fist

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.sqlite3 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,100 @@
+"""
+    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
+[![](https://neurons-lab-public-bucket.s3.eu-central-1.amazonaws.com/GenerativeAIHead.png)](https://docsend.com/view/c6dim5yjb35iijqv)'
+## 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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requirements.txt

@@ -0,0 +1,8 @@
+gradio
+langchain
+Chroma
+chromadb
+openai
+boto3
+unstructured
+tiktoken