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selfie/parsers/chat/discord.py create mode 100644 selfie/parsers/chat/google.py create mode 100644 selfie/parsers/chat/whatsapp.py create mode 100644 selfie/text_generation/__init__.py create mode 100644 selfie/text_generation/generation.py create mode 100644 selfie/text_generation/retrieval_augmentation.py create mode 100644 selfie/types/__init__.py create mode 100644 selfie/types/completion_requests.py create mode 100644 selfie/types/share_gpt.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f552a8f --- /dev/null +++ b/.gitignore @@ -0,0 +1,20 @@ +__pycache__/ +*.pyc +*.pyo +*.egg-info/ +*.log + +venv/ + +# pyinstaller +build/ +dist/ + +.idea/ + +data/* +!data/.gitkeep + +selfie/web/ + +*.env \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..d744f40 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,20 @@ +# Contributing to Selfie + +How you can help: + +- **Submit Pull Requests:** Enhance the Selfie codebase or fix an issue. +- **Improve Documentation:** Help clarify and expand our guides and references. +- **Report Bugs:** If something isn't working, let us know. +- **Suggest Features:** Have a great idea? Share it with us. + +## Do's and Don'ts +**Do:** +- Check if your concern or idea is already addressed in an issue or pull request. +- Use pull requests for code changes. +- Provide detailed issue reports and feature suggestions. +- Engage respectfully with the community. + +**Don't:** +- Ignore issue templates. +- Submit untested changes. +- Hesitate to ask for help or clarification. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..241e082 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Corsali, Inc. DBA Vana. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..b42ea17 --- /dev/null +++ b/README.md @@ -0,0 +1,256 @@ +
+ selfie +
+ selfie + +[//]: # ( selfie) +
+ +# Selfie + +Selfie personalizes text generation, augmenting both local and hosted Large Language Models (LLMs) with your personal data. Selfie exposes an OpenAI-compatible API that wraps the LLM of your choice, and automatically injects relevant context into each text generation request. + +## Features + +* Automatically mix your data into chat and text completions using OpenAI-compatible clients like [OpenAI SDKs](https://platform.openai.com/docs/libraries), [SillyTavern](https://sillytavernai.com), and [Instructor](https://github.com/jxnl/instructor)* (untested). +* Quickly drop in personal messaging data exported from WhatsApp and Google Messages. +* Runs locally by default to keep your data private. +* Unopinionated compatibility with your LLM or provider of choice. +* Easily switch to vanilla text generation modes. + +On the roadmap: +* Load data using any [LlamaHub loader](https://llamahub.ai/?tab=loaders) (partial support is available through the API). +* Directly and selectively query loaded data. +* Easy deployment with Docker and pre-built executables. + +## Overview + +Selfie is designed to compose well with tools on both sides of the text generation process. You can think of it as middleware that intelligently mixes your data into a request. + +Typical request: +``` +Application -(request)-> LLM +``` + +Request through Selfie: +``` +Application -(request)-> Selfie -(request x data)-> LLM + | + Your Data +``` + +On the application side, Selfie exposes text generation APIs, including OpenAI-compatible endpoints. + +On the LLM side, Selfie uses tools like LiteLLM and txtai to support forwarding data-augmented requests to your LLM of choice + +## Getting Started + +To launch Selfie, ensure that [python](https://www.python.org), [poetry](https://python-poetry.org), and [yarn](https://yarnpkg.com) are installed. Then run the following commands in the project directory: + +1. `./scripts/build-ui.sh` (requires `yarn`) +2. `poetry shell` +3. `poetry install`, enable GPU or Metal acceleration via llama.cpp by installing GPU-enabled llama-cpp-python, see Scripts. + +[//]: # (3. `poetry install` or `poetry install -E gpu` (to enable GPU devices via transformers). Enable GPU or Metal acceleration via llama.cpp by installing GPU-enabled llama-cpp-python, see Scripts.) +4. `python -m selfie`. The first time you run this, it will download ~4GB of model weights. While you wait, you can download your WhatsApp Google Takeout data for the next step. + +This starts a local web server and should launch the UI in your browser at http://localhost:8181. API documentation is available at http://localhost:8181/docs. Now that the server is running, you can use the API to import your data and connect to your LLM. + +> Note: You can also host your API at a publicly-accessible URL with [ngrok](https://ngrok.com). Copy `selfie/.env.example` to `selfie/.env`, add your ngrok token, and run `python -m selfie share`. + +### Step 1: Gather Messaging Data + +Future versions of Selfie will support loading any text data. For now, you can import chat logs from popular messaging platforms. + +> Note: If you don't have any chat logs or want to try the app first, you can use the example chat logs provided in the `example-chats` directory.) + +Export chats that you use frequently and contain information you want the LLM to know. + +#### Export Instructions + +The following links provide instructions for exporting chat logs from popular messaging platforms: + +* [Whatsapp](https://faq.whatsapp.com/1180414079177245/?cms_platform=android) +* [Google](https://takeout.google.com/settings/takeout) (select Messages from the list) + +These platforms are not yet supported, but you can create a parser in selfie/parsers/chats to support them (please contribute!): + +* [Instagram](https://help.instagram.com/181231772500920) +* [Facebook Messenger](https://www.facebook.com/help/messenger-app/713635396288741/?cms_platform=iphone-app&helpref=platform_switcher) +* [Telegram](https://www.maketecheasier.com/export-telegram-chat-history/) + +Ensure you ask permission of the friends who are also in the chats you export. + +[//]: # (You can also redact their name, messages, and other personal information in later steps.) + +### Step 2: Import Messaging Data + +1. Place your exported chat logs in a directory on your computer, e.g. `/home/alice/chats`. +2. Open the UI at http://localhost:8181. +3. Add your directory as a Data Source. Give it a name (e.g. My Chats), enter the **absolute** path, and click `Add Directory`. +4. In the Documents table, select the exported chat logs you want to import, and click `Index Selected`. + +If this process is successful, your selected chat logs will show as indexed in the table. You can now use the API to connect to your LLM and generate personalized text completions. + +[//]: # (1. Open http://localhost:8181/docs) +[//]: # (2. Find `POST /v1/index_documents/chat-processor`) +[//]: # (3. Upload one or more exported chat log files. To get these files, export them from platforms that you use frequently and contain information you want the LLM to know. Exports: [Whatsapp](https://faq.whatsapp.com/1180414079177245/?cms_platform=android) | [Google](https://takeout.google.com/settings/takeout) | [Instagram](https://help.instagram.com/181231772500920) | [Facebook Messenger](https://www.facebook.com/help/messenger-app/713635396288741/?cms_platform=iphone-app&helpref=platform_switcher) | [Telegram](https://www.maketecheasier.com/export-telegram-chat-history/). Ensure you ask permission of the friend who is also in the chat you export. You can also redact their name, messages, and other personal information in later steps.) +[//]: # (4. Copy, paste, and edit the example parser_configs JSON. Include one configuration object in the list for each file you upload.) +[//]: # () +[//]: # (![chat-processor.png](docs/images/chat-processor.png)) +[//]: # () +[//]: # (Setting `extract_importance` to `true` will give you better query results, but usually causes the import to take a while.) + +### Step 3: Generate Personalized Text + +You can quickly verify if everything is in order by visiting the summarization endpoint in your browser: http://localhost:8181/v1/index_documents/summary?topic=travel ([docs](http://localhost:8181/docs#/default/get_index_documents_summary_v1_index_documents_summary_get)). + +Next, scroll down to the Playground section in the UI. Enter your name and a simple bio, and try asking some questions whose answers are in your chat logs. + +## Usage Guide + +By default, Selfie augments text completions with local models using llama.cpp and a local txtai embeddings database. + +OpenAI-supported parameters like `messages`, `temperature`, `max_tokens`, etc., should all work out of the box, with some special considerations: + +* `model` should be a local path, HuggingFace model name, or LiteLLM model name, depending on the method you choose. + +You can also include special parameters to direct Selfie in how your request should be handled: + +* `method`: The method to use for text generation. Options are `llama.cpp` for running local model files directly (e.g., `.gguf` files), and `litellm` for everything else. +* `api_base`: The base URL of an OpenAI-compatible API to use for text generation, e.g. if you want to host a local model with another tool. +* `api_key`: The API key required by your API at `api_base`. +* `disable_augmentation`: Set to `true` to generate text without data augmentation. + +Examples and more details are provided in the next sections. + +### Augmenting Local Models + +Selfie uses [txtai](https://neuml.github.io/txtai) to download local models and run them with [llama.cpp](https://github.com/ggerganov/llama.cpp). In completion requests, specify the `llama.cpp` method, or leave it off as the default, and ensure that your model is defined correctly, as a local path or HuggingFace model, according to [txtai's documentation](https://neuml.github.io/txtai/models). + +```json +{ + "prompt": "What is the meaning of life?", + "method": "llama.cpp", + "model": "~/models/dolphin-2.6-mistral-7b-dpo.Q5_K_M.gguf" +} +``` +or +```json +{ + "prompt": "What is the meaning of life?", + "method": "llama.cpp", + "model": "TheBloke/dolphin-2.6-mistral-7B-dpo-GGUF/dolphin-2.6-mistral-7b-dpo.Q5_K_M.gguf" +} +``` +You can even use a local Open-AI compatible API ([LiteLLM OpenAI-Compatible Endpoints docs](https://litellm.vercel.app/docs/providers/openai_compatible)). +```json +{ + "method": "litellm", + "api_base": "http://localhost:5000/v1", + "api_key": "none" +} +``` +Method is optional and defaults to `litellm` when `api_base` is specified. + +### Augmenting Hosted Models + +Selfie can use hosted model providers through [litellm](https://litellm.vercel.app). In completion requests, specify the `litellm` method (optional) and ensure that your model is prefixed correctly according to [litellm's documentation for your provider](https://docs.litellm.ai/docs/providers). + +```json +"method": "litellm", +"model": "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", +``` + +In general, you need an API key for your provided loaded into your environment. A quick way to do that is to specify it when you start the server: +`REPLICATE_API_KEY=replicatekey python -m selfie`. + +### Vanilla Text Generation + +Add `disable_augmentation: true` to your request body to generate text without data augmentation. + +```json +{ + "prompt": "What is the meaning of life?", + "method": "llama.cpp", + "model": "~/models/dolphin-2.6-mistral-7b-dpo.Q5_K_M.gguf", + "disable_augmentation": true +} +``` + +## Integration Recipes + +Selfie can be used to augment text generation in a variety of applications. Here are some examples. + +### Powering the OpenAI SDK + +The OpenAI SDK is a popular way to access OpenAI's text generation models. You can use Selfie to augment the text completions that the SDK generates by setting the `apiBase` and `apiKey` parameters. + +```js +import OpenAI from 'openai'; + +const openai = new OpenAI({ + baseURL: 'http://localhost:8181/v1', + apiKey: '' +}); + +const name = 'Alice'; +const chatCompletion = await openai.chat.completions.create({ + // model: 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q5_K_M.gguf', // Optionally, customize the model used + messages: [ + { role: 'system', content: `Write ${name}'s next reply in a fictional chat with ${name} and their friends.` }, + { role: 'user', content: 'Favorite ice cream?' }, + ] +} as any); + +console.log(chatCompletion.choices[0].message.content); +// "Alice enjoys Bahn Mi and Vietnamese coffee." +``` + +### Powering SillyTavern + +[SillyTavern](https://sillytavernai.com) is a self-hosted application that allows you to chat/roleplay with characters that you create. You can use Selfie to give SillyTavern characters some awareness of your data. + +1. Install and run [SillyTavern](https://github.com/SillyTavern/SillyTavern). +2. Configure a custom chat completion source as `http://localhost:8181`. You can customize the model by setting API parameters for `method` and `model` in Additional Parameters. \ + ![silly-tavern-api.png](docs/images/silly-tavern-api.png) +3. Create a character, customize the text generation settings, etc. +4. Chat with your character to see that it is aware of your data: \ + ![silly-tavern-chat.png](docs/images/silly-tavern-chat.png) + +You can even tell Selfie to use an OpenAI-compatible API for the LLM that it augments: + +Note that model is empty: +![silly-tavern-local-api-model.png](docs/images/silly-tavern-local-api-model.png) +We pass an extra parameter, `instruct_mode`, for text-generation-webui. +![silly-tavern-local-api.png](docs/images/silly-tavern-local-api.png) +> Note: some OpenAI-compatible APIs may not properly handle SillyTavern's use of multiple system messages and non-alternating user/assistant messages (like [text-generation-webui](https://github.com/oobabooga/text-generation-webui)). A text-generation-webui workaround is described [here](https://github.com/SillyTavern/SillyTavern/issues/1722#issuecomment-1902619716). + + +## Experimental Features + +Selfie is a work in progress. Here are some features that are not yet fully supported. + +### Building an Executable + +Build an executable for your platform with `pyinstaller selfie.spec --noconfirm`. + +Start the built service with `./dist/selfie/selfie`. + +### Scripts + +Besides `build-ui.sh`, scripts are provided in scripts/ to help with useful tasks. + +### llama-cpp-python-cublas.sh + +To install llama.cpp with CUDA support for better performance, run `./scripts/llama-cpp-python-cublas.sh`. [ROCm](https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/tag/rocm) and [Metal](https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/tag/metal) versions should be installed manually. + +Alternatively, you can build `llama-cpp-python` manually with the flags of your choice by following [the instructions](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#installation). + +## Contributing + +Selfie is a community project. We welcome contributions of all kinds, including bug reports, feature requests, and pull requests. Please see the [contributing guide](CONTRIBUTING.md) for more information. + +## Community + +Join the [Vana Discord server](https://discord.gg/GhYDaDqENx) to chat with the community and get help with Selfie. diff --git a/docs/images/chat-processor.png b/docs/images/chat-processor.png new file mode 100644 index 0000000000000000000000000000000000000000..69121764b2caf693868df41e0751130fc8041d6b GIT binary patch literal 109445 zcmZs?by%C*(msq8D=wu#ai=XQ?jGC<6u07D3MIG{io3fN_Y{f~q?AH%NGZW7EblGjq>9(XTa?2=Qs~(a_KcRg{4`XlReXXlNLFxY(#) z_$Dg9p`m?5Qvu4p@iE`;x-g(Lfwx@U;@t)HfG&0pr&ewtM{q3>Q6Gt+yblRk38MDA z+yDCVL4Oo$sE9F&8eN-pd$hpp;sw4i?dvzwFPBH$356&P>WrOh;5mW_x4!@LS+m)6 zOL%Pfe?C&6&k#Gy|KoA*K+N&xB(qaSB)=s9N zpy0f^y862T>YLN64B`md7Mt@yZnrdeB>{Ii<=9Xvq}F9fbK*@SjPIZQg-y%6ASU1- z5H?LJ1lM0$Pa>> zr8Up*4KZ$-ku;XSzwDC0Yv@hWEJ@#aAZ?=-mD}CkC;oT#sL3hEZ*_~UT6$j$SL0Ak zQ&MeERf^wFmg)GqDwt7iYz@uLzNMyVFeun8SN78Gwt2Ee_Se$?wZ8p)QV z{(=~^TtF$s(=+EyK{9uiLcG2{OZeAVBx@OC>^}#DQUSdz3-bJ^nSUPAr<-o{@^D_B 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Makes me regret staying in. +4/1/23, 9:20 AM - Alice Johnson: There's always tomorrow! Plus, the park will be gorgeous for our picnic. +4/1/23, 9:22 AM - Bob Smith: Speaking of, I'm bringing the lemonade. Tried a new recipe I found online. +4/1/23, 9:23 AM - Alice Johnson: Yum! I'll make those sandwiches you like. +4/1/23, 10:35 AM - Bob Smith: Can't wait! Also, did you decide on the theme for your mom's birthday party? +4/1/23, 10:37 AM - Alice Johnson: Yes, going with '70s disco. She's going to love it. +4/1/23, 10:40 AM - Bob Smith: That's perfect! I'll look for my bell-bottoms then 😄. +4/1/23, 1:15 PM - Alice Johnson: Just got back from the craft store. I bought so much glitter, it's everywhere. +4/1/23, 1:17 PM - Bob Smith: Glitter bomb! Need help with decorations? +4/1/23, 1:19 PM - Alice Johnson: Would love that, thanks! How about Saturday? +4/1/23, 1:20 PM - Bob Smith: It's a date. I'll bring extra vacuum cleaners for the glitter aftermath. +4/1/23, 3:45 PM - Alice Johnson: Oh, ran into Mark at the store. He's back from his trip to Japan. +4/1/23, 3:47 PM - Bob Smith: Japan? That's awesome. Did he bring back any cool gadgets? +4/1/23, 3:49 PM - Alice Johnson: Yes, a pocket translator and some traditional tea. We should plan a visit. +4/1/23, 3:51 PM - Bob Smith: Definitely. Always wanted to see the cherry blossoms. +4/1/23, 6:00 PM - Alice Johnson: Look what I found while cleaning today! Our photo from the hiking trip last year. +4/1/23, 6:02 PM - Bob Smith: +4/1/23, 6:03 PM - Alice Johnson: Such a great day. We need to go hiking again soon. +4/1/23, 6:05 PM - Bob Smith: Agreed. Let's plan for next weekend before it gets too hot. +4/1/23, 8:10 PM - Alice Johnson: Dinner tonight was amazing. Made that pasta recipe you sent me. +4/1/23, 8:12 PM - Bob Smith: Glad you liked it! Cooking has become my new hobby lately. +4/1/23, 9:30 PM - Alice Johnson: I'm thinking of starting a small garden. Any tips on growing herbs? +4/1/23, 9:33 PM - Bob Smith: Start with basil and mint. They're pretty easy and great for cooking. +4/1/23, 9:35 PM - Alice Johnson: Great idea. I'll pick up some seeds tomorrow. +4/1/23, 9:36 PM - Bob Smith: Let me know if you need help setting it up. I've got a green thumb these days. +4/1/23, 10:00 PM - Alice Johnson: Thanks, Bob! Really appreciate it. Goodnight! +4/1/23, 10:02 PM - Bob Smith: Goodnight, Alice. Talk to you tomorrow. diff --git a/hooks/hook-llama_cpp.py b/hooks/hook-llama_cpp.py new file mode 100644 index 0000000..503e1ac --- /dev/null +++ b/hooks/hook-llama_cpp.py @@ -0,0 +1,21 @@ +from PyInstaller.utils.hooks import collect_data_files, get_package_paths +import os, sys + +# Get the package path +package_path = get_package_paths('llama_cpp')[0] + +# Collect data files +datas = collect_data_files('llama_cpp') + +# Append the additional .dll or .so file +if os.name == 'nt': # Windows + dll_path = os.path.join(package_path, 'llama_cpp', 'llama.dll') + datas.append((dll_path, 'llama_cpp')) +elif sys.platform == 'darwin': # Mac + #so_path = os.path.join(package_path, 'llama_cpp', 'llama.dylib') + so_path = os.path.join(package_path, 'llama_cpp', 'libllama.dylib') + # so_path = os.path.join(package_path, 'llama_cpp', 'libllama.so') + datas.append((so_path, 'llama_cpp')) +elif os.name == 'posix': # Linux + so_path = os.path.join(package_path, 'llama_cpp', 'libllama.so') + datas.append((so_path, 'llama_cpp')) \ No newline at end of file diff --git a/hooks/hook-llama_index.py b/hooks/hook-llama_index.py new file mode 100644 index 0000000..24715d7 --- /dev/null +++ b/hooks/hook-llama_index.py @@ -0,0 +1,9 @@ +from PyInstaller.utils.hooks import collect_data_files, get_package_paths +import os + +# Get the package path +package_path = get_package_paths("llama_index")[0] + +# Collect data files +datas = collect_data_files("llama_index") +datas.append((os.path.join(package_path, "*"), "llama_index/")) diff --git a/hooks/hook-skl2onnx.py b/hooks/hook-skl2onnx.py new file mode 100644 index 0000000..0630292 --- /dev/null +++ b/hooks/hook-skl2onnx.py @@ -0,0 +1,3 @@ +from PyInstaller.utils.hooks import collect_submodules +hiddenimports = collect_submodules('skl2onnx') +module_collection_mode = 'pyz+py' diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..154a818 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,5041 @@ +[[package]] +name = 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"evaluate", "scikit-learn", "scipy", "timm", "tqdm", "transformers"] +test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"] +test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"] +testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "tqdm", "transformers"] + +[[package]] +name = "aiohttp" +version = "3.9.1" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""} +attrs = ">=17.3.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["Brotli", "aiodns", "brotlicffi"] + +[[package]] +name = "aiosignal" +version = "1.3.1" +description = "aiosignal: a list of registered asynchronous callbacks" +category = 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+humanize = "^4.9.0" +litellm = "^1.23.3" +txtai = {version = "^6.3.0", extras = ["pipeline-llm"]} +pyinstaller = "^6.3.0" +sse-starlette = "^2.0.0" +llama-index = "^0.9.38" +html2text = "^2020.1.16" +peewee = "^3.17.0" +pypdf = "^3.17.4" +python-multipart = "^0.0.6" +ngrok = "^1.0.0" +python-dotenv = "^1.0.1" + +# Support GPU Features +auto-gptq = { version = "^0.6.0", optional = true } +optimum = { version = "^1.16.2", optional = true } +autoawq = { version = "^0.1.8", optional = true } + +[tool.poetry.extras] +gpu = ["auto-gptq", "optimum", "autoawq"] + +[tool.poetry.group.dev.dependencies] +colorlog = "^6.8.2" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/scripts/build-ui.sh b/scripts/build-ui.sh new file mode 100755 index 0000000..f8601f4 --- /dev/null +++ b/scripts/build-ui.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +#!/bin/bash + +# Step 1: Build Next.js App +echo "Building Next.js app..." +cd selfie-ui +yarn install +# NODE_ENV=production yarn install --production --frozen-lockfile +yarn run build + +# Step 2: Move the out/ directory to FastAPI static file serving directory +echo "Moving built files to FastAPI app..." +cd .. +rm -rf selfie/web +mkdir -p selfie/web +cp -r selfie-ui/out/* selfie/web/ + +echo "Next.js app built and integrated with FastAPI." diff --git a/scripts/llama-cpp-python-cublas.sh b/scripts/llama-cpp-python-cublas.sh new file mode 100755 index 0000000..21c1f3b --- /dev/null +++ b/scripts/llama-cpp-python-cublas.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +echo "Installing llama-cpp-python with cuBLAS support." + +# Detect installed CUDA version +CUDA_VERSION_OUTPUT=$(nvcc --version | grep "release" | awk '{print $6}' | cut -d'.' -f1-2) +CUDA_VERSION="cu${CUDA_VERSION_OUTPUT//[[:alpha:]|\.]/}" + +if [ -z "$CUDA_VERSION_OUTPUT" ]; then + echo "CUDA version could not be detected. Please ensure CUDA is installed." + exit 1 +else + echo "Detected CUDA version: $CUDA_VERSION" +fi + +# Default to AVX2 CPU architecture +CPU_ARCH="AVX2" + +# Allow user to override CPU architecture +read -p "Enter your CPU architecture (AVX, AVX2, AVX512, basic) or press enter to use default (AVX2): " user_arch +if [ ! -z "$user_arch" ]; then + CPU_ARCH=$user_arch +fi + +# Construct the pip install command +INSTALL_CMD="python -m pip install llama-cpp-python --prefer-binary --force-reinstall --extra-index-url=https://jllllll.github.io/llama-cpp-python-cuBLAS-wheels/${CPU_ARCH}/${CUDA_VERSION}" + +# Execute the installation command +echo "Running installation command:" +echo $INSTALL_CMD +eval $INSTALL_CMD + +echo "Installation complete. Please check for any errors above." diff --git a/selfie-ui/.eslintrc.json b/selfie-ui/.eslintrc.json new file mode 100644 index 0000000..bffb357 --- /dev/null +++ b/selfie-ui/.eslintrc.json @@ -0,0 +1,3 @@ +{ + "extends": "next/core-web-vitals" +} diff --git a/selfie-ui/.gitignore b/selfie-ui/.gitignore new file mode 100644 index 0000000..fd3dbb5 --- /dev/null +++ b/selfie-ui/.gitignore @@ -0,0 +1,36 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js +.yarn/install-state.gz + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# local env files +.env*.local + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/selfie-ui/README.md b/selfie-ui/README.md new file mode 100644 index 0000000..c403366 --- /dev/null +++ b/selfie-ui/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details. diff --git a/selfie-ui/next.config.mjs b/selfie-ui/next.config.mjs new file mode 100644 index 0000000..15eff7c --- /dev/null +++ b/selfie-ui/next.config.mjs @@ -0,0 +1,6 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + output: 'export' +}; + +export default nextConfig; diff --git a/selfie-ui/package.json b/selfie-ui/package.json new file mode 100644 index 0000000..71be605 --- /dev/null +++ b/selfie-ui/package.json @@ -0,0 +1,35 @@ +{ + "name": "selfie-ui", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "next lint" + }, + "dependencies": { + "@heroicons/react": "^2.1.1", + "@tailwindcss/typography": "^0.5.10", + "daisyui": "^4.6.1", + "deep-chat-react": "^1.4.10", + "deep-chat-react-dev": "^9.0.129", + "next": "14.1.0", + "prismjs": "^1.29.0", + "react": "^18", + "react-dom": "^18", + "react-icons": "^5.0.1", + "tailwindcss": "^3.4.1" + }, + "devDependencies": { + "@types/node": "^20", + "@types/prismjs": "^1.26.3", + "@types/react": "^18", + "@types/react-dom": "^18", + "autoprefixer": "^10.4.17", + "eslint": "^8", + "eslint-config-next": "14.1.0", + "postcss": "^8.4.33", + "typescript": "^5" + } +} diff --git a/selfie-ui/postcss.config.js b/selfie-ui/postcss.config.js new file mode 100644 index 0000000..12a703d --- /dev/null +++ b/selfie-ui/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/selfie-ui/public/next.svg b/selfie-ui/public/next.svg new file mode 100644 index 0000000..5174b28 --- /dev/null +++ b/selfie-ui/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/selfie-ui/public/vercel.svg b/selfie-ui/public/vercel.svg new file mode 100644 index 0000000..d2f8422 --- /dev/null +++ b/selfie-ui/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/selfie-ui/src/app/components/Chat.tsx b/selfie-ui/src/app/components/Chat.tsx new file mode 100644 index 0000000..1262c42 --- /dev/null +++ b/selfie-ui/src/app/components/Chat.tsx @@ -0,0 +1,93 @@ +"use client"; + +import dynamic from "next/dynamic"; + +const DeepChat = dynamic( + () => import('deep-chat-react-dev').then((mod) => ({ default: mod.DeepChat })), + { ssr: false } +); + +import React from "react"; +import type { OpenAIChat } from "deep-chat-dev/dist/types/openAI"; + +export const Chat = ({ assistantName = "Assistant", assistantBio = "", userName = "User", disableAugmentation = false, shouldClear = false }) => + {}} + key={`${assistantName}-${assistantBio}-${userName}-${disableAugmentation}`} // Force re-render when props are changed + style={{ borderRadius: '10px', border: 'unset', backgroundColor: '#292929', width: '100%', maxWidth: 'inherit', display: 'block' }} + messageStyles={{ + "default": { + "ai": {"bubble": {"backgroundColor": "#545454", "color": "white"}} + }, + "loading": { + "bubble": {"backgroundColor": "#545454", "color": "white"} + } + }} + textInput={{ + "styles": { + "container": { + "backgroundColor": "#666666", + "border": "unset", + "color": "#e8e8e8" + } + }, + "placeholder": {"text": "Say anything here...", "style": {"color": "#bcbcbc"}} + }} + submitButtonStyles={{ + "submit": { + "container": { + "default": {"bottom": "0.7rem"} + }, + "svg": { + "styles": { + "default": { + "filter": "brightness(0) saturate(100%) invert(70%) sepia(52%) saturate(5617%) hue-rotate(185deg) brightness(101%) contrast(101%)" + } + } + } + } + }} + auxiliaryStyle="::-webkit-scrollbar { + width: 10px; + height: 10px; + } + ::-webkit-scrollbar-thumb { + background-color: grey; + border-radius: 5px; + } + ::-webkit-scrollbar-track { + background-color: unset; + }" + initialMessages={[ + {"text": "Hello! How are you?", "role": "assistant"}, + ]} + + stream={true} + directConnection={{ + openAI: { + chat: { + model: '', + system_prompt: `You are ${assistantName}, chatting with ${userName} in a fictional roleplay in which you are playing yourself. Write 1 reply only. Be proactive, creative, and drive the plot and conversation forward. Write at least 1 sentence, up to 4. Always stay in character and avoid repetition.${ assistantBio ? `\n${assistantBio}` : ''}`, + stop: ['[END]', '[INST]', '[/INST]'], + temperature: 0.3, + frequency_penalty: 0.7, + presence_penalty: 0.7, + top_p: 1, + max_tokens: 350, + } as OpenAIChat, + key: 'ignored' + } + }} + request={{ + url: 'http://localhost:8181/v1/chat/completions', + additionalBodyProps: { + disable_augmentation: disableAugmentation, + + /** Uncomment and edit to use a local OpenAI-compatible API, e.g. text-generation-webui on localhost:5000. + api_base: 'http://localhost:5000/v1', + // When api_base refers to a local text-generation-webui API, we need to specify the instruction template. + instruction_template: 'mistral', + **/ + }, + }} + /> diff --git a/selfie-ui/src/app/components/CodeSnippet.tsx b/selfie-ui/src/app/components/CodeSnippet.tsx new file mode 100644 index 0000000..d80a81f --- /dev/null +++ b/selfie-ui/src/app/components/CodeSnippet.tsx @@ -0,0 +1,20 @@ +import React, { useEffect } from 'react'; +import Prism from 'prismjs'; +import 'prismjs/themes/prism-okaidia.css'; // Or any other theme you prefer + +const CodeSnippet = ({snippet}: {snippet: string}) => { + useEffect(() => { + // Highlight syntax whenever the component mounts or updates + Prism.highlightAll(); + }, []); + + return ( +

