Step 1: Understand the Basic Components of MCP

 

To implement embedding-based context filtering for MCP, begin by understanding its core components. MCP consists of:

• System Instructions:

  Guidelines on behavior and tasks, e.g., "You are a helpful assistant specialized in finance."

• User Profile:

  User information, preferences, and goals.

• Document Context:

  Knowledge base or recent uploads relevant to current interactions.

• Active Tasks/Goals:

  Current objectives or to-dos to focus the model’s operations.

• Tool Access:

  Defines what the model can access, like web tools, databases, etc.

• Rules/Constraints:

  Guidelines that restrict certain outputs or maintain focus within a domain.

 

Step 2: Set Up Your Development Environment

 

Ensure that your development environment is equipped for LLM operation and context filtering.

• Programming Language:

  Python is commonly used for such tasks due to its robust libraries.

• Libraries:

  Install required libraries such as Hugging Face Transformers, NumPy, and any semantic search libraries like FAISS.

pip install transformers numpy faiss-cpu

 

Step 3: Prepare the System Instructions

 

Define the system instructions to guide the language model appropriately. This is typically a text file or a string within your code that sets the overall behavior of the model.

system_instructions = "You are a helpful assistant specialized in finance."

 

Step 4: Implement the User Profile

 

Create a user profile to tailor responses. This will be a dictionary holding user information, preferences, and goals.

user_profile = {
"name": "John Doe",
"preferences": ["finance", "technology"],
"goals": ["learn about stock market"]
}

 

Step 5: Incorporate Document Context

 

Add relevant document context to support the language model with background knowledge.

document_context = [
"The stock market is unpredictable, but historical data can provide insights.",
"Fintech combines finance and technology to offer new services."
]

 

Step 6: Define Active Tasks/Goals

 

Specify the active tasks or goals to ensure the model focuses on the current objectives.

active_tasks = [
"Explain the basics of stock trading.",
"Discuss the impact of technology on finance."
]

 

Step 7: Set Tool Access

 

Outline which external tools the model can access to enhance functionality.

tool_access = {
"web_access": True,
"database_access": False
}

 

Step 8: Establish Rules/Constraints

 

Implement rules to constrain the model’s output where necessary.

rules_constraints = [
"Do not provide medical or legal advice.",
"Focus on finance-related queries."
]

 

Step 9: Implement Embedding-Based Context Filtering

 

To perform context filtering, use embeddings to measure the relevance of various context elements. Compute embeddings for the context and queries and use cosine similarity or another metric to filter pertinent information.

from transformers import AutoTokenizer, AutoModel
import numpy as np

Load your transformer model

tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model = AutoModel.from_pretrained('distilbert-base-uncased')

def embed_text(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
return outputs.last_hidden_state.mean(dim=1).detach().numpy()

Example usage

query_embedding = embed_text("Tell me about fintech.")
document_embeddings = [embed_text(doc) for doc in document_context]

Calculate cosine similarities

similarities = [(doc, np.dot(embed, query_embedding.T)) for doc, embed in zip(document_context, document_embeddings)]
filtered_context = [doc for doc, sim in similarities if sim > 0.8] # Filter based on threshold

 

Step 10: Integrate into MCP Workflow

 

Finally, integrate the filtered context into the MCP workflow, feeding it into the language model alongside the structured components to enhance response accuracy.

Incorporate filtered context into the MCP

mcp_input = {
"system_instructions": system_instructions,
"user_profile": user_profile,
"context": filtered_context,
"active_tasks": active_tasks,
"tool_access": tool_access,
"rules_constraints": rules_constraints
}

Pass this structured input to your LLM

llm_response = yourLLM(mcp_input)

 

Step 11: Evaluate and Iterate

 

Continuously evaluate the effectiveness of the context filtering and adjust components where necessary to enhance the model's performance and adaptability.

Define evaluation criteria and iterate improvements

def evaluate_model_output(output):
return "Satisfactory" if "finance" in output else "Needs Improvement"

Example output evaluation

result = evaluate_model_output(llm_response)

print(result)