bulk-chain 0.25.2

A lightweight, no-strings-attached Chain-of-Thought framework for your LLM, ensuring reliable results for bulk input requests.

bulk-chain 0.25.2

Third-party providers hosting↗️
👉demo👈

A no-strings-attached framework for your LLM that allows applying Chain-of-Thought-alike prompt schema towards a massive textual collections using custom third-party providers ↗️.

Main Features

• ✅ No-strings: you're free to LLM dependencies and flexible venv customization.
• ✅ Support schemas descriptions for Chain-of-Thought concept.
• ✅ Provides iterator over infinite amount of input contexts served in CSV/JSONL.

Extra Features

• ✅ Progress caching [for remote LLMs]: withstanding exception during LLM calls by using sqlite3 engine for caching LLM answers;

Installation

From PyPI:

pip install --no-deps bulk-chain

or latest version from here:

pip install git+https://github.com/nicolay-r/bulk-chain@master

Chain-of-Thought Schema

To declare Chain-of-Though (CoT) schema, this project exploits JSON format. This format adopts name field for declaring a name and schema is a list of CoT instructions for the Large Language Model.

Each step represents a dictionary with prompt and out keys that corresponds to the input prompt and output variable name respectively. All the variable names are expected to be mentioned in {}.

Below, is an example on how to declare your own schema:

{
"name": "schema-name",
"schema": [
    {"prompt": "Given the question '{text}', let's think step-by-step.", 
     "out": "steps"},
    {"prompt": "For the question '{text}' the reasoining steps are '{steps}'. what would be an answer?", 
     "out":  "answer"},
]
}

Usage

Preliminary steps:

1. Define your schema (Example for Sentiment Analysis))
2. Wrap or pick LLM model from the Third-party providers hosting↗️.

Shell

Demo Mode

demo mode to interact with LLM via command line with LLM output streaming support. The video below illustrates an example of application for sentiment analysis on author opinion extraction towards mentioned object in text.

Quck start with launching demo:

1. ⬇️ Download replicate provider for bulk-chain:
2. 📜 Setup your reasoning thor_cot_schema.json according to the following example ↗️
3. 🚀 Launch demo.py as follows:

python3 -m bulk_chain.demo \
    --schema "test/schema/thor_cot_schema.json" \
    --adapter "dynamic:replicate_104.py:Replicate" \
    %%m \
    --model_name "meta/meta-llama-3-70b-instruct" \
    --api_token "<REPLICATE-API-TOKEN>" \
    --stream

📺 This video showcase application of the ↗️ Sentiment Analysis Schema towards LLaMA-3-70B-Instruct hosted by Replicate for reasoning over submitted texts

Inference Mode

NOTE: You have to install source-iter and tqdm packages that actual dependencies of this project

1. ⬇️ Download replicate provider for bulk-chain:

wget https://raw.githubusercontent.com/nicolay-r/nlp-thirdgate/refs/heads/master/llm/replicate_104.py

2. 📜 Setup your reasoning schema.json according to the following example ↗️
3. 🚀 Launch inference using DeepSeek-R1:

python3 -m bulk_chain.infer \
    --src "<PATH-TO-YOUR-CSV-or-JSONL>" \
    --schema "test/schema/default.json" \
    --adapter "replicate_104.py:Replicate" \
    %%m \
    --model_name "deepseek-ai/deepseek-r1" \
    --api_token "<REPLICATE-API-TOKEN>"

API

Please take a look at the related Wiki page

Embed your LLM

All you have to do is to implement BaseLM class, that includes:

• __init__ -- for setting up batching mode support and (optional) model name;
• ask(prompt) -- infer your model with the given prompt.

See examples with models at nlp-thirdgate 🌌.