LLMLight - Lightweight and Flexible Agentic Framework
Key Features
| Feature | Description |
|---|---|
| Local LLM Support | Run LLMs locally with minimal dependencies. |
| Full Prompt Control | Fine-grained control over prompts including Query, Instructions, System, Context, Response Format, Automatic formatting, Temperature, and Top P. |
| Single Endpoint for All Local Models | One unified endpoint to connect different local models. |
| Flexible Embedding Methods | Multiple embedding strategies: TF-IDF for structured documents, Bag of Words (BOW), BERT for free text, BGE-Small. |
| Advanced Retrieval Methods | Supports Naive RAG with fixed chunking and RSE (Relevant Segment Extraction). |
| Context Strategies | Advanced reasoning for complex queries using Global-reasoning and Chunk-wise approaches. |
| Local Memory | Video memory storage for efficient local use. |
| PDF Processing | Native support for reading and processing PDF documents. |
Documentation & Resources
Installation
# Install from PyPI
pip install LLMlight
Quick Start
1. Check Available Models at Endpoint
from LLMlight import LLMlight
# Initialize client
from LLMlight import LLMlight
# Initialize with LM Studio endpoint
client = LLMlight(model='mistralai/mistral-small-3.2',
endpoint="http://localhost:1234/v1/chat/completions")
modelnames = client.get_available_models(validate=False)
print(modelnames)
2. Basic Usage with Endpoint
from LLMlight import LLMlight
# Initialize with default settings
client = LLMlight(model='openai/gpt-oss-20b', endpoint='http://localhost:1234/v1/chat/completions')
# Run a simple query
response = client.prompt('What is the capital of France?',
context='The capital of France is Amsterdam.',
instructions='Do not argue with the information in the context. Only return the information from the context.')
print(response)
# According to the provided context, the capital of France is Amsterdam.
3. Using with LM Studio
https://erdogant.github.io/LLMlight/pages/html/Algorithm.html#get-available-llm-models
3. Query against PDF files
https://erdogant.github.io/LLMlight/pages/html/Examples.html#working-with-files-pdfs
4. Creating Local Memory Database
https://erdogant.github.io/LLMlight/pages/html/Saving%20and%20Loading.html#memory-management
Maintainer
- Erdogan Taskesen, github: erdogant
- Contributions are welcome.
- Yes! This library is entirely free but it runs on coffee! :) Feel free to support with a Coffee.
1 maintainer
Metadata
1 maintainer
Release files for LLMlight 1.2.1
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| File | Size | Uploaded | Provenance | Actions |
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| File | Interpreter | ABI | Platform | Actions Reset |
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| llmlight-1.2.1-py3-none-any.whl | Python 3 | none | any | Details for llmlight-1.2.1-py3-none-any.whl |
Total release size: 142.8 kB
Release files / llmlight-1.2.1.tar.gz
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