refactor(chat_manager.py, utils.py): remove unused imports and functions, move pil_to_base64 and try_setting_streaming_options to utils module

src/backend/langflow/api/chat_manager.py

@@ -1,24 +1,17 @@
 import asyncio
-import base64
-from io import BytesIO
 from typing import Dict, List
 from collections import defaultdict
 from fastapi import WebSocket
 import json
-from langchain.llms import OpenAI, AzureOpenAI
-from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
 from langflow.api.schemas import ChatMessage, ChatResponse, FileResponse
-from langflow.cache.manager import AsyncSubject, Subject
-from langchain.callbacks.base import AsyncCallbackManager
-from langflow.api.callback import StreamingLLMCallbackHandler
+from langflow.cache.manager import Subject
 from langflow.interface.run import (
-    async_get_result_and_steps,
     get_result_and_steps,
     load_or_build_langchain_object,
 )
+from langflow.interface.utils import pil_to_base64, try_setting_streaming_options
 from langflow.utils.logger import logger
 from langflow.cache import cache_manager
-from PIL.Image import Image
 
 
 class ChatHistory(Subject):
@@ -149,6 +142,9 @@ class ChatManager:
                     payload = json.loads(json_payload)
                 except TypeError:
                     payload = json_payload
+                if "clear_history" in payload:
+                    self.chat_history.history[client_id] = []
+
                 with self.cache_manager.set_client_id(client_id):
                     await self.process_message(client_id, payload)
         except Exception as e:
@@ -187,30 +183,3 @@ async def process_graph(
         # Log stack trace
         logger.exception(e)
         raise e
-
-
-def try_setting_streaming_options(langchain_object, websocket):
-    # If the LLM type is OpenAI or ChatOpenAI,
-    # set streaming to True
-    # First we need to find the LLM
-    llm = None
-    if hasattr(langchain_object, "llm"):
-        llm = langchain_object.llm
-    elif hasattr(langchain_object, "llm_chain") and hasattr(
-        langchain_object.llm_chain, "llm"
-    ):
-        llm = langchain_object.llm_chain.llm
-    if isinstance(llm, (OpenAI, ChatOpenAI, AzureOpenAI, AzureChatOpenAI)):
-        llm.streaming = bool(hasattr(llm, "streaming"))
-        stream_handler = StreamingLLMCallbackHandler(websocket)
-        stream_manager = AsyncCallbackManager([stream_handler])
-        llm.callback_manager = stream_manager
-
-    return langchain_object
-
-
-def pil_to_base64(image: Image) -> str:
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue())
-    return img_str.decode("utf-8")

src/backend/langflow/interface/utils.py

@@ -1,5 +1,12 @@
+import base64
+from io import BytesIO
 import json
 import os
+from PIL.Image import Image
+from langchain.callbacks.base import AsyncCallbackManager
+from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
+from langchain.llms import AzureOpenAI, OpenAI
+from langflow.api.callback import StreamingLLMCallbackHandler
 
 import yaml
 
@@ -20,3 +27,30 @@ def load_file_into_dict(file_path: str) -> dict:
         raise ValueError("Unsupported file type. Please provide a JSON or YAML file.")
 
     return data
+
+
+def pil_to_base64(image: Image) -> str:
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue())
+    return img_str.decode("utf-8")
+
+
+def try_setting_streaming_options(langchain_object, websocket):
+    # If the LLM type is OpenAI or ChatOpenAI,
+    # set streaming to True
+    # First we need to find the LLM
+    llm = None
+    if hasattr(langchain_object, "llm"):
+        llm = langchain_object.llm
+    elif hasattr(langchain_object, "llm_chain") and hasattr(
+        langchain_object.llm_chain, "llm"
+    ):
+        llm = langchain_object.llm_chain.llm
+    if isinstance(llm, (OpenAI, ChatOpenAI, AzureOpenAI, AzureChatOpenAI)):
+        llm.streaming = bool(hasattr(llm, "streaming"))
+        stream_handler = StreamingLLMCallbackHandler(websocket)
+        stream_manager = AsyncCallbackManager([stream_handler])
+        llm.callback_manager = stream_manager
+
+    return langchain_object