fix: some bugs

api/core/callback_handler/main_chain_gather_callback_handler.py

@@ -49,13 +49,15 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """Print out that we are entering a chain."""
         if not self._current_chain_result:
-            self._current_chain_result = ChainResult(
-                type=serialized['name'],
-                prompt=inputs,
-                started_at=time.perf_counter()
-            )
-            self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
-            self.agent_loop_gather_callback_handler.current_chain = self._current_chain_message
+            chain_type = serialized['id'][-1]
+            if chain_type:
+                self._current_chain_result = ChainResult(
+                    type=chain_type,
+                    prompt=inputs,
+                    started_at=time.perf_counter()
+                )
+                self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
+                self.agent_loop_gather_callback_handler.current_chain = self._current_chain_message
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""

api/core/callback_handler/std_out_callback_handler.py

@@ -50,8 +50,8 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
         self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
     ) -> None:
         """Print out that we are entering a chain."""
-        class_name = serialized["name"]
-        print_text("\n[on_chain_start]\nChain: " + class_name + "\nInputs: " + str(inputs) + "\n", color='pink')
+        chain_type = serialized['id'][-1]
+        print_text("\n[on_chain_start]\nChain: " + chain_type + "\nInputs: " + str(inputs) + "\n", color='pink')
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""

api/core/embedding/cached_embedding.py

@@ -22,7 +22,7 @@ class CacheEmbedding(Embeddings):
             hash = helper.generate_text_hash(text)
             embedding = db.session.query(Embedding).filter_by(hash=hash).first()
             if embedding:
-                text_embeddings.append(embedding.embedding)
+                text_embeddings.append(embedding.get_embedding())
             else:
                 embedding_queue_texts.append(text)
 
@@ -55,7 +55,7 @@ class CacheEmbedding(Embeddings):
         hash = helper.generate_text_hash(text)
         embedding = db.session.query(Embedding).filter_by(hash=hash).first()
         if embedding:
-            return embedding.embedding
+            return embedding.get_embedding()
 
         embedding_results = self._embeddings.embed_query(text)
 

api/core/index/vector_index/qdrant_vector_index.py

@@ -50,7 +50,7 @@ class QdrantVectorIndex(BaseVectorIndex):
     def to_index_struct(self) -> dict:
         return {
             "type": self.get_type(),
-            "vector_store": {"collection_name": self.get_index_name(self._dataset.get_id())}
+            "vector_store": {"collection_name": self.get_index_name(self._dataset.id)}
         }
 
     def create(self, texts: list[Document], **kwargs) -> BaseIndex:
@@ -58,7 +58,7 @@ class QdrantVectorIndex(BaseVectorIndex):
         self._vector_store = QdrantVectorStore.from_documents(
             texts,
             self._embeddings,
-            collection_name=self.get_index_name(self._dataset.get_id()),
+            collection_name=self.get_index_name(self._dataset.id),
             ids=uuids,
             **self._client_config.to_qdrant_params()
         )
@@ -76,7 +76,7 @@ class QdrantVectorIndex(BaseVectorIndex):
 
         return QdrantVectorStore(
             client=client,
-            collection_name=self.get_index_name(self._dataset.get_id()),
+            collection_name=self.get_index_name(self._dataset.id),
             embeddings=self._embeddings
         )
 

api/core/index/vector_index/vector_index.py

@@ -29,12 +29,13 @@ class VectorIndex:
             return WeaviateVectorIndex(
                 dataset=dataset,
                 config=WeaviateConfig(
-                    endpoint=config.get('WEAVIATE_URL'),
+                    endpoint=config.get('WEAVIATE_ENDPOINT'),
                     api_key=config.get('WEAVIATE_API_KEY'),
                     batch_size=int(config.get('WEAVIATE_BATCH_SIZE'))
                 ),
                 embeddings=embeddings,
-                attributes=['doc_id', 'dataset_id', 'document_id', 'source'],
+                # attributes=['doc_id', 'dataset_id', 'document_id', 'source'],
+                attributes=['doc_id'],
             )
         elif vector_type == "qdrant":
             from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig

api/core/index/vector_index/weaviate_vector_index.py

@@ -4,7 +4,7 @@ import weaviate
 from langchain.embeddings.base import Embeddings
 from langchain.schema import Document, BaseRetriever
 from langchain.vectorstores import VectorStore
-from pydantic import BaseModel
+from pydantic import BaseModel, root_validator
 
 from core.index.base import BaseIndex
 from core.index.vector_index.base import BaseVectorIndex
@@ -17,6 +17,12 @@ class WeaviateConfig(BaseModel):
     api_key: Optional[str]
     batch_size: int = 100
 
+    @root_validator()
+    def validate_config(cls, values: dict) -> dict:
+        if not values['endpoint']:
+            raise ValueError("config WEAVIATE_ENDPOINT is required")
+        return values
+
 
 class WeaviateVectorIndex(BaseVectorIndex):
     def __init__(self, dataset: Dataset, config: WeaviateConfig, embeddings: Embeddings, attributes: list[str]):
@@ -59,7 +65,7 @@ class WeaviateVectorIndex(BaseVectorIndex):
     def to_index_struct(self) -> dict:
         return {
             "type": self.get_type(),
-            "vector_store": {"class_prefix": self.get_index_name(self._dataset.get_id())}
+            "vector_store": {"class_prefix": self.get_index_name(self._dataset.id)}
         }
 
     def create(self, texts: list[Document], **kwargs) -> BaseIndex:
@@ -68,7 +74,7 @@ class WeaviateVectorIndex(BaseVectorIndex):
             texts,
             self._embeddings,
             client=self._client,
-            index_name=self.get_index_name(self._dataset.get_id()),
+            index_name=self.get_index_name(self._dataset.id),
             uuids=uuids,
             by_text=False
         )
@@ -82,7 +88,7 @@ class WeaviateVectorIndex(BaseVectorIndex):
 
         return WeaviateVectorStore(
             client=self._client,
-            index_name=self.get_index_name(self._dataset.get_id()),
+            index_name=self.get_index_name(self._dataset.id),
             text_key='text',
             embedding=self._embeddings,
             attributes=self._attributes,

api/core/llm/provider/azure_provider.py

@@ -42,7 +42,7 @@ class AzureProvider(BaseProvider):
         """
         config = self.get_provider_api_key(model_id=model_id)
         config['openai_api_type'] = 'azure'
-        config['deployment'] = config['deployment_name'] = model_id.replace('.', '') if model_id else None
+        config['deployment_name'] = model_id.replace('.', '') if model_id else None
         return config
 
     def get_provider_name(self):