more work on langchain memory

matrix_pygmalion_bot/bot/ai/langchain.py

@@ -1,7 +1,7 @@
 import asyncio
 import os, time
 from .prompts import *
-#from .langchain_memory import BotConversationSummaryBufferWindowMemory, TestMemory
+from .langchain_memory import CustomMemory # BotConversationSummaryBufferWindowMemory, TestMemory
 from ..utilities.messages import Message
 
 from langchain import PromptTemplate
@@ -90,7 +90,7 @@ class AI(object):
 
         from ..wrappers.langchain_koboldcpp import KoboldCpp
         self.llm_chat = KoboldCpp(temperature=self.bot.temperature, endpoint_url="http://172.16.85.10:5001/api/latest/generate", stop=['<|endoftext|>'])
-        self.llm_summary = KoboldCpp(temperature=0.2, endpoint_url="http://172.16.85.10:5001/api/latest/generate", stop=['<|endoftext|>'], max_tokens=512)
+        self.llm_summary = KoboldCpp(temperature=0.2, endpoint_url="http://172.16.85.10:5002/api/latest/generate", stop=['<|endoftext|>'], max_tokens=512)
         self.text_wrapper = text_wrapper
         self.image_wrapper = image_wrapper
         self.embeddings = SentenceTransformerEmbeddings()
@@ -102,7 +102,7 @@ class AI(object):
     def get_memory(self, room_id, human_prefix="Human"):
         if not room_id in self.rooms:
             self.rooms[room_id] = {}
-            memory = ConversationBufferMemory(memory_key="chat_history", input_key="input", human_prefix=human_prefix, ai_prefix=self.bot.name)
+            memory = CustomMemory(memory_key="chat_history", input_key="input", human_prefix=human_prefix, ai_prefix=self.bot.name, llm=self.llm_summary, summary_prompt=prompt_progressive_summary, max_len=1200, min_len=200)
             self.rooms[room_id]["memory"] = memory
             self.rooms[room_id]["summary"] = "No previous events."
             memory.chat_memory.add_ai_message(self.bot.greeting)
@@ -181,25 +181,15 @@ class AI(object):
 
 
     async def generate_roleplay(self, message, reply_fn, typing_fn):
-        langchain_human_message = HumanMessage(
-            content=message.message,
-            additional_kwargs={
-                "timestamp": message.timestamp,
-                "user_name": message.user_name,
-                "event_id": message.event_id,
-                "user_id": message.user_id,
-                "room_name": message.room_name,
-                "room_id": message.room_id,
-            }
-        )
 
         chat_ai_name = self.bot.name
-        chat_human_name = message.user_name
+        chat_human_name = message.additional_kwargs['user_name']
+        room_id = message.additional_kwargs['room_id']
         if False: # model is vicuna
             chat_ai_name = "### Assistant"
             chat_human_name = "### Human"
 
-        conversation_memory = self.get_memory(message.room_id, message.user_name)
+        conversation_memory = self.get_memory(room_id, chat_human_name)
         conversation_memory.human_prefix = chat_human_name
         readonlymemory = ReadOnlySharedMemory(memory=conversation_memory)
         summary_memory = ConversationSummaryMemory(llm=self.llm_summary, memory_key="summary", input_key="input")
@@ -211,11 +201,11 @@ class AI(object):
 
             async def make_progressive_summary(previous_summary, chat_history_text_string):
                 await asyncio.sleep(0) # yield for matrix-nio
-                #self.rooms[message.room_id]["summary"] = summary_memory.predict_new_summary(conversation_memory.chat_memory.messages, previous_summary).strip()
+                #self.rooms[room_id]["summary"] = summary_memory.predict_new_summary(conversation_memory.chat_memory.messages, previous_summary).strip()
                 summary_chain = LLMChain(llm=self.llm_summary, prompt=prompt_progressive_summary, verbose=True)
-                self.rooms[message.room_id]["summary"] = await summary_chain.apredict(summary=previous_summary, chat_history=chat_history_text_string)
+                self.rooms[room_id]["summary"] = await summary_chain.apredict(summary=previous_summary, chat_history=chat_history_text_string)
                 # ToDo: maybe add an add_task_done callback and don't access the variable directly from here?
-                logger.info(f"New summary is: \"{self.rooms[message.room_id]['summary']}\"")
+                logger.info(f"New summary is: \"{self.rooms[room_id]['summary']}\"")
                 conversation_memory.chat_memory.messages = conversation_memory.chat_memory.messages[-k * 2 :]
                 conversation_memory.load_memory_variables({})
                 #summary = summarize(conversation_memory.buffer)
@@ -224,11 +214,11 @@ class AI(object):
 