+ ); +}; + +export default CodeSnippet; \ No newline at end of file diff --git a/selfie-ui/src/app/components/DataSourceTable/DataSourceActionBar.tsx b/selfie-ui/src/app/components/DataSourceTable/DataSourceActionBar.tsx new file mode 100644 index 0000000..58ef1c8 --- /dev/null +++ b/selfie-ui/src/app/components/DataSourceTable/DataSourceActionBar.tsx @@ -0,0 +1,42 @@ +import React, { useState } from 'react'; + +interface AddDataSourceFormProps { + onAddDataSource: (name: string, directory: string) => void | Promise; +} + +export const DataSourceActionBar: React.FC = ({ onAddDataSource }) => { + const [name, setName] = useState(''); + const [directory, setDirectory] = useState(''); + + const handleSubmit = async (event: React.FormEvent) => { + event.preventDefault(); + await onAddDataSource(name, directory); + // Reset the form fields after submission + setName(''); + setDirectory(''); + }; + + return ( +
+ setName(e.target.value)} + className="input input-sm input-bordered w-full max-w-xs mr-2" + required + /> + setDirectory(e.target.value)} + className="input input-bordered input-sm w-full max-w-xs mr-2" + required + /> + +
+ ); +}; diff --git a/selfie-ui/src/app/components/DataSourceTable/DataSourceTable.tsx b/selfie-ui/src/app/components/DataSourceTable/DataSourceTable.tsx new file mode 100644 index 0000000..9e3672c --- /dev/null +++ b/selfie-ui/src/app/components/DataSourceTable/DataSourceTable.tsx @@ -0,0 +1,54 @@ +import React from 'react'; +import { DataSource } from "@/app/types"; +import { DataSourceActionBar } from "@/app/components/DataSourceTable/DataSourceActionBar"; + +interface DataSourceTableProps { + dataSources: DataSource[]; + onAddDataSource: (name: string, directory: string) => void | Promise; + onDeleteDataSource: (dataSource: DataSource) => void | Promise; +} + +const DataSourceTable = ({ + dataSources, + onAddDataSource, + onDeleteDataSource + }: DataSourceTableProps) => { + + return ( +
+ + +
+ + + + + + + + + + {dataSources.map((dataSource, index) => ( + + + + + + ))} + +
NameInput DirectoryActions
{dataSource.name}{dataSource.config.constructor_kwargs.input_dir} + +
+
+
+ ); +}; + +DataSourceTable.displayName = 'DataSourceTable'; + +export default DataSourceTable; diff --git a/selfie-ui/src/app/components/DataSourceTable/index.tsx b/selfie-ui/src/app/components/DataSourceTable/index.tsx new file mode 100644 index 0000000..2bb09ff --- /dev/null +++ b/selfie-ui/src/app/components/DataSourceTable/index.tsx @@ -0,0 +1,3 @@ +import DataSourceTable from "./DataSourceTable"; + +export { DataSourceTable } diff --git a/selfie-ui/src/app/components/DocumentTable/DocumentTable.tsx b/selfie-ui/src/app/components/DocumentTable/DocumentTable.tsx new file mode 100644 index 0000000..eb78b83 --- /dev/null +++ b/selfie-ui/src/app/components/DocumentTable/DocumentTable.tsx @@ -0,0 +1,143 @@ +import React, { useMemo, useState } from 'react'; +import { ChevronDownIcon, ChevronUpIcon } from "@heroicons/react/20/solid"; +import { DataSource, Document } from "@/app/types"; +import { isDateString, isNumeric } from "@/app/utils"; +import { DocumentTableActionBar } from "./DocumentTableActionBar"; +import DocumentTableRow from './DocumentTableRow'; + +interface DocumentTableProps { + dataSources: DataSource[]; + documents: { [sourceId: string]: Document[] }; + columnNames: string[]; + selectedDocuments: Set; + setSelectedDocuments: (selected: Set) => void; + onToggleDocumentSelection: (docId: string) => void; + onIndexDocument: (doc: Document) => void | Promise; + onUnindexDocument: (doc: Document) => void | Promise; + onIndexDocuments: () => void | Promise; + onUnindexDocuments: () => void | Promise; + disabled?: boolean; +} + +const DocumentTable = ({ + dataSources, + documents, + columnNames, + selectedDocuments, + setSelectedDocuments, + onToggleDocumentSelection, + onIndexDocument, + onUnindexDocument, + onIndexDocuments, + onUnindexDocuments, + disabled = false, + }: DocumentTableProps) => { + const [sortField, setSortField] = useState(); + const [sortDirection, setSortDirection] = useState<'asc'|'desc'>('asc'); + const [selectAll, setSelectAll] = useState(false); + + const handleToggleSelectAll = () => { + setSelectAll(!selectAll); + if (!selectAll) { + const allDocIds = Object.values(documents).flat().map(doc => doc.id); + setSelectedDocuments(new Set(allDocIds)); + } else { + setSelectedDocuments(new Set()); + } + }; + + const handleHeaderClick = (fieldName: string) => { + if (sortField === fieldName) { + setSortDirection(sortDirection === 'asc' ? 'desc' : 'asc'); + } else { + setSortField(fieldName); + setSortDirection('asc'); + } + }; + + const getSortableValue = (value: any) => { + if (isDateString(value)) { + return new Date(value).getTime(); + } else if (isNumeric(value)) { + return parseFloat(value); + } + return value.toString(); + }; + + const sortedDocuments = useMemo(() => { + if (!sortField) return documents; + + return Object.keys(documents).reduce((sortedDocs: any, key: string) => { + sortedDocs[key] = [...documents[key]].sort((a, b) => { + const aValue = getSortableValue(a.metadata[sortField]); + const bValue = getSortableValue(b.metadata[sortField]); + + if (sortDirection === 'asc') { + return aValue > bValue ? 1 : -1; + } else { + return aValue < bValue ? 1 : -1; + } + }); + return sortedDocs; + }, {}); + }, [documents, sortField, sortDirection]); + + return ( + <> + 0} + disabled={disabled} + /> + + + + + {/* TODO: make sortable */} + + + {columnNames.map((colName) => ( + + ))} + + + + + {dataSources.flatMap(dataSource => + sortedDocuments[dataSource.id]?.map((doc: Document) => ( + onIndexDocument(doc)} + onUnindexDocument={(doc) => onUnindexDocument(doc)} + disabled={disabled} + /> + )) + )} + +
+ + SourceIs Indexed handleHeaderClick(colName)}> + {colName} + {sortField === colName && ( + sortDirection === 'asc' ? : + + )} + Actions
+ + ); +}; + +DocumentTable.displayName = 'DocumentTable'; + +export default DocumentTable; diff --git a/selfie-ui/src/app/components/DocumentTable/DocumentTableActionBar.tsx b/selfie-ui/src/app/components/DocumentTable/DocumentTableActionBar.tsx new file mode 100644 index 0000000..fb711d8 --- /dev/null +++ b/selfie-ui/src/app/components/DocumentTable/DocumentTableActionBar.tsx @@ -0,0 +1,45 @@ +import React from 'react'; + +interface IndexDocumentsFormProps { + onIndexDocuments: () => void | Promise; + onUnindexDocuments: () => void | Promise; + hasSelectedDocuments: boolean; + disabled?: boolean; +} + +export const DocumentTableActionBar: React.FC = ({ + onIndexDocuments, + onUnindexDocuments, + hasSelectedDocuments, + disabled = false, + }) => { + const handleSubmit = async (event: React.FormEvent, isIndex: boolean) => { + event.preventDefault(); + if (isIndex) { + await onIndexDocuments(); + } else { + await onUnindexDocuments(); + } + }; + + return ( +
{}} className="flex items-center space-x-4"> + + +
+ ); +}; diff --git a/selfie-ui/src/app/components/DocumentTable/DocumentTableRow.tsx b/selfie-ui/src/app/components/DocumentTable/DocumentTableRow.tsx new file mode 100644 index 0000000..7679590 --- /dev/null +++ b/selfie-ui/src/app/components/DocumentTable/DocumentTableRow.tsx @@ -0,0 +1,86 @@ +import React, {useEffect, useState} from "react"; +import { formatDate, isDateString } from "@/app/utils"; +import { DataSource, Document } from "@/app/types"; + +interface DocumentTableRowProps { + doc: Document; + dataSource: DataSource; + columnNames: string[]; + onToggle: (docId: string) => void; + onIndexDocument: (doc: Document) => void | Promise; + onUnindexDocument: (doc: Document) => void | Promise; + isSelected?: boolean; + disabled?: boolean; +} + +const DocumentTableRow = React.memo(({ + doc, + dataSource, + columnNames, + onToggle, + onIndexDocument, + onUnindexDocument, + isSelected = false, + disabled = false, +}) => { + const [selected, setSelected] = useState(isSelected); + + useEffect(() => { + setSelected(isSelected); + }, [isSelected]); + + const handleCheckboxChange = () => { + setSelected(!selected); + onToggle(doc.id); + }; + + return ( + + + + + +
+ {dataSource.name} +
+ + +
+ {doc.is_indexed ? '✅' : ''} +
+ + {columnNames.map((colName) => ( + +
+ {isDateString(doc.metadata[colName]) ? formatDate(doc.metadata[colName]) : doc.metadata[colName]} +
+ + ))} + + {!doc.is_indexed && } + {doc.is_indexed && } + + + ); +}); + +DocumentTableRow.displayName = 'DocumentTableRow'; + +export default DocumentTableRow; diff --git a/selfie-ui/src/app/components/DocumentTable/index.tsx b/selfie-ui/src/app/components/DocumentTable/index.tsx new file mode 100644 index 0000000..db4ed5b --- /dev/null +++ b/selfie-ui/src/app/components/DocumentTable/index.tsx @@ -0,0 +1,3 @@ +import DocumentTable from "./DocumentTable"; + +export { DocumentTable } diff --git a/selfie-ui/src/app/components/Footer.tsx b/selfie-ui/src/app/components/Footer.tsx new file mode 100644 index 0000000..68c830e --- /dev/null +++ b/selfie-ui/src/app/components/Footer.tsx @@ -0,0 +1,7 @@ +export const Footer = () => ( + +); diff --git a/selfie-ui/src/app/components/Header.tsx b/selfie-ui/src/app/components/Header.tsx new file mode 100644 index 0000000..d78c680 --- /dev/null +++ b/selfie-ui/src/app/components/Header.tsx @@ -0,0 +1,20 @@ +import { FaGithub } from "react-icons/fa"; + +export const Header = () => ( + //
+ //

