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# [Sem_Cache](https://github.com/kjannette/semantic-cache-script) # [Sem_Cache](https://github.com/kjannette/semantic-cache-script)
![Script usage screenshot](semcache.jpg)
Sem_Cache is a tiny command-line tool written in [Python](https://www.python.org/) that Sem_Cache is a tiny command-line tool written in [Python](https://www.python.org/) that
demonstrates semantic caching for Large Language Model (LLM) queries. It uses the demonstrates semantic caching for Large Language Model (LLM) queries. It uses the
[Sentence Transformers](https://www.sbert.net/) library with [NumPy](https://numpy.org/) [Sentence Transformers](https://www.sbert.net/) library with [NumPy](https://numpy.org/)
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--- ---
## How It Works ## How It Works
If two queries mean the same thing, they should return the same answer. Rather than comparing strings character-by-character, it converts each query into a dense vector embedding using the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model (a lightweight transformer that runs locally). It then calculates the cosine similarity between the cached query embedding and the new query embedding. If two queries mean the same thing, they should return the same answer. Rather than comparing strings character-by-character, it converts each query into a dense vector embedding using the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model (a lightweight transformer that runs locally). It then calculates the cosine similarity between the cached query embedding and the new query embedding.
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## Getting Started ## Getting Started
### Prerequisites ### Prerequisites
- [Python](https://www.python.org/) 3.10 or higher - [Python](https://www.python.org/) 3.10 or higher
- [pip](https://pip.pypa.io/) (Python package installer) - [pip](https://pip.pypa.io/) (Python package installer)
### Installation ### Installation
Clone the repository and set up a virtual environment: Clone the repository and set up a virtual environment:
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1. **Sentence 1**: The baseline cached query (simulating a query already in the cache) 1. **Sentence 1**: The baseline cached query (simulating a query already in the cache)
2. **Sentence 2**: The new incoming query (simulating a user's new request) 2. **Sentence 2**: The new incoming query (simulating a user's new request)
### Example Session ### Example Session
``` ```
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## Configuration ## Configuration
The similarity threshold is set to 0.92 by default. This is a highly-conservative value, set to reduce false positives (treating dissimilar queries as matches). To adjust the threshold, The similarity threshold is set to 0.92 by default. This is a highly-conservative value, set to reduce false positives (treating dissimilar queries as matches). To adjust the threshold,
@@ -115,6 +129,8 @@ Higher values reduce false positives but may miss valid semantic matches.
--- ---
## Project Structure ## Project Structure
``` ```
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## Participation ## Participation
### Bug Reports ### Bug Reports
Bug reports are accepted via [Git issues](https://github.com/kjannette/semantic-cache-script/issues). Bug reports are accepted via [Git issues](https://github.com/kjannette/semantic-cache-script/issues).
@@ -146,12 +166,16 @@ This project follows [PEP 8](https://peps.python.org/pep-0008/) style guidelines
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## Author ## Author
- @ sjDev - @ sjDev
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## Ideology ## Ideology
This project does not have a formal Code of Conduct. This project does not have a formal Code of Conduct.
@@ -163,6 +187,8 @@ executes on-device; no external API calls are made by the caching logic itself).
--- ---
## Roadmap / TO-DO ## Roadmap / TO-DO
(This README also serves as a development notebook.) (This README also serves as a development notebook.)
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- [ ] Print result to command line (completed in current version) - [ ] Print result to command line (completed in current version)
- [ ] Perform and output quantified metrics of what a cache hit conserves - [ ] Perform and output quantified metrics of what a cache hit conserves
### Possible Metrics to Implement ### Possible Metrics to Implement
1. **Estimated completion tokens** — Calculate tokens saved by popular model 1. **Estimated completion tokens** — Calculate tokens saved by popular model
@@ -192,6 +220,8 @@ executes on-device; no external API calls are made by the caching logic itself).
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## License ## License
This softeare is released under the [GNU General Public License Version 3](https://opensource.org/license/gpl-3-0). This softeare is released under the [GNU General Public License Version 3](https://opensource.org/license/gpl-3-0).