Agentic MVS Implementation, DataLoader rewrite and more

.gitignore

@@ -166,6 +166,7 @@ cython_debug/
 */__pycache__/*
 .chainlit/translations/
 code/.chainlit/translations/
+**/.chainlit/translations/
 storage/logs/*
 vectorstores/*
 

apps/ai_tutor/app.py

@@ -26,7 +26,7 @@
 import hashlib
 
 # set config
-config = config_manager.get_config().dict()
+config = config_manager.get_config()
 
 # set constants
 GITHUB_REPO = config["misc"]["github_repo"]

apps/ai_tutor/chainlit_app.py

@@ -31,6 +31,9 @@
 from langchain_community.callbacks import get_openai_callback
 from datetime import datetime, timezone
 from config.config_manager import config_manager
+from edubotics_core.chat.agentic.agent import Agent
+
+from config.prompts import prompts
 
 USER_TIMEOUT = 60_000
 SYSTEM = "System"
@@ -40,7 +43,8 @@
 ERROR = "Error"
 
 # set config
-config = config_manager.get_config().dict()
+config = config_manager.get_config()
+print(config)
 
 
 async def setup_data_layer():
@@ -81,6 +85,8 @@ def __init__(self, config):
         Initialize the Chatbot class.
         """
         self.config = config
+        # Initialize Agent instance
+        self.agent = Agent(config=config, prompts=prompts)
 
     @no_type_check
     async def setup_llm(self):
@@ -172,10 +178,14 @@ async def make_llm_settings_widgets(self, config=None):
                 cl.input_widget.Select(
                     id="retriever_method",
                     label="Retriever (Default FAISS)",
-                    values=["FAISS", "Chroma", "RAGatouille", "RAPTOR"],
-                    initial_index=["FAISS", "Chroma", "RAGatouille", "RAPTOR"].index(
-                        config["vectorstore"]["db_option"]
-                    ),
+                    values=["FAISS", "Chroma", "RAGatouille", "RAPTOR", "MVS"],
+                    initial_index=[
+                        "FAISS",
+                        "Chroma",
+                        "RAGatouille",
+                        "RAPTOR",
+                        "MVS",
+                    ].index(config["vectorstore"]["db_option"]),
                 ),
                 cl.input_widget.Slider(
                     id="memory_window",
@@ -297,14 +307,15 @@ async def start(self):
             }
 
         memory = cl.user_session.get("memory", [])
-        self.llm_tutor = LLMTutor(self.config, user=self.user)
+        # Replace LLMTutor usage with Agent if needed
+        # self.llm_tutor = LLMTutor(self.config, user=self.user)
+        # self.chain = self.llm_tutor.qa_bot(memory=memory)
 
-        self.chain = self.llm_tutor.qa_bot(
-            memory=memory,
-        )
-        self.question_generator = self.llm_tutor.question_generator
-        cl.user_session.set("llm_tutor", self.llm_tutor)
-        cl.user_session.set("chain", self.chain)
+        # cl.user_session.set("llm_tutor", self.llm_tutor)
+        # cl.user_session.set("chain", self.chain)
+
+        self.agent.set_thread_id(cl.context.session.thread_id)
+        cl.user_session.set("agent", self.agent)
 
     async def stream_response(self, response):
         """
@@ -344,6 +355,7 @@ async def main(self, message):
 
         # update user info with last message time
         user = cl.user_session.get("user")
+
         await reset_tokens_for_user(
             user,
             self.config["token_config"]["tokens_left"],
@@ -355,7 +367,11 @@ async def main(self, message):
 
         # see if user has token credits left
         # if not, return message saying they have run out of tokens
-        if user.metadata["tokens_left"] <= 0 and "admin" not in user.metadata["role"]:
+        if (
+            user.metadata["tokens_left"] <= 0
+            and "admin" not in user.metadata["role"]
+            and config["chat_logging"]["log_chat"]
+        ):
             current_datetime = get_time()
             cooldown, cooldown_end_time = await check_user_cooldown(
                 user,
@@ -425,84 +441,83 @@ async def main(self, message):
             ),
         }
 
+        response = cl.Message(content="")
         with get_openai_callback() as token_count_cb:
-            if stream:
-                res = chain.stream(user_query=user_query_dict, config=chain_config)
-                res = await self.stream_response(res)
-            else:
-                res = await chain.invoke(
-                    user_query=user_query_dict,
-                    config=chain_config,
-                )
-        token_count += token_count_cb.total_tokens
-
-        answer = res.get("answer", res.get("result"))
-
-        answer_with_sources, source_elements, sources_dict = get_sources(
-            res, answer, stream=stream, view_sources=view_sources
-        )
-        answer_with_sources = answer_with_sources.replace("$$", "$")
+            async for chunk in self.agent.stream(message.content):
+                content = chunk["content"]
+                tokens = chunk["total_tokens"]
+                await response.stream_token(content)
 
-        actions = []
+        token_count = tokens
 
-        if self.config["llm_params"]["generate_follow_up"]:
-            cb_follow_up = cl.AsyncLangchainCallbackHandler()
-            config = {
-                "callbacks": (
-                    [cb_follow_up]
-                    if cl_data._data_layer and self.config["chat_logging"]["callbacks"]
-                    else None
-                )
-            }
-            with get_openai_callback() as token_count_cb:
-                list_of_questions = await self.question_generator.generate_questions(
-                    query=user_query_dict["input"],
-                    response=answer,
-                    chat_history=res.get("chat_history"),
-                    context=res.get("context"),
-                    config=config,
-                )
-
-            token_count += token_count_cb.total_tokens
-
-            for question in list_of_questions:
-                actions.append(
-                    cl.Action(
-                        name="follow up question",
-                        value="example_value",
-                        description=question,
-                        label=question,
-                    )
-                )
+        answer = response.content
+        sources = self.agent.get_sources()
 