Selfie

+ //
+
+ {/*Selfie*/} +

Selfie

+
+ + + API Docs + + + + + +
+); diff --git a/selfie-ui/src/app/components/Sidebar.tsx b/selfie-ui/src/app/components/Sidebar.tsx new file mode 100644 index 0000000..0f5a348 --- /dev/null +++ b/selfie-ui/src/app/components/Sidebar.tsx @@ -0,0 +1,8 @@ +export const Sidebar = () => ( + +) +; diff --git a/selfie-ui/src/app/favicon.ico b/selfie-ui/src/app/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..718d6fea4835ec2d246af9800eddb7ffb276240c GIT binary patch literal 25931 zcmeHv30#a{`}aL_*G&7qml|y<+KVaDM2m#dVr!KsA!#An?kSQM(q<_dDNCpjEux83 zLb9Z^XxbDl(w>%i@8hT6>)&Gu{h#Oeyszu?xtw#Zb1mO{pgX9699l+Qppw7jXaYf~-84xW z)w4x8?=youko|}Vr~(D$UXIbiXABHh`p1?nn8Po~fxRJv}|0e(BPs|G`(TT%kKVJAdg5*Z|x0leQq0 zkdUBvb#>9F()jo|T~kx@OM8$9wzs~t2l;K=woNssA3l6|sx2r3+kdfVW@e^8e*E}v zA1y5{bRi+3Z`uD3{F7LgFJDdvm;nJilkzDku>BwXH(8ItVCXk*-lSJnR?-2UN%hJ){&rlvg`CDTj z)Bzo!3v7Ou#83zEDEFcKt(f1E0~=rqeEbTnMvWR#{+9pg%7G8y>u1OVRUSoox-ovF z2Ydma(;=YuBY(eI|04{hXzZD6_f(v~H;C~y5=DhAC{MMS>2fm~1H_t2$56pc$NH8( 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--foreground-rgb: 255, 255, 255; + --background-start-rgb: 0, 0, 0; + --background-end-rgb: 0, 0, 0; + } +} + +body { + color: rgb(var(--foreground-rgb)); + background: linear-gradient( + to bottom, + transparent, + rgb(var(--background-end-rgb)) + ) + rgb(var(--background-start-rgb)); +} + +@layer utilities { + .text-balance { + text-wrap: balance; + } +} diff --git a/selfie-ui/src/app/layout.tsx b/selfie-ui/src/app/layout.tsx new file mode 100644 index 0000000..3f7d83e --- /dev/null +++ b/selfie-ui/src/app/layout.tsx @@ -0,0 +1,33 @@ +import React from 'react'; + +import { Header } from '@/app/components/Header'; +import { Sidebar } from '@/app/components/Sidebar'; +import { Footer } from "@/app/components/Footer"; + +import './globals.css'; + +import { Metadata } from 'next' + +export const metadata: Metadata = { + title: 'Home', + description: 'Welcome to Next.js', +} + +export default function Layout({ children }: { children: React.ReactNode }) { + return ( + + +
+
+
+ {/**/} +
+ {children} +
+
+
+
+ + + ); +} \ No newline at end of file diff --git a/selfie-ui/src/app/page.tsx b/selfie-ui/src/app/page.tsx new file mode 100644 index 0000000..b088c0c --- /dev/null +++ b/selfie-ui/src/app/page.tsx @@ -0,0 +1,349 @@ +"use client"; + +import React, { useState, useEffect, useMemo } from 'react'; +import CodeSnippet from "./components/CodeSnippet"; +import { Chat } from "./components/Chat"; +import { DocumentTable } from "./components/DocumentTable"; +import { DataSourceTable } from "./components/DataSourceTable"; +import { DataSource, Documents } from "./types"; + +const SelfieManager = () => { + const [dataSources, setDataSources] = useState([]); + const [documents, setDocuments] = useState({}); + const [selectedDocuments, setSelectedDocuments] = useState>(new Set()); + const [isChat, setIsChat] = useState(true); + const [loading, setLoading] = useState(true); + + const [isTaskPending, setIsTaskPending] = useState(false); + const [runningTaskMessage, setRunningTaskMessage] = useState(''); + const [completedTaskMessage, setCompletedTaskMessage] = useState(''); + const [isTaskError, setIsTaskError] = useState(false); + const [disableAugmentation, setDisableAugmentation] = useState(false); + const [shouldClear, setShouldClear] = useState(false); + + const [name, setName] = useState(() => { + if (typeof window !== 'undefined') { + return localStorage.getItem('assistantName') || ''; + } + return ''; + }); + + const [bio, setBio] = useState(() => { + if (typeof window !== 'undefined') { + return localStorage.getItem('assistantBio') || ''; + } + return ''; + }); + + // Effect hook to update localStorage when name or bio changes + useEffect(() => { + if (typeof window !== 'undefined') { + localStorage.setItem('assistantName', name); + } + }, [name]); + + useEffect(() => { + if (typeof window !== 'undefined') { + localStorage.setItem('assistantBio', bio); + } + }, [bio]); + + + useEffect(() => { + fetchDataSources(); + }, []); + + const fetchDataSources = async () => { + try { + const response = await fetch('http://localhost:8181/v1/data-sources'); + const data = await response.json(); + setDataSources(data); + data.forEach((dataSource: any) => fetchDocuments(dataSource.id)); + } catch (error) { + console.error('Error fetching data sources:', error); + } finally { + setLoading(false); + } + }; + + const fetchDocuments = async (sourceId: string) => { + try { + const response = await fetch(`http://localhost:8181/v1/documents?source_id=${sourceId}`); + const docs = await response.json(); + setDocuments(prevDocs => ({ ...prevDocs, [sourceId]: docs })); + } catch (error) { + console.error('Error fetching documents:', error); + } + }; + + const handleScan = async (sourceId: string) => { + await fetch(`http://localhost:8181/v1/data-sources/${sourceId}/scan`, { method: 'POST' }); + await fetchDocuments(sourceId); + }; + + const handleIndex = async (sourceId: string) => { + await fetch(`http://localhost:8181/v1/data-sources/${sourceId}/index`, { method: 'POST' }); + }; + + const toggleDocumentSelection = (docId: string) => { + setSelectedDocuments(prevSelectedDocuments => { + const newSelection = new Set(prevSelectedDocuments); + if (newSelection.has(docId)) { + newSelection.delete(docId); + } else { + newSelection.add(docId); + } + return newSelection; + }); + }; + + const columnNames = useMemo(() => { + const firstDoc = documents[dataSources[0]?.id]?.[0]; + return firstDoc ? Object.keys(firstDoc.metadata) : []; + }, [documents, dataSources]); + + + // const handleDirectoryChange = (event: React.ChangeEvent) => { + // const files = event.target.files; + // const path = files?.[0]?.webkitRelativePath.split('/')[0]; // Get directory name + // console.log(files) + // console.log('Selected directory:', path); + // setSelectedDirectory(path); + // }; + + const handleAddDataSource = async (dataSourceName: string, selectedDirectory: string) => { + try { + const response = await fetch('http://localhost:8181/v1/data-sources', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + name: dataSourceName, + loader_module: "llama_index.readers.SimpleDirectoryReader", + constructor_args: [], + constructor_kwargs: { + input_dir: selectedDirectory, + recursive: true + }, + load_data_args: [], + load_data_kwargs: {} + }) + }); + + if (!response.ok) { + throw new Error(`HTTP error! Status: ${response.status}`); + } + + const result = await response.json(); + console.log("Data source added:", result); + + // Now, scan the directory to populate the list of documents + await handleScan(result.id); + + // Optionally, refresh the list of data sources or update the UI accordingly + await fetchDataSources(); + } catch (error) { + console.error("Failed to add data source:", error); + } + }; + + const handleIndexDocument = async (documentId: string, deleteInstead = false) => { + await handleIndexSelected([documentId], deleteInstead); + }; + + + const handleIndexSelected = async (documentIds = Array.from(selectedDocuments), deleteInstead = false) => { + setRunningTaskMessage(`${ deleteInstead ? 'Unindexing' : 'Indexing' } documents...`); + setIsTaskPending(true); + console.log(`${deleteInstead ? 'Unindexing' : 'Indexing'} documents:`, documentIds); + + try { + const response = await fetch(`http://localhost:8181/v1/documents/${ deleteInstead ? 'unindex' : 'index'}`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + document_ids: documentIds.map((id: any) => id.toString()), + ...(deleteInstead ? {} : {is_chat: isChat}) + }) + }); + + if (!response.ok) { + throw new Error(`Error: ${response.statusText}`); + } + + const result = await response.json(); + setCompletedTaskMessage(`${ deleteInstead ? 'Unindexing' : 'Indexing' } completed successfully.`); + // Update the list of documents to reflect the indexing status + await fetchDataSources(); + } catch (error) { + setCompletedTaskMessage(`Error occurred during ${ deleteInstead ? 'unindexing' : 'indexing' }.`); + setIsTaskError(true); + console.error(`Error ${ deleteInstead ? 'un': '' }indexing documents:`, error); + } finally { + setRunningTaskMessage(''); + setIsTaskPending(false); + } + }; + + + const handleDeleteDataSource = async (dataSourceId: string) => { + try { + const response = await fetch(`http://localhost:8181/v1/data-sources/${dataSourceId}`, { + method: 'DELETE' + }); + + if (!response.ok) { + throw new Error(`HTTP error! Status: ${response.status}`); + } + + // Refresh the list of data sources to reflect the deletion + await Promise.all([fetchDataSources(), fetchDocuments(dataSourceId)]); + } catch (error) { + console.error("Failed to delete data source:", error); + } + }; + + useEffect(() => { + if (!completedTaskMessage) { + return; + } + + setTimeout(() => { + setIsTaskPending(false); + setIsTaskError(false); + setCompletedTaskMessage(''); + }, 5000); + }, [completedTaskMessage]); + + if (loading) return
Loading data sources...
; + + const jsExample = (); + return ( +
+

Usage

+
+

To get started, add a directory containing chat files, then index one or more documents to + add them to the knowledge bank.

+

+ Once indexed, data will be used automatically in chat completions. + {jsExample} +

+

+ Explore and test the full set of API features for indexed data on the + documentation page. + For a quick start, try the Summary API. +

+
+

Source Directories

+ +
+ handleDeleteDataSource(dataSource.id)} + /> +
+ +

Documents

+ + { (runningTaskMessage || completedTaskMessage) && +
+
+ {runningTaskMessage || completedTaskMessage} +
+
+ } + +
+ handleIndexSelected([doc.id])} + onUnindexDocument={(doc) => handleIndexDocument(doc.id, true)} + onIndexDocuments={() => handleIndexSelected()} + onUnindexDocuments={() => handleIndexSelected(undefined, true)} + /> +
+
+

