0.5.1

CHANGELOG.md

@@ -4,6 +4,27 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](http://semver.org/).
 
+## [0.5.1] - 2023-12-12
+
+### Added
+
+- Dark mode
+- Web Caching
+- LateX Support
+- Multiline support for chat textbox
+
+### Changed
+
+- Required Sign In
+- No A/B for Honcho
+- Error handling for content filter
+
+### Security
+
+- Update Langchain version to ^0.0.348
+- Update OpenAI Package to ^1.3.8
+
+
 ## [0.4.1] – 2023-09-14
 
 ### Added

README.md

@@ -1,5 +1,5 @@
 # tutor-gpt
-![Static Badge](https://img.shields.io/badge/Version-0.4.0-blue)
+![Static Badge](https://img.shields.io/badge/Version-0.5.1-blue)
 [![Discord](https://img.shields.io/discord/1076192451997474938?logo=discord&logoColor=%23ffffff&label=Bloom&labelColor=%235865F2)](https://discord.gg/bloombotai)
 ![GitHub License](https://img.shields.io/github/license/plastic-labs/tutor-gpt)
 ![GitHub Repo stars](https://img.shields.io/github/stars/plastic-labs/tutor-gpt)

agent/chain.py

@@ -5,6 +5,9 @@
 )
 from langchain.prompts import load_prompt, ChatPromptTemplate
 from langchain.schema import AIMessage, HumanMessage, BaseMessage
+
+from openai import BadRequestError
+
 from dotenv import load_dotenv
 
 from collections.abc import AsyncIterator
@@ -54,12 +57,11 @@ def think(cls, cache: Conversation, input: str):
         ])
         chain = thought_prompt | cls.llm 
 
-        cache.add_message("thought", HumanMessage(content=input))
+        def save_new_messages(ai_response):
+            cache.add_message("response", HumanMessage(content=input))
+            cache.add_message("response", AIMessage(content=ai_response))
 
-        return Streamable(
-                chain.astream({}, {"tags": ["thought"], "metadata": {"conversation_id": cache.conversation_id, "user_id": cache.user_id}}),
-            lambda thought: cache.add_message("thought", AIMessage(content=thought))
-        )
+        return Streamable(chain.astream({}, {"tags": ["thought"], "metadata": {"conversation_id": cache.conversation_id, "user_id": cache.user_id}}), save_new_messages)
 
     @classmethod
     @sentry_sdk.trace
@@ -72,13 +74,12 @@ def respond(cls, cache: Conversation, thought: str, input: str):
         ])
         chain = response_prompt | cls.llm
 
-        cache.add_message("response", HumanMessage(content=input))
+        def save_new_messages(ai_response):
+            cache.add_message("response", HumanMessage(content=input))
+            cache.add_message("response", AIMessage(content=ai_response))
+
+        return Streamable(chain.astream({ "thought": thought }, {"tags": ["response"], "metadata": {"conversation_id": cache.conversation_id, "user_id": cache.user_id}}), save_new_messages)
 
-        return Streamable(
-            chain.astream({ "thought": thought }, {"tags": ["response"], "metadata": {"conversation_id": cache.conversation_id, "user_id": cache.user_id}}),
-            lambda response: cache.add_message("response", AIMessage(content=response))
-        )
-
     @classmethod
     @sentry_sdk.trace
     async def think_user_prediction(cls, cache: Conversation):
@@ -114,27 +115,45 @@ async def chat(cls, cache: Conversation, inp: str ) -> tuple[str, str]:
         return thought, response
 
 
+
 class Streamable:
     "A async iterator wrapper for langchain streams that saves on completion via callback"
 
     def __init__(self, iterator: AsyncIterator[BaseMessage], callback):
         self.iterator = iterator
         self.callback = callback
         self.content = ""
+        self.stream_error = False
 
     def __aiter__(self):
         return self
 
     async def __anext__(self):
         try:
+            if self.stream_error:
+                raise StopAsyncIteration
+
             data = await self.iterator.__anext__()
             self.content += data.content
             return data.content
         except StopAsyncIteration as e:
             self.callback(self.content)
             raise StopAsyncIteration
+        except BadRequestError as e:
+            if e.code == "content_filter":
+                self.stream_error = True
+                self.message = "Sorry, your message was flagged as inappropriate. Please try again."
+
+                return self.message
+            else: 
+                raise Exception(e)
         except Exception as e:
-            raise e
+            sentry_sdk.capture_exception(e)
+
+            self.stream_error = True
+            self.message = "Sorry, an error occurred while streaming the response. Please try again."
+
+            return self.message
 
     async def __call__(self):
         async for _ in self:

agent/mediator.py

@@ -5,13 +5,17 @@
 from dotenv import load_dotenv
 # Supabase for Postgres Management
 from supabase.client import create_client, Client
+from supabase.lib.client_options import ClientOptions
 from typing import List, Tuple, Dict
 load_dotenv()
 
 class SupabaseMediator:
     @sentry_sdk.trace
     def __init__(self):
-        self.supabase: Client = create_client(os.environ['SUPABASE_URL'], os.environ['SUPABASE_KEY'])
+        # Change the network db timeout to 60 seconds since the default is only 5 seconds
+        timeout_client_options = ClientOptions(postgrest_client_timeout=60)
+        self.supabase: Client = create_client(os.environ['SUPABASE_URL'], os.environ['SUPABASE_KEY'], timeout_client_options)
+
         self.memory_table = os.environ["MEMORY_TABLE"]
         self.conversation_table = os.environ["CONVERSATION_TABLE"]