tmp

matrix_pygmalion_bot/bot/ai/langchain.py

@@ -1,22 +1,29 @@
 import asyncio
-import time
+import os, time
 from .prompts import *
-from .langchain_memory import BotConversationSummerBufferWindowMemory
+#from .langchain_memory import BotConversationSummaryBufferWindowMemory, TestMemory
+from ..utilities.messages import Message
 
 from langchain import PromptTemplate
 from langchain import LLMChain, ConversationChain
 from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory, CombinedMemory, ConversationSummaryMemory
 
 from langchain.chains.base import Chain
-from typing import Dict, List
+from typing import Dict, List, Union
 
 from langchain.document_loaders import TextLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import Chroma
 
+from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser, ZeroShotAgent
+from langchain.schema import AgentAction, AgentFinish
+from langchain.schema import AIMessage, HumanMessage, SystemMessage, ChatMessage
+from langchain.utilities import OpenWeatherMapAPIWrapper, SearxSearchWrapper, PythonREPL
+from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
+
 import humanize
-import datetime as dt
+from datetime import datetime, timedelta
 
 import logging
 
@@ -48,6 +55,30 @@ class RoleplayChain(Chain):
         return {self.output_key: result}
 
 
+class CustomOutputParser(AgentOutputParser):
+    
+    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
+        # Check if agent should finish
+        if "Final Answer:" in llm_output:
+            return AgentFinish(
+                # Return values is generally always a dictionary with a single `output` key
+                # It is not recommended to try anything else at the moment :)
+                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
+                log=llm_output,
+            )
+        # Parse out the action and action input
+        regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
+        match = re.search(regex, llm_output, re.DOTALL)
+        if not match:
+            regex = r"Action\s*\d*\s*:(.*?)[\s]*[\"\'](.*)[\"\']"
+            match = re.search(regex, llm_output, re.DOTALL)
+            if not match:
+                raise ValueError(f"Could not parse LLM output: `{llm_output}`")
+        action = match.group(1).strip()
+        action_input = match.group(2)
+        # Return the action and action input
+        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
+
 
 class AI(object):
 
@@ -59,63 +90,87 @@ 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|>'])
+        self.llm_summary = KoboldCpp(temperature=0.2, endpoint_url="http://172.16.85.10:5001/api/latest/generate", stop=['<|endoftext|>'], max_tokens=512)
         self.text_wrapper = text_wrapper
         self.image_wrapper = image_wrapper
+        self.embeddings = SentenceTransformerEmbeddings()
+        #embeddings = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
+        self.db = Chroma(persist_directory=os.path.join(self.memory_path, f'chroma-db'), embedding_function=self.embeddings)
 
         #self.memory = BotConversationSummerBufferWindowMemory(llm=self.llm_summary, max_token_limit=1200, min_token_limit=200)
 
-    def get_memory(self, message):
+    def get_memory(self, room_id, human_prefix="Human"):
-        if not message.room_id in self.rooms:
+        if not room_id in self.rooms:
-            self.rooms[message.room_id] = {}
+            self.rooms[room_id] = {}
-            memory = ConversationBufferMemory(memory_key="chat_history", input_key="input", human_prefix=message.user_name, ai_prefix=self.bot.name)
+            memory = ConversationBufferMemory(memory_key="chat_history", input_key="input", human_prefix=human_prefix, ai_prefix=self.bot.name)
-            self.rooms[message.room_id]["memory"] = memory
+            self.rooms[room_id]["memory"] = memory
-            self.rooms[message.room_id]["summary"] = "No previous events."
+            self.rooms[room_id]["summary"] = "No previous events."
             memory.chat_memory.add_ai_message(self.bot.greeting)
             #memory.save_context({"input": None, "output": self.bot.greeting})
             memory.load_memory_variables({})
         else:
-            memory = self.rooms[message.room_id]["memory"]
+            memory = self.rooms[room_id]["memory"]
         #print(f"memory: {memory.load_memory_variables({})}")
         #print(f"memory has an estimated {self.llm_chat.get_num_tokens(memory.buffer)} number of tokens")
         return memory
 
+    async def add_chat_message(self, message):
+        conversation_memory = self.get_memory(message.room_id)
+        langchain_message = message.to_langchain()
+        if message.user_id == self.bot.connection.user_id:
+            langchain_message.role = self.bot.name
+        conversation_memory.chat_memory.messages.append(langchain_message)
 
