How to run vector search and inject results into MCP context block?

 

Step 1: Understand the Vector Search Concept

 

• Before injecting search results into an MCP context block, you need to understand what vector search involves. Vector search uses embedding models to convert data (text, images, etc.) into numerical vectors. Similarity between vectors allows you to retrieve related data efficiently.

 

Step 2: Obtain or Develop Vector Embeddings

 

• You first need a source of vector embeddings, which might be pre-trained embedding models or ones you develop yourself on specific datasets. For text, models like BERT or GPT-3 generate embeddings for document retrieval.

 

Step 3: Set Up a Vector Database

 

• Use a vector database like Pinecone, Milvus, or Weaviate to index your embeddings.

• Example: Setting up a vector database with Pinecone.

  
  import pinecone
  
  # Initialize connection to Pinecone
  pinecone.init(api_key='your-api-key', environment='your-environment')
  
  # Create or connect to an index
  index = pinecone.Index('example-index', dimension=128)  # dimension should match your embedding size
  
  # Upsert vectors (assuming embeddings is a list of tuples with ID)
  index.upsert(vectors=embeddings)
  

 

Step 4: Conduct Your Vector Search

 

• With your vectors indexed, you can perform a search to find similar items.

  
  # Generate an embedding for your query
  queryembedding = generateembeddingforquery('your query text')
  
  # Perform the search in the vector database
  result = index.query(queries=[queryembedding], topk=5)  # top_k specifies the number of results
  
  # Access search results
  search_results = result['matches']
  

 

Step 5: Format Results Suitable for MCP

 

• Convert the results from your search into a suitable format for your MCP context block.

• Example formatting search results:

  
  def formatresultsfor_mcp(results):
      formatted_results = []
      for item in results:
          data = retrievedataby_id(item['id'])  # Function to get raw data using ID
          formatted_results.append({
              'title': data['title'],
              'snippet': data['snippet']
          })
      return formatted_results
  
  searchcontext = formatresultsformcp(search_results)
  

 

Step 6: Inject Results into MCP Context Block

 

• With the formatted search results, you can integrate them into your MCP context block.

  
  mcpcontextblock = {
      'system_instructions': 'You are a well-versed assistant in literature.',
      'active_tasks': ['Answer query using relevant documents'],
      'documentcontext': searchcontext,  # Injecting search results here
      'user_profile': {
          'name': 'John Doe',
          'preferences': ['concise answers', 'cite documents']
      }
  }
  

 

Step 7: Implement the MCP with Your Model

 

• Use the MCP context block with a model such as Claude or others that support the MCP format.

  
  def askmodelwithmcp(mcpblock, query):
      response = llm_ask(
          context=mcp_block,
          query=query
      )
      return response
  
  # Final query example
  modelresponse = askmodelwithmcp(mcpcontextblock, 'Tell me about literary styles.')
  

 

Step 8: Refine and Iterate Your Process

 

• Review results for effectiveness. Tweak embedding models, search parameters, or the structuring of the MCP block as needed to improve performance and relevancy.