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"""
Generative AI Chatbot through Document Sources
"""
import boto3
import gradio as gr
from langchain.chains import RetrievalQA
from langchain.embeddings.openai import OpenAIEmbeddings
#from langchain.llms import ChatOpenAI
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
# Get OpenAI API key from SSM Parameter Store
API_KEY_PARAMETER_PATH = '/openai/api_key'
ssm_client = boto3.client('ssm', region_name='us-east-1')
def get_openai_api_key(client, parameter_path):
"""
Get the OpenAI API key from the SSM Parameter Store
Args:
ssm_client: boto3 SSM client
parameter_path: path to the SSM Parameter Store parameter
Returns:
OpenAI API key
"""
try:
response = client.get_parameter(
Name=parameter_path,
WithDecryption=True,
)
return response['Parameter']['Value']
except client.exceptions.ParameterNotFound:
raise Exception(f'Parameter {parameter_path} not found in SSM Parameter Store')
# Get the API key from the SSM Parameter Store
openai_api_key = get_openai_api_key(client=ssm_client, parameter_path=API_KEY_PARAMETER_PATH)
def OpenAIWithChroma(persist_directory='./chroma.db', model_name='gpt-3.5-turbo-16k', chain_type="stuff"):
"""
Create a retrieval chatbot with OpenAI LLM and Chroma
Args:
persist_directory: directory to save the Chroma database
model_name: name of the OpenAI LLM
chain_type: type of chain to use for the retrieval chatbot
Returns:
RetrievalQA: retrieval chatbot
"""
# connect to local Chroma
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
# connect to OpenAI LLM with Chroma
llm = ChatOpenAI(model_name=model_name, temperature=0, openai_api_key=openai_api_key, max_tokens=5000)
chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=db.as_retriever(), return_source_documents=True)
return chain
def message_construction(result):
message = "**Bot Answer:** \n"
message += f"{result['result']}\n"
source_documents = "**Source Documents:**\n"
for d in result['source_documents']:
source_documents += f"* *{d.metadata['source']}* - {d.page_content[0:200].encode('unicode_escape').decode('utf-8')}...\n"
return message + "\n" + source_documents
retrieval_chain = OpenAIWithChroma()
with gr.Blocks(theme=gr.themes.Default(
primary_hue="blue",
secondary_hue="yellow"
)) as demo:
gr.Markdown("""
# Neurons Lab: Generative AI Chatbot through Document Sources
## Document Sources
1. [Generative AI in Finance and Banking: The Current State and Future Implications](https://www.leewayhertz.com/generative-ai-in-finance-and-banking/#Variational-Autoencoders-(VAEs))
2. [McKinsey & Company: The economic potential of generative AI](https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier/the-economic-potential-of-generative-ai-the-next-productivity-frontier-vf.pdf)
3. [Deloitte: Generative AI is all the rage](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-ai-institute-gen-ai-for-enterprises.pdf)
## Prompt Examples
- Provide Generative AI use cases for financial services. Print in table view wiht columns: Use Case Name, Description
- Provide Generative AI models that fit for Financial Services. Print in table view with columns: Model Name, Model Description, Areas of Application in Finance.
- Provide real world example on how Generative AI change Financial Services sector.
- What is difference between traditional AI and Generative AI?
- Summarise the economic potential of generative AI
- How does Generative AI change a future of work?
- How Generative AI can personalise customer experience in finance?
""")
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.ClearButton([msg, chatbot])
def respond(message, chat_history):
result = retrieval_chain({"query": message})
bot_message = message_construction(result)
chat_history.append((message, bot_message))
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
demo.launch()
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