updated RAG Search functionality

backend/api_routes.py

@@ -7,6 +7,8 @@ import hashlib
 import json
 import os
 import uuid
+import math
+import re
 from pathlib import Path
 import io
 import zipfile
@@ -38,6 +40,7 @@ from db_queries import (
     list_rag_chunks,
     list_user_history,
     search_rag_chunks,
+    search_rag_chunks_vector,
     update_audio_post,
     upload_storage_object,
     upsert_archive_metadata,
@@ -61,6 +64,10 @@ WHISPER_COMPUTE_TYPE = os.getenv("WHISPER_COMPUTE_TYPE", "int8")
 ARCHIVE_BUCKET = os.getenv("SUPABASE_BUCKET", os.getenv("SUPABASE_ARCHIVE_BUCKET", "archives"))
 
 _whisper_model: WhisperModel | None = None
+_openai_client = None
+EMBEDDING_DIM = 1536
+EMBEDDING_PROVIDER = (os.getenv("EMBEDDING_PROVIDER") or "local").strip().lower()
+OPENAI_EMBEDDING_MODEL = (os.getenv("OPENAI_EMBEDDING_MODEL") or "text-embedding-3-small").strip()
 
 
 def _model() -> WhisperModel:
@@ -103,6 +110,62 @@ def _build_prompt(transcript_text: str, title: str) -> str:
         f"{transcript_text}\n\n"
         "Answer user questions grounded in this transcript."
     )
+
+
+def _local_embedding(text: str, dim: int = EMBEDDING_DIM) -> list[float]:
+    """
+    Free fallback embedding: hashed bag-of-words + bi-grams, L2-normalized.
+    This is weaker than model embeddings but keeps vector search functional.
+    """
+    if not text:
+        return [0.0] * dim
+
+    vec = [0.0] * dim
+    tokens = re.findall(r"[a-z0-9]+", text.lower())
+    if not tokens:
+        return vec
+
+    for i, tok in enumerate(tokens):
+        idx = int(hashlib.sha256(f"u:{tok}".encode("utf-8")).hexdigest(), 16) % dim
+        vec[idx] += 1.0
+        if i < len(tokens) - 1:
+            bigram = f"{tok}_{tokens[i+1]}"
+            bidx = int(hashlib.sha256(f"b:{bigram}".encode("utf-8")).hexdigest(), 16) % dim
+            vec[bidx] += 0.5
+
+    norm = math.sqrt(sum(v * v for v in vec))
+    if norm > 0:
+        vec = [v / norm for v in vec]
+    return vec
+
+
+def _openai_embedding(text: str) -> list[float] | None:
+    global _openai_client
+    api_key = (os.getenv("OPENAI_API_KEY") or "").strip()
+    if not api_key:
+        return None
+
+    try:
+        if _openai_client is None:
+            from openai import OpenAI
+            _openai_client = OpenAI(api_key=api_key)
+        response = _openai_client.embeddings.create(
+            model=OPENAI_EMBEDDING_MODEL,
+            input=text,
+        )
+        return response.data[0].embedding
+    except Exception:
+        return None
+
+
+def _embed_text(text: str) -> list[float]:
+    if EMBEDDING_PROVIDER == "openai":
+        emb = _openai_embedding(text)
+        if emb:
+            return emb
+    return _local_embedding(text)
+
+
 def _add_audio_url(post: Dict[str, Any]) -> Dict[str, Any]:
     """Add signed audio URL to post if ready"""
     if post.get("status") == "ready":
@@ -281,7 +344,7 @@ def api_upload_post():
                     "end_sec": float(seg.end),
                     "text": segment_text,
                     "confidence": float(seg.avg_logprob) if seg.avg_logprob is not None else None,
-                    "embedding": None,
+                    "embedding": _embed_text(segment_text),
                 }
             )
 
@@ -376,8 +439,21 @@ def api_rag_search():
         return _error("'q' is required.", 400)
 
     try:
-        rows = search_rag_chunks(user_id=user_id, query_text=query_text, page=page, limit=limit)
-        return jsonify({"results": rows, "page": page, "limit": min(max(1, limit), 100)})
+        query_embedding = _embed_text(query_text)
+        rows = search_rag_chunks_vector(user_id=user_id, query_embedding=query_embedding, limit=limit)
+
+        # Fallback in case vector path is unavailable or empty.
+        mode = "vector"
+        if not rows:
+            rows = search_rag_chunks(user_id=user_id, query_text=query_text, page=page, limit=limit)
+            mode = "text_fallback"
+
+        return jsonify({
+            "results": rows,
+            "page": page,
+            "limit": min(max(1, limit), 100),
+            "mode": mode
+        })
     except Exception as e:
         return _error(str(e), 500)