MCP with RAG and Agent

 

                                                                image from Google next

To break down "MCP" and "MCP tools":

• Model Context Protocol (MCP):

  • This is an open standard that aims to standardize how Large Language Models (LLMs) communicate with external applications, data sources, and tools.
  • Essentially, it provides a structured way for LLMs to interact with the "outside world" in a consistent manner.
  • It follows a client-server architecture, where:
    • MCP clients (like LLM applications) request actions.
    • MCP servers provide access to tools and data.

• MCP Tools:

  • These are the specific functions or capabilities that MCP servers expose.
  • They allow LLMs to perform actions, such as:
    • Accessing files.
    • Interacting with databases.
    • Using web services.
  • MCP tools enable LLMs to go beyond their internal knowledge and perform real-world tasks.³
  • Essentially, the tools are the functions that the server provides, that the client can call.
  • There is also a tool called "MCP tools" that is a command line interface that helps with interacting with MCP servers.

In simpler terms, MCP is like a set of rules, and MCP tools are the actions that LLMs can take by following those rules.

It's accurate that building sophisticated AI applications often involves combining multi-agent systems with Retrieval Augmented Generation (RAG). Here's a breakdown of how you can connect multi-agent frameworks like LangGraph or ADK with an agent that utilizes RAG:

Understanding the Components:

• Multi-Agent Systems (LangGraph, ADK):
  • These frameworks allow you to create workflows where multiple AI agents collaborate to achieve a complex goal.¹
  • They handle the orchestration, communication, and state management between these agents.
  • LangGraph, in particular, excels at defining agent interactions as a graph, providing fine-grained control over the workflow.³
• Retrieval Augmented Generation (RAG):
  • RAG enhances LLMs by grounding their responses in external knowledge.
  • It involves retrieving relevant information from a database or knowledge base and providing it to the LLM as context.
  • This enables the LLM to generate more accurate and informed responses.

Connecting Multi-Agents with RAG:

Here's a general approach:

1. Define Agent Roles:

   • Determine the specific roles of each agent in your multi-agent system.
   • One agent might be responsible for handling user queries, another for retrieving information, and another for generating the final response.
   • For example, you could have:
     • A "Query Agent" that receives user input.
     • A "Retrieval Agent" that uses RAG to fetch relevant information.
     • A "Response Agent" that formulates a response based on the retrieved information.

2. Integrate RAG into a Specific Agent:

   • The "Retrieval Agent" or the "Response Agent" is the logical place to integrate your RAG pipeline.
   • This agent would:
     • Receive the user query.
     • Use a vector database or other retrieval mechanism to find relevant documents.
     • Pass the retrieved documents to the LLM along with the query.

3. Orchestrate the Workflow with LangGraph or ADK:

   • Use LangGraph or ADK to define the flow of information between the agents.
   • For example:
     • The "Query Agent" receives a user query and passes it to the "Retrieval Agent."
     • The "Retrieval Agent" performs RAG and passes the retrieved information to the "Response Agent."
     • The "Response Agent" generates a response and returns it to the user.

4. State Management:

   • LangGraph's state management capabilities are crucial for maintaining context across agent interactions.
   • This ensures that the agents can effectively collaborate and build upon previous information.

Key Considerations:

• Tooling: Langchain is a very useful tool for creating the RAG elements of the agents.
• Vector Databases: Vector databases are essential for efficient retrieval in RAG.
• Agent Communication: Ensure that your agents can communicate effectively by defining clear data structures and protocols.

By following these steps, you can create powerful AI applications that combine the strengths of multi-agent systems and RAG.

Lets do some programming with Vertex ai

import vertexai

from vertexai.language_models import TextGenerationModel

from vertexai.generative_models import GenerativeModel, Part

from google.cloud import aiplatform

import google_auth_httplib2

import google.auth

import requests

import json

from google.cloud import storage

import os

# Initialize Vertex AI

PROJECT_ID = "YOUR_PROJECT_ID"  # Replace with your project ID

LOCATION = "us-central1"  # Replace with your location

vertexai.init(project=PROJECT_ID, location=LOCATION)

# Initialize Generative and Text Models

gen_model = GenerativeModel("gemini-pro")

text_model = TextGenerationModel.from_pretrained("text-bison")

# Initialize AI Platform for embeddings (if needed for RAG)

aiplatform.init(project=PROJECT_ID, location=LOCATION)

# Initialize Cloud storage client for RAG data

storage_client = storage.Client(project=PROJECT_ID)

# --- RAG Function (Simplified) ---

def rag_retrieve(query, bucket_name="YOUR_BUCKET_NAME", file_prefix="rag_data/"): #replace with your bucket name

    """

    Simplified retrieval from Cloud Storage. In a real application, you'd use a vector database.

