Text transmutation - recipe for semantic search with embeddings

In the rapidly evolving area of data science and natural language processing (NLP), the ability to intelligently understand and process textual information is crucial. In this article I will show how to create a semantic search aplication using the Candle ML framework written in Rust, coupled with the E5 model for embedding generation.

Before you will continue reading please watch short introduction:

Text embeddings are at the heart of modern natural language processing (NLP). They are the result of transforming textual data into a numerical form that machines can understand.

To calculate embeddings I will use the E5 model (arxiv2212.03533) from Hugging Face to generate text embeddings.

E5 name comes from embeddings from bidirectional encoder representations. Model was trained on Colossal Clean text Pairs from heterogeneous semi-structured data sources like: Reddit (post, comment), Stackexchange (question, upvoted answer), English Wikipedia (entity name + section title, passage), Scientific papers (title, abstract), Common Crawl (title, passage), and others.

To run the E5 model I will use the Candle ML framework written in Rust. Candle supports a wide range of ML models including: Whisper, LLama2, Mistral, Stable Diffusion and others. Moreover we can simply compile and use Candle library inside WebAssembly to calculate text embeddings.

To demonstrate the power of these embeddings, I have created a simple search application. The application contains two parts: rust code which is compiled to WebAssembly and Vue web application.

The rust code is based on the candle Web Assembly example and expose model struct which loads the E5 model and calculates embeddings. Compiled rust struct is used in the Vue typescript webworker.

The web application reads example recipes and calculates embeddings for each.

When user inputs a text application calculates embedding and search the recipe from the list that matches the best, the cosine similarity is used for this purpose.

Cosine similarity measures the cosine of the angle between two vectors, offering a way to judge how similar two texts are in their semantic content.

For handling larger datasets, it becomes impractical to compute cosine similarity for each phrase individually due to scalability issues. In such cases, utilizing a vector database is a more efficient approach.

Application code is available here: https://github.com/onceuponai-dev/stories-text-transmutation The rust part is based on Candle example

You can also quickly test model on: https://stories.onceuponai.dev/stories-text-transmutation/

Tiny LLama: Compact LLM with WebAssembly

Tiny LLama is an ambitious initiative aimed at pretraining a language model on a dataset of 3 trillion tokens. What sets this project apart is not just the size of the data but the efficiency and speed of its processing. Utilizing 16 A100-40G GPUs, the training of Tiny LLama started in September and is planned to span just 90 days.

Before you will continue reading please watch short introduction:

The compactness of Tiny LLama is its standout feature. With only 1.1 billion parameters, it is uniquely tailored for scenarios where computational and memory resources are limited. This makes it an ideal solution for edge devices.

For ease, I’ve prepared a Docker image containing all the necessary tools, including CUDA, mlc-llm, and Emscripten, which are crucial for preparing the model for WebAssembly.

Dockerfile:

 FROM alpine/git:2.36.2 as download

RUN git clone https://github.com/mlc-ai/mlc-llm.git --recursive /mlc-llm

FROM nvidia/cuda:12.2.2-cudnn8-devel-ubuntu22.04

RUN apt update && \
    apt install -yq curl git cmake ack tmux \
        python3-dev vim python3-venv python3-pip \
        protobuf-compiler build-essential


RUN python3 -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
RUN python3 -m pip install --pre -U -f https://mlc.ai/wheels mlc-chat-nightly-cu122 mlc-ai-nightly-cu122

RUN apt install gcc
COPY --from=download /mlc-llm /opt/mlc-llm

RUN cd /opt/mlc-llm && pip3 install .

RUN apt-get install git-lfs -yq

ENV TVM_HOME="/opt/venv/lib/python3.10/site-packages/tvm/"

RUN git clone https://github.com/emscripten-core/emsdk.git /opt/emsdk
RUN cd /opt/emsdk && ./emsdk install latest

ENV PATH="/opt/emsdk:/opt/emsdk/upstream/emscripten:/opt/emsdk/node/16.20.0_64bit/bin:/opt/venv/bin:$PATH"
RUN cd /opt/emsdk/ && ./emsdk activate latest
ENV TVM_HOME=/opt/mlc-llm/3rdparty/tvm

RUN cd /opt/mlc-llm/3rdparty/tvm \
  && git checkout 5828f1e9e \
  && git submodule init \
  && git submodule update --recursive \
  && make webclean \
  && make web


RUN python3 -m pip install auto_gptq>=0.2.0 transformers

CMD /bin/bash

To build docker image we need to run:

docker build -t onceuponai/mlc-llm .

