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import os | |
import logging | |
from typing import Optional | |
from datetime import datetime | |
import chromadb | |
from llama_index.core.tools import QueryEngineTool, FunctionTool, ToolMetadata | |
from llama_index.agent.openai import OpenAIAgent | |
from llama_index.vector_stores.chroma import ChromaVectorStore | |
from llama_index.core import VectorStoreIndex | |
from llama_index.embeddings.openai import OpenAIEmbedding | |
from llama_index.llms.openai import OpenAI | |
from llama_index.core.vector_stores import ( | |
MetadataFilters, | |
MetadataFilter, | |
FilterCondition, | |
) | |
import gradio as gr | |
from gradio.themes.utils import ( | |
fonts, | |
) | |
from utils import init_mongo_db | |
from tutor_prompts import ( | |
TEXT_QA_TEMPLATE, | |
QueryValidation, | |
system_message_validation, | |
system_message_openai_agent, | |
) | |
from call_openai import api_function_call | |
logger = logging.getLogger(__name__) | |
logging.basicConfig(level=logging.INFO) | |
logging.getLogger("httpx").setLevel(logging.WARNING) | |
# # This variables are used to intercept API calls | |
# # launch mitmweb | |
# cert_file = "/Users/omar/Downloads/mitmproxy-ca-cert.pem" | |
# os.environ["REQUESTS_CA_BUNDLE"] = cert_file | |
# os.environ["SSL_CERT_FILE"] = cert_file | |
# os.environ["HTTPS_PROXY"] = "http://127.0.0.1:8080" | |
CONCURRENCY_COUNT = int(os.getenv("CONCURRENCY_COUNT", 64)) | |
MONGODB_URI = os.getenv("MONGODB_URI") | |
AVAILABLE_SOURCES_UI = [ | |
"Gen AI 360: LLMs", | |
"Gen AI 360: LangChain", | |
"Gen AI 360: Advanced RAG", | |
"Towards AI Blog", | |
"Activeloop Docs", | |
"HF Transformers Docs", | |
"Wikipedia", | |
"OpenAI Docs", | |
"LangChain Docs", | |
] | |
AVAILABLE_SOURCES = [ | |
"llm_course", | |
"langchain_course", | |
"advanced_rag_course", | |
"towards_ai", | |
"activeloop", | |
"hf_transformers", | |
"wikipedia", | |
"openai", | |
"langchain_docs", | |
] | |
# Initialize MongoDB | |
mongo_db = ( | |
init_mongo_db(uri=MONGODB_URI, db_name="towardsai-buster") | |
if MONGODB_URI | |
else logger.warning("No mongodb uri found, you will not be able to save data.") | |
) | |
# Initialize vector store and index | |
db2 = chromadb.PersistentClient(path="scripts/ai-tutor-db") | |
chroma_collection = db2.get_or_create_collection("ai-tutor-db") | |
vector_store = ChromaVectorStore(chroma_collection=chroma_collection) | |
index = VectorStoreIndex.from_vector_store(vector_store=vector_store) | |
# Initialize OpenAI models | |
llm = OpenAI(temperature=0, model="gpt-3.5-turbo-0125", max_tokens=None) | |
# embeds = OpenAIEmbedding(model="text-embedding-3-large", mode="text_search") | |
embeds = OpenAIEmbedding(model="text-embedding-3-large", mode="similarity") | |
query_engine = index.as_query_engine( | |
llm=llm, | |
similarity_top_k=5, | |
embed_model=embeds, | |
streaming=True, | |
text_qa_template=TEXT_QA_TEMPLATE, | |
) | |
query_engine_tools = [ | |
QueryEngineTool( | |
query_engine=query_engine, | |
metadata=ToolMetadata( | |
name="AI_information", | |
description="""The 'AI_information' tool serves as a comprehensive repository for insights into the field of artificial intelligence. When utilizing this tool, the input should be the user's complete question. The input can also be adapted to focus on specific aspects or further details of the current topic under discussion. This dynamic input approach allows for a tailored exploration of AI subjects, ensuring that responses are relevant and informative. Employ this tool to fetch nuanced information on topics such as model training, fine-tuning, LLM augmentation, and more, thereby facilitating a rich, context-aware dialogue.""", | |
), | |
) | |
] | |
def initialize_agent(): | |
agent = OpenAIAgent.from_tools( | |
query_engine_tools, | |
llm=llm, | |
verbose=True, | |
system_prompt=system_message_openai_agent, | |
) | |
return agent | |
def reset_agent(agent_state): | |
agent_state = initialize_agent() # Reset the agent by reassigning a new instance | |
chatbot = [[None, None]] | |
return "Agent has been reset.", chatbot | |
def log_emails(email: gr.Textbox): | |
collection = "email_data-test" | |
logger.info(f"User reported {email=}") | |
