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n4ze3m 2023-04-15 21:59:27 +05:30
parent 5d8c2c8df2
commit 5d587351e4
1 changed files with 21 additions and 34 deletions
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53
py_server/handlers/chat.py
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@ -20,11 +20,9 @@ from db.supa import SupaService
supabase = SupaService() supabase = SupaService()
async def chat_app_handler(body: ChatAppBody, jwt: str): async def chat_app_handler(body: ChatAppBody, jwt: str):
try: try:
user = supabase.get_user(jwt) user = supabase.get_user(jwt)
if not user: if not user:
@ -33,10 +31,8 @@ async def chat_app_handler(body: ChatAppBody, jwt: str):
"human_message": body.user_message, "human_message": body.user_message,
} }
user_id = user.user.id user_id = user.user.id
website_response = supabase.find_website(body.id, user_id) website_response = supabase.find_website(body.id, user_id)
website = website_response.data website = website_response.data
@ -49,25 +45,23 @@ async def chat_app_handler(body: ChatAppBody, jwt: str):
website = website[0] website = website[0]
text = website["html"] text = website["html"]
text = text.strip()
result = [LDocument(page_content=text, metadata={"source": "test"})] result = [LDocument(page_content=text, metadata={"source": "test"})]
token_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) token_splitter = CharacterTextSplitter(
chunk_size=1000, chunk_overlap=0)
doc = token_splitter.split_documents(result) doc = token_splitter.split_documents(result)
print(f'Number of documents: {len(doc)}') print(f'Number of documents: {len(doc)}')
vectorstore = Chroma.from_documents(doc, OpenAIEmbeddings()) vectorstore = Chroma.from_documents(doc, OpenAIEmbeddings())
messages = [ messages = [
SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Use the following pieces of context from this webpage to answer the question from the user. SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Answer the question based on the following context from the webpage you are on.
If user want recommendation, help from the context, or any other information, please provide it. Answer must be in markdown format.
If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context. Helpful answer in markdown:
----------------- -----------------
context:
{context} {context}
"""), """),
HumanMessagePromptTemplate.from_template("{question}") HumanMessagePromptTemplate.from_template("{question}")
@ -75,21 +69,20 @@ If the question is not related to the context, politely respond that you are tun
prompt = ChatPromptTemplate.from_messages(messages) prompt = ChatPromptTemplate.from_messages(messages)
chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(
search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,)
chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,) history = [(d["human_message"], d["bot_response"])
for d in body.history]
history = [(d["human_message"], d["bot_response"]) for d in body.history]
response = chat({ response = chat({
"question": body.user_message, "question": body.user_message,
"chat_history": history "chat_history": history
}) })
answer = response["answer"] answer = response["answer"]
answer = answer[answer.find(":")+1:].strip() answer = answer[answer.find(":")+1:].strip()
return { return {
"bot_response": answer, "bot_response": answer,
"human_message": body.user_message, "human_message": body.user_message,
@ -103,9 +96,6 @@ If the question is not related to the context, politely respond that you are tun
} }
async def chat_extension_handler(body: ChatBody): async def chat_extension_handler(body: ChatBody):
try: try:
soup = BeautifulSoup(body.html, 'lxml') soup = BeautifulSoup(body.html, 'lxml')
@ -120,23 +110,22 @@ async def chat_extension_handler(body: ChatBody):
if div: if div:
div.decompose() div.decompose()
text = soup.get_text() text = soup.get_text()
text = text.strip()
result = [LDocument(page_content=text, metadata={"source": "test"})] result = [LDocument(page_content=text, metadata={"source": "test"})]
token_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) token_splitter = CharacterTextSplitter(
chunk_size=1000, chunk_overlap=0)
doc = token_splitter.split_documents(result) doc = token_splitter.split_documents(result)
print(f'Number of documents: {len(doc)}') print(f'Number of documents: {len(doc)}')
vectorstore = Chroma.from_documents(doc, OpenAIEmbeddings()) vectorstore = Chroma.from_documents(doc, OpenAIEmbeddings())
messages = [ messages = [
SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Use the following pieces of context from this webpage to answer the question from the user. SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Answer the question based on the following context from the webpage you are on.
If user want recommendation, help from the context, or any other information, please provide it. Answer must be in markdown format.
If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context. Helpful answer in markdown:
----------------- -----------------
context:
{context} {context}
"""), """),
HumanMessagePromptTemplate.from_template("{question}") HumanMessagePromptTemplate.from_template("{question}")
@ -144,21 +133,20 @@ If the question is not related to the context, politely respond that you are tun
prompt = ChatPromptTemplate.from_messages(messages) prompt = ChatPromptTemplate.from_messages(messages)
chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(
search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,)
chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,) history = [(d["human_message"], d["bot_response"])
for d in body.history]
history = [(d["human_message"], d["bot_response"]) for d in body.history]
response = chat({ response = chat({
"question": body.user_message, "question": body.user_message,
"chat_history": history "chat_history": history
}) })
answer = response["answer"] answer = response["answer"]
answer = answer[answer.find(":")+1:].strip() answer = answer[answer.find(":")+1:].strip()
return { return {
"bot_response": answer, "bot_response": answer,
"human_message": body.user_message, "human_message": body.user_message,
@ -170,4 +158,3 @@ If the question is not related to the context, politely respond that you are tun
"bot_response": "Something went wrong please try again later", "bot_response": "Something went wrong please try again later",
"human_message": body.user_message, "human_message": body.user_message,
} }