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import os
import pinecone
import gradio as gr
from openai import OpenAI
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
pinecone.init(api_key=os.getenv("PINECONE_API_TOKEN"), environment=os.getenv("PINECONE_ENVIRONMENT"))
index = pinecone.Index(os.getenv("PINECONE_INDEX"))
def init_prompt(type_prompt: str) -> str:
if type_prompt == "main":
name_file = 'main_prompt.txt'
else:
name_file = 'standalone_question.txt'
with open(f"prompts/{name_file}", mode='r', encoding='utf-8') as infile:
prompt = infile.read()
return prompt
def get_embedding(text: str) -> list[float]:
response = openai_client.embeddings.create(
input=text,
model='text-embedding-ada-002'
)
return response.data[0].embedding
def call_api(message_history: list[dict]) -> str:
response = openai_client.chat.completions.create(
model='gpt-4-1106-preview',
temperature=0.7,
messages=message_history
)
return response.choices[0].message.content
def get_standalone_question(question: str, message_history: list[dict], prompt_q: str) -> str:
# Format the message history like: Human: blablablá \nAssistant: blablablá
history = ''
for i, msg in enumerate(message_history):
if i == 0:
continue # Omit the prompt
if i % 2 == 0:
history += f'Human: {msg["content"]}\n'
else:
history += f'Assistant: {msg["content"]}\n'
# Add history and question to the prompt and call chatgpt
prompt = [{'role': 'system', 'content': ''}]
content = prompt_q.replace('HISTORY', history).replace('QUESTION', question)
prompt[0]['content'] = content
return call_api(prompt)
def get_context(question: str) -> str:
q_embedding = get_embedding(question)
# Get most similar vectors
result = index.query(
vector=q_embedding,
top_k=10,
include_metadata=True
)['matches']
# Crete a string based on the text of each vector
context = ''
for r in result:
context += r['metadata']['Text'] + '\n'
return context
def get_answer(context: str, message_history: list[dict], question: str, prompt_m: str) -> str:
message_history[0]['content'] = prompt_m.replace('CONTEXT', context)
message_history.append({'role': 'user', 'content': question})
return call_api(message_history)
def ask_query(
msg: str, chat_history: list[list[str | None]], message_history: list[dict], prompt_q: str, prompt_m: str
) -> tuple[str, list[list[str | None]], list[dict]]:
if len(chat_history) == 5:
answer = 'Un placer haberte ayudado, hasta luego!'
else:
question = get_standalone_question(msg, message_history, prompt_q)
context = get_context(question)
answer = get_answer(context, message_history, msg, prompt_m)
message_history.append({'role': 'assistant', 'content': answer})
chat_history.append([msg, answer])
return "", chat_history, message_history
def start_chat(chat_history: list[list[str | None]], prompt_m: str):
greeting = ('Hola 👋, ¡estoy encantada de conversar contigo! Antes de empezar, quiero asegurarte algo '
'importante: tu privacidad y confidencialidad son mi máxima prioridad. Puedes estar '
'tranquila sabiendo que nuestras conversaciones son completamente seguras y nunca '
'serán compartidas con terceros. ¿En qué puedo ayudarte hoy?')
message_history = [
{'role': 'system', 'content': prompt_m},
{'role': 'assistant', 'content': greeting}
]
chat_history.append(['', greeting])
return message_history, chat_history, gr.Button(visible=False), gr.Text(visible=True)
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