commit 08270fc7f9561a706de94fe08c2de412438f50a6 Author: projectmoon Date: Mon Jul 15 16:39:54 2024 +0200 Basic super hacky memory filter. diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9a6bdb7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +chroma/ +chromatest.py +env/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..4d63245 --- /dev/null +++ b/LICENSE @@ -0,0 +1,699 @@ +This software is governed by the terms of the Affero GNU General +Public License. Portions of the code come from the original +MIT-licensed project, and the terms of the MIT license also apply to +those portions. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/memories.py b/memories.py new file mode 100644 index 0000000..3936466 --- /dev/null +++ b/memories.py @@ -0,0 +1,763 @@ +""" +title: Memory Filter +author: projectmoon +author_url: https://git.agnos.is/projectmoon/open-webui-filters +version: 0.0.1 +required_open_webui_version: 0.3.8 +""" + +# System imports +import asyncio +import hashlib +import uuid +import json + +from typing import Optional, List, Dict, Callable, Any, NewType, Tuple, Awaitable +from typing_extensions import TypedDict, NotRequired + +# Libraries available to OpenWebUI +import markdown +from bs4 import BeautifulSoup +from pydantic import BaseModel as PydanticBaseModel, Field +import chromadb +from chromadb import Collection as ChromaCollection +from chromadb.api.types import Document as ChromaDocument + +# OpenWebUI imports +from config import CHROMA_CLIENT +from apps.rag.main import app +from utils.misc import get_last_user_message, get_last_assistant_message +from main import generate_chat_completions + +# OpenWebUI aliases +EMBEDDING_FUNCTION = app.state.EMBEDDING_FUNCTION + +# Custom type declarations +EmbeddingFunc = NewType('EmbeddingFunc', Callable[[str], List[Any]]) + +# Prompts +ENRICHMENT_SUMMARY_PROMPT = """ +You are tasked with analyzing the following Characters and Plot Details +sections and reducing this set of information into lists of the most +important points needed for the continuation of the narrative you are +writing. Remove duplicate or conflicting information. If there is conflicting +information, decide on something consistent and interesting for the story. + +Your reply must consist of two sections: Characters and Plot Details. These +sections must be markdown ### Headers. Under each header, respond with a +list of bullet points. Each bullet point must be one piece of relevant information. + +Limit each bullet point to one sentence. Respond ONLY with the Characters and +Plot Details sections, with the bullet points under them, and nothing else. +Do not respond with any commentary. ONLY respond with the bullet points. +""".replace("\n", " ").strip() + +QUERY_PROMPT = """ +You are tasked with generating questions for a vector database +about the narrative presented below. The queries must be questions about +parts of the story that you need more details on. The questions must be +about past events in the story, or questions about the characters involved +or mentioned in the scene (their appearance, mental state, past actions, etc). + +Your reply must consist of two sections: Characters and Plot Details. These +sections must be markdown ### Headers. Under each header, respond with a +list of bullet points. Each bullet point must be a single question or sentence +that will be given to the vector database. Generate a maximum of 5 Character +queries and 5 Plot Detail queries. + +Limit each bullet point to one sentence. Respond ONLY with the Characters and +Plot Details sections, with the bullet points under them, and nothing else. +Do not respond with any commentary. ONLY respond with the bullet points. +""".replace("\n", " ").strip() + +SUMMARIZER_PROMPT = """ +You are a narrative summarizer. Summarize the given message as if it's +part of a story. Your response must have two separate sections: Characters +and Plot Details. These sections should be markdown ### Headers. Under each +section, respond with a list of bullet points. This knowledge will be stored +in a vector database for your future use. + +The Characters section should note any characters in the scene, and important +things that happen to them. Describe the characters' appearances, actions, +mental states, and emotional states. The Plot Details section should have a +list of important plot details in this scene. + +The bullet points you generate must be in the context of storing future +knowledge about the story. Do not focus on useless details: only focus on +information that you could lose in the