="Status")
process_button.click(process_url, inputs=url_input, outputs=url_output)
with gr.Tab("Chat Interface"):
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Your Question")
clear = gr.Button("Clear")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
bot_message = chat_with_rag_chain(history[-1][0], history)
history[-1][1] = bot_message
return history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch(debug=True) |