Playground

+
+
+
+ +
+ + +
+
+
+ + + + setName(e.target.value)} + className="input input-sm input-bordered w-full max-w-xs mr-2"/> +
+
+ + +
+
+
+
+
+); +}; + +SelfieManager.displayName = 'SelfieManager'; + +export default SelfieManager; diff --git a/selfie-ui/src/app/types/index.ts b/selfie-ui/src/app/types/index.ts new file mode 100644 index 0000000..ef3e899 --- /dev/null +++ b/selfie-ui/src/app/types/index.ts @@ -0,0 +1,26 @@ +export interface Document { + id: string + metadata: { + [key: string]: any + } + is_indexed: boolean +} + +export interface Documents { + [sourceId: string]: Document[] +} + +export interface DataSource { + id: string; + name: string; + loader_module: string; + config: { + constructor_args: any[]; + constructor_kwargs: { + input_dir: string; + recursive: boolean; + }; + load_data_args: any[]; + load_data_kwargs: any; + }; +} diff --git a/selfie-ui/src/app/utils.ts b/selfie-ui/src/app/utils.ts new file mode 100644 index 0000000..e57e4f6 --- /dev/null +++ b/selfie-ui/src/app/utils.ts @@ -0,0 +1,16 @@ +export const toTitleCase = (str: string) => { + return str.replace(/_/g, ' ').replace(/\b(\w)/g, char => char.toUpperCase()); +}; + +export const isNumeric = (str: any) => { + if (typeof str == "string") return false; + return !isNaN(str) && !isNaN(parseFloat(str)); +}; + +export const isDateString = (str: any) => { + return typeof str === 'string' && !isNaN(Date.parse(str)); +}; + +export const formatDate = (dateStr: string) => { + return new Date(dateStr).toLocaleDateString(undefined, { day: 'numeric', month: 'long', year: 'numeric' }); +}; diff --git a/selfie-ui/tailwind.config.ts b/selfie-ui/tailwind.config.ts new file mode 100644 index 0000000..0747b48 --- /dev/null +++ b/selfie-ui/tailwind.config.ts @@ -0,0 +1,20 @@ +import type { Config } from "tailwindcss"; + +const config: Config = { + content: [ + "./src/pages/**/*.{js,ts,jsx,tsx,mdx}", + "./src/components/**/*.{js,ts,jsx,tsx,mdx}", + "./src/app/**/*.{js,ts,jsx,tsx,mdx}", + ], + theme: { + extend: { + backgroundImage: { + "gradient-radial": "radial-gradient(var(--tw-gradient-stops))", + "gradient-conic": + "conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))", + }, + }, + }, + plugins: [require("@tailwindcss/typography"), require("daisyui")], +}; +export default config; diff --git a/selfie-ui/tsconfig.json b/selfie-ui/tsconfig.json new file mode 100644 index 0000000..7b28589 --- /dev/null +++ b/selfie-ui/tsconfig.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], + "exclude": ["node_modules"] +} diff --git a/selfie-ui/yarn.lock b/selfie-ui/yarn.lock new file mode 100644 index 0000000..57bd478 --- /dev/null +++ b/selfie-ui/yarn.lock @@ -0,0 +1,2787 @@ +# THIS IS AN AUTOGENERATED FILE. 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a/selfie.spec b/selfie.spec new file mode 100644 index 0000000..a3cdb9a --- /dev/null +++ b/selfie.spec @@ -0,0 +1,57 @@ +# -*- mode: python ; coding: utf-8 -*- + +# Run poetry shell, then pyinstaller cli.spec --noconfirm + +import os +a = Analysis( + ['selfie/__main__.py'], + debug=True, + pathex=[os.path.abspath(SPECPATH)], + binaries=[], + datas=[ + ('./selfie/parsers/chat/blacklist_patterns.yaml', 'selfie/parsers/chat/'), + ('./selfie/web/', 'selfie/web/'), + ], + hiddenimports=[ + 'tiktoken_ext', + 'tiktoken_ext.openai_public', + 'onnxconverter_common', # Logs say hidden import 'onnxconverter_common' not found + ], + hookspath=['./hooks'], + hooksconfig={}, + runtime_hooks=[], + excludes=[], + noarchive=False, + module_collection_mode={ + 'onnxconverter_common': 'pyz+py', + }, +) + +pyz = PYZ(a.pure) + +exe = EXE( + pyz, + a.scripts, + [], + exclude_binaries=True, + name='selfie', + debug=False, + bootloader_ignore_signals=False, + strip=False, + upx=True, + console=True, + disable_windowed_traceback=False, + argv_emulation=False, + target_arch=None, + codesign_identity=None, + entitlements_file=None, +) +coll = COLLECT( + exe, + a.binaries, + a.datas, + strip=False, + upx=True, + upx_exclude=[], + name='selfie', +) diff --git a/selfie/.env.example b/selfie/.env.example new file mode 100644 index 0000000..c8d927e --- /dev/null +++ b/selfie/.env.example @@ -0,0 +1,2 @@ +PORT=8181 +NGROK_AUTHTOKEN= # Only needed if running using "share" flag \ No newline at end of file diff --git a/selfie/__init__.py b/selfie/__init__.py new file mode 100644 index 0000000..a40d9fe --- /dev/null +++ b/selfie/__init__.py @@ -0,0 +1,20 @@ +import logging +import warnings +from llama_index.llms import litellm +import colorlog + +# Suppress specific warnings +# TODO: Not sure why these aren't quite working +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore") + +handler = colorlog.StreamHandler() +handler.setFormatter(colorlog.ColoredFormatter()) +logging.root.addHandler(handler) + +logging.basicConfig(level=logging.INFO) +logging.getLogger("selfie").setLevel(level=logging.DEBUG) + + +litellm.drop_params = True diff --git a/selfie/__main__.py b/selfie/__main__.py new file mode 100644 index 0000000..a30ade4 --- /dev/null +++ b/selfie/__main__.py @@ -0,0 +1,30 @@ +import uvicorn +import sys +import webbrowser +import ngrok +import logging +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +from selfie.api import app +from selfie.config import port, ngrok_auth_token + +@app.on_event("startup") +async def on_startup(): + webbrowser.open(f"http://localhost:{port}") + + if "share" in sys.argv: + if ngrok_auth_token is None: + raise ValueError("NGROK_AUTHTOKEN environment variable is required to share the API. Visit https://dashboard.ngrok.com/ to get your token.") + listener = await ngrok.forward(port, authtoken_from_env=True) + logger.info(f"API available at {listener.url()}") + + +@app.on_event("shutdown") +def on_shutdown(): + ngrok.disconnect() + + +# Uncomment this for hot-reloading +#uvicorn.run("selfie.api:app", host="0.0.0.0", port=port, reload=True) +uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/selfie/api/__init__.py b/selfie/api/__init__.py new file mode 100644 index 0000000..5a4d713 --- /dev/null +++ b/selfie/api/__init__.py @@ -0,0 +1,70 @@ +import os +import sys + +from fastapi import FastAPI, Request +from fastapi.responses import HTMLResponse, FileResponse +from fastapi.staticfiles import StaticFiles +from starlette.middleware.cors import CORSMiddleware +import logging + +from selfie.api.completions import router as completions_router +from selfie.api.data_sources import router as data_sources_router +from selfie.api.documents import router as documents_router +from selfie.api.index_documents import router as index_documents_router +from selfie.api.models import router as models_router + +logger = logging.getLogger(__name__) + + +app = FastAPI() + +app.add_middleware( + CORSMiddleware, + allow_credentials=True, + allow_headers=["*"], + allow_methods=["*"], + allow_origins=["*"], +) + +if getattr(sys, 'frozen', False): + # Running in a PyInstaller bundle + bundle_dir = os.path.join( + sys._MEIPASS, "selfie" + ) +else: + # Running in a normal Python environment + bundle_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..') + +static_files_dir = os.path.join(bundle_dir, "web") + + +app.mount("/static", StaticFiles(directory=static_files_dir), name="static") + + +app.include_router(completions_router, prefix="/v1") +app.include_router(data_sources_router, prefix="/v1") +app.include_router(documents_router, prefix="/v1") +app.include_router(index_documents_router, prefix="/v1") +app.include_router(models_router, prefix="/v1") + + +@app.get("/", response_class=HTMLResponse) +async def serve_index_html(): + index_html_path = os.path.join(static_files_dir, "index.html") + with open(index_html_path, "r") as f: + return HTMLResponse(content=f.read()) + + +@app.get("/{full_path:path}", include_in_schema=False) +async def serve_spa_or_static(request: Request, full_path: str): + # Skip API routes + if full_path.startswith("api/v1") or full_path.startswith("docs"): + return + + possible_path = os.path.join(static_files_dir, full_path) + if os.path.isfile(possible_path): + # Serve matching file + return FileResponse(possible_path) + else: + # Serve the app + return FileResponse(os.path.join(static_files_dir, "index.html")) diff --git a/selfie/api/completions.py b/selfie/api/completions.py new file mode 100644 index 0000000..fad958a --- /dev/null +++ b/selfie/api/completions.py @@ -0,0 +1,26 @@ +from fastapi import APIRouter +from litellm import ModelResponse as LitellmCompletionResponse +from llama_cpp import ( + CreateCompletionResponse as LlamaCppCompletionResponse, + CreateChatCompletionResponse as LlamaCppChatCompletionResponse, +) +from selfie.types.completion_requests import ChatCompletionRequest, CompletionRequest + +from selfie.text_generation import completion + +router = APIRouter() + + +@router.post("/chat/completions") +async def create_chat_completion( + request: ChatCompletionRequest, +) -> LlamaCppChatCompletionResponse | LitellmCompletionResponse: + return await completion(request) + + +# TODO can StreamingResponse's schema be defined? +@router.post("/completions") +async def create_completion( + request: CompletionRequest, +) -> LlamaCppCompletionResponse | LitellmCompletionResponse: + return await completion(request) diff --git a/selfie/api/data_sources.py b/selfie/api/data_sources.py new file mode 100644 index 0000000..4e29c62 --- /dev/null +++ b/selfie/api/data_sources.py @@ -0,0 +1,55 @@ +from typing import Optional, Any, List, Dict + +from fastapi import APIRouter, HTTPException +from playhouse.shortcuts import model_to_dict +from pydantic import BaseModel + +from selfie.database import DataManager + +router = APIRouter() + + +class DataLoaderRequest(BaseModel): + name: Optional[str] + loader_module: str + constructor_args: Optional[List[Any]] = [] + constructor_kwargs: Optional[Dict[str, Any]] = {} + load_data_args: Optional[List[Any]] = [] + load_data_kwargs: Optional[Dict[str, Any]] = {} + + +@router.get("/data-sources") +async def get_data_sources(): + return DataManager().get_data_sources() + + +@router.post("/data-sources") +async def add_data_source(request: DataLoaderRequest): + return model_to_dict(DataManager().add_data_source( + request.name, + request.loader_module, + request.constructor_args, + request.constructor_kwargs, + request.load_data_args, + request.load_data_kwargs, + )) + + +@router.delete("/data-sources/{source_id}") +async def delete_data_source(source_id: int, delete_documents: bool = True, delete_indexed_data: bool = True): + await DataManager().remove_data_source(source_id, delete_documents, delete_indexed_data) + return {"message": "Data source removed successfully"} + + +@router.post("/data-sources/{source_id}/scan") +async def scan_data_sources(source_id: int): + return DataManager().scan_data_sources([source_id]) + + +@router.post("/data-sources/{source_id}/index") +async def index_data_source(source_id: int): + manager = DataManager() + data_source = manager.get_data_source(source_id) + if isinstance(data_source, dict) and "error" in data_source: + raise HTTPException(status_code=404, detail=data_source["error"]) + return await manager.index_documents(data_source) diff --git a/selfie/api/documents.py b/selfie/api/documents.py new file mode 100644 index 0000000..ef09565 --- /dev/null +++ b/selfie/api/documents.py @@ -0,0 +1,70 @@ +from typing import Optional, List + +from fastapi import APIRouter +from pydantic import BaseModel + +from selfie.database import DataManager +from selfie.parsers.chat import ChatFileParser +from selfie.embeddings import DataIndex + +router = APIRouter() + + +class UnindexDocumentsRequest(BaseModel): + document_ids: List[str] = [] + + +class IndexDocumentsRequest(BaseModel): + is_chat: bool = False + document_ids: List[str] = [] + + +@router.get("/documents") +async def get_documents(source_id: Optional[int] = None): + return DataManager().get_documents(source_id) + + +@router.delete("/documents/{document_id}") +async def delete_data_source(document_id: int, delete_indexed_data: bool = True): + await DataManager().remove_document(document_id, delete_indexed_data) + return {"message": "Document removed successfully"} + + +@router.post("/documents/unindex") +async def unindex_documents(request: UnindexDocumentsRequest): + await DataIndex("n/a").delete_documents_with_source_documents(request.document_ids) + return {"message": "Document unindexed successfully"} + + +@router.post("/documents/index") +async def index_documents(request: IndexDocumentsRequest): + is_chat = request.is_chat + document_ids = request.document_ids + + manager = DataManager() + parser = ChatFileParser() + + # TODO: figure out what to do about this + speaker_aliases = {} + + return [ + await manager.index_document(manager.get_document(document_id), + lambda document: DataIndex.map_share_gpt_data( + parser.parse_document( + document.text, + None, + speaker_aliases, + False, + document.id + ).conversations, + document.source.name, + document.id + ) if is_chat else None) + for document_id in document_ids + ] + + +# @app.delete("/documents/{document-id}") +# async def delete_data_source(document_id: int): +# DataSourceManager().remove_document(document_id) +# return {"message": "Document removed successfully"} diff --git a/selfie/api/index_documents.py b/selfie/api/index_documents.py new file mode 100644 index 0000000..e9b0148 --- /dev/null +++ b/selfie/api/index_documents.py @@ -0,0 +1,151 @@ +from typing import Optional, List + +from fastapi import APIRouter, UploadFile, File, Form + +from selfie.api.data_sources import DataLoaderRequest +from selfie.parsers.chat import ChatFileParser +from selfie.parsers.chat.chat_file_parsing_helper import get_files_with_configs, delete_uploaded_files +from selfie.embeddings import DataIndex +from selfie.embeddings.document_types import Document + +from llama_index.node_parser.text import SentenceSplitter +from datetime import datetime +import importlib + +router = APIRouter() + + +@router.get("/index_documents") +async def get_documents(offset: int = 0, limit: int = 10): + return await DataIndex("n/a").get_documents(offset=offset, limit=limit) + + +@router.get("/index_documents/summary") +async def get_index_documents_summary(topic: str, limit: Optional[int] = 5, min_score: Optional[float] = None): + result = await DataIndex("n/a").recall(topic, limit=limit, min_score=min_score) + return { + "summary": result["summary"], + "score": result["mean_score"], + "documents": result["documents"], + } + + +@router.post("/index_documents") +async def create_index_document(document: Document): + return (await DataIndex("n/a").index([document]))[0] + + +@router.get("/index_documents/{document_id}") +async def get_index_document(document_id: int): + return await DataIndex("n/a").get_document(document_id) + + +@router.put("/index_documents/{document_id}") +async def update_index_document(document_id: int, document: Document): + await DataIndex("n/a").update_document(document_id, document) + return {"message": "Document updated successfully"} + + +@router.delete("/index_documents/{document_id}") +async def delete_index_document(document_id: int): + # Sometimes self.embeddings.save() errors on "database is locked", bricks it + # raise HTTPException(status_code=501, detail="Not implemented") + DataIndex("n/a").delete_document(document_id) + return {"message": "Document deleted successfully"} + + +@router.delete("/index_documents") +async def delete_index_documents(): + DataIndex("n/a").delete_all() + return {"message": "All documents deleted successfully"} + + +@router.post("/index_documents/llama-hub-loader") +async def load_data(request: DataLoaderRequest): + # TODO: extract document metadata from request? + + # Adapted from: + # https://docs.llamaindex.ai/en/stable/examples/low_level/oss_ingestion_retrieval.html#build-an-ingestion-pipeline-from-scratch + module_name, class_name = request.loader_module.rsplit(".", 1) + module = importlib.import_module(module_name) + loader_class = getattr(module, class_name) + + loader = loader_class(*request.constructor_args, **request.constructor_kwargs) + + documents = loader.load_data(*request.load_data_args, **request.load_data_kwargs) + + print(documents) + + text_parser = SentenceSplitter( + chunk_size=1024, + # separator=" ", + ) + + text_chunks = [] + # maintain relationship with source doc index, to help inject doc metadata in (3) + doc_idxs = [] + for doc_idx, doc in enumerate(documents): + cur_text_chunks = text_parser.split_text(doc.text) + text_chunks.extend(cur_text_chunks) + doc_idxs.extend([doc_idx] * len(cur_text_chunks)) + + documents = [] + for idx, text_chunk in enumerate(text_chunks): + src_doc = documents[doc_idxs[idx]] + document = Document( + text=text_chunk, + source=request.loader_module, + # importance=0.0, + # timestamp=datetime.strptime(src_doc.metadata['last_modified'], "%Y-%m-%d"), + # use last_modified if available, otherwise use current time + timestamp=datetime.strptime(src_doc.metadata["last_modified"], "%Y-%m-%d") + if "last_modified" in src_doc.metadata + else datetime.now(), + ) + + documents.append(document) + + return {"documents": await DataIndex("n/a").index(documents, extract_importance=False)} + + +@router.post("/index_documents/chat-processor") +async def process_chat_files( + character_name: str, + files: List[UploadFile] = File(..., description="Upload chat files here."), + parser_configs: str = Form( + "[]", + description="JSON string of parser configurations. Format can be whatsapp, google, discord. Example: " + + '[{"main_speaker": "Alice", "format": "whatsapp", ' + + '"speaker_aliases": {"alicex": "Alice", "bobby": "Bob"}}]', + ), + extract_importance: bool = False, +): + parser = ChatFileParser() + data_index = DataIndex(character_name) + + files_with_settings = get_files_with_configs(files, parser_configs) + + documents = [] + new_document_count = 0 + for file_with_settings in files_with_settings: + file_data = parser.parse_file( + file_with_settings["file"], + file_with_settings["config"].format, + file_with_settings["config"].speaker_aliases, + ) + + mapped_documents = DataIndex.map_share_gpt_data( + file_data.conversations, file_with_settings["file"].split("/")[-1] + ) + + file_documents = await data_index.index(mapped_documents, extract_importance) + new_document_count += len(file_documents) + documents.extend(file_documents) + + delete_uploaded_files(files_with_settings) + + return { + "success": True, + "num_documents": len(documents), + "num_new_documents": new_document_count, + } diff --git a/selfie/api/models.py b/selfie/api/models.py new file mode 100644 index 0000000..bb98713 --- /dev/null +++ b/selfie/api/models.py @@ -0,0 +1,34 @@ +from fastapi import APIRouter +from huggingface_hub import scan_cache_dir + +router = APIRouter() + + +@router.get("/models") +async def get_models(): + hf_cache_info = scan_cache_dir() + models = [] + + for repo in hf_cache_info.repos: + for revision in repo.revisions: + gguf_files = [file for file in revision.files if file.file_name.endswith('.gguf')] + if gguf_files: + for gguf_file in gguf_files: + models.append({ + "id": f"{repo.repo_id}/{gguf_file.file_name}", + "object": "model", + "created": gguf_file.blob_last_modified, + "owned_by": "user" + }) + else: + models.append({ + "id": repo.repo_id, + "object": "model", + "created": min(file.last_modified for file in repo.revisions), + "owned_by": "user" + }) + + return { + "object": "list", + "data": models + } diff --git a/selfie/config.py b/selfie/config.py new file mode 100644 index 0000000..5aa00e1 --- /dev/null +++ b/selfie/config.py @@ -0,0 +1,14 @@ +import os +from dotenv import load_dotenv +load_dotenv() + +port = int(os.environ.get('PORT', '8181')) +ngrok_auth_token = os.environ.get('NGROK_AUTHTOKEN', None) + +default_database_storage_root = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/database") +default_embeddings_storage_root = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/embeddings") +default_db_name = 'selfie.db' +default_local_model = 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf' +default_local_gpu_model = 'TheBloke/Mistral-7B-OpenOrca-GPTQ' +default_local_functionary_model = "meetkai/functionary-7b-v2-GGUF/functionary-7b-v2.q4_0.gguf" +default_hosted_model = "openai/gpt-3.5-turbo" diff --git a/selfie/data_generators/__init__.py b/selfie/data_generators/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/selfie/data_generators/chat_training_data.py b/selfie/data_generators/chat_training_data.py new file mode 100644 index 0000000..dbf54fa --- /dev/null +++ b/selfie/data_generators/chat_training_data.