Update vector_db.py
Browse files- vector_db.py +19 -4
vector_db.py
CHANGED
|
@@ -12,15 +12,30 @@ class VectorDB:
|
|
| 12 |
|
| 13 |
def load_documents(self, path="documents.json"):
|
| 14 |
with open(path, "r", encoding="utf-8") as f:
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 18 |
embeddings = self.model.encode(texts, convert_to_numpy=True)
|
| 19 |
-
|
| 20 |
dim = embeddings.shape[1]
|
| 21 |
self.index = faiss.IndexFlatL2(dim)
|
| 22 |
self.index.add(embeddings)
|
| 23 |
|
|
|
|
| 24 |
def search(self, query, top_k=3):
|
| 25 |
if self.index is None:
|
| 26 |
return []
|
|
|
|
| 12 |
|
| 13 |
def load_documents(self, path="documents.json"):
|
| 14 |
with open(path, "r", encoding="utf-8") as f:
|
| 15 |
+
raw_docs = json.load(f)
|
| 16 |
+
|
| 17 |
+
self.documents = []
|
| 18 |
+
texts = []
|
| 19 |
+
|
| 20 |
+
for i, doc in enumerate(raw_docs):
|
| 21 |
+
content = doc.get("content") or doc.get("text") or doc.get("data")
|
| 22 |
+
if not content:
|
| 23 |
+
print(f"⚠️ Skipping document {i}: no content/text field")
|
| 24 |
+
continue
|
| 25 |
+
|
| 26 |
+
self.documents.append(doc)
|
| 27 |
+
texts.append(content)
|
| 28 |
+
|
| 29 |
+
if not texts:
|
| 30 |
+
raise ValueError("No valid documents found to index")
|
| 31 |
+
|
| 32 |
embeddings = self.model.encode(texts, convert_to_numpy=True)
|
| 33 |
+
|
| 34 |
dim = embeddings.shape[1]
|
| 35 |
self.index = faiss.IndexFlatL2(dim)
|
| 36 |
self.index.add(embeddings)
|
| 37 |
|
| 38 |
+
|
| 39 |
def search(self, query, top_k=3):
|
| 40 |
if self.index is None:
|
| 41 |
return []
|