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webinars/genai-chatbot/.env.example

@@ -0,0 +1,4 @@
+BWB_ENDPOINT_URL=https://bedrock-runtime.us-east-1.amazonaws.com
+ELASTICACHE_ENDPOINT_URL=rediss://elaticache.serverless.use1.cache.amazonaws.com:6379
+BWB_PROFILE_NAME=IF_YOU_NEED_TO_USE_AN_AWS_CLI_PROFILE_IT_GOES_HERE
+BWB_REGION_NAME=REGION_NAME_GOES_HERE_IF_YOU_NEED_TO_OVERRIDE_THE_DEFAULT_REGION

webinars/genai-chatbot/.gitignore

@@ -1,3 +1,4 @@
 .venv/*
 .env.sh
 __pycache__/*
+.env

webinars/genai-chatbot/chatbot_app.py

@@ -1,52 +1,56 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: MIT-0
 
-
 import os
-import streamlit as st #all streamlit commands will be available through the "st" alias
-import chatbot_lib as glib #reference to local lib script
+import logging  # Import the logging module for logging purposes
+import streamlit as st  # Import the Streamlit library for building interactive web apps
+import chatbot_lib as glib  # Import a custom module named 'chatbot_lib'
+from dotenv import load_dotenv  # Import the load_dotenv function from the 'dotenv' module
+
+# Set up the logging configuration with the INFO level and a specific format
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
 
+# Load environment variables from a '.env' file
+load_dotenv()
 
-st.set_page_config(page_title="Chatbot") #HTML title
-st.title("Chatbot") #page title
-# Add a login mechanism for the user
+st.set_page_config(page_title="Chatbot")  # Set the page title for the Streamlit app
+st.title("Chatbot")  # Display the title "Chatbot" on the Streamlit app
 
-username = st.text_input("Enter your username:")
-session_id = username
-print("username being sent to session", session_id)
-redis_url=os.environ.get("ELASTICACHE_ENDPOINT_URL")
-key_prefix="chat_history:"
+username = st.text_input("Enter your username:")  # Get the username from the user input
+session_id = username  # Set the session_id to the username
+redis_url = os.environ.get("ELASTICACHE_ENDPOINT_URL")  # Get the Redis URL from the environment variables
+key_prefix = "chat_history:"  # Set a prefix for the chat history key
 
 if username:
     st.session_state.username = username  # Store the username in the session state
-    st.info(f"Logged in as: {username}")
-
-if 'memory' not in st.session_state: #see if the memory hasn't been created yet
-    print(f"DEBUG: session_id: {session_id}")
-    st.session_state.memory = glib.get_memory(session_id=session_id, url=redis_url, key_prefix=key_prefix) #initialize the memory
-
-if 'chat_history' not in st.session_state: #see if the chat history hasn't been created yet
-    st.session_state.chat_history = [] #initialize the chat history
-
-print(f"DEBUG: session_id2: {session_id}")
-st.session_state.memory = glib.get_memory(session_id=session_id, url=redis_url, key_prefix=key_prefix) #initialize the memory
-
-#Re-render the chat history (Streamlit re-runs this script, so need this to preserve previous chat messages)
-for message in st.session_state.chat_history: #loop through the chat history
-    with st.chat_message(message["role"]): #renders a chat line for the given role, containing everything in the with block
-        st.markdown(message["text"]) #display the chat content
-
-input_text = st.chat_input("Chat with your bot here") #display a chat input box
-
-if input_text: #run the code in this if block after the user submits a chat message
-    with st.chat_message("user"): #display a user chat message
-        st.markdown(input_text) #renders the user's latest message
-    #Debugging : Pring the input_text to the model 
-    print("Input text to the model:", input_text)
-    st.session_state.chat_history.append({"role":"user", "text":input_text}) #append the user's latest message to the chat history
-    #Debugging: Print the memory being sent to the model 
-    print("Memory to the Model:", st.session_state.memory)
-    chat_response = glib.get_chat_response(input_text=input_text, memory=st.session_state.memory) #call the model through the supporting library
-    with st.chat_message("assistant"): #display a bot chat message
-        st.markdown(chat_response) #display bot's latest response
-    st.session_state.chat_history.append({"role":"assistant", "text":chat_response}) #append the bot's latest message to the chat history
+    st.info(f"Logged in as: {username}")  # Display a message with the logged-in username
+
+# If 'memory' is not in the session state, initialize it by calling the 'get_memory' function from 'chatbot_lib'
+if 'memory' not in st.session_state:
+    st.session_state.memory = glib.get_memory(session_id=session_id, url=redis_url, key_prefix=key_prefix)
+
+# If 'chat_history' is not in the session state, initialize it as an empty list
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+# Update the 'memory' in the session state by calling the 'get_memory' function from 'chatbot_lib'
+st.session_state.memory = glib.get_memory(session_id=session_id, url=redis_url, key_prefix=key_prefix)
+
+# Display the chat history by iterating over the messages and rendering them using Streamlit's chat_message
+for message in st.session_state.chat_history:
+    with st.chat_message(message["role"]):
+        st.markdown(message["text"])
+
+input_text = st.chat_input("Chat with your bot here")  # Get the user's input text from the chat input
+
+if input_text:
+    with st.chat_message("user"):
+        st.markdown(input_text)  # Display the user's input text in the chat
+    logging.info(f"Input text to the model: {input_text}")  # Log the user's input text
+    st.session_state.chat_history.append({"role": "user", "text": input_text})  # Add the user's input to the chat history
+    logging.info(f"Memory to the Model: {st.session_state.memory}")  # Log the memory passed to the model
+    chat_response = glib.get_chat_response(input_text=input_text, memory=st.session_state.memory)  # Get the chat response from the 'chatbot_lib' module
+    with st.chat_message("assistant"):
+        st.markdown(chat_response)  # Display the chat response in the chat
+    st.session_state.chat_history.append({"role": "assistant", "text": chat_response})  # Add the chat response to the chat history