 
             logger.info("memory progressive summary scheduled...")
-            await self.bot.schedule(self.bot.queue, make_progressive_summary, self.rooms[message.room_id]["summary"], conversation_memory.buffer) #.add_done_callback(
+            await self.bot.schedule(self.bot.queue, make_progressive_summary, self.rooms[room_id]["summary"], conversation_memory.buffer) #.add_done_callback(
 
 
 
-        #t = datetime.fromtimestamp(message.timestamp)
+        #t = datetime.fromtimestamp(message.additional_kwargs['timestamp'])
         #when = humanize.naturaltime(t)
         #print(when)
 
@@ -241,8 +231,8 @@ class AI(object):
             ai_name=self.bot.name,
             persona=self.bot.persona,
             scenario=self.bot.scenario,
-            summary=self.rooms[message.room_id]["summary"],
-            human_name=message.user_name,
+            summary=self.rooms[room_id]["summary"],
+            human_name=chat_human_name,
             #example_dialogue=replace_all(self.bot.example_dialogue, {"{{user}}": chat_human_name, "{{char}}": chat_ai_name})
             ai_name_chat=chat_ai_name,
         )
@@ -252,48 +242,44 @@ class AI(object):
             prompt=prompt,
             verbose=True,
             memory=readonlymemory,
-            #stop=['<|endoftext|>', '\nYou:', f"\n{message.user_name}:"],
+            #stop=['<|endoftext|>', '\nYou:', f"\n{chat_human_name}:"],
         )
         
-#        output = llm_chain(inputs={"ai_name": self.bot.name, "persona": self.bot.persona, "scenario": self.bot.scenario, "human_name": message.user_name, "ai_name_chat": self.bot.name, "chat_history": "", "input": message.message})['results'][0]['text']
+#        output = llm_chain(inputs={"ai_name": self.bot.name, "persona": self.bot.persona, "scenario": self.bot.scenario, "human_name": chat_human_name, "ai_name_chat": self.bot.name, "chat_history": "", "input": message.content})['results'][0]['text']
 
         #roleplay_chain = RoleplayChain(llm_chain=chain, character_name=self.bot.name, persona=self.bot.persona, scenario=self.bot.scenario, ai_name_chat=chat_ai_name, human_name_chat=chat_human_name)
 
         stop = ['<|endoftext|>', f"\n{chat_human_name}"]
-        #print(f"Message is: \"{message.message}\"")
+        #print(f"Message is: \"{message.content}\"")
         await asyncio.sleep(0)
-        output = await chain.arun({"input":message.message, "stop": stop})
-        output = output.replace("<BOT>", self.bot.name).replace("<USER>", message.user_name)
+        output = await chain.arun({"input":message.content, "stop": stop})
+        output = output.replace("<BOT>", self.bot.name).replace("<USER>", chat_human_name)
         output = output.replace("### Assistant", self.bot.name)
         output = output.replace(f"\n{self.bot.name}: ", " ")
         output = output.strip()
 
+
+        if "*activates the neural uplink*" in output.casefold():
+            pass # call agent
+
+        own_message_resp = await reply_fn(output)
+
         langchain_ai_message = AIMessage(
             content=output,
             additional_kwargs={
                 "timestamp": datetime.now().timestamp(),
                 "user_name": self.bot.name,
-                "event_id": None,
+                "event_id": own_message_resp.event_id,
                 "user_id": None,
-                "room_name": message.room_name,
-                "room_id": message.room_id,
+                "room_name": message.additional_kwargs['room_name'],
+                "room_id": own_message_resp.room_id,
             }
         )
 
-        if "*activates the neural uplink*" in output.casefold():
-            pass # call agent
-
-        #conversation_memory.chat_memory.messages.append(ChatMessage(content=message, role=message.user_name))
-        conversation_memory.chat_memory.add_user_message(message.message)
-        conversation_memory.chat_memory.add_ai_message(output)
+        conversation_memory.save_context({"input": message.content}, {"ouput": output})
         conversation_memory.load_memory_variables({})
 