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 + +from typing import List, Dict +import os +import time +import json +import random +import argparse +from itertools import groupby + +from enum import Enum + +from selfie.parsers.chat import ChatFileParser, Parser +from selfie.types.share_gpt import ShareGPTMessage + + +class Strategy(Enum): + BUNDLE = 'bundle' + CONVERSATION = 'conversation' + + +# TODO: Figure out what to do with time-based conversations that don't start/end with the right speaker +class ChatTrainingDataGenerator: + @staticmethod + def write_jsonl_file(output_file, lines, output_dir="."): + with open(os.path.join(output_dir, output_file), 'w', encoding='utf-8') as file: + for line in lines: + file.write(line + '\n') + + @staticmethod + def write_json_file(output_file, lines, output_dir="."): + with open(os.path.join(output_dir, output_file), 'w', encoding='utf-8') as file: + file.write('[\n') + file.write(',\n'.join(lines)) + file.write('\n]') + + @staticmethod + def group_messages_into_conversations(messages: List[ShareGPTMessage], max_time_gap_seconds: int = 1800) -> List[List[ShareGPTMessage]]: + conversations = [] + current_conversation = [] + + i = 0 + while i < len(messages): + if i > 0: + prev_timestamp = messages[i - 1].timestamp + current_timestamp = messages[i].timestamp + time_gap = (current_timestamp - prev_timestamp).seconds + + # Check if the time gap is exceeded and there's more than one speaker + single_speaker = len(set(msg.from_user for msg in current_conversation)) == 1 + if time_gap > max_time_gap_seconds and not single_speaker: + conversations.append(current_conversation) + current_conversation = [] + + current_conversation.append(messages[i]) + i += 1 + + if current_conversation: + conversations.append(current_conversation) + + return conversations + + @staticmethod + def extract_message_bundles(conversations: List[ShareGPTMessage]): + message_bundles = [] + current_bundle = [] + last_speaker = None + for convo in conversations: + speaker = convo.from_user + if speaker != last_speaker: + if current_bundle: + message_bundles.append(current_bundle) + current_bundle = [] + current_bundle.append(convo) + last_speaker = speaker + if current_bundle: + message_bundles.append(current_bundle) + return message_bundles + + @staticmethod + def group_messages_into_chunks(conversations: List[ShareGPTMessage], overlap: int = 0, max_messages: int = 3, max_characters: int = 0) -> List[List[ShareGPTMessage]]: + chunks = [] + index = 0 + while index < len(conversations): + end_index = index + max_messages + chunk = conversations[index:end_index] + + # If there's a max characters limit, adjust the chunk to not exceed it + if max_characters > 0: + characters_count = sum(len(msg.value.split()) for msg in chunk) + while characters_count > max_characters and len(chunk) > 0: + chunk.pop() # Remove the last message + characters_count = sum(len(msg.value.split()) for msg in chunk) + + chunks.append(chunk) + index += max_messages - overlap + + return chunks + + + @staticmethod + def generate_sharegpt_jsonl_line(messages: List[ShareGPTMessage]) -> str: + """ + Generate a JSONL line in ShareGPT format. + + Args: + messages (List[Dict[str, str]]): List of message dictionaries. + + Returns: + str: A JSONL line. + """ + grouped_messages = [(k, list(g)) for k, g in groupby(messages, key=lambda x: x.from_user)] + concatenated_messages = [] + + for speaker, group in grouped_messages: + concatenated_text = "\n".join([msg.value for msg in group]) + timestamp = group[-1].timestamp + concatenated_messages.append({'from': speaker, 'value': concatenated_text, 'timestamp': timestamp}) + + conversation = {"conversations": concatenated_messages} + + return json.dumps(conversation, ensure_ascii=False, indent=2) + + def __init__(self, target_format='llama-chat', strategy=Strategy.BUNDLE, train_split=0.9, validate_split=None, mask=False, no_overlap=False, final_format='replicate', additional_blacklist_patterns=None): + self.file_parser = ChatFileParser(additional_blacklist_patterns) + self.strategy = strategy + self.target_format = target_format + self.target_format_mapper = { + 'llama-chat': self.generate_llama_jsonl_line, + 'sharegpt': self.generate_sharegpt_jsonl_line, + }[self.target_format] + self.mask = mask + self.no_overlap = no_overlap + self.final_format = final_format + self.train_split = train_split + self.validate_split = 1.0 - self.train_split if validate_split is None else validate_split + self.test_split = 1.0 - (self.train_split + self.validate_split) + + def create_jsonl_line(self, prompt: str, completion: str) -> str: + if self.final_format == 'colab': + example = {"text": f"{prompt} {completion}"} + else: # 'replicate' format + example = {"prompt": prompt, "completion": f'{completion} '} + + return json.dumps(example, ensure_ascii=False) + + def generate_llama_jsonl_line(self, messages: List[ShareGPTMessage]): + grouped_messages = [(speaker, list(g)) for speaker, g in groupby(messages, key=lambda x: x.from_user)] + + inst_flag = True # Whether to wrap the message in [INST] tags + segments = [] + + for speaker, group in grouped_messages: + speaker_text = f"{speaker}: " + "\n".join([msg.value for msg in group]) + + if inst_flag: + segments.append(f"[INST] {speaker_text} [/INST]") + else: + segments.append(f"{speaker_text}") + + inst_flag = not inst_flag + + prompt = f'[INST] <>\nComplete the chat below.\n<>\n\n' + prompt += " ".join(segments[:-1]).removeprefix("[INST] ") + + completion = segments[-1] + " " + + return self.create_jsonl_line(prompt, completion) + + def process_files(self, files_with_settings: List[Dict], output_dir='.'): + print(f"Processing {len(files_with_settings)} files...") + all_conversations = [] + for file_setting in files_with_settings: + input_file = file_setting['file'] + parser_type = file_setting['config'].format or Parser.WHATSAPP.name + rename_speakers = file_setting['config'].speaker_aliases or {} + filter_speaker = file_setting['config'].main_speaker + + if filter_speaker in rename_speakers: + filter_speaker = rename_speakers[filter_speaker] + + share_gpt_data = self.file_parser.parse_file(input_file, parser_type, rename_speakers, self.mask) + print(f"Number of conversations: {len(share_gpt_data.conversations)}") + # print(share_gpt_data) + + if self.strategy == Strategy.BUNDLE: + message_bundles = self.extract_message_bundles(share_gpt_data.conversations) + jsonl_lines = self.generate_jsonl_from_bundles(message_bundles, filter_speaker) + elif self.strategy == Strategy.CONVERSATION: + conversations = self.group_messages_into_conversations(share_gpt_data.conversations, max_time_gap_seconds=1800) + jsonl_lines = self.generate_jsonl_from_conversations(conversations) + else: + raise ValueError(f"Invalid strategy: {self.strategy}") + + all_conversations.extend(jsonl_lines) + + return self.write_output_files(all_conversations, output_dir) + + def generate_jsonl_from_bundles(self, message_bundles: List[ShareGPTMessage], filter_speaker=None): + jsonl_lines = [] + i = 0 + while i <= len(message_bundles) - 4: + speakers = [bundle[-1].from_user for bundle in [message_bundles[i + j] for j in range(4)]] + + # Filtering logic to skip over bundles that don't match the filter speaker + if filter_speaker and filter_speaker != speakers[3]: + i += 1 + continue + + if speakers[0] == speakers[2] and speakers[1] == speakers[3]: + occurrence = [message_bundles[i + j] for j in range(4)] + flattened_occurrence = [msg for bundle in occurrence for msg in bundle] + jsonl_line = self.target_format_mapper(flattened_occurrence) + if jsonl_line is not None: + jsonl_lines.append(jsonl_line) + if self.no_overlap: + i += 4 + else: + i += 1 + else: + i += 1 + return jsonl_lines + + def generate_jsonl_from_conversations(self, conversations: List[List[ShareGPTMessage]]) -> List[str]: + jsonl_lines = [] + + for convo in conversations: + jsonl_line = self.target_format_mapper(convo) + if jsonl_line is not None: + jsonl_lines.append(jsonl_line) + + return jsonl_lines + + def write_output_files(self, all_lines, output_dir): + random.shuffle(all_lines) + current_time_millis = int(time.time() * 1000) + print(current_time_millis) + + train_index = int(self.train_split * len(all_lines)) + validate_index = train_index + int(self.validate_split * len(all_lines)) + + train_lines = all_lines if self.train_split == 1.0 else all_lines[:train_index] + validate_lines = None if self.train_split == 1.0 else all_lines[train_index:validate_index + (1 if self.test_split == 0 and validate_index < len(all_lines) else 0)] + test_lines = all_lines[validate_index:] if self.test_split > 0 else None + + ext = '.jsonl' if self.final_format == 'colab' else '.json' + + train_file_name = f"train_{current_time_millis}{ext}" + validate_file_name = f"validate_{current_time_millis}{ext}" + test_file_name = f"test_{current_time_millis}{ext}" + + if self.final_format == 'colab': + self.write_jsonl_file(train_file_name, train_lines, output_dir) + if validate_lines is not None: + self.write_jsonl_file(validate_file_name, validate_lines, output_dir) + if test_lines: + self.write_jsonl_file(test_file_name, test_lines, output_dir) + else: # 'replicate' format + self.write_json_file(train_file_name, train_lines, output_dir) + if validate_lines is not None: + self.write_json_file(validate_file_name, validate_lines, output_dir) + if test_lines: + self.write_json_file(test_file_name, test_lines, output_dir) + + return train_file_name, validate_file_name, test_file_name + + +def main(): + parser = argparse.ArgumentParser(description='Process chat data into train and validate JSON/JSONL files.') + parser.add_argument('input_files', type=str, nargs='+', help='Paths to the input text files.') + parser.add_argument('--strategy', type=lambda s: Strategy[s.upper()], choices=list(Strategy), default=Strategy.CONVERSATION, help='Strategy for grouping messages.') + parser.add_argument('--target-format', type=str, choices=['llama-chat', 'sharegpt'], default='sharegpt', help='Target format for the output.') + parser.add_argument('--filter-speaker', type=str, help='Filter by speaker name.') + parser.add_argument('--mask', action='store_true', help='Mask the text.') + parser.add_argument('--no-overlap', action='store_true', help='Generate non-overlapping examples.') + parser.add_argument('--final-format', type=str, choices=['replicate', 'colab'], default='colab', help='Final format.') + parser.add_argument('--train-split', type=float, default=1.0, help='Percentage of data for training.') + parser.add_argument('--validate-split', type=float, help='Percentage of data for validation.') + parser.add_argument('--source-type', type=str, choices=[parser.name for parser in Parser], default=Parser.WHATSAPP.name, help='Source platform and format of the input files.') + parser.add_argument('--rename', action='append', help='Rename speakers, e.g., --rename "Self:gpt" --rename "(555) 123-4567:human"') + parser.add_argument('--additional-blacklist', type=str, nargs='*', help='Additional regex patterns to blacklist messages. Messages matching any of these patterns will be excluded from processing. For example, --additional-blacklist "\btwilight\b" "\bromance\b".') + args = parser.parse_args() + + rename_speakers = {original: new for pair in args.rename for original, new in [pair.split(":")]} if args.rename else {} + + # TODO: final_format and target_format are very confusing. Figure out a better way. + if args.final_format == 'replicate' and args.target_format != 'llama-chat': + raise ValueError("Final format 'replicate' is only supported for target format 'llama-chat'.") + + processor = ChatTrainingDataGenerator( + target_format=args.target_format, + strategy=args.strategy, + train_split=args.train_split, + validate_split=args.validate_split, + mask=args.mask, + no_overlap=args.no_overlap, + final_format=args.final_format, + additional_blacklist_patterns=args.additional_blacklist + ) + + files_with_settings = [{'file': file, 'parser': args.source_type, 'rename_speakers': rename_speakers, 'filter_speaker': args.filter_speaker} for file in args.input_files] + + train_file_name, validate_file_name, test_file_name = processor.process_files(files_with_settings) + + print(train_file_name) + if validate_file_name: + print(validate_file_name) + if test_file_name: + print(test_file_name) + + +if __name__ == "__main__": + main() diff --git a/selfie/database/__init__.py b/selfie/database/__init__.py new file mode 100644 index 0000000..e3477b1 --- /dev/null +++ b/selfie/database/__init__.py @@ -0,0 +1,269 @@ +import os +from datetime import datetime, timezone + +from llama_index.node_parser import SentenceSplitter +from peewee import ( + Model, + SqliteDatabase, + CharField, + TextField, + ForeignKeyField, + AutoField, + DoesNotExist, + Proxy, +) +import json +import importlib +from typing import List, Dict, Any, Optional, Callable + +from selfie.config import default_database_storage_root, default_db_name +from selfie.embeddings import DataIndex +from selfie.embeddings.document_types import Document + +import logging +logger = logging.getLogger(__name__) + +database_proxy = Proxy() + + +class BaseModel(Model): + class Meta: + database = database_proxy + + +class DataSource(BaseModel): + id = AutoField() + name = CharField() + loader_module = CharField() + config = TextField() + + class Meta: + table_name = 'selfie_datasource' + + +class SelfieDocument(BaseModel): + id = AutoField() + source = ForeignKeyField(DataSource, backref="documents") + metadata = TextField() + text = TextField() + + class Meta: + table_name = 'selfie_document' + + +class DataManager: + def __init__(self, storage_path: str = default_database_storage_root): + os.makedirs(storage_path, exist_ok=True) + + self.db = SqliteDatabase(os.path.join(storage_path, default_db_name)) + database_proxy.initialize(self.db) + self.db.connect() + self.db.create_tables([DataSource, SelfieDocument]) + + def add_data_source( + self, + name: str, + loader_module: str, + constructor_args: List[Any], + constructor_kwargs: Dict[str, Any], + load_data_args: List[Any], + load_data_kwargs: Dict[str, Any], + ) -> int: + config = { + "constructor_args": constructor_args, + "constructor_kwargs": constructor_kwargs, + "load_data_args": load_data_args, + "load_data_kwargs": load_data_kwargs, + } + return DataSource.create( + name=name, loader_module=loader_module, config=json.dumps(config) + ) + + # def remove_data_source(self, source_id: int): + # DataSource.get_by_id(source_id).delete_instance() + + async def remove_document(self, document_id: int, delete_indexed_data: bool = True): + with self.db.atomic(): + document = SelfieDocument.get_by_id(document_id) + if document is None: + raise ValueError(f"No document found with ID {document_id}") + + if delete_indexed_data: + index = DataIndex("n/a") + await index.delete_documents_with_source_documents([document.source_id]) + + document.delete_instance() + + async def remove_data_source(self, source_id: int, delete_documents: bool = True, delete_indexed_data: bool = True): + data_source = self.get_data_source(source_id) + if data_source is None: + raise ValueError(f"No data source found with ID {source_id}") + + if delete_indexed_data: + source_document_ids = [doc.id for doc in SelfieDocument.select().where(SelfieDocument.source == source_id)] + await DataIndex("n/a").delete_documents_with_source_documents(source_document_ids) + + with self.db.atomic(): + if delete_documents: + SelfieDocument.delete().where(SelfieDocument.source == source_id).execute() + + DataSource.delete().where(DataSource.id == source_id).execute() + + def scan_data_sources(self, source_ids: List[int]) -> Dict[str, Any]: + changes = {} + for source_id in source_ids: + data_source = DataSource.get_by_id(source_id) + documents = self._fetch_documents( + data_source.loader_module, json.loads(data_source.config) + ) + for doc in documents: + document, created = SelfieDocument.get_or_create( + source=data_source, defaults={"metadata": json.dumps(doc.metadata)}, text=doc.text + ) + changes[document.id] = "Created" if created else "Updated" + return changes + + def _fetch_documents(self, loader_module: str, config: Dict[str, Any]) -> List[Any]: + module_name, class_name = loader_module.rsplit(".", 1) + module = importlib.import_module(module_name) + loader_class = getattr(module, class_name) + loader = loader_class( + *config["constructor_args"], **config["constructor_kwargs"] + ) + return loader.load_data(*config["load_data_args"], **config["load_data_kwargs"]) + + def _get_unique_key(self, doc, unique_key_path: str): + keys = unique_key_path.split(".") + value = doc + for key in keys: + if isinstance(value, dict): + value = value.get(key, None) + else: + value = getattr(value, key, None) + return value + + async def index_documents(self, data_source: DataSource): + print("Indexing documents") + + # Ensure that the data source is in the database + self.scan_data_sources([data_source.id]) + + loader_module = data_source.loader_module + + documents = self._fetch_documents(loader_module, json.loads(data_source.config)) + documents = [ + document for doc in documents for document in self._map_selfie_documents_to_index_documents(selfie_document=doc) + ] + + await DataIndex("n/a").index(documents, extract_importance=False) + + # TODO: record the last time this data source was indexed + + return {"message": f"{len(documents)} documents indexed successfully"} + + async def index_document(self, document: SelfieDocument, selfie_documents_to_index_documents: Callable[[SelfieDocument], List[Document]] = None): + print("Indexing document") + + if selfie_documents_to_index_documents is None: + selfie_documents_to_index_documents = self._map_selfie_documents_to_index_documents + + index_documents = selfie_documents_to_index_documents(document) + if not index_documents: + logger.warning("No documents to index") + return {"message": "No documents to index"} + + return await DataIndex("n/a").index(index_documents, extract_importance=False) + + @staticmethod + def _map_selfie_documents_to_index_documents(selfie_document: SelfieDocument): + text_parser = SentenceSplitter(chunk_size=1024) + documents = [] + + for text_chunk in text_parser.split_text(selfie_document.text): + documents.append(Document( + text=text_chunk, + source=selfie_document.source, + timestamp=DataManager._extract_timestamp(selfie_document), + source_document_id=selfie_document.id, + )) + + return documents + + @staticmethod + def _extract_timestamp(doc): + last_modified = doc.metadata.get("last_modified") + if last_modified: + return datetime.strptime(last_modified, "%Y-%m-%d") + return datetime.now() + + @staticmethod + def get_data_source(source_id: int): + try: + return DataSource.get_by_id(source_id) + except DoesNotExist: + return {"error": "Data source not found"} + except Exception as e: + return {"error": str(e)} + + def get_data_sources(self): + return [ + { + "name": source.name, + "id": source.id, + "loader_module": source.loader_module, + "config": json.loads(source.config), + } + for source in DataSource.select() + ] + + def get_documents(self, source_id: Optional[int] = None): + if source_id: + documents = SelfieDocument.select().where(SelfieDocument.source_id == source_id) + else: + documents = SelfieDocument.select() + + one_indexed_document_per_source = DataIndex("n/a").get_one_document_per_source_document([str(document.id) for document in documents]) + indexed_documents = list(set([doc['source_document_id'] for doc in one_indexed_document_per_source])) + return [ + # TODO: for some reason, initializing Embeddings in DataIndex with the SQLAlchemy driver returns indexed_documents as strings, not ints (requires str(doc.id)). + {"id": doc.id, "metadata": json.loads(doc.metadata), "is_indexed": doc.id in indexed_documents} for doc in documents + ] + + def get_document(self, document_id: str): + return SelfieDocument.get_by_id(document_id) + + +if __name__ == "__main__": + manager = DataManager() + + # # Adding a SimpleWebPageReader data source + # constructor_args_web = [] + # constructor_kwargs_web = {"html_to_text": True} + # load_data_args_web = [] + # load_data_kwargs_web = {"urls": ["http://paulgraham.com/worked.html"]} + # source_id_web = manager.add_data_source("llama_index.readers.SimpleWebPageReader", constructor_args_web, constructor_kwargs_web, load_data_args_web, load_data_kwargs_web, "metadata.url").id + # + # # Adding a SimpleDirectoryReader data source + # constructor_args_dir = [] + # constructor_kwargs_dir = {"input_dir": "/home/tnunamak/Downloads/tim_misc_text", "recursive": True, "num_files_limit": 5} + # load_data_args_dir = [] + # load_data_kwargs_dir = {} + # source_id_dir = manager.add_data_source("llama_index.readers.SimpleDirectoryReader", constructor_args_dir, constructor_kwargs_dir, load_data_args_dir, load_data_kwargs_dir, "metadata.file_path").id + + # Scanning data sources + # changes_web = manager.scan_data_sources([source_id_web]) + # changes_dir = manager.scan_data_sources([source_id_dir]) + # print("Web Changes:", changes_web) + # print("Directory Changes:", changes_dir) + + # for data_source in DataSource.select(): + # manager.index_documents(data_source) + # print(data_source.id, data_source.loader_module, data_source.config) + + # source_id = -1 # Example source ID + # result = manager.index_documents(source_id) + # print(result) + + # Cleanup + # manager.remove_data_source(source_id_web) + # manager.remove_data_source(source_id_dir) diff --git a/selfie/embeddings/__init__.py b/selfie/embeddings/__init__.py new file mode 100644 index 0000000..ce6033b --- /dev/null +++ b/selfie/embeddings/__init__.py @@ -0,0 +1,458 @@ +import asyncio +import time +from concurrent.futures import ThreadPoolExecutor +from datetime import datetime, timezone +import os +import shutil +from typing import Optional, List, Dict, Any, Coroutine, Callable + +import humanize + +from selfie.config import default_embeddings_storage_root, default_db_name, default_local_model +from selfie.data_generators.chat_training_data import ( + ChatTrainingDataGenerator, +) +from selfie.types.share_gpt import ShareGPTMessage +from selfie.embeddings.document_types import Document, ScoredDocument +from selfie.embeddings.importance_scorer import ImportanceScorer +from selfie.embeddings.recency_scorer import RecencyScorer +from selfie.embeddings.relevance_scorer import RelevanceScorer +from txtai.embeddings import Embeddings +import logging + +from txtai.pipeline import LLM + +logger = logging.getLogger(__name__) + +default_importance = 0.3 + +llm = LLM( + path=default_local_model, + method="llama.cpp", + n_ctx=8192, + n_gpu_layers=-1, +) + + +class DataIndex: + _singleton = None + + def __new__(cls, *args, **kwargs): + if not cls._singleton: + cls._singleton = super(DataIndex, cls).