cat.llm can optionally stream tokens

core/cat/looking_glass/cheshire_cat.py

@@ -2,24 +2,24 @@
 from copy import deepcopy
 import traceback
 from typing import Literal, get_args
-import langchain
 import os
 import asyncio
+import langchain
+from langchain.llms import Cohere, OpenAI, OpenAIChat, AzureOpenAI, HuggingFaceTextGenInference, HuggingFaceHub
+from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
+from langchain.base_language import BaseLanguageModel
+
 from cat.log import log
+from cat.db import crud
 from cat.db.database import Database
 from cat.rabbit_hole import RabbitHole
 from cat.mad_hatter.mad_hatter import MadHatter
 from cat.memory.working_memory import WorkingMemoryList
 from cat.memory.long_term_memory import LongTermMemory
 from cat.looking_glass.agent_manager import AgentManager
-
-# TODO: natural language dependencies; move to another file
+from cat.looking_glass.callbacks import NewTokenHandler
 import cat.factory.llm as llms
 import cat.factory.embedder as embedders
-from cat.db import crud
-from langchain.llms import Cohere, OpenAI, OpenAIChat, AzureOpenAI, HuggingFaceTextGenInference, HuggingFaceHub
-from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
-from langchain.base_language import BaseLanguageModel
 from cat.factory.custom_llm import CustomOpenAI
 
 
@@ -315,7 +315,7 @@ def recall_relevant_memories_to_working_memory(self):
         # hook to modify/enrich retrieved memories
         self.mad_hatter.execute_hook("after_cat_recalls_memories")
 
-    def llm(self, prompt: str) -> str:
+    def llm(self, prompt: str, chat: bool = False, stream: bool = False) -> str:
         """Generate a response using the LLM model.
 
         This method is useful for generating a response with both a chat and a completion model using the same syntax
@@ -331,13 +331,19 @@ def llm(self, prompt: str) -> str:
             The generated response.
 
         """
+
+        # should we stream the tokens?
+        callbacks = []
+        if stream:
+            callbacks.append( NewTokenHandler(self) )
+
         # Check if self._llm is a completion model and generate a response
         if isinstance(self._llm, langchain.llms.base.BaseLLM):
-            return self._llm(prompt)
+            return self._llm(prompt, callbacks=callbacks)
 
         # Check if self._llm is a chat model and call it as a completion model
         if isinstance(self._llm, langchain.chat_models.base.BaseChatModel):
-            return self._llm.call_as_llm(prompt)
+            return self._llm.call_as_llm(prompt, callbacks=callbacks)
 
     def send_ws_message(self, content: str, msg_type: MSG_TYPES = "notification"):
         """Send a message via websocket.