future as your context window shifts. + +Limit each bullet point to one sentence. The sentence MUST be in the PAST TENSE. +Respond ONLY with the Characters and Plot Details sections, with the bullet points +under them, and nothing else. Do not respond with any commentary. ONLY respond with +the bullet points. +""".replace("\n", " ").strip() + +class Message(TypedDict): + id: NotRequired[str] + role: str + content: str + +class MessageInsertMetadata(TypedDict): + role: str + chapter: str + +class MessageInsert(TypedDict): + message_id: str + content: str + metadata: MessageInsertMetadata + embeddings: List[Any] + + +class BaseModel(PydanticBaseModel): + class Config: + arbitrary_types_allowed = True + +class SummarizerResponse(BaseModel): + characters: List[str] + plot: List[str] + + +class Summarizer(BaseModel): + message: str + model: str + prompt: str = SUMMARIZER_PROMPT + + def extract_section(self, soup: BeautifulSoup, section_name: str) -> List[str]: + for h3 in soup.find_all('h3'): + heading = h3.get_text().strip() + if heading != section_name: + continue + + # Find the next sibling which should be a
    or
      + ul = h3.find_next_sibling('ul') + ol = h3.find_next_sibling('ol') + list_items = [] + + if ul: + list_items = [li.get_text().strip() for li in ul.find_all('li')] + elif ol: + list_items = [li.get_text().strip() for li in ol.find_all('li')] + + return list_items + return [] + + def sanitize_section(self, bullet_points: List[str]) -> List[str]: + return [ + bullet.strip().lstrip("-*•123456789").strip() for bullet in bullet_points + ] + + async def summarize(self) -> SummarizerResponse: + messages: List[Message] = [ + { "role": "system", "content": SUMMARIZER_PROMPT }, + { "role": "user", "content": self.message } + ] + + request = { + "model": self.model, + "messages": messages, + "stream": False, + "keep_alive": "10s" + } + + resp = await generate_chat_completions(request) + if "choices" in resp and len(resp["choices"]) > 0: + content: str = resp["choices"][0]["message"]["content"] + html = markdown.markdown(content) + soup = BeautifulSoup(html, "html.parser") + character_results = self.extract_section(soup, "Characters") + character_results = self.sanitize_section(character_results) + plot_points = self.extract_section(soup, "Plot Details") + plot_points = self.sanitize_section(plot_points) + + return SummarizerResponse(characters=character_results, plot=plot_points) + else: + return SummarizerResponse(characters=[], plot=[]) + +class Chapter(BaseModel): + """ + Focuses on a single 'chapter,' or chunk of a conversation. Provides methods to + search for data in this section of conversational story history. + """ + + convo_id: Optional[str] + client: chromadb.ClientAPI + chapter_id: str + messages: List[Message] + embedding_func: EmbeddingFunc + + def create_metadata(self) -> Dict: + return { "convo_id": self.convo_id, "chapter": self.chapter_id } + + def get_collection(self) -> Optional[ChromaCollection]: + try: + coll = self.client.get_collection("stories") + + if not self.convo_id: + self.convo_id = ( + coll.metadata["current_convo_id"] if "current_convo_id" in coll.metadata else None + ) + + return coll + except ValueError as e: + return None + + + def _create_inserts(self, summary: SummarizerResponse) -> List[MessageInsert]: + inserts = [] + plot_points = summary.plot + character_points = summary.characters + + for plot_point in plot_points: + inserts.append({ + 'id': str(uuid.uuid4()), + 'content': plot_point, + 'metadata': { + "convo_id": self.convo_id, + "chapter": self.chapter_id, + "type": "plot" + }, + 'embedding': self.embedding_func(plot_point) + }) + + for character_point in character_points: + inserts.append({ + 'id': str(uuid.uuid4()), + 'content': character_point, + 'metadata': { + "convo_id": self.convo_id, + "chapter": self.chapter_id, + "type": "character" + }, + 'embedding': self.embedding_func(character_point) + }) + + return inserts + + + def chapter_state(self) -> dict: + """Useful for storing current place in chapter, and convo switching.""" + coll = self.get_collection() + result = coll.get(ids=f"chapter-{self.chapter_id}", include=["metadatas"]) + if len(result.metadatas) > 0: + return result.metadatas[0] + else: + return {} + + + def embed(self, summary: SummarizerResponse): + """ + Store plot points for this chapter in ChromaDB. + """ + coll = self.get_collection() + if not self.convo_id: + return + + inserts = self._create_inserts(summary) + + if len(inserts) > 0: + documents = [entry['content'] for entry in inserts] + metadatas = [entry['metadata'] for entry