|
import gradio as gr |
|
from openai import OpenAI |
|
from pinecone import Pinecone |
|
import os |
|
|
|
DEFAULT_SYSTEM_PROMPT = ''' |
|
Your name is Personata. You are a helpful, respectful AI Chatbot. |
|
You will be representing Divya Prakash Manivannan( First Name: Divya Prakash Last Name: Manivannan) and who goes by pronouns (He/Him). |
|
You will be prompted by the user for his information about his professional profile and you will have answer it on his behalf. |
|
You also have access to RAG vectore database access which has his data, with which you will answer the question asked. |
|
Be careful when giving response, sometime irrelevent Rag content will be there so give response effectivly to user based on the prompt. |
|
You can speak fluently in English. |
|
Always answer as helpfully and logically as possible, while being safe. |
|
Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. |
|
Please ensure that your responses are socially unbiased and positive in nature. |
|
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. |
|
If you don't have the RAG response, answer that " I do not have the information". |
|
Try to give concise answers, wherever required |
|
''' |
|
|
|
|
|
PINE_CONE_API_KEY = os.getenv('PINE_CONE_API_KEY') |
|
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') |
|
|
|
pc = Pinecone( |
|
api_key= PINE_CONE_API_KEY |
|
) |
|
client = OpenAI(api_key= OPENAI_API_KEY) |
|
index = pc.Index("rag-resume") |
|
|
|
def vector_search(query): |
|
rag_data = "" |
|
xq = client.embeddings.create(input=query,model="text-embedding-ada-002") |
|
res = index.query(vector = xq.data[0].embedding, top_k=5, include_metadata=True) |
|
print(res) |
|
for match in res['matches']: |
|
if match['score'] < 0.80: |
|
continue |
|
rag_data += match['metadata']['text'] |
|
return rag_data |
|
|
|
def respond( |
|
message, |
|
history: list[tuple[str, str]] |
|
): |
|
messages = [{"role": "system", "content": DEFAULT_SYSTEM_PROMPT}] |
|
print(message) |
|
rag = vector_search(message) |
|
|
|
for val in history: |
|
if val[0]: |
|
messages.append({"role": "user", "content": val[0] + rag}) |
|
if val[1]: |
|
messages.append({"role": "assistant", "content": val[1]}) |
|
|
|
messages.append({"role": "user", "content": message}) |
|
|
|
response = "" |
|
|
|
response = client.chat.completions.create( |
|
model="gpt-3.5-turbo-1106", |
|
messages = messages |
|
) |
|
|
|
yield response.choices[0].message.content |
|
|
|
|
|
demo = gr.ChatInterface( |
|
respond |
|
) |
|
|
|
demo.queue() |
|
|
|
if __name__ == "__main__": |
|
demo.launch() |
