https://github.com/manav54321/models
https://github.com/manav54321/models
chatmodel cosine-similarity embedding-models langchain llms streamlit transformers
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/manav54321/models
- Owner: Manav54321
- License: mit
- Created: 2025-06-26T10:44:10.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-03T09:51:42.000Z (12 months ago)
- Last Synced: 2025-09-01T20:20:47.368Z (10 months ago)
- Topics: chatmodel, cosine-similarity, embedding-models, langchain, llms, streamlit, transformers
- Language: Python
- Homepage:
- Size: 14.3 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Document Similarity Search with Sentence Transformers
This repository presents a minimal and effective implementation of semantic document retrieval using vector embeddings and cosine similarity. The system is designed to identify the document that best matches the meaning of a user's input query.
---
## Overview
Traditional search methods rely on keyword overlap. This method improves upon that by leveraging **pretrained transformer-based sentence embeddings**, allowing retrieval based on *semantic similarity* rather than lexical similarity.
The process involves:
* Embedding each document and query into a high-dimensional space.
* Computing cosine similarity between the query vector and document vectors.
* Returning the document with the highest semantic alignment.
---
## Mathematical Foundation
### 1. Vector Representation
Each document `d_i` and query `q` is passed through a transformer model `f`, which encodes the text into an embedding vector:
d_i = f(d_i), q = f(q)
Where `f` maps raw text to ℝ^384 using a pre-trained model.
---
### 2. Cosine Similarity
Similarity between query and document embeddings is calculated as:
cos_sim(q, d_i) = (q · d_i) / (||q|| * ||d_i||)
This metric reflects the angle between vectors. Higher values indicate greater similarity in meaning.
### 3. Ranking Mechanism
All cosine scores are computed, sorted, and the top document is returned:
```python
sorted_scores = sorted(enumerate(scores), key=lambda x: x[1])
document_index, score = sorted_scores[-1]
```
---
## Embedding Model
* **Model Name**: `sentence-transformers/all-MiniLM-L6-v2`
* **Architecture**: Distilled bi-encoder (Transformer-based)
* **Embedding Dimension**: 384
* **Provider**: HuggingFace Model Hub
---
## Example Run
```python
query = "Kylie"
```
Returns:
```
query: Kylie
best_match: Kylie Jenner commands attention with her signature style, glamorous beauty, and trendsetting influence on social media.
similarity_score: 0.7823
```
---
## Dependencies
Install required libraries:
```bash
pip install -r requirements.txt
```
---
## Run the Script
```bash
python /Users/manavdesai/Desktop/GitHub/Langchain_Models/document_search_app.py.py
```
---