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import os
import openai
from langchain.chat_models import ChatOpenAI
import chainlit as cl
from langchain.chains import LLMChain, SequentialChain
from langchain.prompts import PromptTemplate
from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.callbacks.base import CallbackManager
from llama_index import (
LLMPredictor,
ServiceContext,
StorageContext,
load_index_from_storage,
)
from llama_index import (
SimpleDirectoryReader,
LLMPredictor,
ServiceContext,
get_response_synthesizer,
)
from llama_index.indices.document_summary import DocumentSummaryIndex
from llama_index.llms import OpenAI
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index import SimpleDirectoryReader
required_exts = [".txt"]
reader = SimpleDirectoryReader(
input_dir="../data",
required_exts=required_exts,
recursive=True,
filename_as_id=True
)
docs = reader.load_data()
print(f"Loaded {len(docs)} docs")
chatgpt = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
service_context = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)
response_synthesizer = get_response_synthesizer(
response_mode="tree_summarize", use_async=True
)
doc_summary_index = DocumentSummaryIndex.from_documents(
docs,
service_context=service_context,
response_synthesizer=response_synthesizer,
)
#print(doc_summary_index.get_document_summary('../data/final-hh.txt'))
@cl.on_chat_start
async def factory():
llm_predictor = LLMPredictor(
llm=ChatOpenAI(
temperature=0,
model_name="gpt-3.5-turbo",
streaming=True,
),
)
service_context = ServiceContext.from_defaults(
llm_predictor=llm_predictor,
chunk_size=512,
callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
)
generation_type = None
twitter_expert_prompt = "You are a twitter expert. Create a twitter thread based on this: "
summary_text = doc_summary_index.get_document_summary('../data/final-hh.txt')
summary_text_without_newlines = summary_text.strip()
prompt = twitter_expert_prompt + summary_text
twitter_prompt = twitter_expert_prompt + summary_text_without_newlines
twitter_prompt_template = PromptTemplate.from_template(
twitter_expert_prompt
)
while generation_type == None:
generation_type = await cl.AskUserMessage(
content="Twitter or blog?", timeout=15
).send()
if generation_type == 'twitter':
print("twitter generation...")
elif generation_type == 'blog':
print("blog generation...")
cl.Message(content=f"generating {generation_type['content']} thread").send()
prompt = 'write a tweet about' + summary_text
print(f'prompt: {prompt}')
llm_twitter_expert = ChatOpenAI(model='gpt-3.5-turbo', temperature=0.7)
tweet_thread_chain = LLMChain(llm=llm_twitter_expert,prompt=twitter_prompt_template)
#tweet_thread = tweet_thread_chain.run(prompt=prompt)
#await cl.Message(content=tweet_thread).send()
the_final_prompt = f"write a twitter thread about {summary_text_without_newlines}"
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a twitter expert."},
#{"role": "assistant", "content": "{summary_text_without_newlines}"},
{"role": "user", "content": the_final_prompt},
]
)
await cl.Message(content=response['choices'][0]['message']['content']).send()
#await cl.Message(content=f"generating {doc_summary_index.get_document_summary('../data/final-hh.txt')}").send()
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