Spaces:
Paused
Paused
File size: 3,352 Bytes
aa12e5a |
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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import os
from typing import List
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.pinecone import Pinecone
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain_core.prompts import PromptTemplate
from langchain.docstore.document import Document
import pinecone
import chainlit as cl
from cleanlab_studio import Studio
pinecone.init(
api_key=os.environ.get("PINECONE_API_KEY"),
environment=os.environ.get("PINECONE_ENV"),
)
studio = Studio(os.getenv("CLEANLAB_API_KEY"))
tlm = studio.TLM(quality_preset='high')
index_name = "tracker"
embeddings = OpenAIEmbeddings()
welcome_message = "Welcome to the Transparency Tracker! Ask me any question related to Anti-Corruption."
@cl.on_chat_start
async def start():
await cl.Message(content=welcome_message,disable_human_feedback=True).send()
docsearch = Pinecone.from_existing_index(
index_name=index_name, embedding=embeddings
)
message_history = ChatMessageHistory()
memory = ConversationBufferMemory(
memory_key="chat_history",
output_key="answer",
chat_memory=message_history,
return_messages=True,
)
with open('./prompt.txt','r') as f:
template = f.read()
prompt = PromptTemplate(input_variables=["context", "question"],template=template)
chain = ConversationalRetrievalChain.from_llm(
llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
streaming=True),
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={'k': 3}), # I only want maximum of three document back with the highest similarity score
memory=memory,
return_source_documents=True,
combine_docs_chain_kwargs={"prompt": prompt}
)
cl.user_session.set("chain", chain)
@cl.action_callback("eval_button")
async def evaluate_response(action):
await action.remove()
arr = action.value.split('|||')
confidence_score = tlm.get_confidence_score(arr[0], response=arr[1])
await cl.Message(content=f"Confidence Score: {confidence_score}",disable_human_feedback=True).send()
@cl.on_message
async def main(message: cl.Message):
chain = cl.user_session.get("chain")
cb = cl.AsyncLangchainCallbackHandler()
res = await chain.acall(message.content, callbacks=[cb])
answer = res["answer"]
source_documents = res["source_documents"]
text_elements = []
if source_documents:
for source_idx, source_doc in enumerate(source_documents):
source_name = f"source_{source_idx}"
text_elements.append(
cl.Text(content=source_doc.page_content, name=source_name)
)
source_names = [text_el.name for text_el in text_elements]
if source_names:
answer += f"\nSources: {', '.join(source_names)}"
else:
answer += "\nNo sources found"
actions = [
cl.Action(name="eval_button",value=f"{message.content}|||{answer}",label='Evaluate with CleanLab',description="Evaluate with CleanLab TLM (*may take a moment*)")
]
await cl.Message(content=answer, elements=text_elements, actions=actions).send()
|