-        if not "messages_today" in self.rooms[message.room_id]:
-            self.rooms[message.room_id]["messages_today"] = []
-        self.rooms[message.room_id]["messages_today"].append(langchain_human_message)
-        self.rooms[message.room_id]["messages_today"].append(langchain_ai_message)
-
-        return output.strip()
+        return output
 
 
     async def summarize(self, text):
@@ -306,10 +292,11 @@ class AI(object):
     async def diary(self, room_id):
         await asyncio.sleep(0) # yield for matrix-nio
         diary_chain = LLMChain(llm=self.llm_summary, prompt=prompt_outline, verbose=True)
+        conversation_memory = self.get_memory(room_id)
         #self.rooms[message.room_id]["summary"]
         string_messages = []
-        for m in self.rooms[room_id]["messages_today"]:
-            string_messages.append(f"{message.user_name}: {message.message}")
+        for m in conversation_memory.chat_memory_day.messages:
+            string_messages.append(f"{message.role}: {message.content}")
         return await diary_chain.apredict(text="\n".join(string_messages))
 
 
@@ -397,12 +384,12 @@ class AI(object):
         # Summarize the last day and save a diary entry
         yesterday = ( datetime.now() - timedelta(days=1) ).strftime('%Y-%m-%d')
         for room_id in self.rooms.keys():
-            if "messages_today" in self.rooms[room_id]:
+            if len(conversation_memory.chat_memory_day.messages) > 0:
                 self.bot.rooms[room_id]["diary"][yesterday] = await self.diary(room_id)
         # Calculate new goals for the character
         # Update stats
         # Let background tasks run
-        self.rooms[room_id]["messages_today"] = []
+        conversation_memory.chat_memory_day.clear()
         await self.bot.write_conf2(self.bot.rooms)
 
 

matrix_pygmalion_bot/bot/ai/langchain_memory.py

@@ -1,15 +1,170 @@
-from typing import Any, Dict, List
+import asyncio
+from typing import Any, Dict, List, Tuple, Optional
+from pydantic import BaseModel, Extra, Field, root_validator
 
 from langchain.chains.llm import LLMChain
-from langchain.memory.chat_memory import BaseChatMemory
+from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
+from langchain.schema import BaseLanguageModel, BaseMessage, BaseChatMessageHistory, BaseMemory, get_buffer_string
+from langchain.schema import AIMessage, HumanMessage, SystemMessage, ChatMessage
+from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
 