__new__(cls) + return cls._singleton + + def __init__(self, character_name, storage_path: str = default_embeddings_storage_root, use_local_llm=True, completion=None): + if not hasattr(self, 'is_initialized'): + logger.info("Initializing DataIndex") + self.storage_path = os.path.join(storage_path, "index") + os.makedirs(storage_path, exist_ok=True) + + async def completion_async(prompt): + return llm(prompt) + + self.completion = completion or completion_async + + self.character_name = character_name + self.embeddings = Embeddings( + path="sentence-transformers/all-MiniLM-L6-v2", + sqlite={"wal": True}, + # For now, sqlite w/the default driver is the only way to use WAL. + content=True + # TODO: may not work on Windows + # https://docs.sqlalchemy.org/en/20/core/engines.html#sqlite + # content=f"sqlite:///{os.path.join(storage_path, default_db_name)}" + ) + self.token_used = { + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0, + } + self.gpt3_call_count = 0 + self.importance_scorer = ImportanceScorer( + score_weight=1, use_local_llm=use_local_llm + ) + self.recency_scorer = RecencyScorer(score_weight=1) + self.relevance_scorer = RelevanceScorer(score_weight=1) + + if os.path.exists(os.path.join(self.storage_path, "embeddings")): + self.embeddings.load(self.storage_path) + else: + logger.info("Embeddings file not found, starting with a new embeddings index.") + # self.embeddings.index(documents=[]) + # self.embeddings.save(self.storage_path) + + # Create a queue for database write operations + self.db_write_queue = asyncio.Queue() + # Start the background task to process database write operations + asyncio.create_task(self.process_db_write_queue()) + self.executor = ThreadPoolExecutor() + + self.is_initialized = True + + async def close(self): + self.executor.shutdown(wait=True) + + def has_data(self): + return os.path.exists(f"{self.storage_path}/embeddings") + + async def process_db_write_queue(self): + while True: + task, future = await self.db_write_queue.get() # Expecting a tuple of (task, future) + try: + if asyncio.iscoroutinefunction(task) or isinstance(task, Coroutine): + # TODO: one of these is correct, figure it out + # result = await task() # Await coroutine and capture result + result = await task # Await coroutine and capture result + else: + loop = asyncio.get_event_loop() + result = await loop.run_in_executor(None, task) # Execute sync function in executor and capture result + future.set_result(result) # Set the result on the future + except Exception as e: + future.set_exception(e) # Set the exception on the future if something goes wrong + finally: + self.db_write_queue.task_done() + + async def enqueue_task(self, task: Callable[..., Any], *args, **kwargs) -> Any: + """Enqueue a task that can be either synchronous or asynchronous.""" + future = asyncio.Future() + if asyncio.iscoroutinefunction(task) or isinstance(task, Coroutine): + # Prepare coroutine for execution; args, kwargs are applied + wrapped_task = asyncio.ensure_future(task(*args, **kwargs)) + else: + # Wrap synchronous function and its arguments in a lambda for deferred execution + wrapped_task = lambda: task(*args, **kwargs) + await self.db_write_queue.put((wrapped_task, future)) + return await future + + async def enqueue_upsert(self, documents: List[Dict[str, Any]] | List[tuple[int, Dict[str, Any]]]): + """Enqueue an upsert operation.""" + return await self.enqueue_task( + lambda: (result := self.embeddings.upsert(documents), self.embeddings.save(self.storage_path), result)[2] + ) + + async def enqueue_index(self, documents: List[Dict[str, Any]] | List[tuple[int, Dict[str, Any]]]): + """Enqueue an index operation.""" + return await self.enqueue_task( + lambda: (result := self.embeddings.index(documents), self.embeddings.save(self.storage_path), result)[2] + ) + + async def enqueue_delete(self, ids: List[int]): + """Enqueue a delete operation.""" + return await self.enqueue_task( + lambda: (result := self.embeddings.delete(ids), self.embeddings.save(self.storage_path), result)[2] + ) + + @staticmethod + def map_share_gpt_data( + conversation: List[ShareGPTMessage], source: str = "Unknown", source_document_id: int = None + ) -> List[Document]: + chunks = ChatTrainingDataGenerator.group_messages_into_chunks( + conversation, overlap=1, max_messages=8, max_characters=0 + ) + documents = [] + for i, conv in enumerate(chunks): + if any("REDACTED" in msg.value for msg in conv): + continue + last_user = "" + formatted_conversation = "\n".join( + f"\n{(last_user := msg.from_user)}:\n{msg.value}" + if msg.from_user != last_user + else f"{msg.value}" + for msg in conv + ).strip() + + document = Document( + text=formatted_conversation, + timestamp=conv[0].timestamp, + source=source, + ) + + if source_document_id: + document.source_document_id = source_document_id + + documents.append(document) + return documents + + @staticmethod + def _calculate_retrieval_score( + recency_score, + relevance_score, + importance_score=None, + importance_weight=1, + recency_weight=1, + relevance_weight=1, + ): + if importance_score is None: + importance_score = default_importance + return ( + (importance_score * importance_weight) + + (recency_score * recency_weight) + + (relevance_score * relevance_weight) + ) / (importance_weight + recency_weight + relevance_weight) + + async def _summarize_documents( + self, character_name: str, documents: List[Document], context, model + ): + logger.info(f"Summarizing {len(documents)} documents") + + if model is None: + model = "gpt-3.5-turbo" + if len(documents) == 0: + return None + document_summary = "\n\n".join( + [ + f"{humanize.naturaltime(datetime.now(timezone.utc) - document.timestamp)}\n{document.text}" + for document in documents + ] + ) + + if character_name: + prompt = f'### Instruction:\nYou are {character_name}. Below are conversation fragments from your recent memories. You are gathering your thoughts in preparation of writing a response for "{context}". In a sentence, concisely tell yourself what you can conclude about this topic from your memories, especially as it relates to you personally.\n\n### Input:\nStart of memories:\n{document_summary}\nEnd of memories.\n\n### Concise response:\n{character_name}:\nBased on the given conversation fragments, it can be concluded that ' + else: + prompt = f'### Instruction:\nBelow are conversation fragments from an unknown person. You are gathering your thoughts in preparation of writing a response for "{context}". In a sentence, concisely answer what you can conclude about this topic from the memories.\n\n### Input:\nStart of memories:\n{document_summary}\nEnd of memories.\n\n### Concise response:\nYou:\nBased on the given conversation fragments, it can be concluded that ' + + logger.debug(f"Model: {model}") + logger.debug(f"Prompt: {prompt}") + + # TODO: truncate the prompt to fit the context window + if model == "local": + return await self.completion(prompt) + else: + from litellm import completion + + openai_response = completion( + model=model, + messages=[{"role": "user", "content": prompt}], + temperature=0, + ) + return openai_response.choices[0].message.content + + def map_document(self, document: Document, extract_importance=True): + return { + **document.to_dict(), + **( + { + "importance": document.importance + if document.importance is not None # Only calculate importance if it's not already set + else self.importance_scorer.calculate_score(document) + + } if extract_importance else {} + ), + } + + def _query( + self, + where: str = None, + parameters: Dict[str, Any] = None, + limit: int = None, + offset: int = None, + order_by: str = None, + group_by: str = None, + ) -> List[Dict[str, Any]]: + parameters = parameters or {} + query_components = [ + f"SELECT score, {', '.join(Document.model_fields.keys())} FROM txtai" + ] + + if where: + query_components.append(f"WHERE {where}") + + if group_by: + query_components.append(f"GROUP BY {group_by}") + + if order_by: + query_components.append(f"ORDER BY {order_by}") + + if offset is not None: + query_components.append("OFFSET :offset") + parameters["offset"] = offset + + logger.debug(f"Query looks like {' '.join(query_components)}") + return self.embeddings.search(" ".join(query_components), parameters=parameters, limit=limit) + + async def index(self, documents: List[Document], extract_importance=True, upsert=False): + start_time = time.time() + logger.info(f"Indexing {len(documents)} documents started at {start_time}") + + documents = [ + await self.find_existing_document(document) or document for document in documents + ] if upsert else documents + + with_importance = [ + self.map_document(document, extract_importance) for document in documents + ] + + logger.info("Starting upsert operation") + # self.embeddings.upsert(with_importance) + await self.enqueue_upsert(with_importance) + logger.info("Upsert operation completed") + + self.embeddings.save(self.storage_path) + + return with_importance + # TODO: return document with ID, if possible + + async def recall( + self, + topic: str, + topic_context=None, + character_name: Optional[str] = None, + limit=5, + importance_weight=0, + recency_weight=1, + relevance_weight=1, + include_summary=True, + local_llm=True, + min_score=0.4, + ): + if min_score is None: + min_score = 0.4 + + if not self.has_data(): + return {"documents": [], "summary": "No documents found.", "mean_score": 0} + self.embeddings.load(self.storage_path) + + results = self._query(where="similar(:topic)", parameters={"topic": topic}, limit=limit) + documents_list: List[ScoredDocument] = [] + for result in results: + document = Document( + text=result["text"], + timestamp=result["timestamp"], + importance=result["importance"], + source=result["source"], + updated_timestamp=result["updated_timestamp"], + source_document_id=result["source_document_id"], + ) + relevance_score = self.relevance_scorer.calculate_score( + document, result["score"] + ) + recency_score = self.recency_scorer.calculate_score(document) + importance_score = document.importance + retrieval_score = self._calculate_retrieval_score( + recency_score, + relevance_score, + importance_score, + importance_weight, + recency_weight, + relevance_weight, + ) + documents_list.append( + ScoredDocument( + **document.model_dump(), + score=retrieval_score, + relevance=relevance_score, + recency=recency_score, + ) + ) + + documents_list = [m for m in documents_list if m.score > min_score] + if len(documents_list) == 0: + return {"documents": [], "summary": "No documents found.", "mean_score": 0} + + documents_list.sort(key=lambda x: x.score, reverse=True) + short_documents_list = documents_list[:limit] + summary = ( + await self._summarize_documents( + character_name, + short_documents_list, + topic_context if topic_context else topic, + ("local" if local_llm else None), + ) + if include_summary + else None + ) + return {"documents": short_documents_list, "summary": summary, "mean_score": sum([m.score for m in short_documents_list]) / len(short_documents_list)} + + # TODO: Fix this + def delete_all(self): + logger.info("Deleting all documents") + if self.has_data(): + shutil.rmtree(f"{self.storage_path}/embeddings") + logger.info(f"Deleted storage path: {self.storage_path}") + else: + logger.info("Storage path not found, nothing to delete.") + + async def delete_documents(self, document_ids): + await self.enqueue_delete(document_ids) + deleted_ids = document_ids # TODO: this is a hack because enqueue_delete doesn't return anything + # TODO: determine whether anything was actually deleted + return deleted_ids + + async def delete_document(self, document_id): + return await self.delete_documents([document_id]) + + async def delete_documents_with_source_documents(self, source_document_ids): + results = self._query(where=f"source_document_id IN ({', '.join([str(doc_id) for doc_id in source_document_ids])})", limit=999999) + print(f"Trying to delete {len(results)} results for source documents: {source_document_ids}") + results = await self.enqueue_delete([result['id'] for result in results]) + print(f"Deleted {len(results)} indexed documents") + return results + + async def update_document(self, document_id, updated_document, extract_importance=True): + await self.enqueue_upsert( + [(document_id, self.map_document(updated_document, extract_importance))] + ) + + def get_document_count(self): + if not self.has_data(): + return 0 + + result = self.embeddings.search("SELECT count(*) FROM txtai") + return result[0]["count(*)"] + + async def get_document(self, document_id): + if not self.has_data(): + return None # TODO: raise exception? + result = self._query(where=f"id = :id", parameters={"id": document_id}) + return result[0] if result else None + + def get_documents_with_source_document(self, source_document_id): + return self._query(where="source_document_id = :source_document_id", parameters={"source_document_id": source_document_id}) if self.has_data() else [] + + def get_one_document_per_source_document(self, source_document_ids: Optional[List[str]] = None): + if not self.has_data(): + return [] + + if source_document_ids: + # sources_placeholder = ', '.join(['?' for _ in sources]) + where_in_clause = f"source_document_id IN ({', '.join(source_document_ids)})" + else: + where_in_clause = "" + + # Bad queries + # query = f"SELECT id, timestamp, text, importance, source, updated_timestamp, source_document_id FROM txtai WHERE source_document_id IS NOT NULL AND source_document_id IN ({', '.join(sources)}) AND id IN (SELECT id FROM txtai WHERE source_document_id IS NOT NULL GROUP BY source_document_id HAVING max(updated_timestamp))" + # query = f"SELECT id, timestamp, text, importance, source, updated_timestamp, source_document_id FROM txtai WHERE source_document_id IS NOT NULL AND source_document_id IN ({', '.join(sources)}) GROUP BY source_document_id HAVING max(updated_timestamp) LIMIT 999999" + # query = f"SELECT id, source_document_id FROM txtai {where_in_clause} GROUP BY source_document_id LIMIT 999999" + # return self.embeddings.search(query) + return self._query(where=where_in_clause, group_by="source_document_id", limit=999999) + + async def find_existing_document(self, document: Document): + if not self.has_data(): + return None + result = self._query( + where="text = :text AND timestamp = :timestamp AND source = :source AND source_document_id = :source_document_id", + parameters={ + "text": document.text, + "timestamp": document.timestamp.isoformat(), + "source": document.source, + "source_document_id": document.source_document_id, + }, + ) + + if len(result) > 1: + logger.warning(f"Found {len(result)} documents matching {document})") + if len(result) > 0: + logger.debug(f"Found an existing document matching") + + # TODO: This is kinda horrible + result[0]['id'] = int(result[0]['id']) + result[0]['source_document_id'] = int(result[0]['source_document_id']) + + return Document(**result[0]) if result else None + + async def get_documents(self, offset=0, limit=10): + if not self.has_data(): + return [] + + return self._query(limit=limit, offset=offset, order_by="timestamp DESC") diff --git a/selfie/embeddings/base_scorer.py b/selfie/embeddings/base_scorer.py new file mode 100644 index 0000000..4cd43c8 --- /dev/null +++ b/selfie/embeddings/base_scorer.py @@ -0,0 +1,28 @@ +from abc import ABC, abstractmethod + +from selfie.embeddings.document_types import Document + + +class BaseScorer(ABC): + def __init__(self, score_weight): + self.score_weight = score_weight + + @abstractmethod + def calculate_raw_score(self, *args, **kwargs): + """ + Calculate the raw score for a document. Implementation is scorer-specific. + """ + pass + + def normalize_score(self, score): + """ + Normalize the score to a range of [0, 1]. + Default implementation. Can be overridden by child classes for specific normalization. + """ + return max(0, min(1, score)) # Default normalization logic + + def calculate_score(self, document: Document, *args, **kwargs): + """ + Wrapper method to calculate and then normalize the score. + """ + return self.normalize_score(self.calculate_raw_score(document, *args, **kwargs)) diff --git a/selfie/embeddings/document_types.py b/selfie/embeddings/document_types.py new file mode 100644 index 0000000..979faad --- /dev/null +++ b/selfie/embeddings/document_types.py @@ -0,0 +1,39 @@ +from datetime import datetime, timezone +from typing import Optional +from pydantic import BaseModel, Field, model_validator + + +class Document(BaseModel): + id: Optional[int] = Field(None, description="ID of the document, auto-generated") + text: str = Field(..., description="Formatted conversation or content of the document") + source: str = Field(..., description="Source of the document, e.g., API") + importance: Optional[float] = Field(None, description="Importance score of the document, [0, 1]") + timestamp: datetime = Field(..., description="The time that the event happened at, e.g. the time a message was sent") + + created_timestamp: datetime = Field(..., description="Time when the document was first indexed") + updated_timestamp: Optional[datetime] = Field(None, description="Time when the document was last updated") + source_document_id: Optional[int] = Field(None, description="ID of the source document") + + def to_dict(self, *args, **kwargs): + d = super().model_dump(*args, **kwargs) + d['timestamp'] = self.timestamp.replace(tzinfo=timezone.utc).isoformat() if self.timestamp else None + d['created_timestamp'] = self.created_timestamp.replace(tzinfo=timezone.utc).isoformat() if self.created_timestamp else None + d['updated_timestamp'] = self.updated_timestamp.replace(tzinfo=timezone.utc).isoformat() if self.updated_timestamp else None + return d + + class Config: + validate_assignment = True + + @model_validator(mode='before') + def autofill_timestamps(cls, values): + if 'created_timestamp' not in values: + values['created_timestamp'] = datetime.utcnow() + values['updated_timestamp'] = datetime.utcnow() + return values + + +class ScoredDocument(Document): + score: float = Field(..., description="Overall score of the document, for a query, [0, 1]") + importance: Optional[float] = Field(..., description="Importance score of the document, [0, 1]") + relevance: float = Field(..., description="Relevance score of the document, for a query, [0, 1]") + recency: float = Field(..., description="Recency score of the document, for a query (time), [0, 1]") diff --git a/selfie/embeddings/importance_scorer.py b/selfie/embeddings/importance_scorer.py new file mode 100644 index 0000000..bf0c4f2 --- /dev/null +++ b/selfie/embeddings/importance_scorer.py @@ -0,0 +1,86 @@ +import json + +from selfie.config import default_local_functionary_model +from selfie.embeddings.base_scorer import BaseScorer +from selfie.embeddings.document_types import Document + + +class ImportanceScorer(BaseScorer): + def __init__(self, score_weight, use_local_llm=True): + super().__init__(score_weight) + self.use_local_llm = use_local_llm + + def calculate_raw_score(self, document: Document): + """ + Calculate the raw importance score for a document using OpenAI's API. + """ + extract_importance_prompt = """ + On the scale of 1 to 10, where 1 is purely mundane (e.g., brushing teeth, making bed) and 10 is extremely + poignant (e.g., a break up, college acceptance), rate the likely poignancy of the following document. + Document: {document} + """.format( + document=document.text + ) + + rate_document = { + "name": "rate_document", + "description": "This function rates document importance for the given part of the conversation.", + "parameters": { + "type": "object", + "properties": { + "importanceScore": { + "type": "number", + "description": "Importance score on the scale of 1 to 10, where 1 is purely mundane and 10 is extremely poignant", + }, + }, + "required": ["importanceScore"], + }, + } + + try: + if self.use_local_llm: + from txtai.pipeline import LLM + + llm = LLM( + default_local_functionary_model, + n_gpu_layers=-1, + method="llama.cpp", + chat_format="functionary", + n_ctx=4096, + ).generator.llm + chat_completion = llm.create_chat_completion + else: + from litellm import completion + + chat_completion = completion + + tool_choice = {"type": "function", "function": {"name": "rate_document"}} + + rate_document_tool = {"type": "function", "function": rate_document} + + response = chat_completion( + # model='gpt-3.5-turbo', + messages=[{"role": "user", "content": extract_importance_prompt}], + # functions=[rate_document], + # function_call={ + # 'name': rate_document['name'], + # }, + tools=[rate_document_tool], + tool_choice=tool_choice, + ) + + json_data = json.loads( + response["choices"][0]["message"]["function_call"]["arguments"] + ) + # json_data = json.loads(openai_response.choices[0].message.function_call.arguments) + return json_data["importanceScore"] + except Exception as e: + print("Error calculating importance score:", str(e)) + return 0 + + def normalize_score(self, score): + """ + Normalize the score to a range of [0, 1]. + Assumes the original score is in [1, 10]. + """ + return 0 if score is 0 else (score - 1) / 9 diff --git a/selfie/embeddings/recency_scorer.py b/selfie/embeddings/recency_scorer.py new file mode 100644 index 0000000..be9db22 --- /dev/null +++ b/selfie/embeddings/recency_scorer.py @@ -0,0 +1,40 @@ +from datetime import datetime +from dateutil import parser +import math + +from selfie.embeddings.base_scorer import BaseScorer +from selfie.embeddings.document_types import Document + +from datetime import timezone + + +class RecencyScorer(BaseScorer): + def __init__(self, score_weight, current_time=datetime.now(timezone.utc)): + super().