# bulk-chain 0.25.0
[](https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb)
[](https://x.com/nicolayr_/status/1847969224636961033)
[](https://pypistats.org/packages/bulk-chain)
<p align="center">
<img src="logo.png"/>
</p>
A lightweight, no-strings-attached **framework** for your LLM that allows applying [Chain-of-Thought](https://arxiv.org/abs/2201.11903) prompt `schema` (See [related section](#chain-of-thought-schema)) towards a massive textual collections.
### 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:
```bash
pip install bulk-chain
```
or latest version from here:
```bash
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:
```python
{
"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"},
]
}
```
Another templates are available [here](/ext/schema/).
# Usage
Preliminary steps:
1. Define your [schema](#chain-of-thought-schema) ([Example for Sentiment Analysis](/ext/schema/thor_cot_schema.json)))
2. Wrap or pick **LLM model** from the [list of presets](/ext/).
## API
Please take a look at the [**related Wiki page**](https://github.com/nicolay-r/bulk-chain/wiki)
## Shell
> **NOTE:** You have to install `source-iter` package
```bash
python3 -m bulk_chain.infer \
--src "<PATH-TO-YOUR-CSV-or-JSONL>" \
--schema "ext/schema/default.json" \
--adapter "dynamic:ext/replicate.py:Replicate" \
%%m \
--api_token "<REPLICATE-API-TOKEN>" \
--temp 0.1
```
# 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 [here](/ext).
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"description": "# bulk-chain 0.25.0\n\n[](https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb)\n[](https://x.com/nicolayr_/status/1847969224636961033)\n[](https://pypistats.org/packages/bulk-chain)\n\n<p align=\"center\">\n <img src=\"logo.png\"/>\n</p>\n\nA lightweight, no-strings-attached **framework** for your LLM that allows applying [Chain-of-Thought](https://arxiv.org/abs/2201.11903) prompt `schema` (See [related section](#chain-of-thought-schema)) towards a massive textual collections.\n\n### Main Features\n* \u2705 **No-strings**: you're free to LLM dependencies and flexible `venv` customization.\n* \u2705 **Support schemas descriptions** for Chain-of-Thought concept.\n* \u2705 **Provides iterator over infinite amount of input contexts** served in `CSV`/`JSONL`.\n\n### Extra Features\n* \u2705 **Progress caching [for remote LLMs]**: withstanding exception during LLM calls by using `sqlite3` engine for caching LLM answers;\n\n\n# Installation\n\nFrom PyPI: \n\n```bash\npip install bulk-chain\n```\n\nor latest version from here:\n\n```bash\npip install git+https://github.com/nicolay-r/bulk-chain@master\n```\n\n## Chain-of-Thought Schema\n\nTo declare Chain-of-Though (CoT) schema, this project exploits `JSON` format.\nThis format adopts `name` field for declaring a name and `schema` is a list of CoT instructions for the Large Language Model.\n\nEach step represents a dictionary with `prompt` and `out` keys that corresponds to the input prompt and output variable name respectively.\nAll the variable names are expected to be mentioned in `{}`.\n\nBelow, is an example on how to declare your own schema:\n\n```python\n{\n\"name\": \"schema-name\",\n\"schema\": [\n {\"prompt\": \"Given the question '{text}', let's think step-by-step.\", \n \"out\": \"steps\"},\n {\"prompt\": \"For the question '{text}' the reasoining steps are '{steps}'. what would be an answer?\", \n \"out\": \"answer\"},\n]\n}\n```\n\nAnother templates are available [here](/ext/schema/).\n\n# Usage\n\nPreliminary steps:\n\n1. Define your [schema](#chain-of-thought-schema) ([Example for Sentiment Analysis](/ext/schema/thor_cot_schema.json)))\n2. Wrap or pick **LLM model** from the [list of presets](/ext/).\n\n## API\n\nPlease take a look at the [**related Wiki page**](https://github.com/nicolay-r/bulk-chain/wiki)\n\n## Shell\n\n> **NOTE:** You have to install `source-iter` package\n\n```bash\npython3 -m bulk_chain.infer \\\n --src \"<PATH-TO-YOUR-CSV-or-JSONL>\" \\\n --schema \"ext/schema/default.json\" \\\n --adapter \"dynamic:ext/replicate.py:Replicate\" \\\n %%m \\\n --api_token \"<REPLICATE-API-TOKEN>\" \\\n --temp 0.1\n```\n\n# Embed your LLM\n\nAll you have to do is to implement `BaseLM` class, that includes:\n* `__init__` -- for setting up *batching mode support* and (optional) *model name*;\n* `ask(prompt)` -- infer your model with the given `prompt`.\n\nSee examples with models [here](/ext).\n",
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