Update helpers/live_sources.py

helpers/live_sources.py

@@ -1,42 +1,43 @@
-# Retrieves links for all modules
-import json
-from pathlib import Path
-import faiss
-from sentence_transformers import SentenceTransformer
-DATA_DIR = Path("data")
-VECTOR_DIR = DATA_DIR / "vector_store"
-LIVE_FAISS_INDEX_PATH = VECTOR_DIR / "live_faiss.index"
-LIVE_METADATA_PATH = VECTOR_DIR / "live_metadata.json"
-index = faiss.read_index(str(LIVE_FAISS_INDEX_PATH))
-with open(LIVE_METADATA_PATH, "r", encoding="utf-8") as f:
-    METADATA = json.load(f)
-model = SentenceTransformer("all-MiniLM-L6-v2")
-def retrieve_live_sources(
-    query: str,
-    *,
-    top_k: int = 2,
-    search_k: int = 2000
-):
-    query_embedding = model.encode(
-        [query],
-        normalize_embeddings=True,
-        convert_to_numpy=True
-    )
-    scores, indices = index.search(query_embedding, search_k)
-    results = []
-    seen_urls = set()
-    for score, idx in zip(scores[0], indices[0]):
-        meta = METADATA[idx]
-        url = meta.get("document_path")
-        if not url or url in seen_urls:
-            continue
-        seen_urls.add(url)
-        results.append({
-            "url": url,
-            "authority": meta.get("authority"),
-            "description": meta.get("text"),
-            "similarity": float(score)
-        })
-        if len(results) >= top_k:
-            break
+# Retrieves links for all modules
+import json
+from pathlib import Path
+import faiss
+from sentence_transformers import SentenceTransformer
+BASE_DIR = Path(__file__).resolve().parent.parent
+DATA_DIR = BASE_DIR / "data"
+VECTOR_DIR = DATA_DIR / "vector_store"
+LIVE_FAISS_INDEX_PATH = VECTOR_DIR / "live_faiss.index"
+LIVE_METADATA_PATH = VECTOR_DIR / "live_metadata.json"
+index = faiss.read_index(str(LIVE_FAISS_INDEX_PATH))
+with open(LIVE_METADATA_PATH, "r", encoding="utf-8") as f:
+    METADATA = json.load(f)
+model = SentenceTransformer("all-MiniLM-L6-v2")
+def retrieve_live_sources(
+    query: str,
+    *,
+    top_k: int = 2,
+    search_k: int = 2000
+):
+    query_embedding = model.encode(
+        [query],
+        normalize_embeddings=True,
+        convert_to_numpy=True
+    )
+    scores, indices = index.search(query_embedding, search_k)
+    results = []
+    seen_urls = set()
+    for score, idx in zip(scores[0], indices[0]):
+        meta = METADATA[idx]
+        url = meta.get("document_path")
+        if not url or url in seen_urls:
+            continue
+        seen_urls.add(url)
+        results.append({
+            "url": url,
+            "authority": meta.get("authority"),
+            "description": meta.get("text"),
+            "similarity": float(score)
+        })
+        if len(results) >= top_k:
+            break
     return results