-    async def generate(self, message, reply_fn, typing_fn):
+    async def clear(self, room_id):
-
+        conversation_memory = self.get_memory(room_id)
-        embeddings = SentenceTransformerEmbeddings()
+        conversation_memory.clear()
-        #embeddings = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
 
-        loader = TextLoader('./germany.txt')
+    async def ingest_textfile(self, filename, category):
+        loader = TextLoader(filename)
         documents = loader.load()
+        documents[0].metadata['indexed'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+        documents[0].metadata['category'] = category
 
         text_splitter = RecursiveCharacterTextSplitter(
             # Set a really small chunk size, just to show.
-            chunk_size = 600,
+            chunk_size = 1024,
-            chunk_overlap  = 100,
+            chunk_overlap  = 80,
             length_function = len,
+            #length_function = self.llm_chat.get_num_tokens,  # The Embeddings are generated with SsentenceTransformers, not this model
         )
 
         docs = text_splitter.split_documents(documents)
 
-        db = Chroma(persist_directory=os.path.join(self.memory_path, f'chroma-db'), embedding_function=embeddings)
+        for i in range(len(docs)):
+            docs[i].metadata['part'] = f"{i}/{len(docs)}"
 
         print(f"Indexing {len(docs)} documents")
         texts = [doc.page_content for doc in docs]
         metadatas = [doc.metadata for doc in docs]
-        #db.add_texts(texts=texts, metadatas=metadatas, ids=None)
+        self.db.add_texts(texts=texts, metadatas=metadatas, ids=None)
-        #db.persist()
+        self.db.persist()
-
+
-        query = "How is climate in Germany?"
+    async def search_vectordb(self, query, category):
-        output_docs = db.similarity_search_with_score(query)
+        #query = "How is climate in Germany?"
+        #retreiver = db.as_retreiver()
+        #docs = retreiver.get_relevant_documents(query)
+        if category:
+            #https://github.com/chroma-core/chroma/blob/main/examples/where_filtering.ipynb
+            output_docs = self.db.similarity_search_with_score(query, filter={"category": category})
+        else:
+            output_docs = self.db.similarity_search_with_score(query)
         print(query)
         print('###')
         for doc, score in output_docs:
             print("-" * 80)
             print("Score: ", score)
-            print(doc.page_content)
+            #print(doc.page_content)
+            print(doc)
             print("-" * 80)
 
+
+    async def generate(self, message, reply_fn, typing_fn):
+
         prompt_template = "{input}"
         chain = LLMChain(
             llm=self.llm_chat,
@@ -126,6 +181,17 @@ 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
@@ -133,19 +199,20 @@ class AI(object):
             chat_ai_name = "### Assistant"
             chat_human_name = "### Human"
 
-        conversation_memory = self.get_memory(message)
+        conversation_memory = self.get_memory(message.room_id, message.user_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")
         #combined_memory = CombinedMemory(memories=[conversation_memory, summary_memory])
 
-        k = 5 #5
+        k = 1 # 5
-        max_k = 12 #10
+        max_k = 3 # 12
         if len(conversation_memory.chat_memory.messages) > max_k*2:
 
             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()
-                summary_chain = LLMChain(llm=self.llm_summary, prompt=prompt_progressive_summary)
+                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)
                 # 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']}\"")
@@ -155,12 +222,13 @@ class AI(object):
                 #print(summary)
                 #return summary
 
+
             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(
 
 
 
-        #t = dt.datetime.fromtimestamp(message.timestamp)
+        #t = datetime.fromtimestamp(message.timestamp)
         #when = humanize.naturaltime(t)
         #print(when)
 
@@ -193,26 +261,150 @@ class AI(object):
 
         stop = ['<|endoftext|>', f"\n{chat_human_name}"]
         #print(f"Message is: \"{message.message}\"")
+        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 = output.replace("### Assistant", self.bot.name)
         output = output.replace(f"\n{self.bot.name}: ", " ")
         output = output.strip()
 
