Comments (2)
To achieve a mixture of Retrieval-Augmented Generation (RAG) and tool usage in Langflow, and maintain the control flow with proper conversation, follow these steps:
1. Setting Up the RAG Model and Tools
First, set up your RAG model and define the tools for specific function calls.
Define the Retriever Tool
Create a retriever tool using the RetrieverToolComponent
:
from langchain.tools.retriever import create_retriever_tool
from langflow import CustomComponent
from langflow.field_typing import BaseRetriever, Tool
class RetrieverToolComponent(CustomComponent):
display_name = "RetrieverTool"
description = "Tool for interacting with retriever"
def build_config(self):
return {
"retriever": {
"display_name": "Retriever",
"info": "Retriever to interact with",
"type": BaseRetriever,
},
"name": {"display_name": "Name", "info": "Name of the tool"},
"description": {"display_name": "Description", "info": "Description of the tool"},
}
def build(
self,
retriever: BaseRetriever,
name: str,
description: str,
) -> Tool:
return create_retriever_tool(
retriever=retriever,
name=name,
description=description,
)
Define the Function for Mobile Data Balance
Implement the function get_mobile_data_balance
and other related functions:
def get_mobile_data_balance(mobile_number):
# Logic to get mobile data balance
balance = "23GB" # Example balance
return balance
def send_msg(mobile_number):
# Logic to send OTP
otp = "8822" # Example OTP
return otp
def otp_validation_and_return_data(otp):
# Logic to validate OTP and return data balance
if otp == "8822":
return "Your data balance is 23GB"
else:
return "Invalid OTP"
2. Maintaining Control Flow
To maintain the control flow and handle the conversation properly, use a state management system like GraphStateManager
.
Example Conversation Flow
Here is an example of how you can handle the conversation flow:
from langflow.graph.state_manager import GraphStateManager
state_manager = GraphStateManager()
def conversation_flow(user_input, user_id):
state = state_manager.get_state(user_id)
if state == "awaiting_mobile_number":
mobile_number = user_input
send_msg(mobile_number)
state_manager.update_state(user_id, "awaiting_otp")
return "Please enter the OTP sent to you"
elif state == "awaiting_otp":
otp = user_input
response = otp_validation_and_return_data(otp)
state_manager.update_state(user_id, "default")
return response
elif "data balance" in user_input:
state_manager.update_state(user_id, "awaiting_mobile_number")
return "Please enter your mobile number"
else:
# Handle general broadband-related queries using RAG model
response = retriever_tool.retrieve(user_input)
return response
# Example integration
retriever_tool = RetrieverToolComponent().build(
retriever=my_retriever_instance,
name="MyRetrieverTool",
description="A tool to retrieve information"
)
# Simulate a user input
user_id = "12345"
user_input = "Can you tell me my data balance?"
response = conversation_flow(user_input, user_id)
print(response) # Output: Please enter your mobile number
Summary
- Define the Retriever Tool: Use the
RetrieverToolComponent
to create a retriever tool for general queries. - Implement Functions: Define the functions
get_mobile_data_balance
,send_msg
, andotp_validation_and_return_data
. - Manage State: Use
GraphStateManager
to manage the conversation state and maintain control flow.
By following these steps, you can achieve a mixture of RAG and tool usage in Langflow, ensuring that the agent can handle both general queries and specific function calls while maintaining a coherent conversation flow.
Details
Sources
- src/backend/langflow/components/tools/RetrieverTool.py
- src/backend/base/langflow/graph/graph/state_manager.py
- src/backend/langflow/components/chains/ConversationChain.py
- src/backend/base/langflow/services/auth/utils.py
- src/backend/base/langflow/services/auth/utils.py
- src/backend/langflow/api/v1/init.py
About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.
Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
To continue the conversation, mention @dosu.
from langflow.
you can run an agent in the Agents section, but you will need to define the two tools you mentioned using custom components.
Contrary to DosuBot's response, if you declare the output type of the custom component as AgentExecutor, you can declare and control the agent directly as you wish.
from langflow.
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