(self, query: list = None, query_vec: list = None, result=None)
| 523 | return model |
| 524 | |
| 525 | def insert_cahing(self, query: list = None, query_vec: list = None, result=None): |
| 526 | # default_ef = embedding_functions.DefaultEmbeddingFunction() |
| 527 | query_vec = None |
| 528 | for i in range(len(query)): |
| 529 | mem = [] |
| 530 | for j in range(result['documents'][i].__len__()): |
| 531 | mem.append((result['documents'][i][j], np.random.rand(384).tolist())) |
| 532 | # mem.append((result['documents'][i][j], self.embedding_model(result['documents'][i][j]))) |
| 533 | |
| 534 | if query_vec is None: |
| 535 | vec = self.embedding_model(query[i])[0] |
| 536 | if query_vec is not None: |
| 537 | vec = query_vec[i] |
| 538 | self.redisCaching.update_cache(query[i], vec, mem) |
| 539 | |
| 540 | def cahing_finished_callback(self, future): |
| 541 | self.my_property = future.result() |
no test coverage detected