Contextual Awareness

[mouimet-infinisoft]

2. Vectorize Codebase into pg_vector for Contextual Awareness

Objective: To enhance the AI's understanding of your codebase by storing vector representations of the code, allowing for more context-aware suggestions and collaborations.

Approach:

• Vectorization: Use a pre-trained model (like OpenAI's Codex or BERT for code) to convert code snippets into vector representations.
• Storage in pg_vector: Store these vector representations in a PostgreSQL table with pg_vector extension for efficient similarity searches.

Implementation Steps:

1. Set Up pg_vector: Ensure pg_vector is enabled on your Supabase instance.

   CREATE EXTENSION IF NOT EXISTS vector;

2. Create a Table for Vectors: Create a table to store the code vectors.

   CREATE TABLE code_vectors (
      id SERIAL PRIMARY KEY,
      file_path TEXT,
      code_snippet TEXT,
      vector vector(1536) -- Adjust the dimension based on the vector size from your model
   );

3. Vectorize Code Snippets: Use a model to generate vectors for your code snippets.

   from transformers import AutoTokenizer, AutoModel
   import torch
   
   tokenizer = AutoTokenizer.from_pretrained("your-code-model")
   model = AutoModel.from_pretrained("your-code-model")
   
   def vectorize_code(code):
      inputs = tokenizer(code, return_tensors='pt')
      outputs = model(**inputs)
      return outputs.last_hidden_state.mean(dim=1).detach().numpy()

4. Store Vectors in Database: Insert the vectors into the database.

   const { data, error } = await supabase
      .from('code_vectors')
      .insert([
          { file_path: 'path/to/file', code_snippet: codeSnippet, vector: vectorRepresentation }
      ]);

5. Search Using Vectors: Implement a search functionality using pg_vector for similarity queries.

   SELECT * FROM code_vectors
   ORDER BY vector <-> '[your_vector_representation]'
   LIMIT 5;