 from ..utilities.messages import Message
 
-class ChatMessageHistory(BaseModel):
-    messages: List[Message] = []
+
+class ChatMessageHistoryCustom(BaseChatMessageHistory, BaseModel):
+    messages: List[BaseMessage] = []
+
+    def add_user_message(self, message: str) -> None:
+        self.messages.append(HumanMessage(content=message))
+
+    def add_ai_message(self, message: str) -> None:
+        self.messages.append(AIMessage(content=message))
+
+    def add_system_message(self, message: str) -> None:
+        self.messages.append(SystemMessage(content=message))
+
+    def add_chat_message(self, message: str) -> None:
+        self.messages.append(ChatMessage(content=message))
+
+    def clear(self) -> None:
+        self.messages = []
+
+
+class CustomMemory(BaseMemory):
+    """Buffer for storing conversation memory."""
+
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    memory_key: str = "history"  #: :meta private:
+
+    chat_memory: BaseChatMessageHistory = Field(default_factory=ChatMessageHistoryCustom)
+    chat_memory_day: BaseChatMessageHistory = Field(default_factory=ChatMessageHistoryCustom)
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    return_messages: bool = False
+
+    max_len: int = 1200
+    min_len: int = 200
+    #length_function: Callable[[str], int] = len,
+    #length_function: Callable[[str], int] = self.llm.get_num_tokens_from_messages,
+
+    moving_summary_buffer: str = ""
+    
+    llm: BaseLanguageModel
+    summary_prompt: BasePromptTemplate = SUMMARY_PROMPT
+    #summary_message_cls: Type[BaseMessage] = SystemMessage
+
+    def _get_input_output(
+        self, inputs: Dict[str, Any], outputs: Dict[str, str]
+    ) -> Tuple[str, str]:
+        if self.input_key is None:
+            prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
+        else:
+            prompt_input_key = self.input_key
+        if self.output_key is None:
+            if len(outputs) != 1:
+                raise ValueError(f"One output key expected, got {outputs.keys()}")
+            output_key = list(outputs.keys())[0]
+        else:
+            output_key = self.output_key
+        return inputs[prompt_input_key], outputs[output_key]
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Save context from this conversation to buffer."""
+        input_str, output_str = self._get_input_output(inputs, outputs)
+        self.chat_memory.add_user_message(input_str)
+        self.chat_memory.add_ai_message(output_str)
+        self.chat_memory_day.add_user_message(input_str)
+        self.chat_memory_day.add_ai_message(output_str)
+        # Prune buffer if it exceeds max token limit
+        buffer = self.chat_memory.messages
+        curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
+        if curr_buffer_length > self.max_len:
+            pruned_memory = []
+            while curr_buffer_length > self.min_len:
+                pruned_memory.append(buffer.pop(0))
+                curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
+            loop = asyncio.get_event_loop()
+            self.moving_summary_buffer = loop.run_until_complete(
+                self.apredict_new_summary(pruned_memory, self.moving_summary_buffer)
+            )
+
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.chat_memory.clear()
+        self.chat_memory_day.clear()
+        self.moving_summary_buffer = ""
+
+    def get_buffer_string(self, messages: List[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI") -> str:
+        """Get buffer string of messages."""
+        string_messages = []
+        for m in messages:
+            if isinstance(m, HumanMessage):
+                role = human_prefix
+            elif isinstance(m, AIMessage):
+                role = ai_prefix
+            elif isinstance(m, SystemMessage):
+                role = "System"
+            elif isinstance(m, ChatMessage):
+                role = m.role
+            else:
+                raise ValueError(f"Got unsupported message type: {m}")
+            string_messages.append(f"{role}: {m.content}")
+        return "\n".join(string_messages)
+
+    @property
+    def buffer(self) -> Any:
+        """String buffer of memory."""
+        if self.return_messages:
+            return self.chat_memory.messages
+        else:
+            return self.get_buffer_string(
+                self.chat_memory.messages,
+                human_prefix=self.human_prefix,
+                ai_prefix=self.ai_prefix,
+            )
+
+    @property
+    def buffer_day(self) -> Any:
+        """String buffer of memory."""
+        if self.return_messages:
+            return self.chat_memory_day.messages
+        else:
+            return self.get_buffer_string(
+                self.chat_memory_day.messages,
+                human_prefix=self.human_prefix,
+                ai_prefix=self.ai_prefix,
+            )
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        """Return history buffer."""
+        return {self.memory_key: self.buffer}
+
+    async def apredict_new_summary(self, messages: List[BaseMessage], existing_summary: str) -> str:
+        new_lines = self.get_buffer_string(
+            messages,
+            human_prefix=self.human_prefix,
+            ai_prefix=self.ai_prefix,
+        )
+
+        chain = LLMChain(llm=self.llm, prompt=self.summary_prompt)
+        await asyncio.sleep(0)
+        return await chain.apredict(summary=existing_summary, new_lines=new_lines)
+
+
+class ChatMessageHistoryMessage(BaseModel):
+    #messages: List[Message] = []
+    messages = []
 
     def add_user_message(self, message: Message) -> None:
         self.messages.append(message)
@@ -32,7 +187,7 @@ class TestMemory(BaseMemory):
 
     human_prefix: str = "Human"
     ai_prefix: str = "AI"
-    chat_memory: ChatMessageHistory = Field(default_factory=ChatMessageHistory)
+    chat_memory: ChatMessageHistory = Field(default_factory=ChatMessageHistoryMessage)
 #    buffer: str = ""
     output_key: Optional[str] = None
     input_key: Optional[str] = None

matrix_pygmalion_bot/bot/ai/prompts.py

@@ -128,6 +128,7 @@ New summary:
 """
 )
 #Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.
+#only include relevant facts for {{char}}'s long-term memory / future
 
 prompt_outline = PromptTemplate.from_template(
 """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

matrix_pygmalion_bot/bot/core.py

@@ -154,9 +154,12 @@ class ChatBot(object):
 
         if message.is_from(self.connection.user_id):
             """Skip messages from ouselves"""
+            message.role = "ai"
             self.chatlog.save(message)
             await self.connection.room_read_markers(room.room_id, event.event_id, event.event_id)
             return
+        else:
+            message.role = "human"
 