in inserts] + ids = [entry['id'] for entry in inserts] + embeddings = [entry['embedding'] for entry in inserts] + coll.upsert(documents=documents, embeddings=embeddings, ids=ids, metadatas=metadatas) + + def query_plot(self, search_term): + return self.query(search_term, "plot") + + def query_characters(self, search_term): + return self.query(search_term, "character") + + def query(self, search_term: str, type: str) -> List[ChromaDocument]: + coll = self.get_collection() + if coll and self.convo_id: + term_embedding = self.embedding_func(search_term) + results = coll.query( + query_embeddings=[term_embedding], + include=["documents", "metadatas"], + where={ + "$and": [ + { "convo_id": self.convo_id }, + { "chapter": self.chapter_id }, + { "type": type } + ] + }, + n_results = 5 + ) + + # flatten out list of list of documents + # because chroma returns a List[List[Document]] for some reason. + if 'documents' in results: + docs = [ + doc + for doc_list in results['documents'] + for doc in doc_list + ] + + metadatas = [ + md + for md_list in results['metadatas'] + for md in md_list + ] + + results = [] + for (doc, metadata) in zip(docs, metadatas): + results.append({ "doc": doc, "metadata": metadata }) + + return results + else: + return [] + else: + return [] + + +class Story(BaseModel): + """Container for chapters. Manages an entire conversation.""" + + convo_id: Optional[str] = None + client: chromadb.ClientAPI + messages: List[Message] + embedding_func: EmbeddingFunc + + def _collection_name(self): + return f"stories" + + def create_metadata(self): + try: + coll = self.client.get_collection(self._collection_name()) + if coll: + # If we have pre-specified a convo id, update metadata + # of collection accordingly. + if self.convo_id: + metadata = coll.metadata + metadata['current_convo_id'] = self.convo_id + metadata["hnsw:space"] = "cosine" + coll = self.client.get_or_create_collection( + name=self._collection_name(), metadata=metadata + ) + else: # Otherwise pull it out of the database. + self.convo_id = ( + coll.metadata['current_convo_id'] if 'current_convo_id' in coll.metadata else None + ) + + return coll.metadata + except ValueError: + return { "current_convo_id": "", "current_chapter": 1 } + + def convo_state(self) -> dict: + """Retrieve information about the current conversation.""" + if not self.convo_id or self.convo_id == "": + return {} + + convo_state_id = f"convo-{self.convo_id}" + coll = self.get_collection() + result = coll.get(ids=[convo_state_id], include=["metadatas"]) + if len(result.metadatas) > 0: + return result.metadatas[0] + else: + # insert convo state + # TODO do something useful with convo summary + convo_summary = f"State for convo {self.convo_id}" + convo_metadata = { "current_chapter": 1 } + + coll.add( + ids=[convo_state_id], + documents=[convo_summary], # maybe store convo summary here? + embeddings=self.embedding_func(convo_summary), + metadatas=[convo_metadata] + ) + + return convo_metadata + + def switch_convo(self): + """Force a switch of current conversation.""" + if not self.convo_id: + # If we have only a user message (i.e. start of + # conversation), forcibly set to + if len(self.messages) < 2: + self.convo_id = "" + else: + # Otherwise attempt to get the cllection, which forces + # metatada creation and updates. + self.get_collection() + + def get_collection(self): + """Retrieve the collection, with its context set to the current convo ID.""" + try: + coll = self.client.get_collection(self._collection_name()) + if coll: + # If we have pre-specified a convo id, update metadata + # of collection accordingly. + if self.convo_id: + metadata = coll.metadata + metadata['current_convo_id'] = self.convo_id + metadata["hnsw:space"] = "cosine" + return self.client.get_or_create_collection( + name=self._collection_name(), metadata=metadata + ) + else: # Otherwise pull existing convo id out of the database. + self.convo_id = ( + coll.metadata['current_convo_id'] if 'current_convo_id' in coll.metadata else None + ) + + return coll + except ValueError: + # if the stories collection does not exist, create it + # completely from scratch. + metadata = { "current_convo_id": "", "hnsw:space": "cosine" } + return self.client.get_or_create_collection(self._collection_name(), metadata=metadata) + + def _current_chapter(self) -> int: + try: + return self.convo_state()["current_chapter"] + except: + return 1 + + def _current_chapter_object(self) -> Chapter: + return Chapter( + convo_id = self.convo_id, chapter_id=str(self._current_chapter()), + messages=self.messages, client=self.client, embedding_func=self.embedding_func + ) + + def embed_summary(self, summary: SummarizerResponse): + self._current_chapter_object().embed(summary) + + def query_plot(self, term: str) -> List[ChromaDocument]: + return self._current_chapter_object().query_plot(term) + + def query_characters(self, term: str) -> List[ChromaDocument]: + return self._current_chapter_object().query_characters(term) + + +# Utils +def create_enrichment_summary_prompt( + narrative: str, + character_details: List[str], + plot_details: List[str] +) -> str: + prompt = ENRICHMENT_SUMMARY_PROMPT + prompt += "Here are the original Character and Plot Details sections." + prompt += " Summarize them according to the instructions.\n\n" + + snippets = "## Character Details:\n" + for character_detail in character_details: + snippets += f"- {character_detail}\n" + + snippets = snippets.strip() + snippets += "\n" + + snippets += "\n\n## Plot Details:\n" + for plot_point in plot_details: + snippets += f"- {plot_point}\n" + + snippets = snippets.strip() + snippets += "\n" + + snippets = snippets.strip() + prompt += snippets + "\n\n" + + + prompt += "Additionally, the narrative you must continue is provided below." + prompt += "\n\n-----\n\n" + prompt += narrative + return prompt.strip() + + +def create_context(results: SummarizerResponse) -> Optional[str]: + if not results: + return None + + character_details = results.characters + plot_details = results.plot + + snippets = "## Relevant Character Details:\n" + snippets += "These are relevant bits of information about characters in the story.\n" + + for character_detail in character_details: + snippets += f"- {character_detail}\n" + + snippets = snippets.strip() + snippets += "\n" + + snippets += "\n\n## Relevant Plot Details:\n" + snippets += "These are relevant plot details that happened earlier in the story.\n" + + for plot_point in plot_details: + snippets += f"- {plot_point}\n" + + snippets = snippets.strip() + snippets += "\n" + + message = ( + "\n\nUse the following context as information about the story, inside XML tags.\n\n" + f"\n{snippets}\n" + "When answering to user:\n" + "- Use the context to enhance your knowledge of the story.\n" + "- If you don't know, do not ask for clarification.\n" + "Do not mention that you obtained the information from the context.\n" + "Do not mention the context.\n" + f"Continue the story according to the user's directions." + ) + + return message + + +def write_log(text): + with open(f"/tmp/test-memories", "a") as file: + file.write(text + "\n") + + +def split_messages(messages, keep_amount): + if len(messages) <= keep_amount: + return messages[:], [] + + recent_messages = messages[-keep_amount:] + old_messages = messages[:-keep_amount] + return recent_messages, old_messages + + +def chunk_messages(messages, chunk_size): + return [messages[i:i + chunk_size] for i in range(0, len(messages), chunk_size)] + +def llm_messages_to_user_messages(messages): + return [ + {'role': 'user', 'content': msg['content']} + for msg in messages if msg['role'] == 'assistant' + ] + +# Das Filter +class Filter: + class Valves(BaseModel): + def summarizer_model(self, body): + if self.summarizer_model_id == "": + # This will be the model ID in the convo. If not base + # model, it will cause problems. + return body["model"] + else: + return self.summarizer_model_id + + summarizer_model_id: str = Field( + default="", + description="Model used to summarize the conversation. Must be a base model.", + ) + + n_last_messages: int = Field( + default=4, description="Number of last messages to retain." + ) + pass + + + + class UserValves(BaseModel): + pass + + def __init__(self): + self.valves = self.Valves() + pass + + def extract_convo_id(self, messages): + """Extract ID of first message to use as conversation ID.""" + if len(messages) > 0: + first_user_message = next( + (message for message in messages if message.get("role") == "user"), None + ) + + if first_user_message and 'id' in first_user_message: + return first_user_message['id'] + else: + raise ValueError("No messages found to extract conversation ID") + else: + raise ValueError("No messages found to extract conversation ID") + + + async def summarize(self, messages) -> Optional[SummarizerResponse]: + message_to_summarize = get_last_assistant_message(messages) + if message_to_summarize: + summarizer = Summarizer(model=self.summarizer_model_id, message=message_to_summarize) + return await summarizer.summarize() + else: + return None + + async def send_outlet_status(self, event_emitter, done: bool): + description = ( + "Analyzing Narrative (do not reply until this is done)" if not done else + "Narrative analysis complete (you may now reply)." + ) + await event_emitter({ + "type": "status", + "data": { + "description": description, + "done": done, + }, + }) + + async def set_enriching_status(self, state: str): + if not self.event_emitter: + return + + done = state == "done" + description = "Enriching Narrative" + + if state == "init": description = f"{description}: Initializing..." + if state == "searching": description = f"{description}: Searching..." + if state == "analyzing": description = f"{description}: Analyzing..." + + description = ( + description if not done else + "Enrichment Complete" + ) + + await self.event_emitter({ + "type": "status", + "data": { + "description": description, + "done": done, + }, + }) + + async def outlet( + self, + body: dict, + __user__: Optional[dict], + __event_emitter__: Callable[[Any], Awaitable[None]], + ) -> dict: + # Useful things to have around. + self.event_emitter = __event_emitter__ + self.summarizer_model_id = self.valves.summarizer_model(body) + + await self.send_outlet_status(__event_emitter__, False) + messages = body['messages'] + convo_id = self.extract_convo_id(messages) + + # summarize into plot points. + summary = await self.summarize(messages) + story = Story( + convo_id=convo_id, client=CHROMA_CLIENT, + embedding_func=EMBEDDING_FUNCTION, + messages=messages + ) + + story.switch_convo() + + if summary: + story.embed_summary(summary) + + await self.send_outlet_status(__event_emitter__, True) + return body + + async def generate_enrichment_queries(self, messages) -> SummarizerResponse: + last_response = get_last_assistant_message(messages) + user_input = get_last_user_message(messages) + + query_message = "" + if last_response: query_message += f"## Assistant\n\n{last_response}\n\n" + if user_input: query_message += f"## User\n\n{user_input}\n\n" + query_message = query_message.strip() + + summarizer = Summarizer( + model=self.summarizer_model_id, + message=query_message, + prompt=QUERY_PROMPT + ) + + return await summarizer.summarize() + + async def summarize_enrichment( + self, + messages, + character_results: List[ChromaDocument], + plot_results: List[ChromaDocument] + ) -> SummarizerResponse: + last_response = get_last_assistant_message(messages) + user_input = get_last_user_message(messages) + + character_details = [r['doc'] for r in character_results] + plot_details = [r['doc'] for r in plot_results] + + narrative_message = "" + if last_response: narrative_message += f"## Assistant\n\n{last_response}\n\n" + if user_input: narrative_message += f"## User\n\n{user_input}\n\n" + narrative_message = narrative_message.strip() + + summarization_prompt = create_enrichment_summary_prompt( + narrative=narrative_message, + plot_details=plot_details, + character_details=character_details + ) + + summarizer = Summarizer( + model=self.summarizer_model_id, + message=narrative_message, + prompt=summarization_prompt + ) + + return await summarizer.summarize() + + + async def enrich(self, story: Story, messages) -> SummarizerResponse: + await self.set_enriching_status("searching") + query_generation_result = await self.generate_enrichment_queries(messages) + character_results = [result + for query in query_generation_result.characters + for result in story.query_characters(query)] + + plot_results = [result + for query in query_generation_result.plot + for result in story.query_plot(query)] + + await self.set_enriching_status("analyzing") + return await self.summarize_enrichment(messages, character_results, plot_results) + + + async def update_system_message(self, messages, system_message): + story = Story( + convo_id=None, client=CHROMA_CLIENT, + embedding_func=EMBEDDING_FUNCTION, + messages=messages + ) + + story.switch_convo() + + if story.convo_id == "": + return + + enrichment_summary: SummarizerResponse = await self.enrich(story, messages) + context = create_context(enrichment_summary) + + if context: + system_message["content"] += context + + + async def inlet( + self, + body: dict, + __user__: Optional[dict], + __event_emitter__: Callable[[Any], Awaitable[None]] + ) -> dict: + # Useful properties to have around. + self.event_emitter = __event_emitter__ + self.summarizer_model_id = self.valves.summarizer_model(body) + await self.set_enriching_status("init") + messages = body["messages"] + + # Ensure we always keep the system prompt + system_prompt = next( + (message for message in messages if message.get("role") == "system"), None + ) + + if system_prompt: + all_messages = [ + message for message in messages if message.get("role") != "system" + ] + + recent_messages, old_messages = split_messages(all_messages, self.valves.n_last_messages) + most_recent_messages = messages[-self.valves.n_last_messages :] + else: + system_prompt = { "id": str(uuid.uuid4()), "role": "system", "content": "" } + recent_messages, old_messages = split_messages(messages, self.valves.n_last_messages) + + await self.update_system_message(messages, system_prompt) + recent_messages.insert(0, system_prompt) + + body["messages"] = recent_messages + await self.set_enriching_status("done") + return body diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..8990589 --- /dev/null +++ b/readme.md @@ -0,0 +1,89 @@ +# Memory Filter + +Super hacky, very basic automatic narrative memory filter for +OpenWebUI, that may or may not actually enhance narrative generation! + +This is intended to be a springboard for a better, more comprehensive +filter that can coherently keep track(ish?) of plot and character +developments in long form story writing/roleplaying scenarios, where +context window length is limited (or ollama crashes on long context +length models despite having 40 GB of unused memory!). + +## Configuration + +The filter exposes two settings: + + - **Summarization model:** This is the model used for extracting and + creating all of the narrative memory, and searching info. It must + be good at following instructions. I use Gemma 2. + - **It must be a base model.** If it's not, things will not work. + - If you don't set this, the filter will attempt to use the model + in the conversation. It must still be a base model. + - **Number of messages to retain:** Number of messages to retain for the + context. All messages before that are dropped in order to manage + context length. + +Ideally, the summarization model is the same model you are using for +the storytelling. Otherwise you may have lots of model swap-outs. + +The filter hooks in to OpenWebUI's RAG settings to generate embeddings +and query the vector database. The filter will use the same embedding +model and ChromaDB instance that's configured in the admin settings. + +## Usage + +Enable the filter on a model that you want to use to generate stories. +It is recommended, although not required, that this be the same model +as the summarizer model (above). If you have lots of VRAM or are very +patient, you can use different models. + +User input is pre-processed to 'enrich' the narrative. Replies from +the language model are analyzed post-delivery to update the story's +knowlege repository. + +You will see status indicators on LLM messages indicating what the +filter is doing. + +Do not reply while the model is updating its knowledge base or funny +things might happen. + +## Functioning + +What does it do? + - When receiving user input, generate search queries for vector DB + based on user input + last model response. + - Search vector DB for theoretically relevant character and plot + information. + - Ask model to summarize results into coherent and more relevant + stuff. + - Inject results as contextual info for the model. + - After receiving model narrative reply, generate character and plot + info and stick them into the vector DB. + +## Limitations and Known Issues + +What does it not do? + - Handle conversational branching/regeneration. In fact, this will + pollute the knowledgebase with extra information! + - Bouncing around some ideas to fix this. Basically requires + building a "canonical" branching story path in the database? + - Proper context "chapter" summarization (planned to change). + - Work properly when switching conversations due to OpenWebUI + limitations. The chat ID is not available on incoming requests for + some reason, so a janky workaround is used when processing LLM + responses. + - Clear out information of old conversations or expire irrelevant + data. + +Other things to do or improve: + - Set a minimum search score, to prevent useless stuff from coming up. + - Figure out how to expire or update information about characters and + events, instead of dumping it all into the vector DB. + - Improve multi-user handling. Should technically sort of work due to + messages having UUIDs, but is a bit messy. Only one collection is + used, so multiple users = concurrency issues. + - Block user input while updating the knowledgebase. + +## License + +AGPL v3.0+. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4d7624a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +chromadb==0.5.4 +pydantic==2.8.2 +Requests==2.32.3