diff --git a/README.md b/README.md index b217686..0b039ec 100644 --- a/README.md +++ b/README.md @@ -1,112 +1,28 @@ # Introduction -This is a simple reproduction of the analysis done in the [CLIO](https://www.anthropic.com/research/clio) paper. I've written a [walkthrough of the code](https://ivanleo.com/blog/understanding-user-conversations) that you can read to understand the high level ideas behind CLIO. The code isn't entirely optimised, I wrote it for fun over the weekend so I apologise in advance for any bugs. +Kura is an open-source tool that helps you to understand your users. It's built using the same ideas as [CLIO](https://www.anthropic.com/research/clio) but open-sourced so that you can try it on your own data. I've written a [walkthrough of the code](https://ivanleo.com/blog/understanding-user-conversations) that you can read to understand the high level ideas behind CLIO. -## What is CLIO? +It's currently under active development and I'm working on adding more features to it but the eventual goal is to make it a very modular tool that you can use to understand general trends in your user base. If you face any issues or have any suggestions, please feel free to open an issue or a PR. -CLIO (Claude Language Insights and Observability) is a framework developed by Anthropic that enables privacy-preserving analysis of user conversations at scale. It works by combining language models and clustering techniques to generate meaningful insights while protecting user privacy. The system processes conversations through multiple stages of summarization and clustering, gradually abstracting away personal details while preserving important patterns and trends. +At it's core, Kura exposes the following few features as shown in the diagram below -At its core, CLIO uses a two-step approach: first, it employs language models to create privacy-preserving summaries of individual conversations, removing any personally identifiable information (PII). Then, it uses clustering algorithms and hierarchical organization to group similar conversations together, allowing analysts to understand broad usage patterns and user needs without accessing sensitive user data. This methodology achieved a 98.5% success rate in PII removal while maintaining the ability to generate actionable insights from user interactions. +- A Summarisation Model that takes user conversations and summarises them into a task description +- Summary Hooks that take the same conversations and extract out valuable metadata from them ( that you specify) +- A base clustering model that takes the summaries and clusters them into groups based off their embeddings +- A clustering model that takes these clusters and further clusters them into more groups based off their embeddings -This repo shows a simplified implementation of the CLIO framework. It's not optimised and it's not designed to be used in production. I've also provided a simple FastHTML app to help visualise the clusters and generate your own visualisations. +This creates a list of hierachal clusters that are labelled with language models with the metadata that you specify and care about. -![](./clio.png) +![](./kura.png) -It's just a fun project to understand the ideas behind CLIO. - -## Instructions - -> Make sure you have a `GOOGLE_API_KEY` set in your shell and `OPENAI_API_KEY` set in your shell. - -I've provided two ways to run the code - -1. Using the FastHTML app - this allows you to visualise the clusters and generate your own visualisations. I recommend this since it's just nicer to use than a CLI -2. Using the scripts directly - this allows you to run it in a script and get the raw `cluster.json` file generated out - -## Using the FastHTML app - -1. First, you need to install the dependencies inside the `pyproject.toml` file. I recommend doing so in a virtual environment. - -```bash -uv venv -source .venv/bin/activate -uv sync -``` - -2. Then just run the `app.py` file - -```bash -python app.py -``` - -3. Then you want to just click the `Start Analysis` button and wait for the clusters to be generated. Depending on the number of conversations you have this could take a while. For ~800 conversations, it took around 1.5 minutes to generate the clusters with a semaphore of 50. - -Once you've done so, I recommend just taking cluster.json and throwing it into something like Claude to ask more questions about the clusters. - -## Using the Scripts - -> Before running the scripts, make sure that you've downloaded your claude chats. A guide on how to do so can be found [here](https://support.anthropic.com/en/articles/9450526-how-can-i-export-my-claude-ai-data). - -1. First, you need to install the dependencies inside the `pyproject.toml` file. I recommend doing so in a virtual environment. - -``` -uv venv -source .venv/bin/activate -uv sync -``` - -2. Once you've done so, make sure that the claude chats are saved in a file called `conversations.json` and that you've set a `GOOGLE_API_KEY` in your shell. If not you'll have to configure it by doing +Once you've installed the package with the command `pip install kura`, you can use the following initialisation code to get started ```python -import google.generativeai as genai - -genai.configure(api_key=) -``` - -Once you've done so, just run `main.py` to generate the initial clusters. Make sure to modify the following variables to your liking: - -```py -SUMMARIES_PER_CLUSTER = 20 # This determines the number of summaries we want on average per cluster -CHILD_CLUSTERS_PER_CLUSTER = 10 # This determines the number of clusters we want on average to have per higher level cluster -MAX_FINAL_CLUSTERS = 10 # This represents the maximum number of clusters we want to have at the end of the process -``` - -Once you've done so, just run `main.py` to generate the initial clusters. This will create a file called `clusters.json` which contains the initial clusters. - -```python -python main.py -``` - -3. Once you've done so, you can run `visualise.py` to visualise the clusters. This will create a print out that looks like a tree. - -```python -python visualise.py -``` - -You'll see an output that looks like this: +from kura import Kura -```bash -Clusters -├── Improve text, code, and generate diverse content -│ Description: Users requested assistance with improving text and code, and generating diverse content and marketing materials. Tasks included rewriting, debugging, analysis, and content creation across -│ various domains. -│ Contains 308 conversations -│ ├── Rewrite and improve text and code -│ │ Description: Users requested assistance with improving and rewriting various types of text, including articles, marketing materials, technical documents, and code. Tasks ranged from enhancing clarity -│ │ and conciseness to debugging code and analyzing data. -│ │ Contains 183 conversations -│ │ ├── Rewrite articles and marketing copy related to AI and databases -│ │ │ Description: Users requested assistance rewriting articles and marketing materials about AI, databases, and related technologies. The requests involved tasks such as improving clarity, style, -│ │ │ conciseness, and creating outlines and summaries. -│ │ │ Contains 28 conversations -│ │ ├── Rewrite or improve text -│ │ │ Description: Users requested assistance with revising, rewriting, or improving existing text, including paragraphs, articles, summaries, and other written content. Tasks ranged from enhancing -│ │ │ clarity and conciseness to translating languages and improving writing style. -│ │ │ Contains 24 conversations -│ │ ├── Improve or create AI-related documents -│ │ │ Description: Users requested assistance with various tasks related to improving or creating documents about AI, including summarization, rewriting, clarification, and the addition of definitions. -│ │ │ These tasks focused on improving clarity, -... more clusters here +kura = Kura() # The default models and parameters are here +kura.load_conversations(conversations) # Load your conversations +kura.cluster_conversations() # This creates the hierachal clusters for your specific usage ``` -This is a tree representation of the clusters. The clusters are grouped by their parent cluster. The clusters are sorted by the number of conversations they contain. You can then use this tree to understand the conversations and the patterns they contain. +We expose the following parameters that you can use diff --git a/clio.png b/clio.png deleted file mode 100644 index caaeb94..0000000 Binary files a/clio.png and /dev/null differ diff --git a/helpers/clusters.py b/helpers/clusters.py deleted file mode 100644 index 89599aa..0000000 --- a/helpers/clusters.py +++ /dev/null @@ -1,408 +0,0 @@ -import instructor -from asyncio import Semaphore -from helpers.embedding import embed_cluster, embed_summary -import math -from helpers.types import ( - CandidateClusters, - Cluster, - ClusterLabel, - ClusterSummary, - ConsolidatedCluster, -) -from helpers.sampling import get_contrastive_examples -from tqdm.asyncio import tqdm_asyncio as asyncio -from openai import AsyncOpenAI -from sklearn.cluster import KMeans -import numpy as np -import random -import os -import json - - -def cluster_items(items: list[dict], items_per_cluster: int) -> list[dict]: - n_clusters = math.ceil(len(items) // items_per_cluster) - embeddings = [item["embedding"] for item in items] - X = np.array(embeddings) - - kmeans = KMeans(n_clusters=n_clusters) - cluster_labels = kmeans.fit_predict(X) - - group_to_clusters = {} - for i, item in enumerate(items): - item_copy = item.copy() - cluster_id = int(cluster_labels[i]) - del item_copy["embedding"] - if cluster_id not in group_to_clusters: - group_to_clusters[cluster_id] = [] - - group_to_clusters[cluster_id].append(item_copy) - - return group_to_clusters - - -async def generate_base_cluster( - client, - sem, - user_requests: list[dict], - negative_examples: list[dict], -): - async with sem: - cluster = await client.chat.completions.create( - messages=[ - { - "role": "system", - "content": """ - You are tasked with summarizing a group of related statements into a short, precise, and accurate description and name. Your goal is to create a concise summary that captures the essence of these statements and distinguishes them from other similar groups of statements. - - Summarize all the statements into a clear, precise, two-sentence description in the past tense. Your summary should be specific to this group and distinguish it from the contrastive answers of the other groups. - - After creating the summary, generate a short name for the group of statements. This name should be at most ten words long (perhaps less) and be specific but also reflective of most of the statements (rather than reflecting only one or two). The name should distinguish this group from the contrastive examples. For instance, "Write fantasy sexual roleplay with octopi and monsters", "Generate blog spam for gambling websites", or "Assist with high school math homework" would be better and more actionable than general terms like "Write erotic content" or "Help with homework". Be as descriptive as possible and assume neither good nor bad faith. Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity is necessary for monitoring - - The cluster name should be a sentence in the imperative that captures the user’s request. For example, ‘Brainstorm ideas for a birthday party’ or ‘Help me find a new job.” - """, - }, - { - "role": "user", - "content": """ - Here are the relevant statements - - Below are the related statements: - - {% for item in user_requests %} - {{ item['user_request'] }} - {% endfor %} - - - For context, here are statements from nearby groups that are NOT part of the group you’re summarizing - - - {% for item in negative_examples %} - {{ item['user_request'] }} - {% endfor %} - - - Remember to analyze both the statements and the contrastive statements carefully to ensure your summary and name accurately represent the specific group while distinguishing it from others. - """, - }, - ], - response_model=ClusterSummary, - context={ - "user_requests": user_requests, - "negative_examples": negative_examples, - }, - ) - - return Cluster( - name=cluster.name, - description=cluster.summary, - chat_ids=[item["chat_id"] for item in user_requests], - parent_id=None, - ) - - -async def embed_summaries( - oai_client: AsyncOpenAI, - sem: Semaphore, - summaries: list[dict], - output_file: str = "checkpoints/embedded_summaries.json", - start_step: str = None, -) -> list[dict]: - should_reembed = start_step in ["embed", "summarize"] if start_step else False - - if not should_reembed and os.path.exists(output_file): - with open(output_file, "r") as f: - return [json.loads(line) for line in f] - - embedded_summaries = await asyncio.gather( - *[ - embed_summary(oai_client, sem, summary, summary["task_description"]) - for summary in summaries - ], - desc="Embedding summaries", - ) - - os.makedirs("checkpoints", exist_ok=True) - with open(output_file, "w") as f: - for summary in embedded_summaries: - f.write(json.dumps(summary) + "\n") - - return embedded_summaries - - -async def cluster_summaries( - client: instructor.Instructor, - sem: Semaphore, - summaries: list[dict], - items_per_cluster: int = 5, - start_step: str = None, -): - oai_client = AsyncOpenAI() - embedded_summaries = await embed_summaries( - oai_client, sem, summaries, start_step=start_step - ) - - cluster_id_to_summaries = cluster_items(embedded_summaries, items_per_cluster) - - return await asyncio.gather( - *[ - generate_base_cluster( - client, - sem, - cluster_id_to_summaries[cluster_id], - get_contrastive_examples( - cluster_id, cluster_id_to_summaries, desired_count=10 - ), - ) - for cluster_id in cluster_id_to_summaries - ] - ) - - -async def label_cluster( - client: instructor.AsyncInstructor, - sem: Semaphore, - cluster: dict, - candidate_clusters: list[str], -) -> dict: - async with sem: - candidate_clusters = candidate_clusters.copy() - random.shuffle(candidate_clusters) - label = await client.chat.completions.create( - messages=[ - { - "role": "system", - "content": """ - You are tasked with categorizing a specific cluster into one of the provided higher-level clusters for observability, monitoring, and content moderation. Your goal is to determine which higher-level cluster best fits the given specific cluster based on its name and description. - - First, here are the ONLY valid higher-level clusters you may select from: - - {% for cluster in candidate_clusters %} - {{ cluster }} - {% endfor %} - - - Here is the specific cluster to categorize: - - Name: {{ cluster["name"] }} - Description: {{ cluster["description"] }} - - - RULES: - 1. You MUST select EXACTLY ONE higher-level cluster from the provided list - 2. You MUST output the higher-level cluster name EXACTLY as written - no modifications allowed - 3. You MUST NOT create new cluster names or combinations - 4. You MUST NOT output any additional text or explanations - 5. You MUST NOT use partial matches or approximate names - - CLASSIFICATION PROCESS: - 1. First, record the exact list of valid higher-level clusters - 2. Read the specific cluster's name and description carefully - 3. Compare the specific cluster's key characteristics against each valid higher-level cluster - 4. Select the single most appropriate higher-level cluster that encompasses the specific cluster - 5. Verify your selected cluster exactly matches one from the valid list - 6. Output ONLY the selected higher-level cluster name, exactly as it appeared in the valid list - - Here is the specific cluster to categorize: - - - Name: {{ cluster["name"] }} - Description: {{ cluster["description"] }} - - - Based on this information, determine the most appropriate higher-level cluster and provide your answer as instructed. - """, - } - ], - context={ - "cluster": cluster, - "candidate_clusters": candidate_clusters, - }, - response_model=ClusterLabel, - max_retries=3, - ) - return { - "cluster": cluster, - "higher_level_cluster": label.higher_level_cluster, - } - - -async def generate_candidate_cluster_names( - client: instructor.Instructor, sem: Semaphore, clusters: list[Cluster] -) -> list[str]: - resp = await client.chat.completions.create( - messages=[ - { - "role": "system", - "content": """ - You are tasked with creating higher-level cluster names based on a given list of clusters and their descriptions. Your goal is to come up with broader categories that could encompass one or more of the provided clusters - - First, review the list of clusters and their descriptions: - - {% for cluster in clusters %} - {{ cluster.name }}: {{ cluster.description }} - {% endfor %} - - - Your task is to create at most {{ desired_number }} higher-level cluster names that could potentially include one or more of the provided clusters. These higher-level clusters should represent broader categories or themes that emerge from the given clusters, while remaining as specific as possible. If there are many clusters with a specific theme, ensure that the higher-level cluster name remains the maximum level of specificity. You are helping to organize user behavior data in order to improve safety, monitoring, and observability. You can generate less than {{ desired_number }} names if you feel that fewer are appropriate and accurately capture the clusters. - - Guidelines for creating higher-level clusters names - 1. Analyze the themes, topics or characteristics common to multiple clusters. - 2. Create names that are specific enough to be meaningful but but not so specific that they can’t meaningfully represent many different clusters. Avoid overly general or vague terms, and do not hesitate to describe socially harmful or sensitive topics (in fact, clusters that clearly describe harmful behavior are slightly preferred); specificity is necessary for observability and enforcement. - 3. Ensure that the higher-level cluster names are distinct from one another. - 4. Use clear, concise, and descriptive language for the cluster names. Assume neither good nor bad faith for the content in the clusters. - - Think about the relationships between the given clusters and potential overarching themes. - - Focus on creating meaningful, distinct and precise ( but not overly specific ) higher-level cluster names that could encompass multiple sub-clusters. - """, - } - ], - response_model=CandidateClusters, - context={ - "clusters": clusters, - "desired_number": math.ceil( - (len(clusters) * 3) // 4 - ), # This ensure we get at least 3/4 of the clusters ( so we have a 25% reduction or more at each stage) - }, - max_retries=3, - ) - - return resp.candidate_cluster_names - - -async def rename_higher_level_cluster( - client: instructor.AsyncInstructor, - sem: Semaphore, - clusters: dict, -) -> dict: - async with sem: - resp = await client.chat.completions.create( - messages=[ - { - "role": "system", - "content": """ - You are tasked with summarizing a group of related cluster names into a short, precise, and accurate overall description and name. Your goal is to create a concise summary that captures the essence of these clusters - - The cluster name should be at most ten words long (perhaps less) and be specific but also reflective of most of the clusters that comprise them. The cluster name should be a sentence in the imperative that captures the user’s request. For example, ‘Brainstorm ideas for a birthday party’ or ‘Help me find a new job are good examples. - - Do not shy away from describing socially harmful or sensitive topics. For instance, "Write fantasy sexual roleplay with octopi and monsters", "Generate blog spam for gambling websites", or "Assist with high school math homework" would be better than "Write erotic content" or "Help with homework". Be as descriptive as possible and assume neither good nor bad faith. Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity is necessary for monitoring. - - The cluster description should be a clear, precise, two-sentence description in the past tense. This description should be specific to this cluster. - - Below are the related cluster names - - {% for cluster in clusters %} - {{ cluster.name }}: {{ cluster.description }} - {% endfor %} - - - Ensure your summary and name accurately represent the clusters and are specific to the clusters. - """, - } - ], - context={"clusters": clusters}, - response_model=ConsolidatedCluster, - ) - - return { - "new_cluster": Cluster( - name=resp.cluster_name, - description=resp.cluster_description, - chat_ids=[chat_id for cluster in clusters for chat_id in cluster.chat_ids], - parent_id=None, - ), - "original_clusters": clusters, - } - - -async def generate_higher_level_cluster( - client: instructor.Instructor, - sem: Semaphore, - clusters: list[Cluster], -) -> list[Cluster]: - if len(clusters) == 1: - original_cluster = clusters[0] - new_cluster = Cluster( - name=original_cluster.name, - description=original_cluster.description, - chat_ids=original_cluster.chat_ids, - parent_id=None, - ) - original_cluster.parent_id = new_cluster.id - return [original_cluster, new_cluster] - - candidate_cluster_names = await generate_candidate_cluster_names( - client, sem, clusters - ) - - new_candidate_clusters = await asyncio.gather( - *[ - label_cluster(client, sem, cluster, candidate_cluster_names) - for cluster in clusters - ], - desc="Generating candidate higher-level clusters", - ) - - cluster_groups: dict[str, list[Cluster]] = {} - for cluster in new_candidate_clusters: - original_cluster = cluster["cluster"] - new_higher_level_cluster = cluster["higher_level_cluster"] - if new_higher_level_cluster not in cluster_groups: - cluster_groups[new_higher_level_cluster] = [] - cluster_groups[new_higher_level_cluster].append(original_cluster) - - new_labelled_clusters = await asyncio.gather( - *[ - rename_higher_level_cluster(client, sem, cluster_group) - for cluster_group in cluster_groups.values() - ], - desc="Generating higher-level clusters", - ) - - res = [] - - for new_labelled_cluster in new_labelled_clusters: - res.append(new_labelled_cluster["new_cluster"]) - - for original_cluster in new_labelled_cluster["original_clusters"]: - original_cluster.parent_id = new_labelled_cluster["new_cluster"].id - res.append(original_cluster) - - return res - - -async def reduce_clusters( - client: instructor.Instructor, - sem: Semaphore, - clusters: list[Cluster], - items_per_cluster: int, -) -> list[Cluster]: - oai_client = AsyncOpenAI() - embedded_clusters = await asyncio.gather( - *[embed_cluster(oai_client, sem, cluster) for cluster in clusters] - ) - - cluster_id_to_cluster = { - cluster_id: [item["cluster"] for item in clusters] - for cluster_id, clusters in cluster_items( - embedded_clusters, items_per_cluster - ).items() - } - - new_clusters = await asyncio.gather( - *[ - generate_higher_level_cluster( - client, - sem, - cluster_id_to_cluster[cluster_id], - ) - for cluster_id in cluster_id_to_cluster - ], - desc="Generating higher-level clusters", - ) - return [ - cluster - for clusters in new_clusters - if clusters is not None - for cluster in clusters - ] diff --git a/helpers/conversation.py b/helpers/conversation.py deleted file mode 100644 index c579306..0000000 --- a/helpers/conversation.py +++ /dev/null @@ -1,95 +0,0 @@ -import json -from helpers.types import Conversation, Message, ConversationSummary -from typing import List -import instructor -from asyncio import Semaphore - - -def load_conversations(path: str) -> List[Conversation]: - """ - This is a function that loads in the conversations from the given path and yields the user's conversations - """ - with open(path, "r") as f: - chat_obj = json.load(f) - return [ - Conversation( - chat_id=chat["uuid"], - created_at=chat["created_at"], - messages=[ - Message( - created_at=message["created_at"], - role=message["sender"], - content="\n".join( - [ - item["text"] - for item in message["content"] - if item["type"] == "text" - ] - ), - ) - for message in chat["chat_messages"] - ], - ) - for chat in chat_obj - ] - - -async def summarise_conversation( - client: instructor.AsyncInstructor, sem: Semaphore, conversation: Conversation -) -> dict: - """ - This is a function that given a user conversation will return a summary of the user request and the task description - - Task Description: The task is to do X - User Request: The user's overall request for the assistant is to do Y - - """ - async with sem: - resp = await client.chat.completions.create( - messages=[ - { - "role": "system", - "content": """Generate a summary of the task that the user is asking and a description of the task that the assistant is trying to complete based off the following conversation. - - Here are some examples of user requests - - - The user’s overall request for the assistant is to help implementing a React component to display a paginated list of users from a database. - - The user’s overall request for the assistant is to debug a memory leak in their Python data processing pipeline. - - The user’s overall request for the assistant is to design and architect a REST API for a social media application. - - - Task descriptions should be concise and short. Here are some examples of task descriptions - - - The task is to help build a frontend component with React and implement database integration - The task is to debug performance issues and optimize memory usage in Python code - The task is to design and architect a RESTful API following best practices - - - Here is the conversation - {% for message in messages %} - - {{message.role}}: {{message.content}} - - {% endfor %} - - When answering, do not include any personally identifiable information (PII), like names, locations, phone numbers, email addressess, and so on. When answering, do not include any proper nouns. Output your answer to the question in English inside tags; be clear and concise and get to the point in at most two sentences (don't say "Based on the conversation..." and avoid mentioning Claude/the chatbot). For example: - - Remember that - - User requests and task descriptions should be concise and short. They should each be at most 1-2 sentences and at most 20 words. - - User requests should start with "The user's overall request for the assistant is to" - - Task descriptions should start with "The task is to" - - Make sure to omit any personally identifiable information (PII), like names, locations, phone numbers, email addressess, company names and so on. - - Make sure to indicate specific details such as programming languages, frameworks, libraries and so on which are relevant to the task. Also mention the specific source of the data if it's relevant to the task. For instance, if the user is asking to perform a calculation, let's talk about the specific source of the data. - """, - } - ], - context={"messages": conversation.messages}, - response_model=ConversationSummary, - ) - return { - "chat_id": conversation.chat_id, - "task_description": resp.task_description, - "user_request": resp.user_request, - "turns": len(conversation.messages), - } diff --git a/helpers/embedding.py b/helpers/embedding.py deleted file mode 100644 index 300d16b..0000000 --- a/helpers/embedding.py +++ /dev/null @@ -1,37 +0,0 @@ -from openai import AsyncOpenAI -from asyncio import Semaphore -from helpers.types import Cluster - - -async def embed_summary( - oai_client: AsyncOpenAI, sem: Semaphore, item: str, text_to_embed: str -) -> list[float]: - return { - "embedding": ( - await oai_client.embeddings.create( - input=text_to_embed, model="text-embedding-3-small" - ) - ) - .data[0] - .embedding, - **item, - } - - -async def embed_cluster( - oai_client: AsyncOpenAI, sem: Semaphore, cluster: Cluster -) -> list[float]: - return { - "embedding": ( - await oai_client.embeddings.create( - input=f""" - Name: {cluster.name} - Description: {cluster.description} - """, - model="text-embedding-3-small", - ) - ) - .data[0] - .embedding, - "cluster": cluster, - } diff --git a/helpers/sampling.py b/helpers/sampling.py deleted file mode 100644 index bf4d5e9..0000000 --- a/helpers/sampling.py +++ /dev/null @@ -1,24 +0,0 @@ -import numpy as np - - -def get_contrastive_examples( - cluster_id: int, - grouped_clusters: dict[int, list[dict]], - desired_count: int = 10, -) -> dict[int, list[dict]]: - other_clusters = [c for c in grouped_clusters.keys() if c != cluster_id] - - if len(other_clusters) == 0: - raise ValueError("No other clusters to compare against") - - # Get all examples from other clusters - all_examples = [] - for cluster in other_clusters: - all_examples.extend(grouped_clusters[cluster]) - - # If we don't have enough examples, use replacement - if len(all_examples) < desired_count: - return all_examples - - # Otherwise sample without replacement - return list(np.random.choice(all_examples, size=desired_count, replace=False)) diff --git a/helpers/types.py b/helpers/types.py deleted file mode 100644 index f80ba31..0000000 --- a/helpers/types.py +++ /dev/null @@ -1,94 +0,0 @@ -from pydantic import BaseModel, field_validator, computed_field, Field, ValidationInfo -import uuid - - -class Message(BaseModel): - created_at: str - role: str - content: str - - -class Conversation(BaseModel): - chat_id: str - created_at: str - messages: list[Message] - - -class Cluster(BaseModel): - id: str = Field( - default_factory=lambda: uuid.uuid4().hex, - ) - name: str - description: str - parent_id: str | None - chat_ids: list[str] - - @computed_field - def count(self) -> int: - return len(self.chat_ids) - - def add_child_count(self, child: "Cluster"): - return Cluster( - name=self.name, - description=self.description, - chat_ids=self.chat_ids + child.chat_ids, - ) - - -class ConversationSummary(BaseModel): - task_description: str - user_request: str - - @field_validator("user_request", "task_description") - def validate_length(cls, v): - if len(v.split()) > 25: - raise ValueError( - f"Please summarize the sentence {v} to at most 20 words. It's currently {len(v.split())} words long. Remove any unnecessary words and make sure to retain the most important information." - ) - return v - - -class ClusterSummary(BaseModel): - """ - This is a summary of the related statements into a single cluster that captures the essence of these statements and distinguishes them from the contrastive answers of other groups. - - Your job is to create a final name and summary for the cluster - """ - - name: str - summary: str - - -class CandidateClusters(BaseModel): - candidate_cluster_names: list[str] - - @field_validator("candidate_cluster_names") - def validate_length(cls, v, info: ValidationInfo): - desired_number = info.context["desired_number"] - if len(v) > desired_number: - raise ValueError( - f"Too many candidate cluster names: got {len(v)}, expected at most {desired_number}. Please combine similar clusters to reduce the total number." - ) - return v - - -class ClusterLabel(BaseModel): - higher_level_cluster: str - - @field_validator("higher_level_cluster") - def validate_length(cls, v, info: ValidationInfo): - candidate_clusters = info.context["candidate_clusters"] - if v not in candidate_clusters: - raise ValueError( - f"The chosen label of {v} is not in the list of candidate clusters provided of {candidate_clusters}. You must choose a label that belongs to the list of candidate clusters - {candidate_clusters}." - ) - return v - - -class ConsolidatedCluster(BaseModel): - """ - This is a consolidated cluster that is the result of merging multiple clusters into a single higher levle cluster - """ - - cluster_name: str - cluster_description: str diff --git a/helpers/visualisation.py b/helpers/visualisation.py deleted file mode 100644 index 7e441a0..0000000 --- a/helpers/visualisation.py +++ /dev/null @@ -1,208 +0,0 @@ -from datetime import datetime, timedelta -import pandas as pd -from fasthtml.common import Script -import json -from typing import List -from helpers.types import Conversation - - -def generate_cumulative_chart(conversations: List[Conversation]) -> Script: - """ - Generate a cumulative word count chart for human messages in conversations. - Returns a Script element containing the Plotly visualization. - """ - # Create DataFrame from messages - messages_data = [] - for conv in conversations: - for msg in conv.messages: - if msg.role == "human": - messages_data.append( - { - "datetime": pd.to_datetime( - msg.created_at.replace("Z", "+00:00") - ), - "words": len(msg.content.split()), - } - ) - - df = pd.DataFrame(messages_data) - df["week_start"] = df["datetime"].dt.to_period("W-MON").dt.start_time - - # Calculate weekly and cumulative word counts - weekly_df = df.groupby("week_start")["words"].sum().reset_index() - weekly_df["cumulative_words"] = weekly_df["words"].cumsum() - weekly_df["week_start"] = weekly_df["week_start"].dt.strftime("%Y-%m-%d") - - data = json.dumps( - { - "data": [ - { - "x": weekly_df["week_start"].tolist(), - "y": weekly_df["cumulative_words"].tolist(), - "type": "scatter", - "mode": "lines", - "name": "Cumulative Words in Human Messages", - "line": {"color": "red"}, - } - ], - "layout": { - "title": "Cumulative Words in Human Messages by Week", - "xaxis": {"title": "Week Starting"}, - "yaxis": {"title": "Total Words"}, - "showlegend": True, - "hovermode": "closest", - }, - } - ) - - return Script(f"var data = {data}; Plotly.newPlot('myDiv', data);") - - -def generate_messages_per_chat_chart(conversations): - # Create DataFrame from messages - messages_data = [] - for conv in conversations: - for msg in conv.messages: - if msg.role == "human": - messages_data.append( - { - "datetime": pd.to_datetime( - msg.created_at.replace("Z", "+00:00") - ), - "chat_id": conv.chat_id, - } - ) - - df = pd.DataFrame(messages_data) - df["week_start"] = df["datetime"].dt.to_period("W-MON").dt.start_time - - # Calculate messages and unique chats per week - weekly_messages = df.groupby("week_start").size().reset_index(name="message_count") - weekly_chats = ( - df.groupby("week_start")["chat_id"].nunique().reset_index(name="chat_count") - ) - - # Merge and calculate average - weekly_df = pd.merge(weekly_messages, weekly_chats, on="week_start") - weekly_df["avg_messages"] = weekly_df["message_count"] / weekly_df["chat_count"] - weekly_df["week_start"] = weekly_df["week_start"].dt.strftime("%Y-%m-%d") - - data = json.dumps( - { - "data": [ - { - "x": weekly_df["week_start"].tolist(), - "y": weekly_df["avg_messages"].tolist(), - "type": "scatter", - "mode": "lines+markers", - "name": "Average Messages per Chat", - "line": {"color": "coral"}, - "marker": {"size": 6}, - } - ], - "layout": { - "title": "Average Number of Messages per Chat by Week", - "xaxis": {"title": "Week Starting"}, - "yaxis": {"title": "Average Messages per Chat"}, - "showlegend": True, - "hovermode": "closest", - }, - } - ) - - return Script( - f"var messages_data = {data}; Plotly.newPlot('messagesDiv', messages_data);" - ) - - -def generate_messages_per_week_chart(conversations: List[Conversation]): - messages_data = [] - for conv in conversations: - for msg in conv.messages: - messages_data.append( - { - "datetime": pd.to_datetime(msg.created_at.replace("Z", "+00:00")), - "chat_id": conv.chat_id, - } - ) - - df = pd.DataFrame(messages_data) - df["week_start"] = df["datetime"].dt.to_period("W-MON").dt.start_time - - # Calculate messages per week - weekly_messages = df.groupby("week_start").size().reset_index(name="message_count") - weekly_messages["week_start"] = weekly_messages["week_start"].dt.strftime( - "%Y-%m-%d" - ) - - data = json.dumps( - { - "data": [ - { - "x": weekly_messages["week_start"].tolist(), - "y": weekly_messages["message_count"].tolist(), - "type": "bar", - "name": "Messages per Week", - "marker": {"color": "coral"}, - } - ], - "layout": { - "title": "Messages per Week", - "xaxis": {"title": "Week Starting"}, - "yaxis": {"title": "Number of Messages"}, - "showlegend": True, - "hovermode": "closest", - "bargap": 0.15, - }, - } - ) - - return Script( - f"var messages_per_week = {data}; Plotly.newPlot('messagesPerWeekDiv', messages_per_week.data, messages_per_week.layout);" - ) - - -def generate_new_chats_per_week_chart(conversations: List[Conversation]): - # Calculate new chats per week - chat_starts = pd.DataFrame( - [ - { - "datetime": pd.to_datetime(conv.created_at.replace("Z", "+00:00")), - "chat_id": conv.chat_id, - } - for conv in conversations - ] - ) - chat_starts["week_start"] = ( - chat_starts["datetime"].dt.to_period("W-MON").dt.start_time - ) - weekly_chats = ( - chat_starts.groupby("week_start").size().reset_index(name="chat_count") - ) - weekly_chats["week_start"] = weekly_chats["week_start"].dt.strftime("%Y-%m-%d") - - data = json.dumps( - { - "data": [ - { - "x": weekly_chats["week_start"].tolist(), - "y": weekly_chats["chat_count"].tolist(), - "type": "bar", - "name": "New Chats per Week", - "marker": {"color": "skyblue"}, - } - ], - "layout": { - "title": "New Chats Created per Week", - "xaxis": {"title": "Week Starting"}, - "yaxis": {"title": "Number of New Chats"}, - "showlegend": True, - "hovermode": "closest", - "bargap": 0.15, - }, - } - ) - - return Script( - f"var chats_per_week = {data}; Plotly.newPlot('chatsPerWeekDiv', chats_per_week.data, chats_per_week.layout);" - ) diff --git a/kura.png b/kura.png new file mode 100644 index 0000000..b6a43af Binary files /dev/null and b/kura.png differ diff --git a/kura/__init__.py b/kura/__init__.py index 45730d2..2768ccb 100644 --- a/kura/__init__.py +++ b/kura/__init__.py @@ -1,14 +1,5 @@ -from .cluster import ClusterModel -from .types import Cluster, ConversationSummary, Conversation, Message -from .llm_cluster import LLMCluster -from .summarise import SummariseBase +from .kura import Kura __all__ = [ - "ClusterModel", - "Cluster", - "ConversationSummary", - "Conversation", - "Message", - "LLMCluster", - "SummariseBase", + "Kura", ] diff --git a/kura/base_classes/__init__.py b/kura/base_classes/__init__.py new file mode 100644 index 0000000..0f7d06c --- /dev/null +++ b/kura/base_classes/__init__.py @@ -0,0 +1,15 @@ +from .embedding import BaseEmbeddingModel +from .summarisation import BaseSummaryModel +from .clustering_method import BaseClusteringMethod +from .cluster import BaseClusterModel +from .meta_cluster import BaseMetaClusterModel +from .dimensionality import BaseDimensionalityReduction + +__all__ = [ + "BaseEmbeddingModel", + "BaseSummaryModel", + "BaseClusteringMethod", + "BaseClusterModel", + "BaseMetaClusterModel", + "BaseDimensionalityReduction", +] diff --git a/kura/base_classes/cluster.py b/kura/base_classes/cluster.py new file mode 100644 index 0000000..bebfa23 --- /dev/null +++ b/kura/base_classes/cluster.py @@ -0,0 +1,10 @@ +from abc import ABC, abstractmethod +from kura.types import Cluster, ConversationSummary + + +class BaseClusterModel(ABC): + @abstractmethod + def cluster_summaries(self, summaries: list[ConversationSummary]) -> list[Cluster]: + pass + + # TODO : Add abstract method for hooks here once we start supporting it diff --git a/kura/base_classes/clustering_method.py b/kura/base_classes/clustering_method.py new file mode 100644 index 0000000..01365d8 --- /dev/null +++ b/kura/base_classes/clustering_method.py @@ -0,0 +1,12 @@ +from abc import ABC, abstractmethod +from typing import TypeVar, Union + +T = TypeVar("T") + + +class BaseClusteringMethod(ABC): + @abstractmethod + def cluster( + self, items: list[dict[str, Union[T, list[float]]]] + ) -> dict[int, list[T]]: + pass diff --git a/kura/base_classes/dimensionality.py b/kura/base_classes/dimensionality.py new file mode 100644 index 0000000..10c2a8c --- /dev/null +++ b/kura/base_classes/dimensionality.py @@ -0,0 +1,12 @@ +from abc import ABC, abstractmethod + +from kura.types import Cluster + + +class BaseDimensionalityReduction(ABC): + @abstractmethod + async def reduce_dimensionality(self, clusters: list[Cluster]) -> list[Cluster]: + """ + This reduces the dimensionality of the individual clusters that we've created so we can visualise them in a lower dimension + """ + pass diff --git a/kura/base_classes/embedding.py b/kura/base_classes/embedding.py new file mode 100644 index 0000000..d1d650f --- /dev/null +++ b/kura/base_classes/embedding.py @@ -0,0 +1,9 @@ +from abc import ABC, abstractmethod +from asyncio import Semaphore + + +class BaseEmbeddingModel(ABC): + @abstractmethod + async def embed(self, text: str) -> list[float]: + """Embed a single text into a list of floats""" + pass diff --git a/kura/base_classes/meta_cluster.py b/kura/base_classes/meta_cluster.py new file mode 100644 index 0000000..8ea6196 --- /dev/null +++ b/kura/base_classes/meta_cluster.py @@ -0,0 +1,8 @@ +from abc import ABC, abstractmethod +from kura.types.cluster import Cluster + + +class BaseMetaClusterModel(ABC): + @abstractmethod + def reduce_clusters(self, clusters: list[Cluster]) -> list[Cluster]: + pass diff --git a/kura/base_classes/summarisation.py b/kura/base_classes/summarisation.py new file mode 100644 index 0000000..4729b37 --- /dev/null +++ b/kura/base_classes/summarisation.py @@ -0,0 +1,28 @@ +from abc import ABC, abstractmethod + +from kura.types import Conversation, ConversationSummary + + +class BaseSummaryModel(ABC): + @abstractmethod + async def summarise( + self, conversations: list[Conversation] + ) -> list[ConversationSummary]: + """Summarise the conversations into a list of ConversationSummary""" + pass + + @abstractmethod + async def summarise_conversation( + self, conversation: Conversation + ) -> ConversationSummary: + """Summarise a single conversation into a single string""" + pass + + @abstractmethod + async def apply_hooks( + self, conversation: ConversationSummary + ) -> ConversationSummary: + """Apply hooks to the conversation summary""" + # Assert that the implementation of the class has a hooks attribute so we can call it in summarise_conversation + assert hasattr(self, "hooks") + pass diff --git a/kura/cli/server.py b/kura/cli/server.py index b4dfc9c..ca88b9d 100644 --- a/kura/cli/server.py +++ b/kura/cli/server.py @@ -2,7 +2,8 @@ from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel from pathlib import Path -from kura.types import Cluster, Conversation, Message +from kura import Kura +from kura.types import ProjectedCluster, Conversation, Message from kura.cli.visualisation import ( generate_cumulative_chart_data, generate_messages_per_chat_data, @@ -57,16 +58,24 @@ async def analyse_conversations(conversation_data: ConversationData): ] # Load clusters from checkpoint file if it exists - clusters_file = Path(__file__).parent.parent.parent / "checkpoints/clusters.json" + clusters_file = ( + Path(__file__).parent.parent.parent + / "checkpoints/dimensionality_checkpoints.json" + ) clusters = [] - print(clusters_file) - if clusters_file.exists(): - print("") - with open(clusters_file) as f: - clusters_data = [] - for line in f: - clusters_data.append(line) - clusters = [Cluster(**json.loads(cluster)) for cluster in clusters_data] + + if not clusters_file.exists(): + kura = Kura() + kura.conversations = conversations + await kura.cluster_conversations() + + with open(clusters_file) as f: + clusters_data = [] + for line in f: + clusters_data.append(line) + clusters = [ + ProjectedCluster(**json.loads(cluster)) for cluster in clusters_data + ] return { "cumulative_words": generate_cumulative_chart_data(conversations), diff --git a/kura/cli/visualisation.py b/kura/cli/visualisation.py index 4e46ec1..3be54aa 100644 --- a/kura/cli/visualisation.py +++ b/kura/cli/visualisation.py @@ -1,6 +1,6 @@ import pandas as pd from typing import List -from helpers.types import Conversation +from kura.types import Conversation def generate_cumulative_chart_data(conversations: List[Conversation]) -> dict: diff --git a/kura/cluster.py b/kura/cluster.py index 31cd10c..b916ad5 100644 --- a/kura/cluster.py +++ b/kura/cluster.py @@ -1,329 +1,163 @@ -from .types import Cluster, GeneratedCluster -from openai import AsyncOpenAI -from asyncio import Semaphore -from tqdm.asyncio import tqdm_asyncio as asyncio -import math -import re -import random +from kura.base_classes import BaseClusterModel, BaseClusteringMethod, BaseEmbeddingModel +from kura.embedding import OpenAIEmbeddingModel +from kura.k_means import KmeansClusteringMethod +from kura.types import ConversationSummary, Cluster, GeneratedCluster +from tqdm.asyncio import tqdm_asyncio import numpy as np -from sklearn.cluster import KMeans -from pydantic import BaseModel, field_validator, ValidationInfo -from instructor import from_gemini, AsyncInstructor +from asyncio import Semaphore +import instructor import google.generativeai as genai +import os -class CandidateClusters(BaseModel): - candidate_cluster_names: list[str] - - @field_validator("candidate_cluster_names") - def validate_candidate_cluster_names( - cls, v: list[str], info: ValidationInfo - ) -> list[str]: - if len(v) == 0: - raise ValueError("Candidate cluster names must be a non-empty list") - - v = [label.strip() for label in v] - v = [label[:-1] if label.endswith(".") else label for label in v] - - return [re.sub(r"\\{1,}", "", label.replace('"', "")) for label in v] - - -class ClusterLabel(BaseModel): - higher_level_cluster: str - - @field_validator("higher_level_cluster") - def validate_higher_level_cluster(cls, v: str, info: ValidationInfo) -> str: - if v not in info.context["candidate_clusters"]: - raise ValueError( - f""" - Invalid higher-level cluster: |{v}| - - Valid clusters are: - {', '.join(f'|{c}|' for c in info.context['candidate_clusters'])} - """ - ) - return v - - -class ClusterModel: +class ClusterModel(BaseClusterModel): def __init__( self, - embed_model: AsyncOpenAI = AsyncOpenAI(), - cluster_model: AsyncInstructor = from_gemini( + clustering_method: BaseClusteringMethod = KmeansClusteringMethod(), + embedding_model: BaseEmbeddingModel = OpenAIEmbeddingModel(), + max_concurrent_requests: int = 50, + client=instructor.from_gemini( genai.GenerativeModel("gemini-1.5-flash-latest"), use_async=True ), - max_concurrent_calls: int = 40, - base_cluster_per_cluster: int = 20, - ) -> None: - self.sem = Semaphore(max_concurrent_calls) - self.embed_model = embed_model - self.cluster_model = cluster_model - self.base_cluster_per_cluster = base_cluster_per_cluster - - async def embed_cluster(self, cluster: Cluster) -> Cluster: - async with self.sem: - embedding = await self.embed_model.embeddings.create( - input=f""" - Name: {cluster.name} - Description: {cluster.description} - """, - model="text-embedding-3-small", - ) - return { - "item": cluster, - "embedding": embedding.data[0].embedding, - } - - def cluster_base_clusters( - self, clusters: list[Cluster] - ) -> dict[int, list[Cluster]]: - n_clusters = math.ceil(len(clusters) / self.base_cluster_per_cluster) - - if n_clusters == 1: - return {0: [item["item"] for item in clusters]} - - embeddings = [item["embedding"] for item in clusters] - X = np.array(embeddings) - - kmeans = KMeans(n_clusters=n_clusters) - cluster_labels = kmeans.fit_predict(X) - - group_to_clusters = {} - for i, item in enumerate(clusters): - cluster_id = int(cluster_labels[i]) - if cluster_id not in group_to_clusters: - group_to_clusters[cluster_id] = [] - - group_to_clusters[cluster_id].append(item["item"]) - - return group_to_clusters - - async def generate_candidate_cluster_names( - self, clusters: list[Cluster] - ) -> list[str]: - async with self.sem: - resp = await self.cluster_model.chat.completions.create( - messages=[ - { - "role": "system", - "content": """ - You are tasked with creating higher-level cluster names based on a given list of clusters and their descriptions. Your goal is to come up with broader categories that could encompass one or more of the provided clusters - - First, review the list of clusters and their descriptions: - - {% for cluster in clusters %} - {{ cluster.name }}: {{ cluster.description }} - {% endfor %} - - - Your task is to create at most {{ desired_number }} higher-level cluster names that could potentially include one or more of the provided clusters. These higher-level clusters should represent broader categories or themes that emerge from the given clusters, while remaining as specific as possible. If there are many clusters with a specific theme, ensure that the higher-level cluster name remains the maximum level of specificity. You are helping to organize user behavior data in order to improve safety, monitoring, and observability. You can generate less than {{ desired_number }} names if you feel that fewer are appropriate and accurately capture the clusters. - - Guidelines for creating higher-level clusters names - 1. Analyze the themes, topics or characteristics common to multiple clusters. - 2. Create names that are specific enough to be meaningful but but not so specific that they can’t meaningfully represent many different clusters. Avoid overly general or vague terms, and do not hesitate to describe socially harmful or sensitive topics (in fact, clusters that clearly describe harmful behavior are slightly preferred); specificity is necessary for observability and enforcement. - 3. Ensure that the higher-level cluster names are distinct from one another. - 4. Use clear, concise, and descriptive language for the cluster names. Assume neither good nor bad faith for the content in the clusters. + checkpoint_dir: str = "checkpoints", + checkpoint_file: str = "cluster_checkpoint.json", + ): + self.clustering_method = clustering_method + self.embedding_model = embedding_model + self.max_concurrent_requests = max_concurrent_requests + self.client = client + self.checkpoint_dir = checkpoint_dir + self.checkpoint_file = checkpoint_file + + if not os.path.exists(self.checkpoint_dir): + print(f"Creating checkpoint directory {self.checkpoint_dir}") + os.makedirs(self.checkpoint_dir) + + def save_checkpoint(self, clusters: list[Cluster]): + with open(os.path.join(self.checkpoint_dir, self.checkpoint_file), "w") as f: + for cluster in clusters: + f.write(cluster.model_dump_json() + "\n") + + print( + f"Saved checkpoint to {os.path.join(self.checkpoint_dir, self.checkpoint_file)}" + ) - Think about the relationships between the given clusters and potential overarching themes. - - Focus on creating meaningful, distinct and precise ( but not overly specific ) higher-level cluster names that could encompass multiple sub-clusters. - """, - } - ], - response_model=CandidateClusters, - context={ - "clusters": clusters, - "desired_number": math.ceil(len(clusters) / 2), - }, - max_retries=3, - ) - return resp.candidate_cluster_names + def load_checkpoint(self) -> list[Cluster]: + print( + f"Loading Cluster Checkpoint from {os.path.join(self.checkpoint_dir, self.checkpoint_file)}" + ) + with open(os.path.join(self.checkpoint_dir, self.checkpoint_file), "r") as f: + return [Cluster.model_validate_json(line) for line in f] - async def label_cluster( + def get_contrastive_examples( + self, + cluster_id: int, + cluster_id_to_summaries: dict[int, list[ConversationSummary]], + desired_count: int = 10, + ): + other_clusters = [c for c in cluster_id_to_summaries.keys() if c != cluster_id] + all_examples = [] + for cluster in other_clusters: + all_examples.extend(cluster_id_to_summaries[cluster]) + + # If we don't have enough examples, return all of them + if len(all_examples) <= desired_count: + return all_examples + + # Otherwise sample without replacement + return list(np.random.choice(all_examples, size=desired_count, replace=False)) + + async def generate_cluster( self, - cluster: Cluster, - candidate_clusters: list[str], - ) -> dict: - async with self.sem: - candidate_clusters = candidate_clusters.copy() - random.shuffle(candidate_clusters) - label = await self.cluster_model.chat.completions.create( + summaries: list[ConversationSummary], + contrastive_examples: list[ConversationSummary], + sem: Semaphore, + ) -> Cluster: + async with sem: + resp = await self.client.chat.completions.create( messages=[ { "role": "system", "content": """ - You are tasked with categorizing a specific cluster into one of the provided higher-level clusters for observability, monitoring, and content moderation. Your goal is to determine which higher-level cluster best fits the given specific cluster based on its name and description. - - First, here are the ONLY valid higher-level clusters you may select from: - - {% for cluster in candidate_clusters %} - {{ cluster }} - {% endfor %} - - - Here is the specific cluster to categorize: - - Name: {{ cluster["name"] }} - Description: {{ cluster["description"] }} - - - RULES: - 1. You MUST select EXACTLY ONE higher-level cluster from the provided list - 2. You MUST output the higher-level cluster name EXACTLY as written - no modifications allowed - 3. You MUST NOT create new cluster names or combinations - 4. You MUST NOT output any additional text or explanations - 5. You MUST NOT use partial matches or approximate names - - CLASSIFICATION PROCESS: - 1. First, record the exact list of valid higher-level clusters - 2. Read the specific cluster's name and description carefully - 3. Compare the specific cluster's key characteristics against each valid higher-level cluster - 4. Select the single most appropriate higher-level cluster that encompasses the specific cluster - 5. Verify your selected cluster exactly matches one from the valid list - 6. Output ONLY the selected higher-level cluster name, exactly as it appeared in the valid list - - Here is the specific cluster to categorize: - - - Name: {{ cluster["name"] }} - Description: {{ cluster["description"] }} - - - Based on this information, determine the most appropriate higher-level cluster and provide your answer as instructed. - """, - } - ], - context={ - "cluster": cluster, - "candidate_clusters": candidate_clusters, - }, - response_model=ClusterLabel, - max_retries=3, - ) - return { - "cluster": cluster, - "higher_level_cluster": label.higher_level_cluster, - } - - async def rename_cluster_group(self, clusters: list[Cluster]) -> Cluster: - async with self.sem: - resp = await self.cluster_model.chat.completions.create( - messages=[ + You are tasked with summarizing a group of related statements into a short, precise, and accurate description and name. Your goal is to create a concise summary that captures the essence of these statements and distinguishes them from other similar groups of statements. + + Summarize all the statements into a clear, precise, two-sentence description in the past tense. Your summary should be specific to this group and distinguish it from the contrastive answers of the other groups. + + After creating the summary, generate a short name for the group of statements. This name should be at most ten words long (perhaps less) and be specific but also reflective of most of the statements (rather than reflecting only one or two). The name should distinguish this group from the contrastive examples. For instance, "Write fantasy sexual roleplay with octopi and monsters", "Generate blog spam for gambling websites", or "Assist with high school math homework" would be better and more actionable than general terms like "Write erotic content" or "Help with homework". Be as descriptive as possible and assume neither good nor bad faith. Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity is necessary for monitoring + + The cluster name should be a sentence in the imperative that captures the user’s request. For example, ‘Brainstorm ideas for a birthday party’ or ‘Help me find a new job.” + """, + }, { - "role": "system", + "role": "user", "content": """ - You are tasked with summarizing a group of related cluster names into a short, precise, and accurate overall description and name. Your goal is to create a concise summary that captures the essence of these clusters - - The cluster name should be at most ten words long (perhaps less) and be specific but also reflective of most of the clusters that comprise them. The cluster name should be a sentence in the imperative that captures the user’s request. For example, ‘Brainstorm ideas for a birthday party’ or ‘Help me find a new job are good examples. - - Do not shy away from describing socially harmful or sensitive topics. For instance, "Write fantasy sexual roleplay with octopi and monsters", "Generate blog spam for gambling websites", or "Assist with high school math homework" would be better than "Write erotic content" or "Help with homework". Be as descriptive as possible and assume neither good nor bad faith. Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity is necessary for monitoring. + Here are the relevant statements - The cluster description should be a clear, precise, two-sentence description in the past tense. This description should be specific to this cluster. Make sure that you've captured the most important details of the cluster. - - Below are the related cluster names - - {% for cluster in clusters %} - {{ cluster.name }}: {{ cluster.description }} - {% endfor %} - - - Ensure your summary and name accurately represent the clusters and are specific to the clusters. - """, - } + Below are the related statements: + + {% for item in positive_examples %} + {{ item.summary }} + {% endfor %} + + + For context, here are statements from nearby groups that are NOT part of the group you’re summarizing + + + {% for item in contrastive_examples %} + {{ item.summary }} + {% endfor %} + + + Remember to analyze both the statements and the contrastive statements carefully to ensure your summary and name accurately represent the specific group while distinguishing it from others. + """, + }, ], - context={"clusters": clusters}, response_model=GeneratedCluster, + context={ + "positive_examples": summaries, + "contrastive_examples": contrastive_examples, + }, ) - res = [] - - new_cluster = Cluster( - name=resp.name, - description=resp.summary, - chat_ids=[chat_id for cluster in clusters for chat_id in cluster.chat_ids], - parent_id=None, - ) - - res.append(new_cluster) - - for cluster in clusters: - res.append( - Cluster( - id=cluster.id, - name=cluster.name, - description=cluster.description, - chat_ids=cluster.chat_ids, - parent_id=new_cluster.id, - ) + return Cluster( + name=resp.name, + description=resp.summary, + chat_ids=[item.chat_id for item in summaries], + parent_id=None, ) - return res + async def cluster_summaries( + self, items: list[ConversationSummary] + ) -> dict[int, list[ConversationSummary]]: + if os.path.exists(os.path.join(self.checkpoint_dir, self.checkpoint_file)): + return self.load_checkpoint() - async def generate_higher_level_cluster( - self, clusters: list[Cluster] - ) -> list[Cluster]: - """ - This takes in a list of clusters and generates a smaller list of clusters that are more general. - - We do so in a 3 step process - - 1. First we generate a list of candidate cluster names - 2. Then we label each base cluster with a potential candidate cluster name - 3. Lastly, for our new clusters, we then generate a new re-labelled cluster with a new title and description that is more representative of these new clusters - - We do have a base case, in which that if a cluster contains a single item, it's just returned as is. - """ - if len(clusters) == 1: - return clusters - - candidates_cluster_names = await self.generate_candidate_cluster_names(clusters) - - labelled_clusters = await asyncio.gather( - *[ - self.label_cluster(cluster, candidates_cluster_names) - for cluster in clusters - ], - disable=True, - ) - label_to_cluster = {} - for labelled_cluster in labelled_clusters: - if labelled_cluster["higher_level_cluster"] not in label_to_cluster: - label_to_cluster[labelled_cluster["higher_level_cluster"]] = [] - - label_to_cluster[labelled_cluster["higher_level_cluster"]].append( - labelled_cluster["cluster"] - ) - - new_clusters = await asyncio.gather( - *[ - self.rename_cluster_group(cluster) - for cluster in label_to_cluster.values() - ], - disable=True, + sem = Semaphore(self.max_concurrent_requests) + embeddings: list[list[float]] = await tqdm_asyncio.gather( + *[self.embedding_model.embed(item.summary, sem) for item in items], + desc="Embedding Summaries", ) - - final_clusters = [] - - for clusters in new_clusters: - final_clusters.extend(clusters) - - return final_clusters - - async def cluster_clusters(self, clusters: list[Cluster]) -> list[Cluster]: - embedded_clusters = await asyncio.gather( - *[self.embed_cluster(cluster) for cluster in clusters], disable=True + cluster_id_to_summaries = self.clustering_method.cluster( + [ + { + "item": item, + "embedding": embedding, + } + for item, embedding in zip(items, embeddings) + ] ) - clusters = self.cluster_base_clusters(embedded_clusters) - new_clusters = await asyncio.gather( + clusters: list[Cluster] = await tqdm_asyncio.gather( *[ - self.generate_higher_level_cluster(cluster) - for cluster in clusters.values() + self.generate_cluster( + summaries, + self.get_contrastive_examples( + cluster_id, cluster_id_to_summaries, 10 + ), + sem, + ) + for cluster_id, summaries in cluster_id_to_summaries.items() ], - desc="Generating New Higher Level Clusters", + desc="Generating Base Clusters", ) - - res = [] - for clusters in new_clusters: - res.extend(clusters) - - return res + self.save_checkpoint(clusters) + return clusters diff --git a/kura/dimensionality.py b/kura/dimensionality.py new file mode 100644 index 0000000..9355f78 --- /dev/null +++ b/kura/dimensionality.py @@ -0,0 +1,77 @@ +from kura.base_classes import BaseDimensionalityReduction, BaseEmbeddingModel +from kura.types import Cluster, ProjectedCluster +from kura.embedding import OpenAIEmbeddingModel +from umap import UMAP +import numpy as np +import asyncio +import os + + +class HDBUMAP(BaseDimensionalityReduction): + def __init__( + self, + embedding_model: BaseEmbeddingModel = OpenAIEmbeddingModel(), + checkpoint_dir: str = "checkpoints", + checkpoint_name: str = "dimensionality_checkpoints.json", + ): + self.embedding_model = embedding_model + self.checkpoint_dir = checkpoint_dir + self.checkpoint_name = checkpoint_name + + async def reduce_dimensionality( + self, clusters: list[Cluster] + ) -> list[ProjectedCluster]: + if os.path.exists(os.path.join(self.checkpoint_dir, self.checkpoint_name)): + with open( + os.path.join(self.checkpoint_dir, self.checkpoint_name), "r" + ) as f: + print( + f"Loading UMAP Checkpoint from {self.checkpoint_dir}/{self.checkpoint_name}" + ) + return [ProjectedCluster.model_validate_json(line) for line in f] + + # Embed all clusters + sem = asyncio.Semaphore(50) + cluster_embeddings = await asyncio.gather( + *[ + self.embedding_model.embed( + f"Name: {c.name}\nDescription: {c.description}", sem + ) + for c in clusters + ] + ) + + # Convert embeddings to numpy array + embeddings = np.array(cluster_embeddings) + + # Project to 2D using UMAP + umap_reducer = UMAP( + n_components=2, + n_neighbors=min(15, len(embeddings) - 1), + min_dist=0, + metric="cosine", + ) + reduced_embeddings = umap_reducer.fit_transform(embeddings) + + # Create projected clusters with 2D coordinates + res = [] + for i, cluster in enumerate(clusters): + projected = ProjectedCluster( + id=cluster.id, + name=cluster.name, + description=cluster.description, + chat_ids=cluster.chat_ids, + parent_id=cluster.parent_id, + x_coord=float(reduced_embeddings[i][0]), + y_coord=float(reduced_embeddings[i][1]), + level=0 if cluster.parent_id is None else 1, + ) + res.append(projected) + + with open(os.path.join(self.checkpoint_dir, self.checkpoint_name), "w") as f: + for c in res: + f.write(c.model_dump_json() + "\n") + + print(f"Saved UMAP Checkpoint to {self.checkpoint_dir}/{self.checkpoint_name}") + + return res diff --git a/kura/embedding.py b/kura/embedding.py new file mode 100644 index 0000000..1a9aaae --- /dev/null +++ b/kura/embedding.py @@ -0,0 +1,20 @@ +from kura.base_classes import BaseEmbeddingModel +from asyncio import Semaphore, wait_for +from tenacity import retry, wait_fixed, stop_after_attempt +from openai import AsyncOpenAI + + +class OpenAIEmbeddingModel(BaseEmbeddingModel): + def __init__(self): + self.client = AsyncOpenAI() + + @retry(wait=wait_fixed(3), stop=stop_after_attempt(3)) + async def embed(self, text: str, sem: Semaphore) -> list[float]: + async with sem: + resp = await wait_for( + self.client.embeddings.create( + input=text, model="text-embedding-3-small" + ), + timeout=5, + ) + return resp.data[0].embedding diff --git a/kura/k_means.py b/kura/k_means.py new file mode 100644 index 0000000..96f5a3d --- /dev/null +++ b/kura/k_means.py @@ -0,0 +1,34 @@ +from kura.base_classes import BaseClusteringMethod +from sklearn.cluster import KMeans +import math +from typing import TypeVar +import numpy as np + +T = TypeVar("T") + + +class KmeansClusteringMethod(BaseClusteringMethod): + def __init__(self, clusters_per_group: int = 10): + self.clusters_per_group = clusters_per_group + + def cluster(self, items: list[T]) -> dict[int, list[T]]: + """ + We perform a clustering here using an embedding defined on each individual item. + + We assume that the item is passed in as a dictionary with + + - its relevant embedding stored in the "embedding" key. + - the item itself stored in the "item" key. + """ + embeddings = [item["embedding"] for item in items] + data: list[T] = [item["item"] for item in items] + n_clusters = math.ceil(len(data) / self.clusters_per_group) + + X = np.array(embeddings) + kmeans = KMeans(n_clusters=n_clusters) + cluster_labels = kmeans.fit_predict(X) + + return { + i: [data[j] for j in range(len(data)) if cluster_labels[j] == i] + for i in range(n_clusters) + } diff --git a/kura/kura.py b/kura/kura.py new file mode 100644 index 0000000..b7f1e87 --- /dev/null +++ b/kura/kura.py @@ -0,0 +1,123 @@ +from kura.dimensionality import HDBUMAP +from kura.types import Conversation, Message, Cluster +from kura.embedding import OpenAIEmbeddingModel +from kura.summarisation import SummaryModel +from kura.meta_cluster import MetaClusterModel +from kura.cluster import ClusterModel +from kura.base_classes import ( + BaseEmbeddingModel, + BaseSummaryModel, + BaseClusterModel, + BaseMetaClusterModel, + BaseDimensionalityReduction, +) +import json +import os + + +class Kura: + def __init__( + self, + conversations: list[Conversation] = [], + embedding_model: BaseEmbeddingModel = OpenAIEmbeddingModel(), + summarisation_model: BaseSummaryModel = SummaryModel(), + cluster_model: BaseClusterModel = ClusterModel(), + meta_cluster_model: BaseMetaClusterModel = MetaClusterModel(), + dimensionality_reduction: BaseDimensionalityReduction = HDBUMAP(), + max_clusters: int = 10, + checkpoint_dir: str = "checkpoints", + cluster_checkpoint_name: str = "clusters.json", + meta_cluster_checkpoint_name: str = "meta_clusters.json", + ): + self.embedding_model = embedding_model + self.summarisation_model = summarisation_model + self.conversations = conversations + self.max_clusters = max_clusters + self.cluster_model = cluster_model + self.meta_cluster_model = meta_cluster_model + self.dimensionality_reduction = dimensionality_reduction + self.checkpoint_dir = checkpoint_dir + self.cluster_checkpoint_name = cluster_checkpoint_name + self.meta_cluster_checkpoint_name = meta_cluster_checkpoint_name + + async def reduce_clusters(self, clusters: list[Cluster]) -> list[Cluster]: + if os.path.exists( + os.path.join(self.checkpoint_dir, self.cluster_checkpoint_name) + ): + print( + f"Loading Meta Cluster Checkpoint from {self.checkpoint_dir}/{self.cluster_checkpoint_name}" + ) + with open( + os.path.join(self.checkpoint_dir, self.cluster_checkpoint_name), "r" + ) as f: + return [Cluster(**json.loads(line)) for line in f] + + root_clusters = clusters + + print(f"Starting with {len(root_clusters)} clusters") + + while len(root_clusters) > self.max_clusters: + # We get the updated list of clusters + new_current_level = await self.meta_cluster_model.reduce_clusters( + root_clusters + ) + + # These are the new root clusters that we've generated + root_clusters = [c for c in new_current_level if c.parent_id is None] + + # We then remove outdated versions of clusters + old_cluster_ids = {rc.id for rc in new_current_level if rc.parent_id} + clusters = [c for c in clusters if c.id not in old_cluster_ids] + + # We then add the new clusters to the list + clusters.extend(new_current_level) + + print(f"Reduced to {len(root_clusters)} clusters") + + with open( + os.path.join(self.checkpoint_dir, self.cluster_checkpoint_name), "w" + ) as f: + print(f"Saving {len(clusters)} clusters to checkpoint") + for c in clusters: + f.write(c.model_dump_json() + "\n") + + return clusters + + async def cluster_conversations(self): + summaries = await self.summarisation_model.summarise(self.conversations) + clusters: list[Cluster] = await self.cluster_model.cluster_summaries(summaries) + processed_clusters = await self.reduce_clusters(clusters) + dimensionality_reduced_clusters = ( + await self.dimensionality_reduction.reduce_dimensionality( + processed_clusters + ) + ) + + return dimensionality_reduced_clusters + + def load_conversations_from_file(self, file_path: str): + """Load a list of conversations from a file in Claude's conversation format dump""" + with open(file_path, "r") as f: + conversations = [] + for conversation in json.load(f): + conversations.append( + Conversation( + chat_id=conversation["uuid"], + created_at=conversation["created_at"], + messages=[ + Message( + created_at=message["created_at"], + role=message["sender"], + content="\n".join( + [ + item["text"] + for item in message["content"] + if item["type"] == "text" + ] + ), + ) + for message in conversation["chat_messages"] + ], + ) + ) + self.conversations = conversations diff --git a/kura/llm_cluster.py b/kura/llm_cluster.py deleted file mode 100644 index 0a382d0..0000000 --- a/kura/llm_cluster.py +++ /dev/null @@ -1,122 +0,0 @@ -from .summarise import SummariseBase -from .summary_cluster import SummaryCluster -from .types import Cluster, Conversation, Message -import json -import os -from .cluster import ClusterModel - - -class LLMCluster: - def __init__( - self, - summariser: SummariseBase = SummariseBase(), - base_cluster_model: SummaryCluster = SummaryCluster(), - cluster_model: ClusterModel = ClusterModel(), - max_clusters: int = 10, - checkpoint_dir: str = "checkpoints", - ): - self.summariser = summariser - self.base_cluster_model = base_cluster_model - self.max_clusters = max_clusters - self.checkpoint_dir = checkpoint_dir - self.cluster_model = cluster_model - - self.clusters = [] - - if not os.path.exists(self.checkpoint_dir): - os.makedirs(self.checkpoint_dir) - - async def reduce_clusters( - self, clusters: list[Cluster], root_clusters: list[Cluster], iteration: int = 1 - ): - while len(root_clusters) > self.max_clusters: - print(f"\n=== Iteration {iteration} ===") - print(f" Starting with {len(root_clusters)} root clusters...") - - # Generate new parent clusters from current level - new_parent_clusters = await self.cluster_model.cluster_clusters( - root_clusters - ) - - # This is the new layer - root_clusters = [c for c in new_parent_clusters if not c.parent_id] - - # Remove the outdated versions of clusters that were just reduced - old_cluster_ids = {rc.id for rc in new_parent_clusters if rc.parent_id} - clusters = [c for c in clusters if c.id not in old_cluster_ids] - - # Add both the updated original clusters and new parent clusters - clusters.extend(new_parent_clusters) - print(f" Successfully reduced to {len(root_clusters)} root clusters") - - iteration += 1 - - return clusters, root_clusters - - async def cluster_conversations(self): - summarized_conversations = await self.summariser.summarise_conversations( - self.conversations - ) - - base_clusters = await self.base_cluster_model.cluster_conversation_summaries( - summarized_conversations - ) - - clusters_path = os.path.join(self.checkpoint_dir, "clusters.json") - - if os.path.exists(clusters_path): - with open(clusters_path, "r") as f: - clusters = [Cluster.model_validate_json(line) for line in f] - else: - clusters = base_clusters - root_clusters = base_clusters - - clusters, root_clusters = await self.reduce_clusters( - clusters, root_clusters - ) - - # Save final clusters to checkpoint directory - - with open(clusters_path, "w") as f: - for cluster in clusters: - f.write(cluster.model_dump_json() + "\n") - - - - - print(f"\nSaved {len(clusters)} total clusters to {clusters_path}") - - return clusters - - def load_claude_messages( - self, path: str, max_conversations: int = -1 - ) -> list[Conversation]: - with open(path, "r") as f: - conversations = [] - for conversation in json.load(f): - conversations.append( - Conversation( - chat_id=conversation["uuid"], - created_at=conversation["created_at"], - messages=[ - Message( - created_at=message["created_at"], - role=message["sender"], - content="\n".join( - [ - item["text"] - for item in message["content"] - if item["type"] == "text" - ] - ), - ) - for message in conversation["chat_messages"] - ], - ) - ) - self.conversations = ( - conversations - if max_conversations == -1 - else conversations[:max_conversations] - ) - print(f"Read in {len(self.conversations)} conversations") diff --git a/kura/meta_cluster.py b/kura/meta_cluster.py new file mode 100644 index 0000000..3f46ddd --- /dev/null +++ b/kura/meta_cluster.py @@ -0,0 +1,308 @@ +from kura.base_classes import ( + BaseMetaClusterModel, + BaseEmbeddingModel, + BaseClusteringMethod, +) +import math +from kura.types.cluster import Cluster, GeneratedCluster +from kura.embedding import OpenAIEmbeddingModel +from kura.k_means import KmeansClusteringMethod +import instructor +import google.generativeai as genai +from tqdm.asyncio import tqdm_asyncio +from asyncio import Semaphore +from pydantic import BaseModel, field_validator, ValidationInfo +import re + + +class CandidateClusters(BaseModel): + candidate_cluster_names: list[str] + + @field_validator("candidate_cluster_names") + def validate_candidate_cluster_names( + cls, v: list[str], info: ValidationInfo + ) -> list[str]: + if len(v) == 0: + raise ValueError("Candidate cluster names must be a non-empty list") + + v = [label.strip() for label in v] + v = [label[:-1] if label.endswith(".") else label for label in v] + + return [re.sub(r"\\{1,}", "", label.replace('"', "")) for label in v] + + +class ClusterLabel(BaseModel): + higher_level_cluster: str + + @field_validator("higher_level_cluster") + def validate_higher_level_cluster(cls, v: str, info: ValidationInfo) -> str: + if v not in info.context["candidate_clusters"]: + raise ValueError( + f""" + Invalid higher-level cluster: |{v}| + + Valid clusters are: + {", ".join(f"|{c}|" for c in info.context["candidate_clusters"])} + """ + ) + return v + + +class MetaClusterModel(BaseMetaClusterModel): + def __init__( + self, + max_concurrent_requests: int = 50, + client=instructor.from_gemini( + genai.GenerativeModel("gemini-1.5-flash-latest"), use_async=True + ), + embedding_model: BaseEmbeddingModel = OpenAIEmbeddingModel(), + clustering_model: BaseClusteringMethod = KmeansClusteringMethod(12), + ): + self.max_concurrent_requests = max_concurrent_requests + self.client = client + self.embedding_model = embedding_model + self.clustering_model = clustering_model + + async def generate_candidate_clusters( + self, clusters: list[Cluster], sem: Semaphore + ) -> list[Cluster]: + async with sem: + resp = await self.client.chat.completions.create( + messages=[ + { + "role": "system", + "content": """ + You are tasked with creating higher-level cluster names based on a given list of clusters and their descriptions. Your goal is to come up with broader categories that could encompass one or more of the provided clusters + + First, review the list of clusters and their descriptions: + + {% for cluster in clusters %} + {{ cluster.name }}: {{ cluster.description }} + {% endfor %} + + + Your task is to create at most {{ desired_number }} higher-level cluster names that could potentially include one or more of the provided clusters. These higher-level clusters should represent broader categories or themes that emerge from the given clusters, while remaining as specific as possible. If there are many clusters with a specific theme, ensure that the higher-level cluster name remains the maximum level of specificity. You are helping to organize user behavior data in order to improve safety, monitoring, and observability. You can generate less than {{ desired_number }} names if you feel that fewer are appropriate and accurately capture the clusters. + + Guidelines for creating higher-level clusters names + 1. Analyze the themes, topics or characteristics common to multiple clusters. + 2. Create names that are specific enough to be meaningful but but not so specific that they can’t meaningfully represent many different clusters. Avoid overly general or vague terms, and do not hesitate to describe socially harmful or sensitive topics (in fact, clusters that clearly describe harmful behavior are slightly preferred); specificity is necessary for observability and enforcement. + 3. Ensure that the higher-level cluster names are distinct from one another. + 4. Use clear, concise, and descriptive language for the cluster names. Assume neither good nor bad faith for the content in the clusters. + + Think about the relationships between the given clusters and potential overarching themes. + + Focus on creating meaningful, distinct and precise ( but not overly specific ) higher-level cluster names that could encompass multiple sub-clusters. + """.strip(), + } + ], + response_model=CandidateClusters, + context={ + "clusters": clusters, + "desired_number": math.ceil(len(clusters) / 2), + }, + max_retries=3, + ) + return resp.candidate_cluster_names + + async def label_cluster(self, cluster: Cluster, candidate_clusters: list[str]): + async with self.sem: + resp = await self.client.chat.completions.create( + messages=[ + { + "role": "system", + "content": """ +You are tasked with categorizing a specific cluster into one of the provided higher-level clusters for observability, monitoring, and content moderation. Your goal is to determine which higher-level cluster best fits the given specific cluster based on its name and description. + +First, here are the ONLY valid higher-level clusters you may select from: + +{% for cluster in candidate_clusters %} +{{ cluster }} +{% endfor %} + + +Here is the specific cluster to categorize: + +Name: {{ cluster.name }} +Description: {{ cluster.description }} + + +RULES: +1. You MUST select EXACTLY ONE higher-level cluster from the provided list +2. You MUST output the higher-level cluster name EXACTLY as written - no modifications allowed +3. You MUST NOT create new cluster names or combinations +4. You MUST NOT output any additional text or explanations +5. You MUST NOT use partial matches or approximate names + +CLASSIFICATION PROCESS: +1. First, record the exact list of valid higher-level clusters +2. Read the specific cluster's name and description carefully +3. Compare the specific cluster's key characteristics against each valid higher-level cluster +4. Select the single most appropriate higher-level cluster that encompasses the specific cluster +5. Verify your selected cluster exactly matches one from the valid list +6. Output ONLY the selected higher-level cluster name, exactly as it appeared in the valid list + +Here is the specific cluster to categorize: + + +Name: {{ cluster.name }} +Description: {{ cluster.description }} + + +Based on this information, determine the most appropriate higher-level cluster and provide your answer as instructed. + """, + } + ], + response_model=ClusterLabel, + context={ + "cluster": cluster, + "candidate_clusters": candidate_clusters, + }, + max_retries=3, + ) + return { + "cluster": cluster, + "label": resp.higher_level_cluster, + } + + async def rename_cluster_group(self, clusters: list[Cluster]) -> list[Cluster]: + async with self.sem: + resp = await self.client.chat.completions.create( + messages=[ + { + "role": "system", + "content": """ + You are tasked with summarizing a group of related cluster names into a short, precise, and accurate overall description and name. Your goal is to create a concise summary that captures the essence of these clusters + + The cluster name should be at most ten words long (perhaps less) and be specific but also reflective of most of the clusters that comprise them. The cluster name should be a sentence in the imperative that captures the user’s request. For example, ‘Brainstorm ideas for a birthday party’ or ‘Help me find a new job are good examples. + + Do not shy away from describing socially harmful or sensitive topics. For instance, "Write fantasy sexual roleplay with octopi and monsters", "Generate blog spam for gambling websites", or "Assist with high school math homework" would be better than "Write erotic content" or "Help with homework". Be as descriptive as possible and assume neither good nor bad faith. Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity is necessary for monitoring. + + The cluster description should be a clear, precise, two-sentence description in the past tense. This description should be specific to this cluster. Make sure that you've captured the most important details of the cluster. + + Below are the related cluster names + + {% for cluster in clusters %} + {{ cluster.name }}: {{ cluster.description }} + {% endfor %} + + + Ensure your summary and name accurately represent the clusters and are specific to the clusters. + """, + } + ], + context={"clusters": clusters}, + response_model=GeneratedCluster, + ) + + res = [] + + new_cluster = Cluster( + name=resp.name, + description=resp.summary, + chat_ids=[ + chat_id for cluster in clusters for chat_id in cluster.chat_ids + ], + parent_id=None, + ) + + res.append(new_cluster) + + for cluster in clusters: + res.append( + Cluster( + id=cluster.id, + name=cluster.name, + description=cluster.description, + chat_ids=cluster.chat_ids, + parent_id=new_cluster.id, + ) + ) + + return res + + async def generate_meta_clusters(self, clusters: list[Cluster]) -> list[Cluster]: + candidate_labels = await self.generate_candidate_clusters( + clusters, Semaphore(self.max_concurrent_requests) + ) + + cluster_labels = await tqdm_asyncio.gather( + *[self.label_cluster(cluster, candidate_labels) for cluster in clusters], + disable=True, + ) + + label_to_clusters = {} + for label in cluster_labels: + if label["label"] not in label_to_clusters: + label_to_clusters[label["label"]] = [] + + label_to_clusters[label["label"]].append(label["cluster"]) + + new_clusters = await tqdm_asyncio.gather( + *[ + self.rename_cluster_group(cluster) + for cluster in label_to_clusters.values() + ], + disable=True, + ) + + res = [] + for new_cluster in new_clusters: + res.extend(new_cluster) + + return res + + async def reduce_clusters(self, clusters: list[Cluster]) -> list[Cluster]: + """ + This takes in a list of existing clusters and generates a few higher order clusters that are more general. This represents a single iteration of the meta clustering process. + + In the event that we have a single cluster, we will just return a new higher level cluster which has the same name as the original cluster. ( This is an edge case which we should definitely handle better ) + """ + if len(clusters) == 1: + new_cluster = Cluster( + name=clusters[0].name, + description=clusters[0].description, + chat_ids=clusters[0].chat_ids, + parent_id=None, + ) + clusters[0].parent_id = new_cluster.id + return [new_cluster, clusters[0]] + + self.sem = Semaphore(self.max_concurrent_requests) + cluster_embeddings: list[list[float]] = await tqdm_asyncio.gather( + *[ + self.embedding_model.embed( + f""" +Name: {cluster.name} +Description: {cluster.description} + """.strip(), + self.sem, + ) + for cluster in clusters + ], + desc="Embedding Clusters", + ) + clusters_and_embeddings = [ + { + "item": cluster, + "embedding": embedding, + } + for cluster, embedding in zip(clusters, cluster_embeddings) + ] + + cluster_id_to_clusters: dict[int, list[Cluster]] = ( + self.clustering_model.cluster(clusters_and_embeddings) + ) + + new_clusters = await tqdm_asyncio.gather( + *[ + 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0:e.precision)>"u"?null:e==null?void 0:e.precision,...xe.errToObj(e==null?void 0:e.message)})}duration(e){return this._addCheck({kind:"duration",...xe.errToObj(e)})}regex(e,r){return this._addCheck({kind:"regex",regex:e,...xe.errToObj(r)})}includes(e,r){return this._addCheck({kind:"includes",value:e,position:r==null?void 0:r.position,...xe.errToObj(r==null?void 0:r.message)})}startsWith(e,r){return this._addCheck({kind:"startsWith",value:e,...xe.errToObj(r)})}endsWith(e,r){return this._addCheck({kind:"endsWith",value:e,...xe.errToObj(r)})}min(e,r){return this._addCheck({kind:"min",value:e,...xe.errToObj(r)})}max(e,r){return this._addCheck({kind:"max",value:e,...xe.errToObj(r)})}length(e,r){return this._addCheck({kind:"length",value:e,...xe.errToObj(r)})}nonempty(e){return this.min(1,xe.errToObj(e))}trim(){return new Hr({...this._def,checks:[...this._def.checks,{kind:"trim"}]})}toLowerCase(){return new Hr({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]})}toUpperCase(){return new Hr({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}get isDatetime(){return!!this._def.checks.find(e=>e.kind==="datetime")}get isDate(){return!!this._def.checks.find(e=>e.kind==="date")}get isTime(){return!!this._def.checks.find(e=>e.kind==="time")}get isDuration(){return!!this._def.checks.find(e=>e.kind==="duration")}get isEmail(){return!!this._def.checks.find(e=>e.kind==="email")}get isURL(){return!!this._def.checks.find(e=>e.kind==="url")}get isEmoji(){return!!this._def.checks.find(e=>e.kind==="emoji")}get isUUID(){return!!this._def.checks.find(e=>e.kind==="uuid")}get isNANOID(){return!!this._def.checks.find(e=>e.kind==="nanoid")}get isCUID(){return!!this._def.checks.find(e=>e.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(e=>e.kind==="cuid2")}get isULID(){return!!this._def.checks.find(e=>e.kind==="ulid")}get isIP(){return!!this._def.checks.find(e=>e.kind==="ip")}get isCIDR(){return!!this._def.checks.find(e=>e.kind==="cidr")}get isBase64(){return!!this._def.checks.find(e=>e.kind==="base64")}get isBase64url(){return!!this._def.checks.find(e=>e.kind==="base64url")}get minLength(){let e=null;for(const r of this._def.checks)r.kind==="min"&&(e===null||r.value>e)&&(e=r.value);return e}get maxLength(){let e=null;for(const r of this._def.checks)r.kind==="max"&&(e===null||r.value{var e;return new Hr({checks:[],typeName:Ae.ZodString,coerce:(e=t==null?void 0:t.coerce)!==null&&e!==void 0?e:!1,...Me(t)})};function qR(t,e){const r=(t.toString().split(".")[1]||"").length,n=(e.toString().split(".")[1]||"").length,i=r>n?r:n,o=parseInt(t.toFixed(i).replace(".","")),s=parseInt(e.toFixed(i).replace(".",""));return o%s/Math.pow(10,i)}class xi extends $e{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(e){if(this._def.coerce&&(e.data=Number(e.data)),this._getType(e)!==pe.number){const o=this._getOrReturnCtx(e);return fe(o,{code:ue.invalid_type,expected:pe.number,received:o.parsedType}),Ee}let n;const i=new Vt;for(const o of this._def.checks)o.kind==="int"?Fe.isInteger(e.data)||(n=this._getOrReturnCtx(e,n),fe(n,{code:ue.invalid_type,expected:"integer",received:"float",message:o.message}),i.dirty()):o.kind==="min"?(o.inclusive?e.datao.value:e.data>=o.value)&&(n=this._getOrReturnCtx(e,n),fe(n,{code:ue.too_big,maximum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="multipleOf"?qR(e.data,o.value)!==0&&(n=this._getOrReturnCtx(e,n),fe(n,{code:ue.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):o.kind==="finite"?Number.isFinite(e.data)||(n=this._getOrReturnCtx(e,n),fe(n,{code:ue.not_finite,message:o.message}),i.dirty()):Fe.assertNever(o);return{status:i.value,value:e.data}}gte(e,r){return this.setLimit("min",e,!0,xe.toString(r))}gt(e,r){return this.setLimit("min",e,!1,xe.toString(r))}lte(e,r){return this.setLimit("max",e,!0,xe.toString(r))}lt(e,r){return this.setLimit("max",e,!1,xe.toString(r))}setLimit(e,r,n,i){return new xi({...this._def,checks:[...this._def.checks,{kind:e,value:r,inclusive:n,message:xe.toString(i)}]})}_addCheck(e){return new xi({...this._def,checks:[...this._def.checks,e]})}int(e){return this._addCheck({kind:"int",message:xe.toString(e)})}positive(e){return this._addCheck({kind:"min",value:0,inclusive:!1,message:xe.toString(e)})}negative(e){return this._addCheck({kind:"max",value:0,inclusive:!1,message:xe.toString(e)})}nonpositive(e){return this._addCheck({kind:"max",value:0,inclusive:!0,message:xe.toString(e)})}nonnegative(e){return this._addCheck({kind:"min",value:0,inclusive:!0,message:xe.toString(e)})}multipleOf(e,r){return this._addCheck({kind:"multipleOf",value:e,message:xe.toString(r)})}finite(e){return this._addCheck({kind:"finite",message:xe.toString(e)})}safe(e){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:xe.toString(e)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:xe.toString(e)})}get minValue(){let e=null;for(const r of this._def.checks)r.kind==="min"&&(e===null||r.value>e)&&(e=r.value);return e}get maxValue(){let e=null;for(const r of this._def.checks)r.kind==="max"&&(e===null||r.valuee.kind==="int"||e.kind==="multipleOf"&&Fe.isInteger(e.value))}get isFinite(){let e=null,r=null;for(const n of this._def.checks){if(n.kind==="finite"||n.kind==="int"||n.kind==="multipleOf")return!0;n.kind==="min"?(r===null||n.value>r)&&(r=n.value):n.kind==="max"&&(e===null||n.valuenew xi({checks:[],typeName:Ae.ZodNumber,coerce:(t==null?void 0:t.coerce)||!1,...Me(t)});class wi extends $e{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(e){if(this._def.coerce)try{e.data=BigInt(e.data)}catch{return this._getInvalidInput(e)}if(this._getType(e)!==pe.bigint)return this._getInvalidInput(e);let n;const i=new Vt;for(const o of this._def.checks)o.kind==="min"?(o.inclusive?e.datao.value:e.data>=o.value)&&(n=this._getOrReturnCtx(e,n),fe(n,{code:ue.too_big,type:"bigint",maximum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="multipleOf"?e.data%o.value!==BigInt(0)&&(n=this._getOrReturnCtx(e,n),fe(n,{code:ue.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):Fe.assertNever(o);return{status:i.value,value:e.data}}_getInvalidInput(e){const r=this._getOrReturnCtx(e);return fe(r,{code:ue.invalid_type,expected:pe.bigint,received:r.parsedType}),Ee}gte(e,r){return this.setLimit("min",e,!0,xe.toString(r))}gt(e,r){return this.setLimit("min",e,!1,xe.toString(r))}lte(e,r){return this.setLimit("max",e,!0,xe.toString(r))}lt(e,r){return this.setLimit("max",e,!1,xe.toString(r))}setLimit(e,r,n,i){return new wi({...this._def,checks:[...this._def.checks,{kind:e,value:r,inclusive:n,message:xe.toString(i)}]})}_addCheck(e){return new wi({...this._def,checks:[...this._def.checks,e]})}positive(e){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:xe.toString(e)})}negative(e){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:xe.toString(e)})}nonpositive(e){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:xe.toString(e)})}nonnegative(e){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:xe.toString(e)})}multipleOf(e,r){return this._addCheck({kind:"multipleOf",value:e,message:xe.toString(r)})}get minValue(){let e=null;for(const r of this._def.checks)r.kind==="min"&&(e===null||r.value>e)&&(e=r.value);return e}get maxValue(){let e=null;for(const r of this._def.checks)r.kind==="max"&&(e===null||r.value{var e;return new wi({checks:[],typeName:Ae.ZodBigInt,coerce:(e=t==null?void 0:t.coerce)!==null&&e!==void 0?e:!1,...Me(t)})};class Qu extends $e{_parse(e){if(this._def.coerce&&(e.data=!!e.data),this._getType(e)!==pe.boolean){const n=this._getOrReturnCtx(e);return fe(n,{code:ue.invalid_type,expected:pe.boolean,received:n.parsedType}),Ee}return Yt(e.data)}}Qu.create=t=>new Qu({typeName:Ae.ZodBoolean,coerce:(t==null?void 0:t.coerce)||!1,...Me(t)});class ea extends $e{_parse(e){if(this._def.coerce&&(e.data=new Date(e.data)),this._getType(e)!==pe.date){const o=this._getOrReturnCtx(e);return fe(o,{code:ue.invalid_type,expected:pe.date,received:o.parsedType}),Ee}if(isNaN(e.data.getTime())){const o=this._getOrReturnCtx(e);return fe(o,{code:ue.invalid_date}),Ee}const n=new Vt;let i;for(const o of this._def.checks)o.kind==="min"?e.data.getTime()o.value&&(i=this._getOrReturnCtx(e,i),fe(i,{code:ue.too_big,message:o.message,inclusive:!0,exact:!1,maximum:o.value,type:"date"}),n.dirty()):Fe.assertNever(o);return{status:n.value,value:new Date(e.data.getTime())}}_addCheck(e){return new ea({...this._def,checks:[...this._def.checks,e]})}min(e,r){return this._addCheck({kind:"min",value:e.getTime(),message:xe.toString(r)})}max(e,r){return this._addCheck({kind:"max",value:e.getTime(),message:xe.toString(r)})}get minDate(){let e=null;for(const r of this._def.checks)r.kind==="min"&&(e===null||r.value>e)&&(e=r.value);return e!=null?new Date(e):null}get maxDate(){let e=null;for(const r of this._def.checks)r.kind==="max"&&(e===null||r.valuenew ea({checks:[],coerce:(t==null?void 0:t.coerce)||!1,typeName:Ae.ZodDate,...Me(t)});class jc extends $e{_parse(e){if(this._getType(e)!==pe.symbol){const n=this._getOrReturnCtx(e);return fe(n,{code:ue.invalid_type,expected:pe.symbol,received:n.parsedType}),Ee}return Yt(e.data)}}jc.create=t=>new jc({typeName:Ae.ZodSymbol,...Me(t)});class Ju extends $e{_parse(e){if(this._getType(e)!==pe.undefined){const n=this._getOrReturnCtx(e);return fe(n,{code:ue.invalid_type,expected:pe.undefined,received:n.parsedType}),Ee}return Yt(e.data)}}Ju.create=t=>new Ju({typeName:Ae.ZodUndefined,...Me(t)});class es extends $e{_parse(e){if(this._getType(e)!==pe.null){const n=this._getOrReturnCtx(e);return fe(n,{code:ue.invalid_type,expected:pe.null,received:n.parsedType}),Ee}return Yt(e.data)}}es.create=t=>new es({typeName:Ae.ZodNull,...Me(t)});class Ja extends $e{constructor(){super(...arguments),this._any=!0}_parse(e){return Yt(e.data)}}Ja.create=t=>new Ja({typeName:Ae.ZodAny,...Me(t)});class Xi extends $e{constructor(){super(...arguments),this._unknown=!0}_parse(e){return Yt(e.data)}}Xi.create=t=>new Xi({typeName:Ae.ZodUnknown,...Me(t)});class qn extends $e{_parse(e){const r=this._getOrReturnCtx(e);return fe(r,{code:ue.invalid_type,expected:pe.never,received:r.parsedType}),Ee}}qn.create=t=>new qn({typeName:Ae.ZodNever,...Me(t)});class Nc extends $e{_parse(e){if(this._getType(e)!==pe.undefined){const n=this._getOrReturnCtx(e);return fe(n,{code:ue.invalid_type,expected:pe.void,received:n.parsedType}),Ee}return Yt(e.data)}}Nc.create=t=>new Nc({typeName:Ae.ZodVoid,...Me(t)});class Kr extends $e{_parse(e){const{ctx:r,status:n}=this._processInputParams(e),i=this._def;if(r.parsedType!==pe.array)return fe(r,{code:ue.invalid_type,expected:pe.array,received:r.parsedType}),Ee;if(i.exactLength!==null){const s=r.data.length>i.exactLength.value,l=r.data.lengthi.maxLength.value&&(fe(r,{code:ue.too_big,maximum:i.maxLength.value,type:"array",inclusive:!0,exact:!1,message:i.maxLength.message}),n.dirty()),r.common.async)return Promise.all([...r.data].map((s,l)=>i.type._parseAsync(new vn(r,s,r.path,l)))).then(s=>Vt.mergeArray(n,s));const o=[...r.data].map((s,l)=>i.type._parseSync(new vn(r,s,r.path,l)));return Vt.mergeArray(n,o)}get element(){return this._def.type}min(e,r){return new Kr({...this._def,minLength:{value:e,message:xe.toString(r)}})}max(e,r){return new Kr({...this._def,maxLength:{value:e,message:xe.toString(r)}})}length(e,r){return new Kr({...this._def,exactLength:{value:e,message:xe.toString(r)}})}nonempty(e){return this.min(1,e)}}Kr.create=(t,e)=>new Kr({type:t,minLength:null,maxLength:null,exactLength:null,typeName:Ae.ZodArray,...Me(e)});function Ba(t){if(t instanceof ct){const e={};for(const r in t.shape){const n=t.shape[r];e[r]=fn.create(Ba(n))}return new ct({...t._def,shape:()=>e})}else return t instanceof Kr?new Kr({...t._def,type:Ba(t.element)}):t instanceof fn?fn.create(Ba(t.unwrap())):t instanceof Si?Si.create(Ba(t.unwrap())):t instanceof yn?yn.create(t.items.map(e=>Ba(e))):t}class ct extends $e{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const e=this._def.shape(),r=Fe.objectKeys(e);return this._cached={shape:e,keys:r}}_parse(e){if(this._getType(e)!==pe.object){const d=this._getOrReturnCtx(e);return fe(d,{code:ue.invalid_type,expected:pe.object,received:d.parsedType}),Ee}const{status:n,ctx:i}=this._processInputParams(e),{shape:o,keys:s}=this._getCached(),l=[];if(!(this._def.catchall instanceof qn&&this._def.unknownKeys==="strip"))for(const d in i.data)s.includes(d)||l.push(d);const f=[];for(const d of s){const h=o[d],m=i.data[d];f.push({key:{status:"valid",value:d},value:h._parse(new vn(i,m,i.path,d)),alwaysSet:d in i.data})}if(this._def.catchall instanceof qn){const d=this._def.unknownKeys;if(d==="passthrough")for(const h of l)f.push({key:{status:"valid",value:h},value:{status:"valid",value:i.data[h]}});else if(d==="strict")l.length>0&&(fe(i,{code:ue.unrecognized_keys,keys:l}),n.dirty());else if(d!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const d=this._def.catchall;for(const h of l){const m=i.data[h];f.push({key:{status:"valid",value:h},value:d._parse(new vn(i,m,i.path,h)),alwaysSet:h in i.data})}}return i.common.async?Promise.resolve().then(async()=>{const d=[];for(const h of f){const m=await h.key,v=await h.value;d.push({key:m,value:v,alwaysSet:h.alwaysSet})}return d}).then(d=>Vt.mergeObjectSync(n,d)):Vt.mergeObjectSync(n,f)}get shape(){return this._def.shape()}strict(e){return xe.errToObj,new ct({...this._def,unknownKeys:"strict",...e!==void 0?{errorMap:(r,n)=>{var i,o,s,l;const f=(s=(o=(i=this._def).errorMap)===null||o===void 0?void 0:o.call(i,r,n).message)!==null&&s!==void 0?s:n.defaultError;return r.code==="unrecognized_keys"?{message:(l=xe.errToObj(e).message)!==null&&l!