-        if "*activates the neural uplink*" in output:
+        langchain_ai_message = AIMessage(
+            content=output,
+            additional_kwargs={
+                "timestamp": datetime.now().timestamp(),
+                "user_name": self.bot.name,
+                "event_id": None,
+                "user_id": None,
+                "room_name": message.room_name,
+                "room_id": message.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.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()
 
 
     async def summarize(self, text):
-            summary_chain = LLMChain(llm=llm_summary, prompt=prompt_summary, verbose=True)
+        await asyncio.sleep(0) # yield for matrix-nio
+        summary_chain = LLMChain(llm=self.llm_summary, prompt=prompt_summary, verbose=True)
         return await summary_chain.arun(text=text)
         #ToDo: We can summarize the whole dialogue here, let half of it in the buffer but skip doing a summary until this is flushed, too?
+        #ToDo: max_tokens and stop
+
+    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)
+        #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}")
+        return await diary_chain.apredict(text="\n".join(string_messages))
+
+
+    async def agent(self):
+
+        os.environ["OPENWEATHERMAP_API_KEY"] = "82452fdb0d1e0e805ac096db87914342"
+        # Tools
+        search = DuckDuckGoSearchAPIWrapper()
+        weather = OpenWeatherMapAPIWrapper()
+        search2 = SearxSearchWrapper(searx_host="https://search.mdosch.de")
+        python_repl = PythonREPL()
+
+        tools = [
+            Tool(
+                name = "Search",
+                func=search.run,
+                description="useful for when you need to answer questions about current events"
+            ),
+            Tool(
+                name = "Searx Search",
+                func=search.run,
+                description="useful for when you need to answer questions about current events"
+            ),
+            Tool(
+                name = "Weather",
+                func=weather.run,
+                description="Useful for fetching current weather information for a specified location. Input should be a location string (e.g. 'London,GB')."
+            ),
+            Tool(
+                name = "Summary",
+                func=summry_chain.run,
+                description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary."
+            )
+        ]
+
+        prompt = ZeroShotAgent.create_prompt(
+            tools=tools, 
+            prefix=prefix, 
+            suffix=suffix, 
+            input_variables=["input", "chat_history", "agent_scratchpad"]
+        )
+
+        output_parser = CustomOutputParser()
+
+        # LLM chain consisting of the LLM and a prompt
+        llm_chain = LLMChain(llm=llm, prompt=prompt_agent)
+
+        agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+        #agent = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True, memory=memory)
+
+        #tool_names = [tool.name for tool in tools]
+        #agent = LLMSingleActionAgent(
+        #    llm_chain=llm_chain, 
+        #    output_parser=output_parser,
+        #    stop=["\nObservation:"], 
+        #    allowed_tools=tool_names,
+        #    verbose=True,
+        #)
+
+        agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
+
+
+        await agent_executor.arun(input="How many people live in canada as of 2023?")
+
+
+    async def sleep(self):
+        # Write Date into chat history
+        for room_id in self.rooms.keys():
+            #fake_message = Message(datetime.now().timestamp(), self.bot.name, "", event_id=None, user_id=None, room_name=None, room_id=room_id)
+            conversation_memory = self.get_memory(room_id)
+            message = SystemMessage(
+                content=f"~~~~ {datetime.now().strftime('%A, %B %d, %Y')} ~~~~",
+                additional_kwargs={
+                    "timestamp": datetime.now().timestamp(),
+                    "user_name": self.bot.name,
+                    "event_id": None,
+                    "user_id": None,
+                    "room_name": None,
+                    "room_id": room_id,
+                }
+            )
+            conversation_memory.chat_memory.messages.append(message)
+            #conversation_memory.chat_memory.add_system_message(message)
+
+        # 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]:
+                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"] = []
+        await self.bot.write_conf2(self.bot.rooms)
+
 
     async def prime_llm(self, text):
         self.llm_chat(text, max_tokens=1)

matrix_pygmalion_bot/bot/ai/langchain_memory.py

@@ -6,8 +6,65 @@ from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts.base import BasePromptTemplate
 from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
 
