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import gradio as gr
import os
import openai
openai.api_key = os.environ['OPENAI_API_KEY']
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.chains import LLMChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
chat_model = ChatOpenAI(model_name="gpt-3.5-turbo")
# The SystemMessage is associated with the system role
system_message = SystemMessage(content="You are a food critic.")
# The HumanMessage is associated with the user role
user_message = HumanMessage(content="Do you think Kraft Dinner constitues fine dining?")
# The AIMessage is associated with the assistant role
assistant_message = AIMessage(content="Egads! No, it most certainly does not!")
second_user_message = HumanMessage(content="What about Red Lobster, surely that is fine dining!")
# create the list of prompts
list_of_prompts = [
system_message,
user_message,
assistant_message,
second_user_message
]
# we can just call our chat_model on the list of prompts!
chat_model(list_of_prompts)
# we can signify variables we want access to by wrapping them in {}
system_prompt_template = "You are an expert in {SUBJECT}, and you're currently feeling {MOOD}"
system_prompt_template = SystemMessagePromptTemplate.from_template(system_prompt_template)
user_prompt_template = "{CONTENT}"
user_prompt_template = HumanMessagePromptTemplate.from_template(user_prompt_template)
# put them together into a ChatPromptTemplate
chat_prompt = ChatPromptTemplate.from_messages([system_prompt_template, user_prompt_template])
chain = LLMChain(llm=chat_model, prompt=chat_prompt)
with open("guide1.txt") as f:
hitchhikersguide = f.read()
text_splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 1000,
chunk_overlap=0,
length_function = len,
)
texts = text_splitter.split_text(hitchhikersguide)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
def query(query):
docs = docsearch.get_relevant_documents(query)
return chain.run(input_documents=docs, question=query)
iface = gr.Interface(fn=query, inputs="text", outputs="text")
iface.launch()
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