    """

    bucket = storage_client.bucket(bucket_name)

    blobs = bucket.list_blobs(prefix=file_prefix)

    retrieved_content = ""

    for blob in blobs:

        if query.lower() in blob.name.lower():  # Basic keyword search

            retrieved_content += blob.download_as_text() + "\n"

    return retrieved_content

# --- Agent Functions ---

def query_agent(user_input):

    """

    Agent to handle user queries.

    """

    return user_input

def retrieval_agent(query):

    """

    Agent to retrieve information using RAG.

    """

    retrieved_info = rag_retrieve(query)

    return retrieved_info

def response_agent(query, retrieved_info):

    """

    Agent to generate a response.

    """

    prompt = f"""

    Based on the following information: {retrieved_info}

    Answer the user's query: {query}

    """

    response = gen_model.generate_content(prompt) #using gemini-pro for better response quality.

    return response.text

# --- LangGraph-like Orchestration (Simplified) ---

def multi_agent_workflow(user_query):

    """

    Simulated multi-agent workflow.

    """

    query = query_agent(user_query)

    retrieved_info = retrieval_agent(query)

    response = response_agent(query, retrieved_info)

    return response

# --- Example Usage ---

user_input = "What are the main benefits?"

final_response = multi_agent_workflow(user_input)

print(final_response)

# example of using the text model.

text_response = text_model.predict(

    f"""

    Based on the following information: {rag_retrieve(user_input)}

    Answer the user's query: {user_input}

    """

)

print(text_response.text)

With ADK

import vertexai

from vertexai.language_models import TextGenerationModel

from vertexai.generative_models import GenerativeModel

from google.cloud import aiplatform

from google_cloud_aiplatform.experimental.generative_ai import foundation_model_adapter as adk

from google.cloud import storage

# Initialize Vertex AI

PROJECT_ID = "YOUR_PROJECT_ID"  # Replace with your project ID

LOCATION = "us-central1"  # Replace with your location

vertexai.init(project=PROJECT_ID, location=LOCATION)

# Initialize Models

gen_model = GenerativeModel("gemini-pro")

text_model = TextGenerationModel.from_pretrained("text-bison")

# Initialize AI Platform

aiplatform.init(project=PROJECT_ID, location=LOCATION)

# Initialize Cloud Storage client

storage_client = storage.Client(project=PROJECT_ID)

# --- RAG Function (Simplified) ---

def rag_retrieve(query, bucket_name="YOUR_BUCKET_NAME", file_prefix="rag_data/"): #replace with your bucket name

    """

    Simplified retrieval from Cloud Storage. In a real application, you'd use a vector database.

    """

    bucket = storage_client.bucket(bucket_name)

    blobs = bucket.list_blobs(prefix=file_prefix)

    retrieved_content = ""

    for blob in blobs:

        if query.lower() in blob.name.lower():  # Basic keyword search

            retrieved_content += blob.download_as_text() + "\n"

    return retrieved_content

# --- ADK Agent Function ---

def adk_agent(user_input):

    """

    Agent using ADK for response generation.

    """

    rag_data = rag_retrieve(user_input)

    prompt = f"""

    Based on the following information: {rag_data}

    Answer the user's query: {user_input}

    """

    model = adk.FoundationModelAdapter(

        model_name="gemini-pro",

        project=PROJECT_ID,

        location=LOCATION,

    )

    response = model.predict(

        prompt,

    )

    return response.text

# --- Example Usage ---

user_input = "What are the key features?"

response = adk_agent(user_input)

print(response)

# Example using the text model.

text_response = text_model.predict(

    f"""

    Based on the following information: {rag_retrieve(user_input)}

    Answer the user's query: {user_input}

    """

)

print(text_response.text)

Will update you with some latest ADK framework and code soon. You can follow Google ADK tutorial here https://google.github.io/adk-docs/get-started/quickstart/