feat(ask): non-streaming now calls streaming

core/quivr_core/brain/brain.py

@@ -545,34 +545,28 @@ def ask(
         print(answer.answer)
         ```
         """
-        llm = self.llm
-
-        # If you passed a different llm model we'll override the brain  one
-        if retrieval_config:
-            if retrieval_config.llm_config != self.llm.get_config():
-                llm = LLMEndpoint.from_config(config=retrieval_config.llm_config)
-        else:
-            retrieval_config = RetrievalConfig(llm_config=self.llm.get_config())
-
-        if rag_pipeline is None:
-            rag_pipeline = QuivrQARAGLangGraph
-
-        rag_instance = rag_pipeline(
-            retrieval_config=retrieval_config, llm=llm, vector_store=self.vector_db
-        )
+        async def collect_streamed_response():
+            full_answer = ""
+            async for response in self.ask_streaming(
+                question=question,
+                retrieval_config=retrieval_config,
+                rag_pipeline=rag_pipeline,
+                list_files=list_files,
+                chat_history=chat_history
+            ):
+                full_answer += response.answer
+            return full_answer
+
+        # Run the async function in the event loop
+        loop = asyncio.get_event_loop()
+        full_answer = loop.run_until_complete(collect_streamed_response())
 
         chat_history = self.default_chat if chat_history is None else chat_history
-        list_files = [] if list_files is None else list_files
-
-        parsed_response = rag_instance.answer(
-            question=question, history=chat_history, list_files=list_files
-        )
-
         chat_history.append(HumanMessage(content=question))
-        chat_history.append(AIMessage(content=parsed_response.answer))
+        chat_history.append(AIMessage(content=full_answer))
 
-        # Save answer to the chat history
-        return parsed_response
+        # Return the final response
+        return ParsedRAGResponse(answer=full_answer)
 
     async def ask_streaming(
         self,
@@ -635,3 +629,4 @@ async def ask_streaming(
         chat_history.append(HumanMessage(content=question))
         chat_history.append(AIMessage(content=full_answer))
         yield response
+

core/quivr_core/config.py

@@ -233,7 +233,7 @@ class LLMEndpointConfig(QuivrBaseConfig):
 
     Attributes:
         supplier (DefaultModelSuppliers): The LLM provider (default: OPENAI).
-        model (str): The specific model to use (default: "gpt-3.5-turbo-0125").
+        model (str): The specific model to use (default: "gpt-4o").
         context_length (int | None): The maximum context length for the model.
         tokenizer_hub (str | None): The tokenizer to use for this model.
         llm_base_url (str | None): Base URL for the LLM API.
@@ -247,7 +247,7 @@ class LLMEndpointConfig(QuivrBaseConfig):
     """
 
     supplier: DefaultModelSuppliers = DefaultModelSuppliers.OPENAI
-    model: str = "gpt-3.5-turbo-0125"
+    model: str = "gpt-4o"
     context_length: int | None = None
     tokenizer_hub: str | None = None
     llm_base_url: str | None = None

core/quivr_core/utils.py

@@ -26,7 +26,7 @@ def model_supports_function_calling(model_name: str):
         "gpt-4",
         "gpt-4-1106-preview",
         "gpt-4-0613",
-        "gpt-3.5-turbo-0125",
+        "gpt-4o",
         "gpt-3.5-turbo-1106",
         "gpt-3.5-turbo-0613",
         "gpt-4-0125-preview",

core/tests/rag_config.yaml

@@ -27,7 +27,7 @@ retrieval_config:
     supplier: "openai"
 
     # The model to use for the LLM for the given supplier
-    model: "gpt-3.5-turbo-0125"
+    model: "gpt-4o"
 
     max_input_tokens: 2000
 

core/tests/rag_config_workflow.yaml

@@ -40,7 +40,7 @@ retrieval_config:
     supplier: "openai"
 
     # The model to use for the LLM for the given supplier
-    model: "gpt-3.5-turbo-0125"
+    model: "gpt-4o"
 
     max_input_tokens: 2000
 

core/tests/test_config.py

@@ -5,7 +5,7 @@ def test_default_llm_config():
     config = LLMEndpointConfig()
 
     assert config.model_dump(exclude={"llm_api_key"}) == LLMEndpointConfig(
-        model="gpt-3.5-turbo-0125",
+        model="gpt-4o",
         llm_base_url=None,
         llm_api_key=None,
         max_input_tokens=2000,