+from ..utilities.messages import Message
 
-class BotConversationSummerBufferWindowMemory(BaseChatMemory):
+class ChatMessageHistory(BaseModel):
+    messages: List[Message] = []
+
+    def add_user_message(self, message: Message) -> None:
+        self.messages.append(message)
+
+    def add_ai_message(self, message: Message) -> None:
+        self.messages.append(message)
+
+    def add_system_message(self, message: Message) -> None:
+        self.messages.append(message)
+
+    def add_chat_message(self, message: Message) -> None:
+        self.messages.append(message)
+
+    def clear(self) -> None:
+        self.messages = []
+
+
+class TestMemory(BaseMemory):
+    """Buffer for storing conversation memory."""
+
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    chat_memory: ChatMessageHistory = Field(default_factory=ChatMessageHistory)
+#    buffer: str = ""
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    memory_key: str = "history"  #: :meta private:
+
+
+    @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."""
+        string_messages = []
+        for m in chat_memory.messages:
+            string_messages.append(f"{message.user_name}: {message.message}")
+        return {self.memory_key: "\n".join(string_messages)}
+
+    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)
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.chat_memory.clear()
+
+
+class BotConversationSummaryBufferWindowMemory(BaseChatMemory):
     """Buffer for storing conversation memory."""
 
     human_prefix: str = "Human"

matrix_pygmalion_bot/bot/ai/prompts.py

@@ -129,6 +129,71 @@ New summary:
 )
 #Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary.
 
+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.
+
+### Instruction:
+Provide an outline of the character's day in keywords.
+
+### Input:
+{text}
+
+### Response:
+"""
+)
+# briefly, as a list, use bullet points, outline the main points what character needs to remember about the day, in note form, review ... point by point
+
+
+prompt_agent  = 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 and explain what actions were used.
+
+### Instruction:
+Answer the following questions as best you can. Speak like a priate when you give the Final answer. You have access to the following tools:
+
+{tools}
+
+Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
+
+### Input:
+{input}
+
+### Response:
+{agent_scratchpad}
+"""
+)
+
+prompt_agent2  = 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 and explain what actions were used.
+
+### Instruction:
+Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:
+
+{tools}
+
+### Input:
+Previous conversation:
+{chat_history}
+
+Question: {input}
+
+Begin!
+
+### Response:
+{agent_scratchpad}
+"""
+)
+
 
 # Roleplay the character that is described in the following lines. You always stay in character.
 
@@ -140,6 +205,27 @@ New summary:
 # \n <EXAMPLE CHAT> YOU / MIKU:
 
 
+#        "instruction": "Using the given facts, create a detailed profile of the character.",
+#        "input": "Name: Sarah Johnson\nAge: 18\nOccupation: Waitress\nLocation: Los Angeles",
+
+# "instruction": "Please summarize the main events in the story and explain how the characters evolve throughout the narrative."
+
+# "instruction": "Describe the given incident in a crisp and vivid way",
+
+# "instruction": "Describe a movie scene using vivid and descriptive language.",
+
+# "instruction": "Imagine that you are the protagonist of the following story and generate an appropriate dialogue",
+
+# "instruction": "Generate some ideas on what the protagonist in this story could do next.",
+
+# "instruction": "Classify the dialogue into one of the following categories: 1) making or cancelling orders; 2) shipping & delivery; 3) change and return; 4) technical issue with website or app.",
+
+# "instruction": "Generate a dialogue between two characters, Jack and Susan, in a restaurant.",
+
+# You are the narrator. Add some detail to the dialogue below. Write what the character Julia thinks and does. Write a vivid and graphic description of her and her surroundings for the reader.
+
+
+
     # https://github.com/ggerganov/llama.cpp/tree/master/examples
 ##    prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n"
 #    prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n"

matrix_pygmalion_bot/bot/core.py

@@ -1,10 +1,13 @@
 import asyncio
+import concurrent.futures
 import os, sys
 import time
 import importlib
 import re
 import json
 import logging
+from datetime import datetime, timedelta
+import psutil
 from functools import partial
 from .memory.chatlog import ChatLog
 from .utilities.messages import Message
@@ -26,6 +29,13 @@ class ChatBot(object):
         task = asyncio.create_task(self.worker(f'worker-{self.name}', self.queue))
         self.background_tasks.add(task)
         task.add_done_callback(self.background_tasks.discard)
+        #loop = asyncio.get_running_loop()
+        #with concurrent.futures.ThreadPoolExecutor() as pool:
+        #    task = loop.run_in_executor(pool, self.worker, f'worker-{self.name}', self.queue)
+
+        event_loop_task = asyncio.create_task(self.event_loop())
+        self.background_tasks.add(event_loop_task)
+        event_loop_task.add_done_callback(self.background_tasks.discard)
         #print(f"Hello, I'm {name}")
 
     def init_character(self, persona, scenario, greeting, example_dialogue=[], nsfw=False, temperature=0.72):
@@ -59,8 +69,8 @@ class ChatBot(object):
             json.dump(data, f)
 
     async def connect(self):
-        await self.connection.login()
         self.connection.callbacks.add_message_callback(self.message_cb, self.redaction_cb)
+        await self.connection.login()
         await self.schedule(self.queue, print, f"Hello, I'm {self.name}")
 
     async def disconnect(self):
@@ -123,18 +133,29 @@ class ChatBot(object):
         reply_fn = partial(self.connection.send_message, room.room_id)
         typing_fn = lambda : self.connection.room_typing(room.room_id, True, 15000)
 
