quick edit readme

README.md

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 # [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
 demonstrates semantic caching for Large Language Model (LLM) queries. It uses the
 [Sentence Transformers](https://www.sbert.net/) library with [NumPy](https://numpy.org/)
@@ -21,6 +23,8 @@ The output can be diff’d against the behavior of the system under development
 
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 ## 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.
@@ -31,13 +35,19 @@ a **cache miss** and routes the request to the LLM.
 
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 ## Getting Started
 
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 ### Prerequisites
 
 - [Python](https://www.python.org/) 3.10 or higher
 - [pip](https://pip.pypa.io/) (Python package installer)
 
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 ### Installation
 
 Clone the repository and set up a virtual environment:
@@ -70,6 +80,8 @@ The program will prompt you to enter two sentences:
 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)
 
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 ### Example Session
 
 ```
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 ## 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,
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 ## Project Structure
 
 ```
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 ## Participation
 
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 ### Bug Reports
 
 Bug reports are accepted via [Git issues](https://github.com/kjannette/semantic-cache-script/issues).
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 ## Author
 
 - @ sjDev
 
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 ## Ideology
 
 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).
 
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 ## Roadmap / TO-DO
 
 (This README also serves as a development notebook.)
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 - [ ] Print result to command line (completed in current version)
 - [ ] Perform and output quantified metrics of what a cache hit conserves
 
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 ### Possible Metrics to Implement
 
 1. **Estimated completion tokens** — Calculate tokens saved by popular model
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 ## License
 
 This softeare is released under the [GNU General Public License Version 3](https://opensource.org/license/gpl-3-0).