feat: ChatKnowledge add prompt token count

dbgpt/app/knowledge/service.py

@@ -120,7 +120,11 @@ def create_knowledge_document(self, space, request: KnowledgeDocumentRequest):
             content=request.content,
             result="",
         )
-        return knowledge_document_dao.create_knowledge_document(document)
+        doc_id = knowledge_document_dao.create_knowledge_document(document)
+        if doc_id is None:
+            raise Exception(f"create document failed, {request.doc_name}")
+        return doc_id
+
 
     def get_knowledge_space(self, request: KnowledgeSpaceRequest):
         """get knowledge space
@@ -229,10 +233,20 @@ def batch_document_sync(
                 raise Exception(
                     f" doc:{doc.doc_name} status is {doc.status}, can not sync"
                 )
-            # space_context = self.get_space_context(space_name)
-            self._sync_knowledge_document(
-                space_name, doc, sync_request.chunk_parameters
-            )
+            chunk_parameters = sync_request.chunk_parameters
+            if "Automatic" == chunk_parameters.chunk_strategy:
+                space_context = self.get_space_context(space_name)
+                chunk_parameters.chunk_size = (
+                    CFG.KNOWLEDGE_CHUNK_SIZE
+                    if space_context is None
+                    else int(space_context["embedding"]["chunk_size"])
+                )
+                chunk_parameters.chunk_overlap = (
+                    CFG.KNOWLEDGE_CHUNK_OVERLAP
+                    if space_context is None
+                    else int(space_context["embedding"]["chunk_overlap"])
+                )
+            self._sync_knowledge_document(space_name, doc, chunk_parameters)
             doc_ids.append(doc.id)
         return doc_ids
 

dbgpt/app/scene/chat_knowledge/v1/chat.py

@@ -20,6 +20,7 @@
 from dbgpt.model import DefaultLLMClient
 from dbgpt.model.cluster import WorkerManagerFactory
 from dbgpt.rag.retriever.rewrite import QueryRewrite
+from dbgpt.util.prompt_util import PromptHelper
 from dbgpt.util.tracer import trace
 
 CFG = Config()
@@ -78,13 +79,15 @@ def __init__(self, chat_param: Dict):
             vector_store_config=config,
         )
         query_rewrite = None
+        self.worker_manager = CFG.SYSTEM_APP.get_component(
+            ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
+        ).create()
+        self.llm_client = DefaultLLMClient(worker_manager=self.worker_manager)
         if CFG.KNOWLEDGE_SEARCH_REWRITE:
-            worker_manager = CFG.SYSTEM_APP.get_component(
-                ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
-            ).create()
-            llm_client = DefaultLLMClient(worker_manager=worker_manager)
             query_rewrite = QueryRewrite(
-                llm_client=llm_client, model_name=self.llm_model, language=CFG.LANGUAGE
+                llm_client=self.llm_client,
+                model_name=self.llm_model,
+                language=CFG.LANGUAGE,
             )
         self.embedding_retriever = EmbeddingRetriever(
             top_k=self.top_k,
@@ -149,9 +152,7 @@ async def generate_input_values(self) -> Dict:
                 if len(chucks) > 0:
                     self.chunks_with_score.append((chucks[0], chunk.score))
 
-            context = [doc.content for doc in candidates_with_scores]
-
-        context = context[: self.max_token]
+            context = "\n".join([doc.content for doc in candidates_with_scores])
         self.relations = list(
             set(
                 [
@@ -165,6 +166,19 @@ async def generate_input_values(self) -> Dict:
             "question": self.current_user_input,
             "relations": self.relations,
         }
+        prompt = self.prompt_template.format(**input_values)
+        model_metadata = await self.worker_manager.get_model_metadata(
+            {"model": self.llm_model}
+        )
+        try:
+            current_token_count = self.llm_client.count_token(prompt)
+        except Exception:
+            prompt_util = PromptHelper(context_window=model_metadata.context_length)
+            current_token_count = prompt_util.token_count(prompt)
+        if current_token_count > model_metadata.context_length:
+            input_values["context"] = input_values["context"][
+                : current_token_count - model_metadata.context_length
+            ]
         return input_values
 
     def parse_source_view(self, chunks_with_score: List):

dbgpt/util/prompt_util.py

@@ -93,6 +93,11 @@ def __init__(
             separator=separator,
         )
 
+    def token_count(self, prompt_template: str) -> int:
+        """Get token count of prompt template."""
+        empty_prompt_txt = get_empty_prompt_txt(prompt_template)
+        return len(self._tokenizer(empty_prompt_txt))
+
     @classmethod
     def from_llm_metadata(
         cls,