Now we are ready to run container:

docker run --rm -it --name mlc-llm -v $(pwd)/data:/data --gpus all onceuponai/mlc-llm

and execute mlc-llm command:

python3 -m mlc_llm.build --hf-path TinyLlama/TinyLlama-1.1B-Chat-v0.6  --target webgpu --quantization q4f32_0 --use-safetensors

where (Documentation): hf-path - is huggingface model name in this case TinyLlama/TinyLlama-1.1B-Chat-v0.6 target - is platfrom for which we prepare the model available options:

• auto (will detect from cuda, metal, vulkan and opencl)
• metal (for M1/M2)
• metal_x86_64 (for Intel CPU)
• iphone
• vulkan
• cuda
• webgpu
• android
• opencl

quantization - is quantization mode: available options: quantization: qAfB(_0) A - number of bits for weights B - number of bits for activations available options: autogptq_llama_q4f16_0, autogptq_llama_q4f16_1, q0f16, q0f32, q3f16_0, q3f16_1, q4f16_0, q4f16_1, q4f16_2, q4f16_ft, q4f32_0, q4f32_1 q8f16_ft, q8f16_1

In our case we will use webgpu target and q4f32_0 quantization to obtaind wasm file and converted model. I have shared several converted models on HuggingFace and Github.

Model can be simply used on web application.

Example typescript code is available here: https://github.com/onceuponai-dev/stories-thumbellama

You can also quickly test model on: https://stories.onceuponai.dev/stories-thumbellama/

Transform Your Coding Journey: Interactive Cheat Sheets with LLM Assistance

Cheat sheets are common companions in the journey through programming. They are incredibly helpful, offering quick references.

But what if we could take them a step further? Imagine these cheat sheets not just as static helpers, but as dynamic, interactive guides with the power of large language models. These enhanced cheat sheets don’t just provide information; they interact, they understand, and they assist. Let’s explore how we can make this leap.

Before you will continue reading please watch short introduction:

In the first step I have built Vue web application with responsive cheatsheet layout.

Next, I have brought Python into the browser using the Pyodide library. Pyodide is a port of CPython to WebAssembly. This means that we can run Python code right in the web browser, seamlessly integrating live coding examples and real-time feedback into cheatsheets.

The final, and perhaps the most exciting step, was adding LLM genie, our digital assistant. Using the mlc-llm library, I have embedded a powerful large language models into the web application. Currently we can choose and test several models like: RedPajama, LLama2 or Mistral. First and foremost, the LLM model, is designed to run directly in your browser on your device. This means that once the LLM is downloaded, all its processing and interactions happen locally, thus its performance depends on your device capabilities. If you want you to test it on my website:

https://www.onceuponai.dev/

Here, you can test the interactive cheat sheets and challenge the LLM with your code.

Data anonymization with AI

Data anonymization is the process of protecting private or sensitive information by erasing or encrypting identifiers that link an individual to stored data. This method is often used in situations where privacy is necessary, such as when sharing data or making it publicly available. The goal of data anonymization is to make it impossible (or at least very difficult) to identify individuals from the data, while still allowing the data to be useful for analysis and research purposes.

Before you will continue reading please watch short introduction:

I have decided to create a library which will help to simply anonymize data with high-performance. That’s why I have used Rust to code it. The library will use three algorithms which will anonymize data. Named Entity Recognition method enables the library to identify and anonymize sensitive named entities in your data, like names, organizations, locations, and other personal identifiers.

Here you can use existing models from HuggingFace for different languages for example:

• dslim bert-base-NER for english
• clarin FastPDN for polish

The models are based on external libraries like pytorch. To avoid external dependencies I have used rust tract library which is a rust onnx implementation.

To use models we need to convert them to onnx format using the transformers library.

import os
import transformers
from transformers import AutoModelForMaskedLM, AutoTokenizer, AutoModelForTokenClassification
from transformers.onnx import FeaturesManager
from pathlib import Path
from transformers import pipeline

model_id='dslim/bert-base-NER'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

feature='token-classification'

model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(model, feature=feature)
onnx_config = model_onnx_config(model.config)

output_dir = "./dslim"
os.makedirs(output_dir, exist_ok=True)

# export
onnx_inputs, onnx_outputs = transformers.onnx.export(
        preprocessor=tokenizer,
        model=model,
        config=onnx_config,
        opset=13,
        output=Path(output_dir+"/model.onnx")
)

print(onnx_inputs)
print(onnx_outputs)
tokenizer.save_pretrained(output_dir)

Now we are ready to use the NER algorithm. We can simply run docker images with a yaml configuration file where we define an anonymization pipeline.

pipeline:
  - kind: ner
    model_path: ./dslim/model.onnx
    tokenizer_path: ./dslim/tokenizer.json
    token_type_ids_included: true
    id2label:
      "0": ["O", false]
      "1": ["B-MISC", true]
      "2": ["I-MISC", true]
      "3": ["B-PER", true]
      "4": ["I-PER", true]
      "5": ["B-ORG", true]
      "6": ["I-ORG", true]
      "7": ["B-LOC", true]
      "8": ["I-LOC", true]

docker run -it -v $(pwd):/app/ -p 8080:8080 qooba/anonymize-rs server --host 0.0.0.0 --port 8080 --config config.yaml