-        # # update user info with token count
-        tokens_left = await update_user_from_chainlit(user, token_count)
-
-        answer_with_sources += (
-            '\n\n<footer><span style="font-size: 0.8em; text-align: right; display: block;">Tokens Left: '
-            + str(tokens_left)
-            + "</span></footer>\n"
-        )
-
-        await cl.Message(
-            content=answer_with_sources,
-            elements=source_elements,
-            author=LLM,
-            actions=actions,
-        ).send()
+        if len(sources) > 0:
+            sources_text = "\n\nSources: \n" + "\n".join(
+                [f"- {source}" for source in sources]
+            )
+            response.content += sources_text
+
+        await response.send()
+
+        # answer_with_sources, source_elements, sources_dict = get_sources(
+        #     res, answer, stream=stream, view_sources=view_sources
+        # )
+        # answer_with_sources = answer_with_sources.replace("$$", "$")
+
+        # actions = []
+
+        # if self.config["llm_params"]["generate_follow_up"]:
+        #     cb_follow_up = cl.AsyncLangchainCallbackHandler()
+        #     config = {
+        #         "callbacks": (
+        #             [cb_follow_up]
+        #             if cl_data._data_layer and self.config["chat_logging"]["callbacks"]
+        #             else None
+        #         )
+        #     }
+        #     with get_openai_callback() as token_count_cb:
+        #         list_of_questions = await self.question_generator.generate_questions(
+        #             query=user_query_dict["input"],
+        #             response=answer,
+        #             chat_history=res.get("chat_history"),
+        #             context=res.get("context"),
+        #             config=config,
+        #         )
+
+        #     token_count += token_count_cb.total_tokens
+
+        #     for question in list_of_questions:
+        #         actions.append(
+        #             cl.Action(
+        #                 name="follow up question",
+        #                 value="example_value",
+        #                 description=question,
+        #                 label=question,
+        #             )
+        #         )
+
+        # # # update user info with token count
+        # tokens_left = await update_user_from_chainlit(user, token_count)
+
+        # answer_with_sources += (
+        #     '\n\n<footer><span style="font-size: 0.8em; text-align: right; display: block;">Tokens Left: '
+        #     + str(tokens_left)
+        #     + "</span></footer>\n"
+        # )
+
+        # await cl.Message(
+        #     content=answer_with_sources,
+        #     elements=source_elements,
+        #     author=LLM,
+        #     actions=actions,
+        # ).send()
 
     async def on_chat_resume(self, thread: ThreadDict):
-        # thread_config = None
-        steps = thread["steps"]
-        k = self.config["llm_params"][
-            "memory_window"
-        ]  # on resume, alwyas use the default memory window
-        conversation_list = get_history_chat_resume(steps, k, SYSTEM, LLM)
-        # thread_config = get_last_config(
-        #     steps
-        # )  # TODO: Returns None for now - which causes config to be reloaded with default values
-        cl.user_session.set("memory", conversation_list)
-        await self.start()
+        thread_id = thread["id"]
+        self.agent.set_thread_id(thread_id)
+        self.agent.populate_conversation_history(thread)
 
     @cl.header_auth_callback
     def header_auth_callback(headers: dict) -> Optional[cl.User]:

apps/ai_tutor/config/constants.py

@@ -7,9 +7,14 @@
 
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY")
-HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+COHERE_API_KEY = os.getenv("COHERE_API_KEY")
+
 LITERAL_API_KEY_LOGGING = os.getenv("LITERAL_API_KEY_LOGGING")
 LITERAL_API_URL = os.getenv("LITERAL_API_URL")
+
+HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+
 CHAINLIT_URL = os.getenv("CHAINLIT_URL")
 EMAIL_ENCRYPTION_KEY = os.getenv("EMAIL_ENCRYPTION_KEY")
 

apps/ai_tutor/config/prompts.py

@@ -13,15 +13,16 @@
         ),
         "prompt_with_history": {
             "normal": (
-                "You are an AI Tutor for the course DS598, taught by Prof. Thomas Gardos. Answer the user's question using the provided context. Only use the context if it is relevant. The context is ordered by relevance. "
-                "If you don't know the answer, do your best without making things up. Keep the conversation flowing naturally. "
+                "You are an AI Tutor for the course DS542, taught by Prof. Thomas Gardos. Answer the user's question using the provided context. Only use the context if it is relevant. The context is ordered by relevance. "
+                "If you don't know the answer, do not make things up, just say you don't know and ask the user to rephrase. Keep the conversation flowing naturally. "
                 "Use chat history and context as guides but avoid repeating past responses. Provide links from the source_file metadata. Use the source context that is most relevant. "
                 "Render math equations in LaTeX format between $ or $$ signs, stick to the parameter and variable icons found in your context. Be sure to explain the parameters and variables in the equations."
                 "Speak in a friendly and engaging manner, like talking to a friend. Avoid sounding repetitive or robotic.\n\n"
                 "Do not get influenced by the style of conversation in the chat history. Follow the instructions given here."
                 "Chat History:\n{chat_history}\n\n"
                 "Context:\n{context}\n\n"
                 "Answer the student's question below in a friendly, concise, and engaging manner. Use the context and history only if relevant, otherwise, engage in a free-flowing conversation.\n"
+                "If the provided context is not relevant, just say you don't know and ask the user to attach the relevant documents. Do not make things up."
                 "Student: {input}\n"
                 "AI Tutor:"
             ),