__init__(score_weight) + self.current_time = current_time + + def calculate_raw_score( + self, document: Document, document_index: int = None, num_documents: int = None + ): + """ + Calculate recency score using an exponential decay function. + """ + time_difference_seconds = (self.current_time - document.timestamp).total_seconds() + + a = 0 + b_years = 0.75 + seconds_per_year = 31536000 + # You can visualize this curve on https://www.desmos.com/calculator + # Type in: + # exp(0 - 0.75 * x) for a = 0, b = 0.75 + # This will display the curve in years. + by_date = math.exp(a - (b_years / seconds_per_year) * time_difference_seconds) + + if document_index is None or num_documents is None: + return by_date + + by_position = 1 - ((num_documents - document_index) / num_documents) + + # TODO: Revisit this + # Give equal weight to time and position + return (by_date + by_position) / 2 diff --git a/selfie/embeddings/relevance_scorer.py b/selfie/embeddings/relevance_scorer.py new file mode 100644 index 0000000..73d845a --- /dev/null +++ b/selfie/embeddings/relevance_scorer.py @@ -0,0 +1,17 @@ +from selfie.embeddings.base_scorer import BaseScorer +from selfie.embeddings.document_types import Document + + +# TODO: rename this to IdentityScorer +# TODO: query weaviate for certainty instead of distance +# See https://weaviate.io/developers/weaviate/config-refs/distances#distance-vs-certainty +class RelevanceScorer(BaseScorer): + def __init__(self, score_weight): + super().__init__(score_weight) + + def calculate_raw_score(self, document: Document, certainty: float): + # TODO use certainty? + return certainty + + def normalize_score(self, score): + return score diff --git a/selfie/parsers/__init__.py b/selfie/parsers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/selfie/parsers/chat/__init__.py b/selfie/parsers/chat/__init__.py new file mode 100644 index 0000000..7d1f33f --- /dev/null +++ b/selfie/parsers/chat/__init__.py @@ -0,0 +1,126 @@ +import os +import re +import sys +from enum import unique, Enum +from typing import Dict +import yaml + +from selfie.parsers.chat.discord import DiscordParser +from selfie.parsers.chat.whatsapp import WhatsAppParser +from selfie.parsers.chat.google import GoogleTakeoutMessagesParser +from selfie.types.share_gpt import ShareGPTConversation, ShareGPTMessage + +import logging +logger = logging.getLogger(__name__) + + +# TODO: Clean this up +# Check if we are running in a PyInstaller bundle +if getattr(sys, "frozen", False): + # If so, the base path is the path to the _MEIPASS folder + base_path = sys._MEIPASS + blacklist_file_path = os.path.join( + base_path, "selfie/parsers/chat/blacklist_patterns.yaml" + ) +else: + # If not, the base path is the root of your project + base_path = os.path.dirname( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + ) + blacklist_file_path = os.path.join( + base_path, "parsers/chat/blacklist_patterns.yaml" + ) +# current_dir = os.path.dirname(os.path.abspath(__file__)) +# blacklist_file_path = os.path.join(current_dir, "blacklist_patterns.yaml") + +with open(blacklist_file_path, "r") as f: + default_blacklist_patterns = yaml.safe_load(f) + default_blacklist_patterns = [ + pattern.strip() for pattern in default_blacklist_patterns + ] + + +@unique +class Parser(Enum): + WHATSAPP = WhatsAppParser + DISCORD = DiscordParser + GOOGLE_MESSAGES = GoogleTakeoutMessagesParser + + +class ChatFileParser: + @staticmethod + def mask_text(text: str, mask: bool) -> str: + return "*" * len(text) if mask else text + + def __init__(self, blacklist_patterns=None, rewrite_placeholder: str = "REDACTED"): + self.parser_cache = {} + self.blacklist_patterns = [ + re.compile(pattern, re.IGNORECASE) + for pattern in default_blacklist_patterns + (blacklist_patterns or []) + ] + self.rewrite_placeholder = rewrite_placeholder + + def select_parser(self, parser_type: str = None, document: str = None): + parsers = [p.value() for p in Parser] if not parser_type else [Parser[parser_type.upper()].value()] + matching_parsers = [parser for parser in parsers if parser.can_parse(document)] + if len(matching_parsers) != 1: + raise ValueError(f"{'Multiple' if matching_parsers else 'No'} parsers match.") + logger.info(f"Selected parser: {matching_parsers[0].__class__.__name__}") + return matching_parsers[0] + + def is_blacklisted(self, line: str) -> bool: + messages = line.split("\n") + return any( + any(pattern.search(message) for pattern in self.blacklist_patterns) + for message in messages + ) + + def rewrite_blacklisted(self, text: str) -> str: + if self.is_blacklisted(text): + # logger.trace(f"Rewriting blacklisted text: {text}") + return self.rewrite_placeholder + return text + + def parse_file( + self, + input_file: str, + parser_type: str = None, + rename_speakers: Dict[str, str] = None, + mask: bool = False, + ) -> ShareGPTConversation: + with open(input_file, "r", encoding="utf-8") as file: + return self.parse_document(''.join(file.readlines()), parser_type, rename_speakers, mask, input_file) + + def parse_document( + self, + document: str, + parser_type: str = None, + rename_speakers: Dict[str, str] = None, + mask: bool = False, + document_name: str = None, + ) -> ShareGPTConversation: + rename_speakers = rename_speakers or {} + try: + parser_instance = self.select_parser(parser_type=parser_type, document=document) + except ValueError as e: + raise ValueError(f"Could not select parser for document {document_name}: {e}") + + parsed_data = parser_instance.parse_chat(document) + + # Check if there are any conversations in the parsed data + if not parsed_data.conversations: + return ShareGPTConversation(conversations=[]) + + # Process each message in the conversations + processed_conversations = [ + ShareGPTMessage.model_validate( + { + "from": rename_speakers.get(msg.from_user, msg.from_user), + "value": self.mask_text(self.rewrite_blacklisted(msg.value), mask), + "timestamp": msg.timestamp.isoformat(), + } + ) + for msg in parsed_data.conversations + ] + + return ShareGPTConversation(conversations=processed_conversations) diff --git a/selfie/parsers/chat/base.py b/selfie/parsers/chat/base.py new file mode 100644 index 0000000..f16819d --- /dev/null +++ b/selfie/parsers/chat/base.py @@ -0,0 +1,204 @@ +import json +import re +from typing import List, Dict, Any +from datetime import timezone + +from pydantic import ValidationError + +from selfie.types.share_gpt import ShareGPTConversation, ShareGPTMessage + +import logging +logger = logging.getLogger(__name__) + + +class ChatParser: + """ + The base class for chat parsers. Don't use this class directly; it's meant to be subclassed. + """ + def __init__(self, timezone=timezone.utc): + self.timezone = timezone + + def preprocess(self, document: str) -> str: + return self._preprocess_hook(document) + + def _preprocess_hook(self, document: str) -> str: + return document + + def can_parse(self, document: str) -> bool: + """ + Check if this parser can parse the given document. + + Args: + document (str): The document to check. + + Returns: + bool: True if this parser can parse the given document. + """ + preprocessed_document = self.preprocess(document) + return self._can_parse_hook(preprocessed_document) + + def _can_parse_hook(self, document: str) -> bool: + """ + Hook method for subclasses to implement can_parse logic. + """ + return False + + @staticmethod + def remove_non_printable(text: str) -> str: + """ + Remove non-printable characters from a text string. + + Args: + text (str): Input string, e.g., "Hello\rWorld\u200e" + + Returns: + str: A new string with non-printable characters removed, e.g., "HelloWorld" + """ + text = text.replace('\r', '').replace('\u200e', '') + return ''.join(char for char in text if char.isprintable()) + + def parse_chat(self, document: str) -> ShareGPTConversation: + """ + Parse raw chat data into a structured format. + + Args: + document (str): The raw chat data. Could be text or JSON. + + Returns: + Dict: A dictionary containing the parsed chat data. + Example structure: + { + "conversations": [ + {"from": "Alice", "value": "Hi!", "timestamp": "2021-01-01T12:00:00Z"}, + {"from": "Bob", "value": "Hello!", "timestamp": "2021-01-01T12:01:00Z"} + ] + } + """ + preprocessed_document = self.preprocess(document) + return self._parse_chat_hook(preprocessed_document) + + def _parse_chat_hook(self, document: str) -> ShareGPTConversation: + """ + Hook method for subclasses to implement parse_chat logic. + """ + raise NotImplementedError + + +class TextBasedChatParser(ChatParser): + def _can_parse_hook(self, document: str) -> bool: + logger.debug(f"Trying to parse {document[:10]} with {self.__class__.__name__}") + return any(re.match(fmt['regex'], document.splitlines()[0], re.DOTALL) for fmt in self.SUPPORTED_FORMATS) + + """ + Parser for chat data that is text-based like WhatsApp. + """ + SUPPORTED_FORMATS: List[Dict[str, str]] = [] + + def is_new_message(self, line: str) -> bool: + """ + Checks if a line of text is the start of a new message. + """ + return any(bool(re.match(format['regex'], line)) for format in self.SUPPORTED_FORMATS) + + def group_lines(self, raw_lines: List[str]) -> List[List[str]]: + """ + Groups lines of text that belong to the same message. + + Args: + raw_lines (List[str]): List of chat lines. + e.g., ["[2021-01-01] Alice: Hi", "How are you?", "[2021-01-01] Bob: Good"] + + Returns: + List[List[str]]: List of message groups, where each group is a list of lines belonging to the same message. + e.g., [["[2021-01-01] Alice: Hi", "How are you?"], ["[2021-01-01] Bob: Good"]] + """ + lines = [] + current_message = [] + for line in raw_lines: + if self.is_new_message(line): + if current_message: + lines.append(current_message) + current_message = [line.strip()] + else: + current_message.append(line.strip()) + if current_message: + lines.append(current_message) + return lines + + def _parse_chat_hook(self, document: str) -> ShareGPTConversation: + """ + Parse a document of line-by-line messages into a structured chat format. + + Args: + document (str): A list of lines from the chat log. + e.g., ["[2021-01-01] Alice: Hi", "[2021-01-01] Bob: Hello"] + + Returns: + Dict: A dictionary containing the parsed chat data. + Example structure: + { + "conversations": [ + {"from": "Alice", "value": "Hi", "timestamp": "2021-01-01T12:00:00Z"}, + {"from": "Bob", "value": "Hello", "timestamp": "2021-01-01T12:01:00Z"} + ] + } + """ + lines = [self.remove_non_printable(line) for line in document.splitlines()] + grouped_lines = self.group_lines(lines) + parsed_data = [self.parse_message(bundle) for bundle in grouped_lines] + + if len(parsed_data) == 0: + raise ValueError("Parser found no messages in chat log.") + + return ShareGPTConversation(conversations=[msg for msg in parsed_data if msg is not None]) + + def parse_message(self, raw_message: List[str]) -> ShareGPTMessage: + """ + Parse a single message from a list of lines that make up that message. + + Args: + raw_message (List[str]): List of lines belonging to the same message. + e.g., ["[2021-01-01] Alice: Hi", "How are you?"] + + Returns: + Dict[str, str]: A dictionary representing the parsed message. + Example structure: + { + "from": "Alice", + "value": "Hi\nHow are you?", + "timestamp": "2021-01-01T12:00:00Z" + } + """ + pass + + +class JsonBasedChatParser(ChatParser): + """ + Parser for chat data that is JSON-based like Discord exports. + """ + SUPPORTED_SCHEMAS: List[Any] = [] + + def _can_parse_hook(self, document: str) -> bool: + logger.debug(f"Trying to parse {document[:10]} with {self.__class__.__name__}") + try: + return any(schema.parse_obj(json.loads(document)) for schema in self.SUPPORTED_SCHEMAS) + except (json.JSONDecodeError, ValidationError) as e: + logger.debug(f"Failed to parse {document[:10]} with {self.__class__.__name__}: {e}") + return False + + def _parse_chat_hook(self, document: str) -> ShareGPTConversation: + return self.extract_conversations(json.loads(document)) + + def extract_conversations(self, data: Any) -> ShareGPTConversation: + """ + Extract conversations from parsed JSON data. + + Args: + data (Any): The parsed JSON data. + + Returns: + Dict: A dictionary containing the parsed chat data. + Must be implemented by subclasses. + """ + raise NotImplementedError + diff --git a/selfie/parsers/chat/blacklist_patterns.yaml b/selfie/parsers/chat/blacklist_patterns.yaml new file mode 100644 index 0000000..3942a69 --- /dev/null +++ b/selfie/parsers/chat/blacklist_patterns.yaml @@ -0,0 +1,66 @@ +# This file contains a list of regex patterns that will be used to filter out +# matching content from the chat. This was generated by ChatGPT and modified. + +# Add any regex pattern you want to exclude from the chat here. + +# Personal Identifiers +- | # Social Security Numbers + \d{3}-\d{2}-\d{4} +- | # Driver's License Numbers + \b\d{8}\b +- | # Passport Numbers + P\d{7} +- | # Employee IDs + E\d{6} +- | # Phone Numbers + \+?\d{1,4}?[-.\s]?\(?\d{1,3}?\)?[-.\s]?\d{1,4}[-.\s]?\d{1,4}[-.\s]?\d{9} + +# Financial Information +- | # Credit Card Numbers + \b\d{4}-?\d{4}-?\d{4}-?\d{4}\b +- | # Bank Account Numbers + \b\d{9,18}\b +- | # Wire Transfer Details + \b\d{3}-?\d{3}-?\d{3}\b +- | # Ethereum Addresses + 0x[a-fA-F0-9]{40} +- | # With prefixes other than 0x + [a-fA-F0-9]{40} +- | # Bitcoin Addresses + ^(bc1|[13])[a-zA-HJ-NP-Z0-9]{25,39}$ + +# Health Information +- | # Medical Record Numbers + MR\d{8} +- | # Health Insurance IDs + HI\d{8} + +# Geo-Location Data +- | # GPS Coordinates + -?\d{1,3}\.\d+,-?\d{1,3}\.\d+ + +# Family Information +- | # Names of Family Members + (?<=\bName:\s)([a-zA-Z]+\s?[a-zA-Z]*) + +# Internet Data +- | # IP Addresses + \b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b +- | # Mac Addresses + ([0-9A-Fa-f]{2}[:-]){5}([0-9A-Fa-f]{2}) +- | # Email Addresses + [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} + +# Sensitive Content +- | # Profanity + \b(?:cunt|shithead)\b +- | # Sexual Content + \b(?:sex|nude|xxx)\b +- | # Drugs + \b(?:drugs?|weed|cocaine|heroin|meth|lsd|mushroom|mushrooms?)\b +- | # Drug Emojis + 🍄 + +# Miscellaneous +- | # Biometric Data + \b(?:fingerprint|retina\sscan)\b diff --git a/selfie/parsers/chat/chat_file_parsing_helper.py b/selfie/parsers/chat/chat_file_parsing_helper.py new file mode 100644 index 0000000..13e66df --- /dev/null +++ b/selfie/parsers/chat/chat_file_parsing_helper.py @@ -0,0 +1,56 @@ +import json +from pathlib import Path +from typing import Optional, Dict, List + +from fastapi import UploadFile, HTTPException +from pydantic import BaseModel, field_validator + +from selfie.parsers.chat import Parser + + +class ChatFileParserConfig(BaseModel): + main_speaker: Optional[str] = None + format: Optional[str] = None + speaker_aliases: Optional[Dict[str, str]] = {} # TODO: invert them + + @field_validator('format') + def validate_format(cls, v: str): + upper_v = v.upper() + if upper_v is not None and upper_v not in Parser.__members__: + raise ValueError(f"format must be one of: {list(Parser.__members__.keys())}") + return upper_v + + +def save_file(f): + path = Path(f.filename) + path.parent.mkdir(parents=True, exist_ok=True) + f.file.seek(0) # Not sure if this is needed + file_content = f.file.read() + path.write_bytes(file_content) + return f.filename + +def get_files_with_full_configs(files: List[UploadFile], parser_configs: List[ChatFileParserConfig]): + parser_configs += [ChatFileParserConfig()] * (len(files) - len(parser_configs)) + saved_files = [save_file(f) for f in files] + return [{"file": saved_file, "config": setting} for saved_file, setting in zip(saved_files, parser_configs)] + +def get_files_with_configs(files: List[UploadFile], parser_configs: str): + try: + parser_configs: List[ChatFileParserConfig] = [ + ChatFileParserConfig(**config) + for config in json.loads(parser_configs) + ] + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid parser config") + + parser_configs += [ChatFileParserConfig()] * (len(files) - len(parser_configs)) + saved_files = [save_file(f) for f in files] + files_with_configs = [{"file": saved_file, "config": setting} for saved_file, setting in zip(saved_files, parser_configs)] + + return files_with_configs + + +def delete_uploaded_files(files_with_configs): + saved_files = [file_with_settings['file'] for file_with_settings in files_with_configs] + for file in saved_files: + Path(file).unlink() diff --git a/selfie/parsers/chat/discord.py b/selfie/parsers/chat/discord.py new file mode 100644 index 0000000..e18d2bb --- /dev/null +++ b/selfie/parsers/chat/discord.py @@ -0,0 +1,54 @@ +from datetime import datetime +from typing import Dict, Any, List + +from pydantic import BaseModel + +from selfie.parsers.chat.base import JsonBasedChatParser +from selfie.types.share_gpt import ShareGPTConversation + + +class Author(BaseModel): + name: str + + +class Message(BaseModel): + author: Author + content: str + timestamp: datetime + + +class DiscordData(BaseModel): + messages: List[Message] + + +# TODO: This is a hypothetical parser for Discord data. It's not tested. +# TODO: Consider compatibility with https://github.com/run-llama/llama-hub/blob/main/llama_hub/discord/base.py +class DiscordParser(JsonBasedChatParser): + SUPPORTED_SCHEMAS = [ + DiscordData + ] + + def extract_conversations(self, data: Any) -> ShareGPTConversation: + """ + Extract conversations from parsed Discord JSON data. + + Args: + data (Any): The parsed JSON data should have a structure like: + { + "messages": [ + {"author": {"name": "Alice"}, "content": "Hi", "timestamp": "2021-01-01T12:00:00Z"}, + {"author": {"name": "Bob"}, "content": "Hello", "timestamp": "2021-01-01T12:01:00Z"} + ] + } + + Returns: + Dict: A dictionary containing the parsed chat data. + Example structure: + { + "conversations": [ + {"from": "Alice", "value": "Hi", "timestamp": "2021-01-01T12:00:00Z"}, + {"from": "Bob", "value": "Hello", "timestamp": "2021-01-01T12:01:00Z"} + ] + } + """ + return ShareGPTConversation(conversations=[{"from": msg['author']['name'], "value": msg['content'], "timestamp": msg['timestamp']} for msg in data['messages']]) diff --git a/selfie/parsers/chat/google.py b/selfie/parsers/chat/google.py new file mode 100644 index 0000000..e81e051 --- /dev/null +++ b/selfie/parsers/chat/google.py @@ -0,0 +1,71 @@ +from datetime import datetime +from typing import Dict, Any + +from pydantic import BaseModel, Field, RootModel + +from selfie.parsers.chat.base import JsonBasedChatParser +from selfie.types.share_gpt import ShareGPTConversation + + +class Author(BaseModel): + name: str + + +class Message(BaseModel): + sender_id: str = Field(alias="SenderId") + message: str = Field(alias="Message") + timestamp: datetime = Field(alias="Timestamp") + attachment_count: int = Field(alias="Attachment count") + + +GoogleData = RootModel(root=dict[str, Message]) + + +class GoogleTakeoutMessagesParser(JsonBasedChatParser): + SUPPORTED_SCHEMAS = [ + GoogleData + ] + + def extract_conversations(self, data: Dict[str, Any]) -> ShareGPTConversation: + """ + Extract conversations from parsed Google Takeout JSON data. + + Args: + data (Dict[str, Any]): The parsed JSON data should have a structure like: + { + "uuid_1": {"SenderId": "Alice", "Message": "Hello", "Timestamp": "2021-01-01 12:00:00", "Attachment count": "0"}, + "uuid_2": {"SenderId": "Bob", "Message": "Hi", "Timestamp": "2021-01-01 12:01:00", "Attachment count": "0"} + } + + Returns: + Dict: A dictionary containing the parsed chat data. + Example structure: + { + "conversations": [ + {"from": "Alice", "value": "Hello", "timestamp": "2021-01-01T12:00:00Z"}, + {"from": "Bob", "value": "Hi", "timestamp": "2021-01-01T12:01:00Z"} + ] + } + """ + parsed_data = [] + for message_id, message_data in data.items(): + if message_id == "footer": + continue # Skip the footer if it exists + from_user = message_data["SenderId"] + value = message_data["Message"] + timestamp_str = message_data["Timestamp"] + timestamp_iso = datetime.strptime(timestamp_str, "%Y-%m-%d %H:%M:%S").replace(tzinfo=self.timezone).isoformat() + + if value == "Failed to load message": + continue + + if value == "Failed to load message\nFailed to load message": + continue + + parsed_data.append({ + "from": from_user, + "value": value, + "timestamp": timestamp_iso + }) + + return ShareGPTConversation(conversations=parsed_data) diff --git a/selfie/parsers/chat/whatsapp.py b/selfie/parsers/chat/whatsapp.py new file mode 100644 index 0000000..affc3a7 --- /dev/null +++ b/selfie/parsers/chat/whatsapp.py @@ -0,0 +1,95 @@ +from datetime import datetime +import logging +from typing import List, Dict +import re + +from selfie.parsers.chat.base import TextBasedChatParser +from selfie.types.share_gpt import ShareGPTMessage + +logger = logging.getLogger(__name__) + + +# TODO: Figure out a better way to solve or at least apply this +def parse_time_with_periods(time_str, format_str): + # Remove periods from the meridian indicator for compatibility with %p + return datetime.strptime(time_str.replace('.', ''), format_str) + + +class WhatsAppParser(TextBasedChatParser): + SUPPORTED_FORMATS = [ + { + 'regex': r"(?P\d{1,2}/\d{1,2}/\d{2}, \d{1,2}:\d{2}\s(AM|PM)) - (?P.