| 15 | |
| 16 | |
| 17 | class BasicMemory(BaseMemory): |
| 18 | def __init__( |
| 19 | self, |
| 20 | memory_path: str, |
| 21 | vectorstore: VectorStore, |
| 22 | memory: Optional[Dict] = None, |
| 23 | ) -> None: |
| 24 | if memory is None: |
| 25 | self.memory = {} |
| 26 | else: |
| 27 | self.memory = memory |
| 28 | self.memory_path = memory_path |
| 29 | self.vectorstore = vectorstore |
| 30 | |
| 31 | def add( |
| 32 | self, |
| 33 | data: Dict, |
| 34 | embedding_key: str, |
| 35 | **kwargs, |
| 36 | ) -> None: |
| 37 | """ |
| 38 | Add data to memory. |
| 39 | """ |
| 40 | name = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime()) # the unique id of the added unit. |
| 41 | self.memory[name] = data |
| 42 | |
| 43 | assert embedding_key in data, f"embedding_key {embedding_key} not in data" |
| 44 | embeddings = data[embedding_key] |
| 45 | |
| 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, |
| 73 | embedding_query: str, |
| 74 | top_k: int = 3, |