+        message.user_name = message.user_name.title()
+        if self.name.casefold() == message.user_name.casefold():
+            """Bot and user have the same name"""
+            message.user_name += " 2" # or simply "You"
+
         if not room.room_id in self.rooms:
             self.rooms[room.room_id] = {}
-            self.write_conf2(self.rooms)
+            self.rooms[room.room_id]['tick'] = 0
+            self.rooms[room.room_id]['num_messages'] = 0
+            self.rooms[room.room_id]['diary'] = {}
+            await self.write_conf2(self.rooms)
             # ToDo: set ticks 0 / start
 
         if not self.connection.synced:
+            if not message.is_command() and not message.is_error():
+                await self.ai.add_chat_message(message)
             self.chatlog.save(message, False)
             return
 
         if message.is_from(self.connection.user_id):
             """Skip messages from ouselves"""
             self.chatlog.save(message)
+            await self.connection.room_read_markers(room.room_id, event.event_id, event.event_id)
             return
 
 #        if event.decrypted:
@@ -154,11 +175,7 @@ class ChatBot(object):
 #        print(f"room.users: {room.users}")
 #        print(f"room.room_id: {room.room_id}")
 
-        if self.name.casefold() == message.user_name.casefold():
-            """Bot and user have the same name"""
-            message.user_name += " 2" # or simply "You"
 
-        message.user_name = message.user_name.title()
 
         if hasattr(self, "owner"):
             if not message.is_from(self.owner):
@@ -176,6 +193,8 @@ class ChatBot(object):
 #            # send, mail, drop, snap picture, photo, image, portrait
         else:
             await self.schedule(self.queue, self.process_message, message, reply_fn, typing_fn)
+        self.rooms[room.room_id]['num_messages'] += 1
+        self.last_conversation = datetime.now()
         self.chatlog.save(message)
         print("done")
 
@@ -216,16 +235,19 @@ class ChatBot(object):
             self.temperature = float( message.message.removeprefix('!temperature').strip() )
         elif message.message.startswith('!begin'):
             self.rooms[message.room_id]["disabled"] = False
-            self.write_conf2(self.rooms)
+            await self.write_conf2(self.rooms)
             self.chatlog.clear(message.room_id)
+            await self.ai.clear(message.room_id)
             # ToDo reset time / ticks
             await reply_fn(self.greeting)
         elif message.message.startswith('!start'):
             self.rooms[message.room_id]["disabled"] = False
-            self.write_conf2(self.rooms)
+            await self.write_conf2(self.rooms)
         elif message.message.startswith('!stop'):
             self.rooms[message.room_id]["disabled"] = True
-            self.write_conf2(self.rooms)
+            await self.write_conf2(self.rooms)
+        elif message.message.startswith('!sleep'):
+            await self.schedule(self.queue, self.ai.sleep)
         elif message.message.startswith('!!'):
             if self.chatlog.chat_history_len(message.room_id) > 2:
                 for _ in range(2):
@@ -241,6 +263,24 @@ class ChatBot(object):
         await reply_fn(output)
 
 
+    async def event_loop(self):
+        try:
+            while True:
+                await asyncio.sleep(60)
+                for room_id in self.rooms.keys():
+                    self.rooms[room_id]["tick"] += 1
+                if datetime.now().hour >= 1 and datetime.now().hour < 5:
+                    load1, load5, load15 = [x / psutil.cpu_count() * 100 for x in psutil.getloadavg()]
+                    if load5 < 25 and load1 < 25:
+                        if not hasattr(self, "last_sleep") or self.last_sleep + timedelta(hours=6) < datetime.now():
+                            await self.ai.sleep()
+                            self.last_sleep = datetime.now()
+
+
+        finally:
+            pass
+#            await self.write_conf2(self.name)
+
     async def worker(self, name: str, q: asyncio.Queue) -> None:
         while True:
             cb, args, kwargs = await q.get()

matrix_pygmalion_bot/bot/utilities/messages.py

@@ -1,3 +1,4 @@
+from langchain.schema import AIMessage, HumanMessage, SystemMessage, ChatMessage
 
 
 class Message(object):
@@ -14,11 +15,28 @@ class Message(object):
     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):
+        return ChatMessage(
+            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,
+            }
+        )
+
     def is_from(self, user_id):
         return self.user_id == user_id
 
     def is_command(self):
         return self.message.startswith('!')
 
