https://github.com/liteobject/demo-semantic-search
https://github.com/liteobject/demo-semantic-search
dense-retrieval embeddings llm machine-learning python semantic-search slm vector-database weaviate
Last synced: 4 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/liteobject/demo-semantic-search
- Owner: LiteObject
- Created: 2024-07-23T16:54:33.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-22T22:17:59.000Z (11 months ago)
- Last Synced: 2025-05-21T19:13:48.826Z (5 months ago)
- Topics: dense-retrieval, embeddings, llm, machine-learning, python, semantic-search, slm, vector-database, weaviate
- Language: Jupyter Notebook
- Homepage:
- Size: 11.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
## Semantic Search Demo
Semantic search using Large Language Models (LLMs) represents a significant advancement in information retrieval, leveraging deep learning to understand the context and intent behind search queries. Unlike traditional keyword-based search, semantic search focuses on the meaning and relationships of words to deliver more relevant results.
### What is Semantic Search?
Semantic search aims to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable data. This involves natural language processing (NLP) to interpret the nuances of language, such as synonyms, homonyms, and the broader context within which terms are used.
### Role of Large Language Models
Large Language Models, such as GPT-4, BERT, and RoBERTa, have revolutionized semantic search. These models are trained on extensive datasets and can capture intricate patterns in language, enabling them to understand context, disambiguate meaning, and even infer user intent. For instance, a query like "best places to visit in Paris" would not just match documents containing these keywords but would also recognize and prioritize content discussing popular tourist attractions in Paris.
### Key Features and Benefits
1. **Contextual Understanding**: LLMs can interpret the context of words, making search results more relevant. For example, they can distinguish between "apple" as a fruit and "Apple" as a technology company based on surrounding text.
2. **Synonym Recognition**: They recognize synonyms and related terms, ensuring that variations in the way users phrase queries don't hinder the search process.
3. **User Intent**: LLMs can infer the user's intent behind a query, providing results that align closely with what the user is seeking, even if the exact keywords aren't present in the documentation.
4. **Improved Accuracy**: By understanding the semantics of the language, these models can significantly reduce irrelevant results, enhancing the overall search experience.
### Applications
Semantic search using LLMs is widely applicable across various domains:
- **E-commerce**: Enhances product search by understanding user queries better, leading to improved product discovery and increased sales.
- **Healthcare**: Helps in retrieving relevant medical information quickly by understanding complex medical terminology and user queries.
- **Legal**: Facilitates legal research by comprehending legal jargon and retrieving pertinent case laws and documents.
- **Content Management**: Improves content discovery in large databases of articles, research papers, and multimedia content by accurately matching user queries with relevant documents.
### Conclusion
Semantic search powered by Large Language Models represents a leap forward in search technology, transforming how users find information by focusing on context and meaning rather than simple keyword matching. As these models continue to evolve, the accuracy and relevance of search results will only improve, making information retrieval faster, more intuitive, and significantly more effective.
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## Links:
- [The Cohere & Weaviate Multilingual Wikipedia Search](https://wiki-multilingual-cohere.demo.weaviate.network/)
- [Generative Search - Cohere](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/generative-cohere)
- [Generative Search - Ollama](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/generative-ollama)