fixed a few bugs related to vector embedding + added additional end to end documentation to showcase local and external embedders step-by-step + added example mock embedder python script

mock_embedder.py

@@ -0,0 +1,34 @@
+from flask import Flask, request, jsonify
+import numpy as np
+
+app = Flask(__name__)
+
+@app.route('/v1/embeddings', methods=['POST'])
+def embeddings():
+    data = request.json
+    inputs = data.get('input', [])
+    if isinstance(inputs, str):
+        inputs = [inputs]
+    
+    # Generate deterministic 768-dim embeddings (hash-based)
+    embeddings = []
+    for text in inputs:
+        # Simple hash to vector
+        vec = np.zeros(768)
+        for i, char in enumerate(text[:768]):
+            vec[i % 768] += ord(char) / 255.0
+        # Normalize
+        norm = np.linalg.norm(vec)
+        if norm > 0:
+            vec = vec / norm
+        embeddings.append(vec.tolist())
+    
+    return jsonify({
+        "data": [{"embedding": emb} for emb in embeddings],
+        "model": data.get('model', 'mock'),
+        "usage": {"total_tokens": sum(len(t) for t in inputs)}
+    })
+
+if __name__ == '__main__':
+    app.run(host='127.0.0.1', port=8081)
+