webinars/genai-chatbot/chatbot_lib.py

@@ -1,62 +1,101 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: MIT-0
 
-
 import os
+import logging
+from dotenv import load_dotenv
 from langchain.memory import ConversationSummaryBufferMemory, RedisChatMessageHistory
 from langchain.llms.bedrock import Bedrock
 from langchain.chains import ConversationChain
 from langchain.prompts.prompt import PromptTemplate
 
+# Load environment variables from .env file
+load_dotenv()
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
 
-redis_url=os.environ.get("ELASTICACHE_ENDPOINT_URL")
+# Get Redis URL from environment variable
+redis_url = os.environ.get("ELASTICACHE_ENDPOINT_URL")
 
 
 def get_llm():
-    model_kwargs =  { 
+    """
+    Returns an instance of the Bedrock LLM with the specified model and configuration.
+    Amazon Bedrock endpoints and quotas => https://docs.aws.amazon.com/general/latest/gr/bedrock.html
+
+    Returns:
+        Bedrock: An instance of the Bedrock LLM.
+    """
+    model_kwargs = {
         "max_tokens_to_sample": 8000,
-            "temperature": 0, 
-            "top_k": 50, 
-            "top_p": 1, 
-            "stop_sequences": ["\n\nHuman:"] 
+        "temperature": 0.5,
+        "top_k": 50,
+        "top_p": 1,
+        "stop_sequences": ["\n\nHuman:"]
     }
-    # Amazon Bedrock endpoints and quotas => https://docs.aws.amazon.com/general/latest/gr/bedrock.html
     llm = Bedrock(
-        credentials_profile_name=os.environ.get("BWB_PROFILE_NAME"), #sets the profile name to use for AWS credentials (if not the default)
-        region_name=os.environ.get("BWB_REGION_NAME"), #sets the region name (if not the default)
-        endpoint_url=os.environ.get("BWB_ENDPOINT_URL"), #sets the endpoint URL (if necessary)
-        model_id="anthropic.claude-v2", #use the Anthropic Claude model
-        model_kwargs=model_kwargs) #configure the properties for Claude
+        credentials_profile_name=os.getenv("BWB_PROFILE_NAME"),
+        region_name=os.getenv("BWB_REGION_NAME"),
+        endpoint_url=os.getenv("BWB_ENDPOINT_URL"),
+        model_id="anthropic.claude-v2",
+        model_kwargs=model_kwargs)
     return llm
-    
-    
+
+
 def get_chat_history():
-    chat_history=RedisChatMessageHistory(session_id='username', url=redis_url, key_prefix="chat_history:") 
+    """
+    Returns an instance of RedisChatMessageHistory for storing chat history.
+
+    Returns:
+        RedisChatMessageHistory: An instance of RedisChatMessageHistory.
+    """
+    chat_history = RedisChatMessageHistory(session_id='username', url=redis_url, key_prefix="chat_history:")
     return chat_history
 