==void 0?l:f}:{message:f}}}:{}})}strip(){return new ct({...this._def,unknownKeys:"strip"})}passthrough(){return new ct({...this._def,unknownKeys:"passthrough"})}extend(e){return new ct({...this._def,shape:()=>({...this._def.shape(),...e})})}merge(e){return new ct({unknownKeys:e._def.unknownKeys,catchall:e._def.catchall,shape:()=>({...this._def.shape(),...e._def.shape()}),typeName:Ae.ZodObject})}setKey(e,r){return this.augment({[e]:r})}catchall(e){return new ct({...this._def,catchall:e})}pick(e){const r={};return Fe.objectKeys(e).forEach(n=>{e[n]&&this.shape[n]&&(r[n]=this.shape[n])}),new ct({...this._def,shape:()=>r})}omit(e){const r={};return Fe.objectKeys(this.shape).forEach(n=>{e[n]||(r[n]=this.shape[n])}),new ct({...this._def,shape:()=>r})}deepPartial(){return Ba(this)}partial(e){const r={};return Fe.objectKeys(this.shape).forEach(n=>{const i=this.shape[n];e&&!e[n]?r[n]=i:r[n]=i.optional()}),new ct({...this._def,shape:()=>r})}required(e){const r={};return Fe.objectKeys(this.shape).forEach(n=>{if(e&&!e[n])r[n]=this.shape[n];else{let o=this.shape[n];for(;o instanceof fn;)o=o._def.innerType;r[n]=o}}),new ct({...this._def,shape:()=>r})}keyof(){return bC(Fe.objectKeys(this.shape))}}ct.create=(t,e)=>new ct({shape:()=>t,unknownKeys:"strip",catchall:qn.create(),typeName:Ae.ZodObject,...Me(e)});ct.strictCreate=(t,e)=>new ct({shape:()=>t,unknownKeys:"strict",catchall:qn.create(),typeName:Ae.ZodObject,...Me(e)});ct.lazycreate=(t,e)=>new ct({shape:t,unknownKeys:"strip",catchall:qn.create(),typeName:Ae.ZodObject,...Me(e)});class ts extends $e{_parse(e){const{ctx:r}=this._processInputParams(e),n=this._def.options;function i(o){for(const l of o)if(l.result.status==="valid")return l.result;for(const l of o)if(l.result.status==="dirty")return r.common.issues.push(...l.ctx.common.issues),l.result;const s=o.map(l=>new vr(l.ctx.common.issues));return fe(r,{code:ue.invalid_union,unionErrors:s}),Ee}if(r.common.async)return Promise.all(n.map(async o=>{const s={...r,common:{...r.common,issues:[]},parent:null};return{result:await o._parseAsync({data:r.data,path:r.path,parent:s}),ctx:s}})).then(i);{let o;const s=[];for(const f of n){const d={...r,common:{...r.common,issues:[]},parent:null},h=f._parseSync({data:r.data,path:r.path,parent:d});if(h.status==="valid")return h;h.status==="dirty"&&!o&&(o={result:h,ctx:d}),d.common.issues.length&&s.push(d.common.issues)}if(o)return r.common.issues.push(...o.ctx.common.issues),o.result;const l=s.map(f=>new vr(f));return fe(r,{code:ue.invalid_union,unionErrors:l}),Ee}}get options(){return this._def.options}}ts.create=(t,e)=>new ts({options:t,typeName:Ae.ZodUnion,...Me(e)});const Nn=t=>t instanceof is?Nn(t.schema):t instanceof Zr?Nn(t.innerType()):t instanceof as?[t.value]:t instanceof _i?t.options:t instanceof os?Fe.objectValues(t.enum):t instanceof us?Nn(t._def.innerType):t instanceof Ju?[void 0]:t instanceof es?[null]:t instanceof fn?[void 0,...Nn(t.unwrap())]:t instanceof Si?[null,...Nn(t.unwrap())]:t instanceof Xb||t instanceof ls?Nn(t.unwrap()):t instanceof ss?Nn(t._def.innerType):[];class Lf extends $e{_parse(e){const{ctx:r}=this._processInputParams(e);if(r.parsedType!==pe.object)return fe(r,{code:ue.invalid_type,expected:pe.object,received:r.parsedType}),Ee;const n=this.discriminator,i=r.data[n],o=this.optionsMap.get(i);return o?r.common.async?o._parseAsync({data:r.data,path:r.path,parent:r}):o._parseSync({data:r.data,path:r.path,parent:r}):(fe(r,{code:ue.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[n]}),Ee)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(e,r,n){const i=new Map;for(const o of r){const s=Nn(o.shape[e]);if(!s.length)throw new Error(`A discriminator value for key \`${e}\` could not be extracted from all schema options`);for(const l of s){if(i.has(l))throw new Error(`Discriminator property ${String(e)} has duplicate value ${String(l)}`);i.set(l,o)}}return new Lf({typeName:Ae.ZodDiscriminatedUnion,discriminator:e,options:r,optionsMap:i,...Me(n)})}}function Og(t,e){const r=Mn(t),n=Mn(e);if(t===e)return{valid:!0,data:t};if(r===pe.object&&n===pe.object){const i=Fe.objectKeys(e),o=Fe.objectKeys(t).filter(l=>i.indexOf(l)!==-1),s={...t,...e};for(const l of o){const f=Og(t[l],e[l]);if(!f.valid)return{valid:!1};s[l]=f.data}return{valid:!0,data:s}}else if(r===pe.array&&n===pe.array){if(t.length!==e.length)return{valid:!1};const i=[];for(let o=0;o{if(_g(o)||_g(s))return Ee;const l=Og(o.value,s.value);return l.valid?((Sg(o)||Sg(s))&&r.dirty(),{status:r.value,value:l.data}):(fe(n,{code:ue.invalid_intersection_types}),Ee)};return n.common.async?Promise.all([this._def.left._parseAsync({data:n.data,path:n.path,parent:n}),this._def.right._parseAsync({data:n.data,path:n.path,parent:n})]).then(([o,s])=>i(o,s)):i(this._def.left._parseSync({data:n.data,path:n.path,parent:n}),this._def.right._parseSync({data:n.data,path:n.path,parent:n}))}}rs.create=(t,e,r)=>new rs({left:t,right:e,typeName:Ae.ZodIntersection,...Me(r)});class yn extends $e{_parse(e){const{status:r,ctx:n}=this._processInputParams(e);if(n.parsedType!==pe.array)return fe(n,{code:ue.invalid_type,expected:pe.array,received:n.parsedType}),Ee;if(n.data.lengththis._def.items.length&&(fe(n,{code:ue.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),r.dirty());const o=[...n.data].map((s,l)=>{const f=this._def.items[l]||this._def.rest;return f?f._parse(new vn(n,s,n.path,l)):null}).filter(s=>!!s);return n.common.async?Promise.all(o).then(s=>Vt.mergeArray(r,s)):Vt.mergeArray(r,o)}get items(){return this._def.items}rest(e){return new yn({...this._def,rest:e})}}yn.create=(t,e)=>{if(!Array.isArray(t))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new yn({items:t,typeName:Ae.ZodTuple,rest:null,...Me(e)})};class ns extends $e{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(e){const{status:r,ctx:n}=this._processInputParams(e);if(n.parsedType!==pe.object)return fe(n,{code:ue.invalid_type,expected:pe.object,received:n.parsedType}),Ee;const i=[],o=this._def.keyType,s=this._def.valueType;for(const l in n.data)i.push({key:o._parse(new vn(n,l,n.path,l)),value:s._parse(new vn(n,n.data[l],n.path,l)),alwaysSet:l in n.data});return n.common.async?Vt.mergeObjectAsync(r,i):Vt.mergeObjectSync(r,i)}get element(){return this._def.valueType}static create(e,r,n){return r instanceof $e?new ns({keyType:e,valueType:r,typeName:Ae.ZodRecord,...Me(n)}):new ns({keyType:Hr.create(),valueType:e,typeName:Ae.ZodRecord,...Me(r)})}}class Mc extends $e{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(e){const{status:r,ctx:n}=this._processInputParams(e);if(n.parsedType!==pe.map)return fe(n,{code:ue.invalid_type,expected:pe.map,received:n.parsedType}),Ee;const i=this._def.keyType,o=this._def.valueType,s=[...n.data.entries()].map(([l,f],d)=>({key:i._parse(new vn(n,l,n.path,[d,"key"])),value:o._parse(new vn(n,f,n.path,[d,"value"]))}));if(n.common.async){const l=new Map;return Promise.resolve().then(async()=>{for(const f of s){const d=await f.key,h=await f.value;if(d.status==="aborted"||h.status==="aborted")return Ee;(d.status==="dirty"||h.status==="dirty")&&r.dirty(),l.set(d.value,h.value)}return{status:r.value,value:l}})}else{const l=new Map;for(const f of s){const d=f.key,h=f.value;if(d.status==="aborted"||h.status==="aborted")return Ee;(d.status==="dirty"||h.status==="dirty")&&r.dirty(),l.set(d.value,h.value)}return{status:r.value,value:l}}}}Mc.create=(t,e,r)=>new Mc({valueType:e,keyType:t,typeName:Ae.ZodMap,...Me(r)});class ta extends $e{_parse(e){const{status:r,ctx:n}=this._processInputParams(e);if(n.parsedType!==pe.set)return fe(n,{code:ue.invalid_type,expected:pe.set,received:n.parsedType}),Ee;const i=this._def;i.minSize!==null&&n.data.sizei.maxSize.value&&(fe(n,{code:ue.too_big,maximum:i.maxSize.value,type:"set",inclusive:!0,exact:!1,message:i.maxSize.message}),r.dirty());const o=this._def.valueType;function s(f){const d=new Set;for(const h of f){if(h.status==="aborted")return Ee;h.status==="dirty"&&r.dirty(),d.add(h.value)}return{status:r.value,value:d}}const l=[...n.data.values()].map((f,d)=>o._parse(new vn(n,f,n.path,d)));return n.common.async?Promise.all(l).then(f=>s(f)):s(l)}min(e,r){return new ta({...this._def,minSize:{value:e,message:xe.toString(r)}})}max(e,r){return new ta({...this._def,maxSize:{value:e,message:xe.toString(r)}})}size(e,r){return this.min(e,r).max(e,r)}nonempty(e){return this.min(1,e)}}ta.create=(t,e)=>new ta({valueType:t,minSize:null,maxSize:null,typeName:Ae.ZodSet,...Me(e)});class Ha extends $e{constructor(){super(...arguments),this.validate=this.implement}_parse(e){const{ctx:r}=this._processInputParams(e);if(r.parsedType!==pe.function)return fe(r,{code:ue.invalid_type,expected:pe.function,received:r.parsedType}),Ee;function n(l,f){return Cc({data:l,path:r.path,errorMaps:[r.common.contextualErrorMap,r.schemaErrorMap,Tc(),Qa].filter(d=>!!d),issueData:{code:ue.invalid_arguments,argumentsError:f}})}function i(l,f){return Cc({data:l,path:r.path,errorMaps:[r.common.contextualErrorMap,r.schemaErrorMap,Tc(),Qa].filter(d=>!!d),issueData:{code:ue.invalid_return_type,returnTypeError:f}})}const o={errorMap:r.common.contextualErrorMap},s=r.data;if(this._def.returns instanceof eo){const l=this;return Yt(async function(...f){const d=new vr([]),h=await l._def.args.parseAsync(f,o).catch(g=>{throw d.addIssue(n(f,g)),d}),m=await Reflect.apply(s,this,h);return await l._def.returns._def.type.parseAsync(m,o).catch(g=>{throw d.addIssue(i(m,g)),d})})}else{const l=this;return Yt(function(...f){const d=l._def.args.safeParse(f,o);if(!d.success)throw new vr([n(f,d.error)]);const h=Reflect.apply(s,this,d.data),m=l._def.returns.safeParse(h,o);if(!m.success)throw new vr([i(h,m.error)]);return m.data})}}parameters(){return this._def.args}returnType(){return this._def.returns}args(...e){return new Ha({...this._def,args:yn.create(e).rest(Xi.create())})}returns(e){return new Ha({...this._def,returns:e})}implement(e){return this.parse(e)}strictImplement(e){return this.parse(e)}static create(e,r,n){return new Ha({args:e||yn.create([]).rest(Xi.create()),returns:r||Xi.create(),typeName:Ae.ZodFunction,...Me(n)})}}class is extends $e{get schema(){return this._def.getter()}_parse(e){const{ctx:r}=this._processInputParams(e);return this._def.getter()._parse({data:r.data,path:r.path,parent:r})}}is.create=(t,e)=>new is({getter:t,typeName:Ae.ZodLazy,...Me(e)});class as extends $e{_parse(e){if(e.data!==this._def.value){const r=this._getOrReturnCtx(e);return fe(r,{received:r.data,code:ue.invalid_literal,expected:this._def.value}),Ee}return{status:"valid",value:e.data}}get value(){return this._def.value}}as.create=(t,e)=>new as({value:t,typeName:Ae.ZodLiteral,...Me(e)});function bC(t,e){return new _i({values:t,typeName:Ae.ZodEnum,...Me(e)})}class _i extends $e{constructor(){super(...arguments),Du.set(this,void 0)}_parse(e){if(typeof e.data!="string"){const r=this._getOrReturnCtx(e),n=this._def.values;return fe(r,{expected:Fe.joinValues(n),received:r.parsedType,code:ue.invalid_type}),Ee}if(kc(this,Du)||mC(this,Du,new Set(this._def.values)),!kc(this,Du).has(e.data)){const r=this._getOrReturnCtx(e),n=this._def.values;return fe(r,{received:r.data,code:ue.invalid_enum_value,options:n}),Ee}return Yt(e.data)}get options(){return this._def.values}get enum(){const e={};for(const r of this._def.values)e[r]=r;return e}get Values(){const e={};for(const r of this._def.values)e[r]=r;return e}get Enum(){const e={};for(const r of this._def.values)e[r]=r;return e}extract(e,r=this._def){return _i.create(e,{...this._def,...r})}exclude(e,r=this._def){return _i.create(this.options.filter(n=>!e.includes(n)),{...this._def,...r})}}Du=new WeakMap;_i.create=bC;class os extends $e{constructor(){super(...arguments),Lu.set(this,void 0)}_parse(e){const r=Fe.getValidEnumValues(this._def.values),n=this._getOrReturnCtx(e);if(n.parsedType!==pe.string&&n.parsedType!==pe.number){const i=Fe.objectValues(r);return fe(n,{expected:Fe.joinValues(i),received:n.parsedType,code:ue.invalid_type}),Ee}if(kc(this,Lu)||mC(this,Lu,new Set(Fe.getValidEnumValues(this._def.values))),!kc(this,Lu).has(e.data)){const i=Fe.objectValues(r);return fe(n,{received:n.data,code:ue.invalid_enum_value,options:i}),Ee}return Yt(e.data)}get enum(){return this._def.values}}Lu=new WeakMap;os.create=(t,e)=>new os({values:t,typeName:Ae.ZodNativeEnum,...Me(e)});class eo extends $e{unwrap(){return this._def.type}_parse(e){const{ctx:r}=this._processInputParams(e);if(r.parsedType!==pe.promise&&r.common.async===!1)return fe(r,{code:ue.invalid_type,expected:pe.promise,received:r.parsedType}),Ee;const n=r.parsedType===pe.promise?r.data:Promise.resolve(r.data);return Yt(n.then(i=>this._def.type.parseAsync(i,{path:r.path,errorMap:r.common.contextualErrorMap})))}}eo.create=(t,e)=>new eo({type:t,typeName:Ae.ZodPromise,...Me(e)});class Zr extends $e{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===Ae.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(e){const{status:r,ctx:n}=this._processInputParams(e),i=this._def.effect||null,o={addIssue:s=>{fe(n,s),s.fatal?r.abort():r.dirty()},get path(){return n.path}};if(o.addIssue=o.addIssue.bind(o),i.type==="preprocess"){const s=i.transform(n.data,o);if(n.common.async)return Promise.resolve(s).then(async l=>{if(r.value==="aborted")return Ee;const f=await this._def.schema._parseAsync({data:l,path:n.path,parent:n});return f.status==="aborted"?Ee:f.status==="dirty"||r.value==="dirty"?qa(f.value):f});{if(r.value==="aborted")return Ee;const l=this._def.schema._parseSync({data:s,path:n.path,parent:n});return l.status==="aborted"?Ee:l.status==="dirty"||r.value==="dirty"?qa(l.value):l}}if(i.type==="refinement"){const s=l=>{const f=i.refinement(l,o);if(n.common.async)return Promise.resolve(f);if(f instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return l};if(n.common.async===!1){const l=this._def.schema._parseSync({data:n.data,path:n.path,parent:n});return l.status==="aborted"?Ee:(l.status==="dirty"&&r.dirty(),s(l.value),{status:r.value,value:l.value})}else return this._def.schema._parseAsync({data:n.data,path:n.path,parent:n}).then(l=>l.status==="aborted"?Ee:(l.status==="dirty"&&r.dirty(),s(l.value).then(()=>({status:r.value,value:l.value}))))}if(i.type==="transform")if(n.common.async===!1){const s=this._def.schema._parseSync({data:n.data,path:n.path,parent:n});if(!Ji(s))return s;const l=i.transform(s.value,o);if(l instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:r.value,value:l}}else return this._def.schema._parseAsync({data:n.data,path:n.path,parent:n}).then(s=>Ji(s)?Promise.resolve(i.transform(s.value,o)).then(l=>({status:r.value,value:l})):s);Fe.assertNever(i)}}Zr.create=(t,e,r)=>new Zr({schema:t,typeName:Ae.ZodEffects,effect:e,...Me(r)});Zr.createWithPreprocess=(t,e,r)=>new Zr({schema:e,effect:{type:"preprocess",transform:t},typeName:Ae.ZodEffects,...Me(r)});class fn extends $e{_parse(e){return this._getType(e)===pe.undefined?Yt(void 0):this._def.innerType._parse(e)}unwrap(){return this._def.innerType}}fn.create=(t,e)=>new fn({innerType:t,typeName:Ae.ZodOptional,...Me(e)});class Si extends $e{_parse(e){return this._getType(e)===pe.null?Yt(null):this._def.innerType._parse(e)}unwrap(){return this._def.innerType}}Si.create=(t,e)=>new Si({innerType:t,typeName:Ae.ZodNullable,...Me(e)});class us extends $e{_parse(e){const{ctx:r}=this._processInputParams(e);let n=r.data;return r.parsedType===pe.undefined&&(n=this._def.defaultValue()),this._def.innerType._parse({data:n,path:r.path,parent:r})}removeDefault(){return this._def.innerType}}us.create=(t,e)=>new us({innerType:t,typeName:Ae.ZodDefault,defaultValue:typeof e.default=="function"?e.default:()=>e.default,...Me(e)});class ss extends $e{_parse(e){const{ctx:r}=this._processInputParams(e),n={...r,common:{...r.common,issues:[]}},i=this._def.innerType._parse({data:n.data,path:n.path,parent:{...n}});return Yu(i)?i.then(o=>({status:"valid",value:o.status==="valid"?o.value:this._def.catchValue({get error(){return new vr(n.common.issues)},input:n.data})})):{status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new vr(n.common.issues)},input:n.data})}}removeCatch(){return this._def.innerType}}ss.create=(t,e)=>new ss({innerType:t,typeName:Ae.ZodCatch,catchValue:typeof e.catch=="function"?e.catch:()=>e.catch,...Me(e)});class Ic extends $e{_parse(e){if(this._getType(e)!==pe.nan){const n=this._getOrReturnCtx(e);return fe(n,{code:ue.invalid_type,expected:pe.nan,received:n.parsedType}),Ee}return{status:"valid",value:e.data}}}Ic.create=t=>new Ic({typeName:Ae.ZodNaN,...Me(t)});const FR=Symbol("zod_brand");class Xb extends $e{_parse(e){const{ctx:r}=this._processInputParams(e),n=r.data;return this._def.type._parse({data:n,path:r.path,parent:r})}unwrap(){return this._def.type}}class Hs extends $e{_parse(e){const{status:r,ctx:n}=this._processInputParams(e);if(n.common.async)return(async()=>{const o=await this._def.in._parseAsync({data:n.data,path:n.path,parent:n});return o.status==="aborted"?Ee:o.status==="dirty"?(r.dirty(),qa(o.value)):this._def.out._parseAsync({data:o.value,path:n.path,parent:n})})();{const i=this._def.in._parseSync({data:n.data,path:n.path,parent:n});return i.status==="aborted"?Ee:i.status==="dirty"?(r.dirty(),{status:"dirty",value:i.value}):this._def.out._parseSync({data:i.value,path:n.path,parent:n})}}static create(e,r){return new Hs({in:e,out:r,typeName:Ae.ZodPipeline})}}class ls extends $e{_parse(e){const r=this._def.innerType._parse(e),n=i=>(Ji(i)&&(i.value=Object.freeze(i.value)),i);return Yu(r)?r.then(i=>n(i)):n(r)}unwrap(){return this._def.innerType}}ls.create=(t,e)=>new ls({innerType:t,typeName:Ae.ZodReadonly,...Me(e)});function xC(t,e={},r){return t?Ja.create().superRefine((n,i)=>{var o,s;if(!t(n)){const l=typeof e=="function"?e(n):typeof e=="string"?{message:e}:e,f=(s=(o=l.fatal)!==null&&o!==void 0?o:r)!==null&&s!==void 0?s:!0,d=typeof l=="string"?