For the NER algorithm we can configure if the predicted entity will be replaced or not. For the example request we will receive an anonymized response and replace items.

curl -X GET "http://localhost:8080/api/anonymize?text=I like to eat apples and bananas and plums" -H "accept: application/json" -H "Content-Type: application/json"

Response:

{
    "text": "I like to eat FRUIT_FLASH0 and FRUIT_FLASH1 and FRUIT_REGEX0",
    "items": {
        "FRUIT_FLASH0": "apples",
        "FRUIT_FLASH1": "banans",
        "FRUIT_REGEX0": "plums"
    }
}

If needed we can deanonymize the data using a separate endpoint.

curl -X POST "http://localhost:8080/api/deanonymize" -H "accept: application/json" -H "Content-Type: application/json" -d '{
    "text": "I like to eat FRUIT_FLASH0 and FRUIT_FLASH1 and FRUIT_REGEX0",
    "items": {
        "FRUIT_FLASH0": "apples",
        "FRUIT_FLASH1": "banans",
        "FRUIT_REGEX0": "plums"
    }
}'

Response:

{
    "text": "I like to eat apples and bananas and plums"
}

If we prefer we can use the library from python code in this case we simply install the library. And we can use it in python.

We have discussed the first anonymization algorithm but what if it is not enough ? There are two additional methods. First is Flush Text algorithm which is a fast method for searching and replacing words in large datasets, used to anonymize predefined sensitive information. For flush text we can define configuration where we can read keywords in separate file where each line is a keyword or in the keyword configuration section.

The last method is simple Regex where we can define patterns which will be replaced.

We can combine several methods and build an anonymization pipeline:

pipeline:
  - kind: ner
    model_path: ./dslim/model.onnx
    tokenizer_path: ./dslim/tokenizer.json
    token_type_ids_included: true
    id2label:
      "0": ["O", false]
      "1": ["B-MISC", true]
      "2": ["I-MISC", true]
      "3": ["B-PER", true]
      "4": ["I-PER", true]
      "5": ["B-ORG", true]
      "6": ["I-ORG", true]
      "7": ["B-LOC", true]
      "8": ["I-LOC", true]
  - kind: flashText
    name: FRUIT_FLASH
    file: ./tests/config/fruits.txt
    keywords:
    - apple
    - banana
    - plum
  - kind: regex
    name: FRUIT_REGEX
    file: ./tests/config/fruits_regex.txt
    patterns:
    - \bapple\w*\b
    - \bbanana\w*\b
    - \bplum\w*\b

Remember that it uses automated detection mechanisms, and there is no guarantee that it will find all sensitive information. You should always ensure that your data protection measures are comprehensive and multi-layered.

How to use large language models on CPU with Rust ?

Currently large language models gain popularity due to their impressive capabilities. However, running these models often requires powerful GPUs, which can be a barrier for many developers. LLM a Rust library developed by the Rustformers GitHub organization is designed to run several large language models on CPU, making these powerful tools more accessible than ever.

Before you will continue reading please watch short introduction:

Currently GGML a tensor library written in C that provides a foundation for machine learning applications is used as a LLM backend.

GGML library uses a technique called model quantization. Model quantization is a process that reduces the precision of the numbers used in a machine learning model. For instance, a model might use 32-bit floating-point numbers in its calculations. Through quantization, these can be reduced to lower-precision formats, such as 16-bit integers or even 8-bit integers.

The GGML library, which Rustformers is built upon, supports a number of different quantization strategies. These include 4-bit, 5-bit, and 8-bit quantization. Each of these offers different trade-offs between efficiency and performance. For instance, 4-bit quantization will be more efficient in terms of memory and computational requirements, but it might lead to a larger decrease in model performance compared to 8-bit quantization.

LLM supports a variety of large language models, including:

• Bloom
• GPT-2
• GPT-J
• GPT-NeoX
• Llama
• MPT

The models needs to be converted into form readable by GGML library but thanks to the authors you can find ready to use models on huggingface.

To test it you can install llm-cli packge. Then you can chat with the model in the console.

cargo install llm-cli --git https://github.com/rustformers/llm

llm gptj infer -m ./gpt4all-j-q4_0-ggjt.bin -p "Rust is a cool programming language because"

To be able to talk with the model using http I have used actix server and built Rest API. Api expose endpoint which returns response asyncronously.

The solution is acomplished with simple UI interface.

To run it you need to clone the repository.

git clone https://github.com/qooba/llm-ui.git

Download selected model from hugging face.

curl -LO https://huggingface.co/rustformers/gpt4all-j-ggml/resolve/main/gpt4all-j-q4_0-ggjt.bin

In our case we will use gpt4all-j model with 4-bit quantization.

Finally we use cargo run in release mode with additional arguments host, port, model type and path to the model.

cargo run --release -- --host 0.0.0.0 --port 8089 gptj ./gpt4all-j-q4_0-ggjt.bin

Now we are ready to call rest api or talk with the model using ui interface.