AI automation & assistants

Detailed Technical Deep Dive: AI & Intelligent Automation

How I put AI to work: retrieval-augmented search over your own documents, models small enough for microcontrollers, and real-time vision pipelines.

1. Robust Retrieval-Augmented Generation (RAG) Vector Search

In corporate environments, generic AI chatbots are liability hazards due to hallucinations. Solving this requires scoping queries strictly using a Postgres vector database extension (pgvector) to verify factual boundaries before invoking the Large Language Model.

Below is the SQL implementation I execute to build semantic search indexing and custom matching inside Supabase:

-- Create database schema to store technical document embeddings
CREATE EXTENSION IF NOT EXISTS pgvector;

CREATE TABLE IF NOT EXISTS document_chunks (
  id           UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  content      TEXT NOT NULL,
  metadata     JSONB,
  embedding    vector(1536) -- Match standard OpenAI / Gemini embedding dimensions
);

-- Build Hierarchical Navigable Small World (HNSW) index for sub-millisecond retrieval
CREATE INDEX ON document_chunks USING hnsw (embedding vector_cosine_ops);

-- Create database function to perform cosine similarity search
CREATE OR REPLACE FUNCTION match_documents (
  query_embedding vector(1536),
  match_threshold float,
  match_count int
)
RETURNS TABLE (
  id uuid,
  content text,
  metadata jsonb,
  similarity float
)
LANGUAGE plpgsql AS $$
BEGIN
  RETURN QUERY
  SELECT
    dc.id,
    dc.content,
    dc.metadata,
    1 - (dc.embedding <=> query_embedding) AS similarity
  FROM document_chunks dc
  WHERE 1 - (dc.embedding <=> query_embedding) > match_threshold
  ORDER BY dc.embedding <=> query_embedding
  LIMIT match_count;
END;
$$;

2. Edge AI Model Quantization for Microcontrollers

Running deep learning models locally on edge microcontrollers (like wake-word detection or gesture classification) requires converting 32-bit floats into 8-bit integers using Post-Training Quantization (PTQ).

The Post-Training Quantization Pipeline in Python:

• Quantization Configuration: Use tf.lite.TFLiteConverter to scale parameters.
• Representative Dataset: Feed a sample dataset to map float parameters to integer ranges.
• Flashing Header Export: Convert the resulting .tflite model into a C++ hex byte array to compile directly with the firmware source.

import tensorflow as tf

# Load baseline float model
converter = tf.lite.TFLiteConverter.from_saved_model('model_dir')
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Define representative dataset for integer range mapping
def representative_dataset():
    for data in sample_data_generator():
        yield [data.astype(np.float32)]

converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8

# Convert and save
quantized_model = converter.convert()
with open('wake_word_model.tflite', 'wb') as f:
    f.write(quantized_model)

3. Real-Time Gesture Tracking Pipelines

To translate webcam frame inputs into robotic movements (like my Smart Robotic Arm control loop), joint offsets are processed locally on the client host using OpenCV and Google's MediaPipe:

  Webcam Frame (30FPS)
          |
          v
  MediaPipe Landmarker (Joints extraction)
          |
          v
  Vector Mapping (Mouth/Hand angles)
          |
          v
  Jitter Smoothing (Moving average)
          |
          v
  WebSocket Client (Send servo angles)
          |
          v
  PCA9685 PWM Driver (Move joints)

By smoothing coordinates on the host side using a moving average filter, we prevent servo jitters and ensure fluid robotic joints rotation with sub-50ms latency.