Retrieve the keys from the vectorstores.
(
self,
data: Dict,
embedding_query: str,
top_k: int = 3,
**kwargs)
| 46 | self.vectorstore.add_embeddings([name], [embeddings]) |
| 47 | |
| 48 | def similarity_search( |
| 49 | self, |
| 50 | data: Dict, |
| 51 | embedding_query: str, |
| 52 | top_k: int = 3, |
| 53 | **kwargs) -> Tuple[List[Dict[str, Any]], List[float]]: |
| 54 | """ |
| 55 | Retrieve the keys from the vectorstores. |
| 56 | """ |
| 57 | assert embedding_query in data, f"embedding_query {embedding_query} not in data" |
| 58 | |
| 59 | query_embedding = data[embedding_query] |
| 60 | |
| 61 | try: |
| 62 | key_and_score = self.vectorstore.similarity_search(query_embedding, top_k) |
| 63 | items = [self.memory[k] for k, score in key_and_score] |
| 64 | scores = [score for k, score in key_and_score] |
| 65 | except: |
| 66 | items = [] |
| 67 | scores = [] |
| 68 | |
| 69 | return items, scores |
| 70 | |
| 71 | def query(self, |
| 72 | data: Dict, |