add tool resource

api/core/workflow/nodes/llm/knowledge_resource.py

@@ -14,3 +14,12 @@ class KnowledgeResource(BaseModel):
     score: Optional[float] = None
     metadata: Optional[dict[str, Any]] = None
 
+    def to_dict(self):
+        return {
+            'content': self.content,
+            'title': self.title,
+            'url': self.url,
+            'icon': self.icon,
+            'score': self.score,
+            'metadata': self.metadata
+        }

api/core/workflow/nodes/llm/llm_node.py

@@ -22,6 +22,7 @@ from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResu
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.llm.entities import LLMNodeData, ModelConfig
+from core.workflow.nodes.llm.knowledge_resource import KnowledgeResource
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from extensions.ext_database import db
 from models.model import Conversation
@@ -262,7 +263,7 @@ class LLMNode(BaseNode):
                 for item in context_value:
                     if isinstance(item, str):
                         context_str += item + '\n'
-                    else:
+                    elif isinstance(item, dict):
                         if 'content' not in item:
                             raise ValueError(f'Invalid context structure: {item}')
 
@@ -271,6 +272,12 @@ class LLMNode(BaseNode):
                         retriever_resource = self._convert_to_original_retriever_resource(item)
                         if retriever_resource:
                             original_retriever_resource.append(retriever_resource)
+                    elif isinstance(item, KnowledgeResource):
+                        context_str += item.content + '\n'
+
+                        retriever_resource = self._convert_to_original_retriever_resource(item.to_dict())
+                        if retriever_resource:
+                            original_retriever_resource.append(retriever_resource)
 
                 if self.callbacks and original_retriever_resource:
                     for callback in self.callbacks:

api/core/workflow/nodes/question_classifier/template_prompts.py

@@ -6,7 +6,7 @@ QUESTION_CLASSIFIER_SYSTEM_PROMPT = """
     ### Task
     Your task is to assign one categories ONLY to the input text and only one category may be assigned returned in the output.Additionally, you need to extract the key words from the text that are related to the classification.
     ### Format
-    The input text is in the variable text_field.Categories are specified as a category list in the variable categories or left empty for automatic determination.Classification instructions may be included to improve the classification accuracy.
+    The input text is in the variable text_field.Categories are specified as a category list with two filed category_id and category_name in the variable categories .Classification instructions may be included to improve the classification accuracy.
     ### Constraint
     DO NOT include anything other than the JSON array in your response.
     ### Memory
@@ -24,7 +24,8 @@ QUESTION_CLASSIFIER_USER_PROMPT_1 = """
 
 QUESTION_CLASSIFIER_ASSISTANT_PROMPT_1 = """
 ```json
-    {"category_id": "f5660049-284f-41a7-b301-fd24176a711c",
+    {"keywords": ["recently", "great experience", "company", "service", "prompt", "staff", "friendly"],
+    "category_id": "f5660049-284f-41a7-b301-fd24176a711c",
     "category_name": "Customer Service"}
 ```
 """
@@ -37,7 +38,8 @@ QUESTION_CLASSIFIER_USER_PROMPT_2 = """
 
 QUESTION_CLASSIFIER_ASSISTANT_PROMPT_2 = """
 ```json
-    {"category_id": "f6ff5bc3-aca0-4e4a-8627-e760d0aca78f",
+    {"keywords": ["bad service", "slow", "food", "tip", "terrible", "waitresses"],
+    "category_id": "f6ff5bc3-aca0-4e4a-8627-e760d0aca78f",
     "category_name": "Experience"}
 ```
 """
@@ -61,9 +63,9 @@ DO NOT include anything other than the JSON array in your response.
 Here is the chat example between human and assistant, inside <example></example> XML tags.
 <example>
 User:{{"input_text": ["I recently had a great experience with your company. The service was prompt and the staff was very friendly."], "categories": [{{"category_id":"f5660049-284f-41a7-b301-fd24176a711c","category_name":"Customer Service"}},{{"category_id":"8d007d06-f2c9-4be5-8ff6-cd4381c13c60","category_name":"Satisfaction"}},{{"category_id":"5fbbbb18-9843-466d-9b8e-b9bfbb9482c8","category_name":"Sales"}},{{"category_id":"23623c75-7184-4a2e-8226-466c2e4631e4","category_name":"Product"}}], "classification_instructions": ["classify the text based on the feedback provided by customer"]}}
-Assistant:{{"category_id": "f5660049-284f-41a7-b301-fd24176a711c","category_name": "Customer Service"}}
+Assistant:{{"keywords": ["recently", "great experience", "company", "service", "prompt", "staff", "friendly"],"category_id": "f5660049-284f-41a7-b301-fd24176a711c","category_name": "Customer Service"}}
 User:{{"input_text": ["bad service, slow to bring the food"], "categories": [{{"category_id":"80fb86a0-4454-4bf5-924c-f253fdd83c02","category_name":"Food Quality"}},{{"category_id":"f6ff5bc3-aca0-4e4a-8627-e760d0aca78f","category_name":"Experience"}},{{"category_id":"cc771f63-74e7-4c61-882e-3eda9d8ba5d7","category_name":"Price"}}], "classification_instructions": []}}
-Assistant:{{"category_id": "f6ff5bc3-aca0-4e4a-8627-e760d0aca78f","category_name": "Customer Service"}}
+Assistant:{{"keywords": ["bad service", "slow", "food", "tip", "terrible", "waitresses"],"category_id": "f6ff5bc3-aca0-4e4a-8627-e760d0aca78f","category_name": "Customer Service"}}
 </example> 
 ### Memory
 Here is the chat histories between human and assistant, inside <histories></histories> XML tags.

api/core/workflow/nodes/tool/tool_node.py

@@ -200,7 +200,7 @@ class ToolNode(BaseNode):
                     chunk.metadata = {
                         '_source': 'tool'
                     }
-                    all_chunks.append(chunk)
+                    all_chunks.append(chunk.to_dict())
         return all_chunks