{message:l}:l;i.addIssue({code:"custom",...d,fatal:f})}}):Ja.create()}const UR={object:ct.lazycreate};var Ae;(function(t){t.ZodString="ZodString",t.ZodNumber="ZodNumber",t.ZodNaN="ZodNaN",t.ZodBigInt="ZodBigInt",t.ZodBoolean="ZodBoolean",t.ZodDate="ZodDate",t.ZodSymbol="ZodSymbol",t.ZodUndefined="ZodUndefined",t.ZodNull="ZodNull",t.ZodAny="ZodAny",t.ZodUnknown="ZodUnknown",t.ZodNever="ZodNever",t.ZodVoid="ZodVoid",t.ZodArray="ZodArray",t.ZodObject="ZodObject",t.ZodUnion="ZodUnion",t.ZodDiscriminatedUnion="ZodDiscriminatedUnion",t.ZodIntersection="ZodIntersection",t.ZodTuple="ZodTuple",t.ZodRecord="ZodRecord",t.ZodMap="ZodMap",t.ZodSet="ZodSet",t.ZodFunction="ZodFunction",t.ZodLazy="ZodLazy",t.ZodLiteral="ZodLiteral",t.ZodEnum="ZodEnum",t.ZodEffects="ZodEffects",t.ZodNativeEnum="ZodNativeEnum",t.ZodOptional="ZodOptional",t.ZodNullable="ZodNullable",t.ZodDefault="ZodDefault",t.ZodCatch="ZodCatch",t.ZodPromise="ZodPromise",t.ZodBranded="ZodBranded",t.ZodPipeline="ZodPipeline",t.ZodReadonly="ZodReadonly"})(Ae||(Ae={}));const WR=(t,e={message:`Input not instance of ${t.name}`})=>xC(r=>r 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+ + diff --git a/kura/static/dist/vite.svg b/kura/static/dist/vite.svg new file mode 100644 index 0000000..e7b8dfb --- /dev/null +++ b/kura/static/dist/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/kura/summarisation.py b/kura/summarisation.py new file mode 100644 index 0000000..7be65f7 --- /dev/null +++ b/kura/summarisation.py @@ -0,0 +1,116 @@ +from kura.base_classes import BaseSummaryModel +from kura.types import Conversation, ConversationSummary +from kura.types.summarisation import GeneratedSummary +from asyncio import Semaphore +from tqdm.asyncio import tqdm_asyncio +import google.generativeai as genai +import instructor +import os + + +class SummaryModel(BaseSummaryModel): + def __init__( + self, + max_concurrent_requests: int = 50, + checkpoint_dir: str = "checkpoints", + checkpoint_file: str = "summarisation_checkpoint.json", + ): + self.sem = Semaphore(max_concurrent_requests) + self.client = instructor.from_gemini( + genai.GenerativeModel( + model_name="gemini-1.5-flash-latest", + ), + use_async=True, + ) + self.checkpoint_dir = checkpoint_dir + self.checkpoint_file = checkpoint_file + + if not os.path.exists(self.checkpoint_dir): + print(f"Creating checkpoint directory {self.checkpoint_dir}") + os.makedirs(self.checkpoint_dir) + + def load_checkpoint(self): + print( + f"Loading Summary Checkpoint from {os.path.join(self.checkpoint_dir, self.checkpoint_file)}" + ) + with open(os.path.join(self.checkpoint_dir, self.checkpoint_file), "r") as f: + return [ConversationSummary.model_validate_json(line) for line in f] + + def save_checkpoint(self, summaries: list[ConversationSummary]): + with open(os.path.join(self.checkpoint_dir, self.checkpoint_file), "w") as f: + for summary in summaries: + f.write(summary.model_dump_json() + "\n") + + print( + f"Saved {len(summaries)} summaries to {os.path.join(self.checkpoint_dir, self.checkpoint_file)}" + ) + + async def summarise( + self, conversations: list[Conversation] + ) -> list[ConversationSummary]: + if os.path.exists(os.path.join(self.checkpoint_dir, self.checkpoint_file)): + return self.load_checkpoint() + + summaries = await tqdm_asyncio.gather( + *[ + self.summarise_conversation(conversation) + for conversation in conversations + ], + desc=f"Summarising {len(conversations)} conversations", + ) + + self.save_checkpoint(summaries) + return summaries + + async def apply_hooks( + self, conversation: ConversationSummary + ) -> ConversationSummary: + # TODO: Implement hooks here for extra metadata extraction + return await super().apply_hooks(conversation) + + async def summarise_conversation( + self, conversation: Conversation + ) -> ConversationSummary: + resp = await self.client.chat.completions.create( + messages=[ + { + "role": "system", + "content": """ + Generate a summary of the task that the user is asking the language model to do based off the following conversation. + + + The summary should be concise and short. It should be at most 1-2 sentences and at most 30 words. Here are some examples of summaries: + - The user’s overall request for the assistant is to help implementing a React component to display a paginated list of users from a database. + - The user’s overall request for the assistant is to debug a memory leak in their Python data processing pipeline. + - The user’s overall request for the assistant is to design and architect a REST API for a social media application. + + + Here is the conversation + + {% for message in messages %} + {{message.role}}: {{message.content}} + {% endfor %} + + + When answering, do not include any personally identifiable information (PII), like names, locations, phone numbers, email addressess, and so on. When answering, do not include any proper nouns. Make sure that you're clear, concise and that you get to the point in at most two sentences. + + For example: + + Remember that + - Summaries should be concise and short. They should each be at most 1-2 sentences and at most 30 words. + - Summaries should start with "The user's overall request for the assistant is to" + - Make sure to omit any personally identifiable information (PII), like names, locations, phone numbers, email addressess, company names and so on. + - Make sure to indicate specific details such as programming languages, frameworks, libraries and so on which are relevant to the task. + """, + } + ], + context={"messages": conversation.messages}, + response_model=GeneratedSummary, + ) + return ConversationSummary( + chat_id=conversation.chat_id, + summary=resp.summary, + metadata={ + "conversation_turns": len(conversation.messages), + }, + ) diff --git a/kura/summarise.py b/kura/summarise.py deleted file mode 100644 index 0fee4b6..0000000 --- a/kura/summarise.py +++ /dev/null @@ -1,113 +0,0 @@ -from asyncio import Semaphore -from tqdm.asyncio import tqdm_asyncio as asyncio -from .types import Conversation, ConversationSummary -from instructor import AsyncInstructor, from_gemini -import google.generativeai as genai -from pydantic import BaseModel, field_validator -import os - - -class GeneratedSummary(BaseModel): - task_description: str - user_request: str - - @field_validator("user_request") - def validate_user_request_length(cls, v): - if len(v.split()) > 30: - raise ValueError( - f"User request is {len(v.split())} words long. Please condense the user request by removing any unnecessary/less important details while retaining the core request." - ) - return v - - -class SummariseBase: - def __init__( - self, - client: AsyncInstructor = from_gemini( - genai.GenerativeModel("gemini-1.5-flash-latest"), use_async=True - ), - max_concurrent_summaries: int = 40, - checkpoint_dir: str = "checkpoints", - checkpoint_file_name: str = "summarized_conversations.json", - ): - self.sem = Semaphore(max_concurrent_summaries) - self.client = client - self.checkpoint_dir = checkpoint_dir - self.checkpoint_file_name = checkpoint_file_name - - def load_checkpoint(self) -> list[ConversationSummary]: - with open( - os.path.join(self.checkpoint_dir, self.checkpoint_file_name), "r" - ) as f: - return [ConversationSummary.model_validate_json(line) for line in f] - - def save_checkpoint(self, data: list[ConversationSummary]): - with open( - os.path.join(self.checkpoint_dir, self.checkpoint_file_name), "w" - ) as f: - for item in data: - f.write(item.model_dump_json() + "\n") - - async def summarise_conversations( - self, conversations: list[Conversation] - ) -> list[ConversationSummary]: - if os.path.exists(os.path.join(self.checkpoint_dir, self.checkpoint_file_name)): - return self.load_checkpoint() - - coros = [self._summarise(conversation) for conversation in conversations] - res = await asyncio.gather(*coros, desc="Summarising Conversations") - self.save_checkpoint(res) - return res - - async def _summarise(self, conversation: Conversation) -> ConversationSummary: - async with self.sem: - resp = await self.client.chat.completions.create( - messages=[ - { - "role": "system", - "content": """Generate a summary of the task that the user is asking and a description of the task that the assistant is trying to complete based off the following conversation. - - Here are some examples of user requests - - - The user’s overall request for the assistant is to help implementing a React component to display a paginated list of users from a database. - - The user’s overall request for the assistant is to debug a memory leak in their Python data processing pipeline. - - The user’s overall request for the assistant is to design and architect a REST API for a social media application. - - - Task descriptions should be concise and short. Here are some examples of task descriptions - - - The task is to help build a frontend component with React and implement database integration - The task is to debug performance issues and optimize memory usage in Python code - The task is to design and architect a RESTful API following best practices - - - Here is the conversation - {% for message in messages %} - - {{message.role}}: {{message.content}} - - {% endfor %} - - When answering, do not include any personally identifiable information (PII), like names, locations, phone numbers, email addressess, and so on. When answering, do not include any proper nouns. Output your answer to the question in English inside tags; be clear and concise and get to the point in at most two sentences (don't say "Based on the conversation..." and avoid mentioning Claude/the chatbot). For example: - - Remember that - - User requests and task descriptions should be concise and short. They should each be at most 1-2 sentences and at most 30 words. - - User requests should start with "The user's overall request for the assistant is to" - - Task descriptions should start with "The task is to" - - Make sure to omit any personally identifiable information (PII), like names, locations, phone numbers, email addressess, company names and so on. - - Make sure to indicate specific details such as programming languages, frameworks, libraries and so on which are relevant to the task. Also mention the specific source of the data if it's relevant to the task. For instance, if the user is asking to perform a calculation, let's talk about the specific source of the data. - """, - } - ], - context={"messages": conversation.messages}, - response_model=GeneratedSummary, - ) - return ConversationSummary( - chat_id=conversation.chat_id, - task_description=resp.task_description, - user_request=resp.user_request, - metadata={ - "turns": len(conversation.messages), - }, - ) diff --git a/kura/summary_cluster.py b/kura/summary_cluster.py deleted file mode 100644 index ad67c63..0000000 --- a/kura/summary_cluster.py +++ /dev/null @@ -1,185 +0,0 @@ -from asyncio import Semaphore -from kura.types import ConversationSummary, GeneratedCluster, Cluster -from instructor import from_gemini -import google.generativeai as genai -from openai import AsyncOpenAI -from tqdm.asyncio import tqdm_asyncio as asyncio -import numpy as np -from sklearn.cluster import KMeans -import math -import os -import json - - -class SummaryCluster: - def __init__( - self, - cluster_model=from_gemini( - genai.GenerativeModel("gemini-1.5-flash-latest"), use_async=True - ), - embedding_model=AsyncOpenAI(), - max_concurrent_calls: int = 40, - summaries_per_cluster: int = 10, - checkpoint_dir: str = "checkpoints", - checkpoint_file_name: str = "base_clusters.json", - ): - self.sem = Semaphore(max_concurrent_calls) - self.cluster_model = cluster_model - self.embedding_model = embedding_model - self.summaries_per_cluster = summaries_per_cluster - self.checkpoint_dir = checkpoint_dir - self.checkpoint_file_name = checkpoint_file_name - - async def _embed(self, summary: ConversationSummary): - async with self.sem: - embedding = await self.embedding_model.embeddings.create( - input=summary.user_request, - model="text-embedding-3-small", - ) - return { - "item": summary, - "embedding": embedding.data[0].embedding, - } - - async def cluster_summaries( - self, summaries: list[ConversationSummary] - ) -> dict[int, list[ConversationSummary]]: - n_clusters = math.ceil(len(summaries) / self.summaries_per_cluster) - embeddings = [summary["embedding"] for summary in summaries] - X = np.array(embeddings) - - kmeans = KMeans(n_clusters=n_clusters) - cluster_labels = kmeans.fit_predict(X) - - group_to_clusters = {} - for i, summary in enumerate(summaries): - cluster_id = int(cluster_labels[i]) - if cluster_id not in group_to_clusters: - group_to_clusters[cluster_id] = [] - group_to_clusters[cluster_id].append(summary["item"]) - - return group_to_clusters - - def get_contrastive_examples( - self, - cluster_id: int, - cluster_id_to_summaries: dict[int, list[ConversationSummary]], - desired_count: int = 10, - ): - other_clusters = [c for c in cluster_id_to_summaries.keys() if c != cluster_id] - all_examples = [] - for cluster in other_clusters: - all_examples.extend(cluster_id_to_summaries[cluster]) - - # If we don't have enough examples, return all of them - if len(all_examples) <= desired_count: - return all_examples - - # Otherwise sample without replacement - return list(np.random.choice(all_examples, size=desired_count, replace=False)) - - async def generate_base_cluster( - self, - cluster_id: int, - cluster_id_to_summaries: dict[int, list[ConversationSummary]], - ): - async with self.sem: - contrastive_examples = self.get_contrastive_examples( - cluster_id, cluster_id_to_summaries - ) - positive_examples = cluster_id_to_summaries[cluster_id] - cluster = await self.cluster_model.chat.completions.create( - messages=[ - { - "role": "system", - "content": """ - You are tasked with summarizing a group of related statements into a short, precise, and accurate description and name. Your goal is to create a concise summary that captures the essence of these statements and distinguishes them from other similar groups of statements. - - Summarize all the statements into a clear, precise, two-sentence description in the past tense. Your summary should be specific to this group and distinguish it from the contrastive answers of the other groups. - - After creating the summary, generate a short name for the group of statements. This name should be at most ten words long (perhaps less) and be specific but also reflective of most of the statements (rather than reflecting only one or two). The name should distinguish this group from the contrastive examples. For instance, "Write fantasy sexual roleplay with octopi and monsters", "Generate blog spam for gambling websites", or "Assist with high school math homework" would be better and more actionable than general terms like "Write erotic content" or "Help with homework". Be as descriptive as possible and assume neither good nor bad faith. Do not hesitate to identify and describe socially harmful or sensitive topics specifically; specificity is necessary for monitoring - - The cluster name should be a sentence in the imperative that captures the user’s request. For example, ‘Brainstorm ideas for a birthday party’ or ‘Help me find a new job.” - """, - }, - { - "role": "user", - "content": """ - Here are the relevant statements - - Below are the related statements: - - {% for item in positive_examples %} - {{ item.user_request }} - {% endfor %} - - - For context, here are statements from nearby groups that are NOT part of the group you’re summarizing - - - {% for item in contrastive_examples %} - {{ item.user_request }} - {% endfor %} - - - Remember to analyze both the statements and the contrastive statements carefully to ensure your summary and name accurately represent the specific group while distinguishing it from others. - """, - }, - ], - response_model=GeneratedCluster, - context={ - "positive_examples": positive_examples, - "contrastive_examples": contrastive_examples, - }, - ) - - return Cluster( - name=cluster.name, - description=cluster.summary, - chat_ids=[item.chat_id for item in positive_examples], - parent_id=None, - ) - - async def embed_summaries(self, summaries: list[ConversationSummary]): - coros = [self._embed(summary) for summary in summaries] - return await asyncio.gather(*coros, desc="Embedding Summaries") - - def save_checkpoint(self, data: list[ConversationSummary]): - with open( - os.path.join(self.checkpoint_dir, self.checkpoint_file_name), "w" - ) as f: - for item in data: - f.write(item.model_dump_json() + "\n") - - def load_checkpoint(self) -> list[ConversationSummary]: - with open( - os.path.join(self.checkpoint_dir, self.checkpoint_file_name), "r" - ) as f: - return [Cluster(**json.loads(line)) for line in f] - - def save_checkpoint_clusters(self, clusters: list[Cluster]): - with open( - os.path.join(self.checkpoint_dir, self.checkpoint_file_name), "w" - ) as f: - for cluster in clusters: - f.write(cluster.model_dump_json() + "\n") - - async def cluster_conversation_summaries( - self, summaries: list[ConversationSummary] - ) -> list[Cluster]: - # Load Checkpoint By Default - if os.path.exists(os.path.join(self.checkpoint_dir, self.checkpoint_file_name)): - print("Loading Base Cluster Checkpoint") - return self.load_checkpoint() - - embedded_summaries = await self.embed_summaries(summaries) - cluster_id_to_summaries = await self.cluster_summaries(embedded_summaries) - clusters = await asyncio.gather( - *[ - self.generate_base_cluster(cluster_id, cluster_id_to_summaries) - for cluster_id in cluster_id_to_summaries.keys() - ], - desc="Generating Base Clusters", - ) - self.save_checkpoint(clusters) - return clusters diff --git a/kura/types/__init__.py b/kura/types/__init__.py new file mode 100644 index 0000000..27ad4ba --- /dev/null +++ b/kura/types/__init__.py @@ -0,0 +1,14 @@ +from .conversation import Conversation, Message +from .summarisation import ConversationSummary, GeneratedSummary +from .cluster import Cluster, GeneratedCluster +from .dimensionality import ProjectedCluster + +__all__ = [ + "Cluster", + "Conversation", + "Message", + "ConversationSummary", + "GeneratedSummary", + "GeneratedCluster", + "ProjectedCluster", +] diff --git a/kura/types.py b/kura/types/cluster.py similarity index 51% rename from kura/types.py rename to kura/types/cluster.py index 7b6a06e..ea38a15 100644 --- a/kura/types.py +++ b/kura/types/cluster.py @@ -1,28 +1,8 @@ from pydantic import BaseModel, Field, computed_field -from datetime import datetime import uuid from typing import Union -class Message(BaseModel): - created_at: datetime - role: str - content: str - - -class Conversation(BaseModel): - chat_id: str - created_at: datetime - messages: list[Message] - - -class ConversationSummary(BaseModel): - chat_id: str - task_description: str - user_request: str - metadata: dict - - class Cluster(BaseModel): id: str = Field( default_factory=lambda: uuid.uuid4().hex, @@ -40,7 +20,3 @@ def count(self) -> int: class GeneratedCluster(BaseModel): name: str summary: str - - -class CachedCluster(Cluster): - umap_embedding: list[float] diff --git a/kura/types/conversation.py b/kura/types/conversation.py new file mode 100644 index 0000000..6b2e03f --- /dev/null +++ b/kura/types/conversation.py @@ -0,0 +1,13 @@ +from pydantic import BaseModel +from datetime import datetime + +class Message(BaseModel): + created_at: datetime + role: str + content: str + + +class Conversation(BaseModel): + chat_id: str + created_at: datetime + messages: list[Message] \ No newline at end of file diff --git a/kura/types/dimensionality.py b/kura/types/dimensionality.py new file mode 100644 index 0000000..e0a93e4 --- /dev/null +++ b/kura/types/dimensionality.py @@ -0,0 +1,7 @@ +from .cluster import Cluster + + +class ProjectedCluster(Cluster): + x_coord: float + y_coord: float + level: int diff --git a/kura/types/summarisation.py b/kura/types/summarisation.py new file mode 100644 index 0000000..873e1b4 --- /dev/null +++ b/kura/types/summarisation.py @@ -0,0 +1,11 @@ +from pydantic import BaseModel + + +class ConversationSummary(BaseModel): + chat_id: str + summary: str + metadata: dict + + +class GeneratedSummary(BaseModel): + summary: str diff --git a/pyproject.toml b/pyproject.toml index 315f6fa..a6d16b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "kura" -version = "0.2.0" +version = "0.4.0" description = "Kura is a tool for analysing and visualising chat data" readme = "README.md" authors = [ @@ -15,6 +15,8 @@ dependencies = [ "uvicorn>=0.34.0", "fastapi[standard]>=0.115.6", "umap-learn>=0.5.7", + "hdbscan>=0.8.40", + "eval-type-backport>=0.2.2", ] diff --git a/ui/bun.lockb b/ui/bun.lockb index 149b1bd..d3e592e 100755 Binary files a/ui/bun.lockb and b/ui/bun.lockb differ diff --git a/ui/package.json b/ui/package.json index 73a64ea..5398c6a 100644 --- a/ui/package.json +++ b/ui/package.json @@ -13,6 +13,7 @@ "@radix-ui/react-scroll-area": "^1.2.2", "@radix-ui/react-separator": "^1.1.1", "@radix-ui/react-slot": "^1.1.1", + "@radix-ui/react-tabs": "^1.1.2", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "lucide-react": "^0.469.0", diff --git a/ui/src/App.css b/ui/src/App.css deleted file mode 100644 index b9d355d..0000000 --- a/ui/src/App.css +++ /dev/null @@ -1,42 +0,0 @@ -#root { - max-width: 1280px; - margin: 0 auto; - padding: 2rem; - text-align: center; -} - -.logo { - height: 6em; - padding: 1.5em; - will-change: filter; - transition: filter 300ms; -} -.logo:hover { - filter: drop-shadow(0 0 2em #646cffaa); -} -.logo.react:hover { - filter: drop-shadow(0 0 2em #61dafbaa); -} - -@keyframes logo-spin { - from { - transform: rotate(0deg); - } - to { - transform: rotate(360deg); - } -} - -@media (prefers-reduced-motion: no-preference) { - a:nth-of-type(2) .logo { - animation: logo-spin infinite 20s linear; - } -} - -.card { - padding: 2em; -} - -.read-the-docs { - color: #888; -} diff --git a/ui/src/App.tsx b/ui/src/App.tsx index 4424ba4..4cb2597 100644 --- a/ui/src/App.tsx +++ b/ui/src/App.tsx @@ -1,5 +1,4 @@ import { useState } from "react"; -import "./App.css"; import { UploadButton } from "./components/upload-button"; import type { Analytics } from "./types/analytics"; import { AnalyticsCharts } from "./components/analytics-charts"; @@ -14,18 +13,20 @@ function App() { return ( <> -
-
-