+?): (?P.+)", + 'timestamp_format': "%m/%d/%y, %I:%M %p" + }, + { + 'regex': r"(?P\d{4}-\d{1,2}-\d{1,2}, \d{1,2}:\d{2}\s?(a\.m\.|p\.m\.)) - (?P.+?): (?P.+)", + 'timestamp_format': "%Y-%m-%d, %I:%M%p" + }, + { + 'regex': r"\[(?P\d{4}-\d{1,2}-\d{1,2}, \d{1,2}:\d{2}:\d{2}\s(AM|PM))\] (?P.+?): (?P.+)", + 'timestamp_format': "%Y-%m-%d, %I:%M:%S %p" + }, + # Deprecated or unofficial formats + { + 'regex': r"\[(?P\d{1,2}/\d{1,2}/\d{2,4}, \d{1,2}:\d{2}:\d{2}\s(AM|PM))\] (?P.+?): (?P.+)", + 'timestamp_format': "%m/%d/%y, %I:%M:%S %p" + }, + { + 'regex': r"(?P\d{1,2}/\d{1,2}/\d{2}, \d{1,2}:\d{2}(AM|PM)) - (?P.+?): (?P.+)", + 'timestamp_format': "%m/%d/%y, %I:%M%p" + }, + ] + + DROP_LINES_LIKE = [ + { + 'regex': r"(?P\d{1,2}/\d{1,2}/\d{2}, \d{1,2}:\d{2}\s(AM|PM)) - (?P.+)", + }, + { + 'regex': r"(?P\d{4}-\d{1,2}-\d{1,2}, \d{1,2}:\d{2}\s?(a\.m\.|p\.m\.)) - (?P.+)", + }, + { + 'regex': r"\[(?P\d{4}-\d{1,2}-\d{1,2}, \d{1,2}:\d{2}:\d{2}\s(AM|PM))\] (?P.+)", + }, + ] + + def _preprocess_hook(self, document: str) -> str: + """ + WhatsApp includes messages like "You added Alice" and "Messages and calls are encrypted", remove them. + """ + # Remove lines that do not match the supported formats and do match the filter lines + keep_formats, remove_formats = [re.compile(fmt['regex']) for fmt in self.SUPPORTED_FORMATS], [re.compile(drp['regex']) for drp in self.DROP_LINES_LIKE] + new_doc = '\n'.join([ + line for line in document.split('\n') + if not any(fmt.match(line) for fmt in remove_formats) or any(flt.match(line) for flt in keep_formats) + ]) + removed_count = len(document.splitlines()) - len(new_doc.splitlines()) + if removed_count: + logger.debug(f"Ignoring {removed_count} of {len(document.splitlines())} lines from WhatsApp chat") + return new_doc + + def parse_message(self, raw_message: List[str]) -> ShareGPTMessage: + """ + Parse a single WhatsApp message from a list of lines that make up that message. + + Args: + raw_message (List[str]): List of lines belonging to the same message. + e.g., ["[1/1/21, 12:00 AM] Alice: Hi", "How are you?"] + + Returns: + Dict[str, str]: A dictionary representing the parsed message. + Example structure: + { + "from": "Alice", + "value": "Hi\nHow are you?", + "timestamp": "2021-01-01T12:00:00Z" + } + """ + + full_message = "\n".join(raw_message) + for format in self.SUPPORTED_FORMATS: + match = re.match(format['regex'], full_message, flags=re.DOTALL) + if match: + groups = match.groupdict() + timestamp_dt = parse_time_with_periods(groups['timestamp'], format['timestamp_format']).replace(tzinfo=self.timezone) + groups['timestamp'] = timestamp_dt.isoformat() + return ShareGPTMessage.model_validate(groups) diff --git a/selfie/text_generation/__init__.py b/selfie/text_generation/__init__.py new file mode 100644 index 0000000..2803519 --- /dev/null +++ b/selfie/text_generation/__init__.py @@ -0,0 +1,42 @@ +import logging + +from selfie.text_generation.retrieval_augmentation import augment +from selfie.types.completion_requests import SelfieCompletionResponse, ChatCompletionRequest, CompletionRequest +from selfie.text_generation.generation import completion as generate_text + +logger = logging.getLogger(__name__) + + +async def completion( + request: CompletionRequest | ChatCompletionRequest +) -> SelfieCompletionResponse: + logger.debug(f"Received completion request: {request}") + if not request.disable_augmentation: + logger.debug("Augmenting request") + + async def augmentation_completion(prompt): + # Create a request with the same parameters as the original request, except for the inference parameters. + simple_result = await completion(ChatCompletionRequest( + **{ + **request.extra_params(), + **request.selfie_params(), + 'disable_augmentation': True, + # TODO: Add a system message? + 'messages': [{"content": prompt, "role": "user"}], + 'max_tokens': 512, + }, + )) + + # TODO: test whether this is necessary + if isinstance(simple_result, dict): + return simple_result["choices"][0]["message"]["content"] + else: + return simple_result.choices[0].message.content + + await augment(request, augmentation_completion) + + logger.debug(f"Augmented request: {request}") + else: + logger.debug("Skipping augmentation") + + return await generate_text(request) diff --git a/selfie/text_generation/generation.py b/selfie/text_generation/generation.py new file mode 100644 index 0000000..9c02877 --- /dev/null +++ b/selfie/text_generation/generation.py @@ -0,0 +1,170 @@ +import logging +import json +import os + +import litellm +from fastapi import HTTPException +from sse_starlette import EventSourceResponse +from txtai.pipeline import GenerationFactory, LLM + +from selfie.config import default_local_model, default_hosted_model +from selfie.types.completion_requests import SelfieCompletionResponse, CompletionRequest, ChatCompletionRequest + +logger = logging.getLogger(__name__) + + +async def completion(request: CompletionRequest | ChatCompletionRequest) -> SelfieCompletionResponse: + logger.debug(f"Handling a completion request: {request}") + + if request.model == "": + request.model = None + + chat_mode = isinstance(request, ChatCompletionRequest) + + if request.method and request.method is not "litellm" and request.api_base: + logger.warning("Ignoring api_base because method is not litellm") + method = request.method + elif (request.method is "litellm" and request.model is None) or (request.method is None and request.api_base is not None): + request.method = "litellm" + request.model = default_hosted_model if request.model is None else request.model + method = "litellm" + elif request.method is None and request.model: + method = GenerationFactory.method(request.model, request.method) + elif request.method is None: + method = 'llama.cpp' + else: + method = request.method + + open_ai_params = request.openai_params() + + logger.debug(f"OpenAI params: {open_ai_params}") + + if method == "llama.cpp": + model = request.model or default_local_model + logger.info(f"Using model {model}") + llm = LLM( + # verbose=True, + path=model, + method="llama.cpp", + n_ctx=8192, + n_gpu_layers=-1, + ).generator.llm + + completion_fn = (llm.create_chat_completion if chat_mode else llm.create_completion) + + result = completion_fn(**open_ai_params) + + if request.stream: + logger.debug("Streaming response") + return EventSourceResponse( + # [logger.debug(f"Sending event: {json.dumps(item)}"), {"data": json.dumps(item)}][1] for item in result + {"data": json.dumps(item)} for item in result + ) + elif method == "litellm": + logger.info(f"Using model {request.model or 'litellm default'}") + if not chat_mode: + open_ai_params["messages"] = [{"content": open_ai_params["prompt"], "role": "user"}] + del open_ai_params["prompt"] + + if "temperature" in open_ai_params and open_ai_params["temperature"] == 0.0: + open_ai_params["temperature"] = 0.0000001 + + result = litellm.completion(**open_ai_params, base_url=request.api_base, api_key=request.api_key or "none") + if request.stream: + logger.debug("Streaming response") + return EventSourceResponse( + # [logger.debug(f"Sending event: {item.model_dump()}"), {"data": json.dumps(item.model_dump())}][1] for item in result + {"data": json.dumps(item.model_dump())} for item in result + ) + elif method == "transformers": # TODO: Check GPU support + # # TODO: TL;DR this seems like way too much. Look for another library. + # model = request.model or default_local_gpu_model + # logger.info(f"Using model {model}") + # + # import torch + # import transformers + # + # device = "cuda:0" if torch.cuda.is_available() else "cpu" + # + # from transformers import TextStreamer + # + # llm = LLM( + # path=model, + # method="transformers", + # device_map="auto", + # **({ + # 'quantization_config': transformers.GPTQConfig(bits=4, use_exllama=False) + # } if device is "cpu" else {}) + # ) + # + # tokenizer = llm.generator.llm.pipeline.tokenizer + # + # # TODO: needs work + # def transform_openai_params(open_ai_params: Dict[str, Any]) -> Dict[str, Any]: + # filtered_params = {key: value for key, value in open_ai_params.items() + # if key not in + # ['model', 'prompt', 'stream', 'logit_bias', 'max_tokens']} # TODO not ideal + # if chat_mode: + # filtered_params['text'] = tokenizer.apply_chat_template(request.messages, tokenize=False, add_generation_prompt=True) + # del filtered_params['messages'] + # else: + # filtered_params['text'] = request.prompt + # + # del filtered_params['frequency_penalty'] + # del filtered_params['presence_penalty'] + # + # if request.stream: + # filtered_params['streamer'] = TextStreamer(tokenizer, skip_prompt=True) + # + # return filtered_params + # + # id = _generate_id() + # created = round(datetime.now().timestamp()) + # transformed_openai_params = transform_openai_params(open_ai_params) + # + # def make_response(text: str, finish_reason: str = None): + # prompt_tokens = tokenizer(transformed_openai_params['text'], return_tensors='pt')['input_ids'].size(1) + # completion_tokens = tokenizer(text, return_tensors='pt')['input_ids'].size(1) + # + # return { + # 'id': id, + # 'object': 'chat.completion' if chat_mode else 'text_completion', + # 'model': model, + # 'created': created, + # 'choices': [ + # { + # **({ + # "message": { + # "content": text, + # "role": "assistant" + # } + # } if chat_mode else { + # "text": text, + # }), + # "index": 0, + # "logprobs": None, + # "finish_reason": finish_reason + # } + # ], + # 'usage': {'prompt_tokens': prompt_tokens, 'completion_tokens': completion_tokens, 'total_tokens': prompt_tokens + completion_tokens} + # } + # + # generation = llm(**transformed_openai_params) + # # print(f'Generation: {generation}') + # if request.stream: + # return EventSourceResponse( + # (*({"data": json.dumps(make_response(item))} for item in generation), + # {"data": json.dumps(make_response("", finish_reason="stop"))}) + # ) + # + # result = make_response(generation, "stop") + + # # TODO: Make error response OpenAI-compatible + raise HTTPException(status_code=501, detail="Method not yet implemented") + else: + # TODO: don't use HTTPException here + raise HTTPException(status_code=400, detail="Invalid method") + + logger.debug(f"Result: {result}") + + return result diff --git a/selfie/text_generation/retrieval_augmentation.py b/selfie/text_generation/retrieval_augmentation.py new file mode 100644 index 0000000..9d14c2b --- /dev/null +++ b/selfie/text_generation/retrieval_augmentation.py @@ -0,0 +1,70 @@ +import logging + +from selfie.embeddings import DataIndex +from selfie.types.completion_requests import CompletionRequest, ChatCompletionRequest, Message + +logger = logging.getLogger(__name__) + + +# TODO: add a token budget for the augmentation +async def augment(request: CompletionRequest | ChatCompletionRequest, completion): + chat_mode = isinstance(request, ChatCompletionRequest) + data_index = DataIndex("n/a", completion=completion) + + # Step 1: Retrieval + + if chat_mode: + # Provide the last 15 user messages as context + context = "" + i = len(request.messages) - 1 + while i >= 0 and len(context) < 512 and len(context.split("\n")) < 15: + message = request.messages[i] + if message.role != "system": + context = f"{message.content}\n{context}" + i -= 1 + else: + context = request.prompt + + # Strategy A: use the last user message as the query + # logger.debug("Using strategy: use the last user message as the query") + # last_user_message = next( + # ( + # message + # for message in reversed(request.messages) + # if message.role == "user" + # ), + # None, + # ) + # + # recall_query = last_user_message.content if last_user_message else "" + # End strategy + + # Strategy B: generate a query for relevant context based on the conversation history + logger.debug("Using strategy: generate a query for relevant context based on the conversation history") + prompt_prefix = f"Given only the conversation snippet below, what is the most salient topic whose answer would be relevant in continuing the conversation? For example, if the conversation was about fire and moved on to scuba diving, you should answer 'scuba diving, ocean, favorite hobbies' (only an example!). State your answer without explanation, your entire response will be fed directly into a query engine:" + prompt = f"{prompt_prefix}:\n\n{context}\nSalient query: " + logger.debug(f"Query generation prompt: {prompt}") + topic = (await completion(prompt)).strip() + # End strategy + + logger.debug(f"Generated query: {topic}") + documents = await data_index.recall(topic, context) + + # Step 2: Augmentation + # Augment the request by prepending the document summary to the system message. + # Find the first system message and insert the document summary at the end. If there isn't one, + # insert a new system message at the beginning. + system_message = f"Here are some things you know, that may or may not be relevant to the current conversation. Do not assume that you are currently talking to any named individuals. Use this knowledge if and only if you are 90%+ convinced that it is relevant to the conversation, otherwise ignore it: {documents['summary']}" + # TODO: Incorporate name and bio + # system_message = f"About {name}: {bio}\n\n{system_message}" + + logger.debug(f"Augmenting by appending to system message: {system_message}") + + if chat_mode: + system_msgs = [m for m in request.messages if m.role == "system"] + if system_msgs: + system_msgs[0].content += f"\n\n{system_message}" + else: + request.messages.insert(0, Message(role="system", content=system_message)) + else: + request.prompt = system_message diff --git a/selfie/types/__init__.py b/selfie/types/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/selfie/types/completion_requests.py b/selfie/types/completion_requests.py new file mode 100644 index 0000000..f9107a6 --- /dev/null +++ b/selfie/types/completion_requests.py @@ -0,0 +1,164 @@ +from typing import Optional, Literal, List, ClassVar, Union, Dict, Any +from litellm import ModelResponse as LitellmCompletionResponse +from llama_cpp import ( + CreateCompletionResponse as LlamaCppCompletionResponse, + CreateChatCompletionResponse as LlamaCppChatCompletionResponse, +) +from openai.types.chat import ChatCompletionMessage +from openai.types.completion_create_params import CompletionCreateParams, CompletionCreateParamsNonStreaming, CompletionCreateParamsStreaming +from openai.types.chat import CompletionCreateParams as ChatCompletionCreateParams +from pydantic import BaseModel, TypeAdapter, Extra + +from sse_starlette import EventSourceResponse +# from typing_extensions import TypedDict + +########################## + + +class Message(BaseModel): + role: str + content: str + + +class FunctionCall(BaseModel): + name: str + parameters: Optional[Dict[str, Any]] = None + + +class Tool(BaseModel): + type: str + function: Optional[FunctionCall] = None + + +class BaseCompletionRequest(BaseModel): + # OpenAI parameters + model: Optional[str] = None + frequency_penalty: Optional[float] = 0.0 + logit_bias: Optional[Dict[int, float]] = None + # logprobs: Optional[bool] = False + max_tokens: Optional[int] = None + # n: Optional[int] = 1 + presence_penalty: Optional[float] = 0.0 + response_format: Optional[Dict[str, str]] = None + seed: Optional[int] = None + stop: Optional[Union[str, List[str]]] = None + stream: Optional[bool] = False + temperature: Optional[float] = 1.0 + top_p: Optional[float] = 1.0 + tools: Optional[List[Tool]] = None + tool_choice: Optional[Union[str, Dict[str, Any]]] = None + # user: Optional[str] = None + + # Selfie parameters + method: Optional[Literal["litellm", "llama.cpp", "transformers"]] = None + api_base: Optional[str] = None + api_key: Optional[str] = None + disable_augmentation: Optional[bool] = False + + # Custom parameters, e.g. for a custom API + class Config: + extra = Extra.allow + + custom_params: ClassVar[List[str]] = ["method", "api_base", "api_key", "disable_augmentation"] + + def openai_params(self): + return { + k: v + for k, v in self.model_dump().items() + if k not in BaseCompletionRequest.custom_params and v is not None + } + + def selfie_params(self): + return {k: v for k, v in self.model_dump().items() if k in BaseCompletionRequest.custom_params and v is not None} + + def extra_params(self): + """ + Returns all extra parameters (not OpenAI or Selfie parameters). + :return: A dictionary of extra parameters + """ + return {k: v for k, v in self.model_dump().items() if k not in self.model_fields.keys()} + + +class ChatCompletionRequest(BaseCompletionRequest): + messages: List[Message] + + +class CompletionRequest(BaseCompletionRequest): + prompt: Union[str, List[str]] + # best_of: Optional[int] = None + echo: Optional[bool] = None + logprobs: Optional[int] = None + # n: Optional[int] = None + suffix: Optional[str] = None + + +class ChatCompletionResponse(BaseModel): + id: str + object: str = "chat.completion" + created: int + model: Optional[str] + choices: List[Dict[str, Any]] + usage: Dict[str, int] + + +########################## +# Unfortunately, these OpenAI typeddicts can't seem to be used by FastAPI for some reason + +# Message = ChatCompletionMessage +# class BaseCompletionRequest(BaseModel): +# model: Optional[str] = None +# method: Optional[Literal["litellm", "llama.cpp", "transformers"]] = None +# api_base: Optional[str] = None +# api_key: Optional[str] = None +# +# custom_params: ClassVar[List[str]] = ["method", "api_base", "api_key"] +# +# def openai_params(self): +# return { +# k: v +# for k, v in self.model_dump().items() +# if k not in BaseCompletionRequest.custom_params and v is not None +# } +# + +# Union[CompletionCreateParamsNonStreaming, CompletionCreateParamsStreaming] + +# class BaseCompletionRequest(TypedDict, total=False): +# method: Optional[Literal["litellm", "llama.cpp", "transformers"]] +# api_base: Optional[str] +# api_key: Optional[str] +# +# +# class CompletionRequestNonStreaming(BaseCompletionRequest, CompletionCreateParamsNonStreaming, total=False): +# pass +# +# +# class CompletionRequestStreaming(BaseCompletionRequest, CompletionCreateParamsStreaming, total=False): +# pass +# +# +# class ChatCompletionRequestNonStreaming(BaseCompletionRequest, CompletionCreateParamsNonStreaming, total=False): +# pass +# +# +# class ChatCompletionRequestStreaming(BaseCompletionRequest, CompletionCreateParamsStreaming, total=False): +# pass + + +# CompletionRequest = TypeAdapter(Union[CompletionRequestNonStreaming, CompletionRequestStreaming]) +# ChatCompletionRequest = TypeAdapter(Union[ChatCompletionRequestNonStreaming, ChatCompletionRequestStreaming]) +# SelfieCompletionRequest = TypeAdapter(Union[CompletionRequestNonStreaming, CompletionRequestStreaming, ChatCompletionRequestNonStreaming, ChatCompletionRequestStreaming]) +# +# +# class RequestHandler: +# def __init__(self, request: SelfieCompletionRequest): +# self.request = request +# +# def openai_params(self): +# # Extract and return the relevant parameters for the OpenAI API call +# # Note: Adjust the logic here as per your requirement +# return {k: v for k, v in self.request.items() if v is not None and k not in ["method", "api_base", "api_key"]} + + +SelfieCompletionRequest = CompletionRequest | ChatCompletionRequest +SelfieCompletionResponse = LlamaCppCompletionResponse | LlamaCppChatCompletionResponse | LitellmCompletionResponse | EventSourceResponse diff --git a/selfie/types/share_gpt.py b/selfie/types/share_gpt.py new file mode 100644 index 0000000..3c9d7cb --- /dev/null +++ b/selfie/types/share_gpt.py @@ -0,0 +1,20 @@ +from pydantic import BaseModel, Field +from typing import List +from datetime import datetime + + +class ShareGPTMessage(BaseModel): + from_user: str = Field(..., alias='from', description="The sender of the message") + value: str = Field(..., description="The content of the message") + timestamp: datetime = Field(..., description="Timestamp of when the message was sent") + + +class ShareGPTConversation(BaseModel): + conversations: List[ShareGPTMessage] = Field(..., description="List of messages in the conversation") + + +class ShareGPT(BaseModel): + data: ShareGPTConversation = Field(..., description="Structured conversation data") + + class Config: + populate_by_name = True

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