Nicolay Rusnachenko's picture

Nicolay Rusnachenko

nicolay-r

AI & ML interests

Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

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1907
📢For those who wish to quick start with reasoning / cot application over rows of tabular data but with minimal dependencies, this post would be valuable.

🔎 I found that the problem is that given a bulk of Chain-of-Though (CoT) 🔗 queries for remotely accessed LLM 🤖 (like openrouter / Replicate / OpenAI) might result in connection loss which may lead exception 💥 and challenges with generated content restoration.

Here, is where I contribute with the bulk-chain.
https://github.com/nicolay-r/bulk-chain

Currently working on 0.24.3 version, in which I am happy to announce the API for developing your apps that are based on CoT schema declaration in JSON (details in attached images 📸)

All you have to do is:
✅ 1. Declare CoT-schema in json
✅ 2. Declare the model or use the preset
✅ 3. Launch code

One example is to use ReplicateIO provider:
https://github.com/nicolay-r/bulk-chain/blob/master/ext/replicate.py

Each model has a wrapped call for inference in try-catch block
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450
If you're coming towards Information Retrieval with pre-processing techniques for LLM, this post might be relevant.

Excited to share of releasing a new 0.25.1 version of the AREkit library! 🎉🥳🎊🎁

AREkit represent an NLP toolkit of components for deep understanding textual narratives through the extraction of inner relations via various techniqes, including machine learning techniques. This toolkit is helpful if you wish to structure your dataset for IR problem. It allows you to turn your narratives into structured datasets of mentioned relations in sentences (sampling).

In the era of GenAI world, AREkit contributes with no-string NLP pipelines and related elements for building your own NLP workflow with any thirdparty ML / LLM / API you wish.

🌟 https://github.com/nicolay-r/AREkit/releases/tag/v0.25.1-rc

In 0.25.1, the following steps were made towards it:
1. ✅ Native batching support for pipelines
2. 📦 Formed thirdparty projects for several text-preprocessing elements:
bulk-translate with GoogleTranslate or any other you wish: https://github.com/nicolay-r/bulk-translate
bulk-ner for NER with DeepPavlov models or any other you wish: https://github.com/nicolay-r/bulk-ner
bulk-chain for reasoning with any LLM you wish: https://github.com/nicolay-r/bulk-chain
* (soon support for AREkit)
3. ❌ Removed convential neural network related components

📺 One of the demo is ARElight which repsent a granular viewer / GUI for network-based representation of infromation extracted from narratives:
ARElight: https://github.com/nicolay-r/ARElight

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