 #        if event.decrypted:
 #            encrypted_symbol = "🛡 "
@@ -191,6 +194,8 @@ class ChatBot(object):
             await self.schedule(self.queue, self.process_command, message, reply_fn, typing_fn)
 #        elif re.search("^(?=.*\bsend\b)(?=.*\bpicture\b).*$", event.body, flags=re.IGNORECASE):
 #            # send, mail, drop, snap picture, photo, image, portrait
+        elif message.is_error():
+            return
         else:
             await self.schedule(self.queue, self.process_message, message, reply_fn, typing_fn)
         self.rooms[room.room_id]['num_messages'] += 1
@@ -257,10 +262,10 @@ class ChatBot(object):
 
 
     async def process_message(self, message, reply_fn, typing_fn):
-        output = await self.ai.generate_roleplay(message, reply_fn, typing_fn)
+        output = await self.ai.generate_roleplay(message.to_langchain(), reply_fn, typing_fn)
         #output = await self.ai.generate(message, reply_fn, typing_fn)
         # typing false
-        await reply_fn(output)
+        #await reply_fn(output)
 
 
     async def event_loop(self):

matrix_pygmalion_bot/bot/utilities/messages.py

@@ -2,7 +2,7 @@ from langchain.schema import AIMessage, HumanMessage, SystemMessage, ChatMessage
 
 
 class Message(object):
-    def __init__(self, timestamp, user_name, message, event_id=None, user_id=None, room_name=None, room_id=None):
+    def __init__(self, timestamp, user_name, message, event_id=None, user_id=None, room_name=None, room_id=None, role=None):
         self.timestamp = timestamp
         self.user_name = user_name
         self.message = message
@@ -10,12 +10,53 @@ class Message(object):
         self.user_id = user_id
         self.room_name = room_name
         self.room_id = room_id
+        self.role = role
 
     @classmethod
     def from_matrix(cls, room, event):
         return cls(event.server_timestamp/1000, room.user_name(event.sender), event.body, event.event_id, event.sender, room.display_name, room.room_id)
 
     def to_langchain(self):
+        if self.role == "human":
+            return HumanMessage(
+                content=self.message,
+                role=self.user_name, # "chat"
+                additional_kwargs={
+                    "timestamp": self.timestamp,
+                    "user_name": self.user_name,
+                    "event_id": self.event_id,
+                    "user_id": self.user_id,
+                    "room_name": self.room_name,
+                    "room_id": self.room_id,
+                }
+            )
+        elif self.role == "ai":
+            return AIMessage(
+                content=self.message,
+                role=self.user_name, # "chat"
+                additional_kwargs={
+                    "timestamp": self.timestamp,
+                    "user_name": self.user_name,
+                    "event_id": self.event_id,
+                    "user_id": self.user_id,
+                    "room_name": self.room_name,
+                    "room_id": self.room_id,
+                }
+            )
+        elif self.role == "system":
+            return SystemMessage(
+                content=self.message,
+                role=self.user_name, # "chat"
+                additional_kwargs={
+                    "timestamp": self.timestamp,
+                    "user_name": self.user_name,
+                    "event_id": self.event_id,
+                    "user_id": self.user_id,
+                    "room_name": self.room_name,
+                    "room_id": self.room_id,
+                }
+            )
+        else:
             return ChatMessage(
                 content=self.message,
                 role=self.user_name, # "chat"

matrix_pygmalion_bot/bot/wrappers/langchain_koboldcpp.py

@@ -10,7 +10,7 @@ import functools
 
 from langchain.llms.base import LLM
 
-from langchain.schema import BaseMessage
+from langchain.schema import BaseMessage, AIMessage, HumanMessage, SystemMessage, ChatMessage
 
 logger = logging.getLogger(__name__)
 
@@ -164,8 +164,9 @@ class KoboldCpp(LLM):
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
-                num_tokens += len(self.get_num_tokens(value))
-                if key == "name":
+                if key == "content":
+                    num_tokens += self.get_num_tokens(value)
+                elif key == "name":
                     num_tokens += tokens_per_name
         num_tokens += 3
         return num_tokens