Chat Analysis

-

- Detailed metrics and insights about chat activity -

+
+
+
+

Chat Analysis

+

+ Detailed metrics and insights about chat activity +

+
+ + {data && }
- - {data && }
+ {data && } - {/* {data && } */} ); } diff --git a/ui/src/components/cluster-details.tsx b/ui/src/components/cluster-details.tsx index e32ed4f..b641a96 100644 --- a/ui/src/components/cluster-details.tsx +++ b/ui/src/components/cluster-details.tsx @@ -1,6 +1,5 @@ import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; import { ScrollArea } from "@/components/ui/scroll-area"; -import { Separator } from "@/components/ui/separator"; import { Badge } from "@/components/ui/badge"; import { FolderOpen, MessageCircle } from "lucide-react"; import { Cluster } from "@/types/analytics"; @@ -50,7 +49,6 @@ export default function ClusterDetail({ cluster }: ClusterDetailProps) {
- diff --git a/ui/src/components/cluster-tree.tsx b/ui/src/components/cluster-tree.tsx index 92b823c..32b7225 100644 --- a/ui/src/components/cluster-tree.tsx +++ b/ui/src/components/cluster-tree.tsx @@ -9,6 +9,7 @@ import { Cluster } from "@/types/analytics"; interface ClusterTreeProps { clusters: Cluster[]; selectedClusterId: string | null; + levelMap: Map; onSelectCluster: (cluster: Cluster) => void; } @@ -18,6 +19,7 @@ interface ClusterTreeItemProps { level: number; selectedClusterId: string | null; onSelectCluster: (cluster: Cluster) => void; + levelMap: Map; } function ClusterTreeItem({ @@ -25,12 +27,15 @@ function ClusterTreeItem({ allClusters, level, selectedClusterId, + levelMap, onSelectCluster, }: ClusterTreeItemProps) { const [isExpanded, setIsExpanded] = useState(false); const childClusters = allClusters.filter((c) => c.parent_id === cluster.id); - const hasChildren = childClusters.length > 0; - const percentage = ((cluster.count / 10000) * 100).toFixed(1); + + const total_count = + levelMap.get(level)?.reduce((acc, curr) => acc + curr.count, 0) || 0; + const percentage = (cluster.count / total_count) * 100; return (
@@ -47,7 +52,7 @@ function ClusterTreeItem({ style={{ paddingLeft: `${level * 16 + 12}px` }} onClick={() => onSelectCluster(cluster)} > - {hasChildren ? ( + {childClusters.length > 0 ? (
{isExpanded && @@ -80,6 +85,7 @@ function ClusterTreeItem({ allClusters={allClusters} level={level + 1} selectedClusterId={selectedClusterId} + levelMap={levelMap} onSelectCluster={onSelectCluster} /> ))} @@ -91,8 +97,9 @@ export function ClusterTree({ clusters, selectedClusterId, onSelectCluster, + levelMap, }: ClusterTreeProps) { - const rootClusters = clusters.filter((cluster) => cluster.parent_id === null); + const rootClusters = levelMap.get(0) || []; return (
@@ -106,6 +113,7 @@ export function ClusterTree({ { + const levelMap = new Map(); + let currentLevel = 0; + let currentLevelNodes = clusters.filter((item) => item.parent_id === null); + + while (currentLevelNodes.length > 0) { + levelMap.set(currentLevel, currentLevelNodes); + + const parentIds = currentLevelNodes.map((n) => n.id); + currentLevelNodes = clusters.filter((item) => + parentIds.includes(item.parent_id ?? "") + ); + currentLevel++; + } + + return levelMap; +}; const ClusterVisualisation = ({ clusters }: { clusters: Cluster[] }) => { const [selectedClusterId, setSelectedClusterId] = useState( null ); + const selectedCluster = clusters.find((c) => c.id === selectedClusterId); const updateSelectedCluster = (cluster: Cluster) => { setSelectedClusterId(cluster.id); }; + const levelMap = getClusterDepth(clusters); + return ( -
-
-

Clusters

+
+
+

Clusters

Visualise your clusters here

- - - - - - - - - +
+ + + Tree View + Map View + + +
+
+ +
+
+ +
+
+ + +
+ +
+ + +
+ +
); }; diff --git a/ui/src/components/ui/tabs.tsx b/ui/src/components/ui/tabs.tsx new file mode 100644 index 0000000..85d83be --- /dev/null +++ b/ui/src/components/ui/tabs.tsx @@ -0,0 +1,53 @@ +import * as React from "react" +import * as TabsPrimitive from "@radix-ui/react-tabs" + +import { cn } from "@/lib/utils" + +const Tabs = TabsPrimitive.Root + +const TabsList = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +TabsList.displayName = TabsPrimitive.List.displayName + +const TabsTrigger = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +TabsTrigger.displayName = TabsPrimitive.Trigger.displayName + +const TabsContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +TabsContent.displayName = TabsPrimitive.Content.displayName + +export { Tabs, TabsList, TabsTrigger, TabsContent } diff --git a/ui/src/components/umap_visualisation.tsx b/ui/src/components/umap_visualisation.tsx new file mode 100644 index 0000000..72c2cbb --- /dev/null +++ b/ui/src/components/umap_visualisation.tsx @@ -0,0 +1,175 @@ +import { useState, useRef, useEffect } from "react"; +import { + ScatterChart, + Scatter, + ResponsiveContainer, + XAxis, + YAxis, + Tooltip, +} from "recharts"; +import { ScrollArea } from "@/components/ui/scroll-area"; +import { Cluster } from "@/types/analytics"; +import { Button } from "./ui/button"; +import { ChevronDown, ChevronUp } from "lucide-react"; + +type Props = { + clusters: Cluster[]; + levelMap: Map; +}; + +const ClusterListItem = ({ + cluster, + isHovered, +}: { + cluster: Cluster; + isHovered: boolean; +}) => { + return ( +
+
{cluster.name}
+
+ {cluster.count.toLocaleString()} conversations +
+
+ ID: {cluster.id} +
+
+ {cluster.description} +
+
+ ); +}; + +const PointVisualisation = ({ levelMap }: Props) => { + const [currentLevel, setCurrentLevel] = useState(0); + const [hoveredNodeId, setHoveredNodeId] = useState(null); + const scrollAreaRef = useRef(null); + + const currentNodes = levelMap.get(currentLevel) || []; + const maxLevel = levelMap.size - 1; + const nodeCoordinates = currentNodes.map((item) => ({ + label: item.name, + x: item.x_coord, + y: item.y_coord, + id: item.id, + })); + + useEffect(() => { + if (hoveredNodeId) { + const element = document.querySelector(`div[id="${hoveredNodeId}"]`); + const viewport = scrollAreaRef.current?.querySelector( + "[data-radix-scroll-area-viewport]" + ); + + if (element && viewport) { + const elementRect = element.getBoundingClientRect(); + const viewportRect = viewport.getBoundingClientRect(); + + // Check if element is outside viewport + if ( + elementRect.top < viewportRect.top || + elementRect.bottom > viewportRect.bottom + ) { + viewport.scrollTo({ + top: + (element as HTMLElement).offsetTop - + viewport.clientHeight / 2 + + element.clientHeight / 2, + behavior: "smooth", + }); + } + } + } + }, [hoveredNodeId]); + + return ( +
+
+ + + + + { + if (payload && payload[0]) { + return ( +
+ {payload[0].payload.label} +
+ ); + } + return null; + }} + /> + label} + onMouseEnter={(data) => { + if (data && data.id) { + setHoveredNodeId(data.id); + } + }} + onMouseLeave={() => setHoveredNodeId(null)} + /> + + +
+
+
+

+ All Clusters - Level {currentLevel} ({currentNodes.length}) +

+
+ + +
+
+ +
+ {currentNodes.map((cluster) => ( + + ))} +
+ +
+
+ ); +}; + +export default PointVisualisation; diff --git a/ui/src/components/upload-button.tsx b/ui/src/components/upload-button.tsx index 8b2c4ff..0288e19 100644 --- a/ui/src/components/upload-button.tsx +++ b/ui/src/components/upload-button.tsx @@ -52,6 +52,7 @@ export function UploadButton({ onSuccess }: UploadButtonProps) { onSuccess(validatedData); } catch (error) { + console.error(error); alert("Error uploading file"); } finally { setIsLoading(false); @@ -75,7 +76,7 @@ export function UploadButton({ onSuccess }: UploadButtonProps) { disabled={isLoading} > - Select conversations.json + {selectedFile ? `${selectedFile.name}` : "Upload your conversations"}
@@ -86,7 +87,33 @@ export function UploadButton({ onSuccess }: UploadButtonProps) { disabled={isLoading || !selectedFile} onClick={handleSubmit} > - {isLoading ? "Uploading..." : "Submit File"} + {isLoading ? ( +
+ + + + + Processing... +
+ ) : ( + "Submit File" + )}
); diff --git a/ui/src/index.css b/ui/src/index.css index 83bebd5..7b71f99 100644 --- a/ui/src/index.css +++ b/ui/src/index.css @@ -3,7 +3,7 @@ @tailwind utilities; @layer base { :root { - --background: 0 0% 100%; + --foreground: 0 0% 3.9%; --card: 0 0% 100%; --card-foreground: 0 0% 3.9%; diff --git a/ui/src/lib/area-calculations.ts b/ui/src/lib/area-calculations.ts new file mode 100644 index 0000000..1448f65 --- /dev/null +++ b/ui/src/lib/area-calculations.ts @@ -0,0 +1,46 @@ +interface Point { + x: number; + y: number; +} + +export function calculateBoundingBox(points: Point[]) { + if (points.length === 0) return { minX: 0, minY: 0, maxX: 0, maxY: 0 }; + + return points.reduce( + (bounds, point) => ({ + minX: Math.min(bounds.minX, point.x), + minY: Math.min(bounds.minY, point.y), + maxX: Math.max(bounds.maxX, point.x), + maxY: Math.max(bounds.maxY, point.y), + }), + { + minX: points[0].x, + minY: points[0].y, + maxX: points[0].x, + maxY: points[0].y, + } + ); +} + +export function calculateClusterSize(children: Point[], paddingFactor = 1.5) { + if (children.length === 0) return { size: 140, width: 50, height: 50 }; + + const bounds = calculateBoundingBox(children); + const width = bounds.maxX - bounds.minX; + const height = bounds.maxY - bounds.minY; + + // Calculate diagonal length for better coverage + const diagonal = Math.sqrt(width * width + height * height); + + // Use diagonal as base size and apply padding factor + const baseSize = diagonal * paddingFactor; + + // Add padding proportional to the cluster size + const padding = baseSize * 0.2; // 20% of baseSize as padding + + return { + size: baseSize * 100, // Scale for visualization + width: (width + padding) * paddingFactor, + height: (height + padding) * paddingFactor, + }; +} diff --git a/ui/src/types/analytics.ts b/ui/src/types/analytics.ts index 7af55f8..bc93c85 100644 --- a/ui/src/types/analytics.ts +++ b/ui/src/types/analytics.ts @@ -12,6 +12,9 @@ export const Cluster = z.object({ chat_ids: z.array(z.string()), parent_id: z.string().nullable(), count: z.number(), + x_coord: z.number(), + y_coord: z.number(), + level: z.number(), }); export const analyticsSchema = z.object({ diff --git a/ui/tsconfig.app.tsbuildinfo b/ui/tsconfig.app.tsbuildinfo index 861a0cf..c3aa9ec 100644 --- a/ui/tsconfig.app.tsbuildinfo +++ b/ui/tsconfig.app.tsbuildinfo @@ -1 +1 @@ -{"root":["./src/app.tsx","./src/main.tsx","./src/vite-env.d.ts","./src/components/analytics-charts.tsx","./src/components/cluster-details.tsx","./src/components/cluster-tree.tsx","./src/components/cluster.tsx","./src/components/upload-button.tsx","./src/components/ui/badge.tsx","./src/components/ui/button.tsx","./src/components/ui/card.tsx","./src/components/ui/chart.tsx","./src/components/ui/resizable.tsx","./src/components/ui/scroll-area.tsx","./src/components/ui/separator.tsx","./src/lib/utils.ts","./src/types/analytics.ts"],"version":"5.6.3"} \ No newline at end of file +{"root":["./src/app.tsx","./src/main.tsx","./src/vite-env.d.ts","./src/components/analytics-charts.tsx","./src/components/cluster-details.tsx","./src/components/cluster-tree.tsx","./src/components/cluster.tsx","./src/components/umap_visualisation.tsx","./src/components/upload-button.tsx","./src/components/ui/badge.tsx","./src/components/ui/button.tsx","./src/components/ui/card.tsx","./src/components/ui/chart.tsx","./src/components/ui/resizable.tsx","./src/components/ui/scroll-area.tsx","./src/components/ui/separator.tsx","./src/components/ui/tabs.tsx","./src/lib/area-calculations.ts","./src/lib/utils.ts","./src/types/analytics.ts"],"version":"5.6.3"} \ No newline at end of file diff --git a/uv.lock b/uv.lock index 5a7d05e..88afeba 100644 --- a/uv.lock +++ b/uv.lock @@ -316,6 +316,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d7/ee/bf0adb559ad3c786f12bcbc9296b3f5675f529199bef03e2df281fa1fadb/email_validator-2.2.0-py3-none-any.whl", hash = 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