+    def is_error(self):
+        return self.message.startswith('<ERROR>')
+
     def __str__(self):
         return str("{}: {}".format(self.user_name, self.message))

matrix_pygmalion_bot/bot/wrappers/langchain_koboldcpp.py

@@ -6,6 +6,7 @@ from typing import Any, List, Mapping, Optional
 
 import json
 import requests
+import functools
 
 from langchain.llms.base import LLM
 
@@ -122,7 +123,9 @@ class KoboldCpp(LLM):
         TRIES = 30
         for i in range(TRIES):
             try:
-                r = requests.post(self.endpoint_url, json=input_data, headers=headers, timeout=600)
+                loop = asyncio.get_running_loop()
+                #r = requests.post(self.endpoint_url, json=input_data, headers=headers, timeout=600)
+                r = await loop.run_in_executor(None, functools.partial(requests.post, self.endpoint_url, json=input_data, headers=headers, timeout=600))
                 r_json = r.json()
             except requests.exceptions.RequestException as e:
                 raise ValueError(f"http connection error.")

matrix_pygmalion_bot/connections/matrix.py

@@ -15,13 +15,15 @@ logger = logging.getLogger(__name__)
 class Callbacks(object):
     """Class to pass client to callback methods."""
 
-    def __init__(self, client: AsyncClient):
+    def __init__(self):
+        self.message_callbacks = []
+        self.message_redaction_callbacks = []
+
+    def setup_callbacks(self, client: AsyncClient):
         self.client = client
         self.client.add_event_callback(self.message_cb, RoomMessageText)
         self.client.add_event_callback(self.invite_cb, InviteEvent)
         self.client.add_event_callback(self.redaction_cb, RedactionEvent)
-        self.message_callbacks = []
-        self.message_redaction_callbacks = []
 
     def add_message_callback(self, callback, redaction_callback=None):
         self.message_callbacks.append(callback)
@@ -153,6 +155,7 @@ class ChatClient(object):
         self.password = password
         self.device_name = device_name
         self.synced = False
+        self.callbacks = Callbacks()
 
     async def persist(self, data_dir):
         #self.data_dir = data_dir
@@ -186,7 +189,7 @@ class ChatClient(object):
                 config=client_config,
             )
 
-            self.callbacks = Callbacks(self.client)
+            self.callbacks.setup_callbacks(self.client)
 
             resp = await self.client.login(self.password, device_name=self.device_name)
             # check that we logged in succesfully
@@ -209,7 +212,7 @@ class ChatClient(object):
                     config=client_config,
                 )
 
-                self.callbacks = Callbacks(self.client)
+                self.callbacks.setup_callbacks(self.client)
 
 #                self.client.user_id=config["user_id"],
 #                self.client.device_id=config["device_id"],
@@ -328,7 +331,7 @@ class ChatClient(object):
         await self.client.synced.wait()
         logger.info("Client is synced")
         self.synced = True
-        logger.info(f"{self.client.user_id}, {self.client.rooms}, {self.client.invited_rooms}, {self.client.encrypted_rooms}")
+        #logger.info(f"{self.client.user_id}, {self.client.rooms}, {self.client.invited_rooms}, {self.client.encrypted_rooms}")
 #        if os.path.exists(self.store_path + "megolm_keys"):
 #            os.remove(self.store_path + "megolm_keys", "pass")
 #        await self.client.export_keys(self.store_path + "megolm_keys", "pass")

requirements.txt

@@ -1,4 +1,5 @@
 asyncio
+requests
 matrix-nio[e2e]
 transformers
 huggingface_hub
@@ -10,3 +11,7 @@ langchain
 chromadb
 sentence-transformers
 humanize
+psutil
+#git+https://github.com/suno-ai/bark.git
+#SpeechRecognition
+#TTS #(Coqui-TTS or Uberduck ??)