 
-def get_memory(session_id, url, key_prefix): # create memory for this chat session
+def get_memory(session_id, url, key_prefix):
+    """
+    Creates and returns a ConversationSummaryBufferMemory instance for maintaining conversation history.
+
+    Args:
+        session_id (str): The session ID for the chat session.
+        url (str): The URL of the Redis instance.
+        key_prefix (str): The key prefix for storing chat history in Redis.
+
+    Returns:
+        ConversationSummaryBufferMemory: An instance of ConversationSummaryBufferMemory.
+    """
     # ConversationSummaryBufferMemory requires an LLM for summarizing older messages
-    # this allows us to maintain the "big picture" of a long-running conversation
+    # This allows us to maintain the "big picture" of a long-running conversation
     llm = get_llm()
-    chat_history=RedisChatMessageHistory(session_id=session_id, url=url, key_prefix=key_prefix) 
-    memory = ConversationSummaryBufferMemory(ai_prefix="AI Assistant",llm=llm, max_token_limit=1024, chat_memory=chat_history) #Maintains a summary of previous messages
-    # memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=1024) # Maintains a summary of previous messages
+    chat_history = RedisChatMessageHistory(session_id=session_id, url=url, key_prefix=key_prefix)
+    memory = ConversationSummaryBufferMemory(ai_prefix="AI Assistant", llm=llm, max_token_limit=1024, chat_memory=chat_history)
     return memory
 
 
-def get_chat_response(input_text, memory): #chat client function
+def get_chat_response(input_text, memory):
+    """
+    Generates a chat response based on the input text and conversation history.
+
+    Args:
+        input_text (str): The input text from the user.
+        memory (ConversationSummaryBufferMemory): The conversation history and summary.
+
+    Returns:
+        str: The chat response generated by the LLM.
+    """
     llm = get_llm()
-    template = """The following is a friendly conversation between a human and an AI. 
+    template = """The following is a friendly conversation between a human and an AI.
     AI provide very concise responses. If the AI does not know the answer to a question, it truthfully says it does not know.
     Current conversation:{history}. Human: {input}  AI Assistant:"""
     PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
-    conversation_with_summary = ConversationChain( #create a chat client
-        llm = llm, #using the Bedrock LLM
-        memory = memory, #with the summarization memory
+    conversation_with_summary = ConversationChain(
+        llm=llm,        # using the Bedrock LLM
+        memory=memory,  # with the summarization memory
         prompt=PROMPT,
-        verbose = True #print out some of the internal states of the chain while running
+        verbose=False   # disable printing internal states
     )
-    chat_response = conversation_with_summary.predict(input=input_text) #pass the user message and summary to the model
+    chat_response = conversation_with_summary.predict(input=input_text)  # pass the user message and summary to the model
+    logging.debug(f"Chat response: {chat_response}")
     return chat_response
-

webinars/genai-chatbot/requirements.lock

@@ -0,0 +1,81 @@
+aiohappyeyeballs==2.3.4
+aiohttp==3.10.0
+aiosignal==1.3.1
+altair==5.3.0
+annotated-types==0.7.0
+anthropic==0.32.0
+anyio==3.7.1
+attrs==23.2.0
+blinker==1.8.2
+boto3==1.34.151
+botocore==1.34.151
+cachetools==5.4.0
+certifi==2024.7.4
+charset-normalizer==3.3.2
+click==8.1.7
+dataclasses-json==0.6.7
+distro==1.9.0
+filelock==3.15.4
+frozenlist==1.4.1
+fsspec==2024.6.1
+gitdb==4.0.11
+GitPython==3.1.43
+greenlet==3.0.3
+h11==0.14.0
+hiredis==3.0.0
+httpcore==1.0.5
+httpx==0.27.0
+huggingface-hub==0.24.5
+idna==3.7
+Jinja2==3.1.4
+jiter==0.5.0
+jmespath==1.0.1
+jsonpatch==1.33
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+langchain==0.0.317
+langsmith==0.0.51
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+marshmallow==3.21.3
+mdurl==0.1.2
+multidict==6.0.5
+mypy-extensions==1.0.0
+numpy==1.26.4
+packaging==24.1
+pandas==2.2.2
+pillow==10.4.0
+protobuf==5.27.3
+pyarrow==17.0.0
+pydantic==2.8.2
+pydantic_core==2.20.1
+pydeck==0.9.1
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+pytz==2024.1
+PyYAML==6.0.1
+redis==5.0.8
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.19.1
+s3transfer==0.10.2
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+SQLAlchemy==2.0.31
+streamlit==1.37.0
+tenacity==8.5.0
+tokenizers==0.19.1
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4.1
+tqdm==4.66.4
+typing-inspect==0.9.0
+typing_extensions==4.12.2
+tzdata==2024.1
+urllib3==2.2.2
+watchdog==4.0.1
+yarl==1.9.4