email_document = {"email": email} | |
try: | |
mongo_db[collection].insert_one(email_document) | |
logger.info("") | |
except: | |
logger.info("Something went wrong logging") | |
return "" | |
def format_sources(completion) -> str: | |
if len(completion.source_nodes) == 0: | |
return "" | |
# Mapping of source system names to user-friendly names | |
display_source_to_ui = { | |
src: ui for src, ui in zip(AVAILABLE_SOURCES, AVAILABLE_SOURCES_UI) | |
} | |
documents_answer_template: str = ( | |
"π Here are the sources I used to answer your question:\n\n{documents}" | |
) | |
document_template: str = "[π {source}: {title}]({url}), relevance: {score:2.2f}" | |
documents = "\n".join( | |
[ | |
document_template.format( | |
title=src.metadata["title"], | |
score=src.score, | |
source=display_source_to_ui.get( | |
src.metadata["source"], src.metadata["source"] | |
), | |
url=src.metadata["url"], | |
) | |
for src in completion.source_nodes | |
] | |
) | |
return documents_answer_template.format(documents=documents) | |
def add_sources(history, completion): | |
if completion is None: | |
yield history | |
formatted_sources = format_sources(completion) | |
if formatted_sources == "": | |
yield history | |
history[-1][1] += "\n\n" + formatted_sources | |
yield history | |
def user(user_input, history, agent_state): | |
agent = agent_state | |
return "", history + [[user_input, None]] | |
def get_answer(history, agent_state): | |
user_input = history[-1][0] | |
history[-1][1] = "" | |
completion = agent_state.stream_chat(user_input) | |
for token in completion.response_gen: | |
history[-1][1] += token | |
yield history, completion | |
logger.info(f"completion: {history[-1][1]=}") | |
example_questions = [ | |
"What is the LLama model?", | |
"What is a Large Language Model?", | |
"What is an embedding?", | |
] | |
theme = gr.themes.Soft() | |
with gr.Blocks( | |
theme=gr.themes.Soft( | |
primary_hue="blue", | |
secondary_hue="blue", | |
font=fonts.GoogleFont("Source Sans Pro"), | |
font_mono=fonts.GoogleFont("IBM Plex Mono"), | |
), | |
fill_height=True, | |
) as demo: | |
agent_state = gr.State(initialize_agent()) | |
with gr.Row(): | |
gr.HTML( | |
"<h3><center>Towards AI π€: A Question-Answering Bot for anything AI-related</center></h3>" | |
) | |
chatbot = gr.Chatbot( | |
elem_id="chatbot", | |
show_copy_button=True, | |
scale=2, | |
likeable=True, | |
show_label=False, | |
) | |
with gr.Row(): | |
question = gr.Textbox( | |
label="What's your question?", | |
placeholder="Ask a question to the AI tutor here...", | |
lines=1, | |
scale=7, | |
show_label=False, | |
) | |
submit = gr.Button(value="Send", variant="primary", scale=1) | |
reset_button = gr.Button("Reset Chat", variant="secondary", scale=1) | |
with gr.Row(): | |
examples = gr.Examples( | |
examples=example_questions, | |
inputs=question, | |
) | |
with gr.Row(): | |
email = gr.Textbox( | |
label="Want to receive updates about our AI tutor?", | |
placeholder="Enter your email here...", | |
lines=1, | |
scale=6, | |
) | |
submit_email = gr.Button(value="Submit", variant="secondary", scale=1) | |
gr.Markdown( | |
"This application uses GPT3.5-Turbo to search the docs for relevant information and answer questions." | |
) | |
completion = gr.State() | |
submit.click( | |
user, [question, chatbot, agent_state], [question, chatbot], queue=False | |
).then( | |
get_answer, | |
inputs=[chatbot, agent_state], | |
outputs=[chatbot, completion], | |
).then( | |
add_sources, inputs=[chatbot, completion], outputs=[chatbot] | |
) | |
# .then( | |
# save_completion, inputs=[completion, chatbot] | |
# ) | |
question.submit( | |
user, [question, chatbot, agent_state], [question, chatbot], queue=False | |
).then( | |
get_answer, | |
inputs=[chatbot, agent_state], | |
outputs=[chatbot, completion], | |
).then( | |
add_sources, inputs=[chatbot, completion], outputs=[chatbot] | |
) | |
# .then( | |
# save_completion, inputs=[completion, chatbot] | |
# ) | |
reset_button.click( | |
reset_agent, inputs=[agent_state], outputs=[agent_state, chatbot] | |
) | |
submit_email.click(log_emails, email, email) | |
email.submit(log_emails, email, email) | |
demo.queue(default_concurrency_limit=CONCURRENCY_COUNT) | |
demo.launch(debug=False, share=False) | |