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from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain_groq import ChatGroq
from langchain.chains import LLMChain
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
from langchain_core.messages import SystemMessage
from src.config import AppConfig
class EmotionalChatbot:
def __init__(self, chat_history=None):
"""
Initialize the chatbot with Memory and Groq integration.
:param chat_history: Existing chat history to load into memory.
"""
self.memory = ConversationBufferWindowMemory(
k=AppConfig.MEMORY_WINDOW,
memory_key="chat_history",
return_messages=True
)
# Load existing chat history into memory if available
if chat_history:
for message in chat_history:
try:
self.memory.save_context(
{"input": message["human"]},
{"output": message["AI"]}
)
except KeyError:
print(f"Skipping invalid message: {message}")
self.default_max_tokens = 100
# Initialize ChatGroq with default max_tokens
self.groq_chat = ChatGroq(**AppConfig.LLM_CONFIG, max_tokens=self.default_max_tokens)
self.prompt_template = ChatPromptTemplate.from_messages(
[
SystemMessage(content=AppConfig.SYSTEM_PROMPT),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{human_input}")
]
)
self.conversation = LLMChain(
llm=self.groq_chat,
prompt=self.prompt_template,
memory=self.memory,
verbose=True
)
def adjust_max_tokens(self, user_input):
"""
Dynamically adjust the max_tokens based on the user's input complexity.
"""
input_length = len(user_input.split())
if any(keyword in user_input.lower() for keyword in [
"explain", "detail", "elaborate", "describe", "clarify", "expand",
"analyze", "break down", "in-depth", "why", "how", "discuss",
"thorough", "comprehensive", "what do you mean"
]) or input_length >= 15:
return self.default_max_tokens # More tokens for detailed queries
else: # Moderate queries
return 50 # Balanced token usage
def generate_response(self, user_input):
"""
Generate a response using the LLM, with dynamically adjusted max_tokens.
:param user_input: The user's message.
:return: The AI's response.
"""
# Adjust max_tokens dynamically
max_tokens = self.adjust_max_tokens(user_input)
# Update the LLM configuration dynamically
self.groq_chat.max_tokens = max_tokens
# Generate the response
return self.conversation.predict(human_input=user_input)
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