make callback manager local

backend/app/rag/chat.py

@@ -6,7 +6,7 @@
 
 import jinja2
 from sqlmodel import Session, select
-from llama_index.core import VectorStoreIndex, Settings
+from llama_index.core import VectorStoreIndex, ServiceContext
 from llama_index.core.base.llms.base import ChatMessage
 from llama_index.core.prompts.base import PromptTemplate
 from llama_index.core.base.response.schema import StreamingResponse
@@ -56,10 +56,10 @@ def __init__(
 
         self.chat_engine_config = ChatEngineConfig.load_from_db(db_session, engine_name)
         self.db_chat_engine = self.chat_engine_config.get_db_chat_engine()
-        self._llm = self.chat_engine_config.get_llama_llm()
+        # self._llm = self.chat_engine_config.get_llama_llm()
         self._dspy_lm = self.chat_engine_config.get_dspy_lm()
         self._fast_dspy_lm = self.chat_engine_config.get_fast_dspy_lm()
-        self._embed_model = self.chat_engine_config.get_embedding_model()
+        # self._embed_model = self.chat_engine_config.get_embedding_model()
         self._reranker = self.chat_engine_config.get_reranker()
 
     def chat(
@@ -153,7 +153,10 @@ def _chat(
             ),
         )
 
-        def _set_langfuse_callback_manager():
+        _llm = self.chat_engine_config.get_llama_llm()
+        _embed_model = self.chat_engine_config.get_embedding_model()
+
+        def _get_langfuse_callback_manager():
             # Why we don't use high-level decorator `observe()` as \
             #   `https://langfuse.com/docs/integrations/llama-index/get-started` suggested?
             # track:
@@ -162,8 +165,10 @@ def _set_langfuse_callback_manager():
             observation = langfuse.trace(id=trace_id)
             langfuse_handler = LlamaIndexCallbackHandler()
             langfuse_handler.set_root(observation)
-            Settings.callback_manager = CallbackManager([langfuse_handler])
-            self._llm.callback_manager = Settings.callback_manager
+            callback_manager = CallbackManager([langfuse_handler])
+            _llm.callback_manager = callback_manager
+            _embed_model.callback_manager = callback_manager
+            return callback_manager
 
         # Frontend requires the empty event to start the chat
         yield ChatEvent(
@@ -188,23 +193,23 @@ def _set_langfuse_callback_manager():
         )
 
         # 1. Retrieve entities, relations, and chunks from the knowledge graph
-        _set_langfuse_callback_manager()
+        callback_manager = _get_langfuse_callback_manager()
         kg_config = self.chat_engine_config.knowledge_graph
         if kg_config.enabled:
             graph_store = TiDBGraphStore(
                 dspy_lm=self._fast_dspy_lm,
                 session=self.db_session,
-                embed_model=self._embed_model,
+                embed_model=_embed_model,
             )
             graph_index: KnowledgeGraphIndex = KnowledgeGraphIndex.from_existing(
                 dspy_lm=self._fast_dspy_lm,
                 kg_store=graph_store,
-                callback_manager=Settings.callback_manager,
+                callback_manager=callback_manager,
             )
 
             if kg_config.using_intent_search:
-                with Settings.callback_manager.as_trace("retrieve_with_weight"):
-                    with Settings.callback_manager.event(
+                with callback_manager.as_trace("retrieve_with_weight"):
+                    with callback_manager.event(
                         MyCBEventType.RETRIEVE_FROM_GRAPH,
                         payload={
                             EventPayload.QUERY_STR: {
@@ -253,13 +258,13 @@ def _set_langfuse_callback_manager():
                 display="Refine the user question ...",
             ),
         )
-        _set_langfuse_callback_manager()
-        with Settings.callback_manager.as_trace("condense_question"):
-            with Settings.callback_manager.event(
+        callback_manager = _get_langfuse_callback_manager()
+        with callback_manager.as_trace("condense_question"):
+            with callback_manager.event(
                 MyCBEventType.CONDENSE_QUESTION,
                 payload={EventPayload.QUERY_STR: user_question},
             ) as event:
-                refined_question = self._llm.predict(
+                refined_question = _llm.predict(
                     get_prompt_by_jinja2_template(
                         self.chat_engine_config.llm.condense_question_prompt,
                         graph_knowledges=graph_knowledges_context,
@@ -279,7 +284,7 @@ def _set_langfuse_callback_manager():
                 display="Search related documents ...",
             ),
         )
-        _set_langfuse_callback_manager()
+        callback_manager = _get_langfuse_callback_manager()
         text_qa_template = get_prompt_by_jinja2_template(
             self.chat_engine_config.llm.text_qa_prompt,
             graph_knowledges=graph_knowledges_context,
@@ -288,18 +293,24 @@ def _set_langfuse_callback_manager():
             self.chat_engine_config.llm.refine_prompt,
             graph_knowledges=graph_knowledges_context,
         )
+        service_context = ServiceContext.from_defaults(
+            llm=_llm,
+            embed_model=_embed_model,
+            callback_manager=callback_manager,
+        )
         vector_store = TiDBVectorStore(session=self.db_session)
         vector_index = VectorStoreIndex.from_vector_store(
             vector_store,
-            embed_model=self._embed_model,
+            service_context=service_context,
         )
         query_engine = vector_index.as_query_engine(
-            llm=self._llm,
+            llm=_llm,
             node_postprocessors=[self._reranker],
             streaming=True,
             text_qa_template=text_qa_template,
             refine_template=refine_template,
             similarity_top_k=100,
+            service_context=service_context,
         )
         response: StreamingResponse = query_engine.query(refined_question)
         source_documents = self._get_source_documents(response)