1. The knowledge base store adds update and upsert methods.

2. Modify the document ID to use the text UUID as the value.

agentuniverse/agent/action/knowledge/store/chroma_store.py

@@ -99,6 +99,32 @@ class ChromaStore(Store):
                 ids=[document.id]
             )
 
+    def upsert_document(self, documents: List[Document], **kwargs):
+        """Upsert document into the store."""
+        for document in documents:
+            embedding = document.embedding
+            if self.embedding_model is not None and len(embedding) == 0:
+                embedding = self.embedding_model.get_embeddings([document.text])[0]
+            self.collection.upsert(
+                documents=[document.text],
+                metadatas=[document.metadata],
+                embeddings=[embedding] if embedding is not None else None,
+                ids=[document.id]
+            )
+
+    def update_document(self, documents: List[Document], **kwargs):
+        """Update document into the store."""
+        for document in documents:
+            embedding = document.embedding
+            if self.embedding_model is not None and len(embedding) == 0:
+                embedding = self.embedding_model.get_embeddings([document.text])[0]
+            self.collection.update(
+                documents=[document.text],
+                metadatas=[document.metadata],
+                embeddings=[embedding] if embedding is not None else None,
+                ids=[document.id]
+            )
+
     @staticmethod
     def to_documents(query_result: QueryResult) -> List[Document]:
         """Convert the query results of ChromaDB to the AgentUniverse(AU) document format."""

agentuniverse/agent/action/knowledge/store/document.py

@@ -8,7 +8,7 @@ import uuid
 from typing import Dict, Any, Optional, List
 
 from langchain_core.documents.base import Document as LCDocument
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 
 class Document(BaseModel):
@@ -21,11 +21,18 @@ class Document(BaseModel):
         embedding (List[float]): Embedding data associated with the document
     """
 
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
+    id: str = None
     text: Optional[str] = ""
     metadata: Optional[Dict[str, Any]] = None
     embedding: List[float] = Field(default_factory=list)
 
+    @model_validator(mode='before')
+    def create_id(cls, values):
+        text: str = values.get('text', '')
+        if not values.get('id'):
+            values['id'] = str(uuid.uuid5(uuid.NAMESPACE_URL, text))
+        return values
+
     def as_langchain(self) -> LCDocument:
         """Convert to LangChain document format."""
         metadata = self.metadata or {}

agentuniverse/agent/action/knowledge/store/store.py

@@ -87,3 +87,19 @@ class Store(BaseModel):
     async def async_delete_document(self, document_id: str, **kwargs):
         """Asynchronously delete the specific document by the document id."""
         raise NotImplementedError
+
+    def upsert_document(self, documents: List[Document], **kwargs):
+        """Upsert document into the store."""
+        raise NotImplementedError
+
+    async def async_upsert_document(self, documents: List[Document], **kwargs):
+        """Asynchronously upsert documents into the store."""
+        raise NotImplementedError
+
+    def update_document(self, documents: List[Document], **kwargs):
+        """Update document into the store."""
+        raise NotImplementedError
+
+    async def async_update_document(self, documents: List[Document], **kwargs):
+        """Asynchronously update documents into the store."""
+        raise NotImplementedError

tests/test_agentuniverse/unit/agent/action/knowledge/test_knowledge.py

@@ -27,6 +27,16 @@ class KnowledgeTest(unittest.TestCase):
             embedding_model_name='text-embedding-ada-002'))
         self.knowledge = Knowledge(**init_params)
 
+    def test_store_update_documents(self) -> None:
+        store = self.knowledge.store
+        store.update_document([Document(text='This is an iPhone', metadata={'type': 'Electronic products'}),
+                               Document(text='This is a Tesla.', metadata={'type': 'Industrial products'})])
+
+    def test_store_upsert_documents(self) -> None:
+        store = self.knowledge.store
+        store.upsert_document([Document(text='This is an iPhone', metadata={'type': 'Cell phone'}),
+                               Document(text='This is a Tesla.', metadata={'type': 'Car'})])
+
     def test_store_insert_documents(self) -> None:
         store = self.knowledge.store
         store.insert_documents([Document(text='This is a document about engineer'),
@@ -34,7 +44,7 @@ class KnowledgeTest(unittest.TestCase):
 
     def test_query(self) -> None:
         store = self.knowledge.store
-        query = Query(query_str='Which stock is the best?', similarity_top_k=1)
+        query = Query(query_str='Which one is a cell phone?', similarity_top_k=1)
         res = store.query(query)
         print(res)