Get Embeddings From Qwen 2

Get Embeddings from qwen 2 using ollama.

Notebook: https://github.com/mosh98/RAG_With_Mo...

AI Consulting: https://calendly.com/mosleh-rdge/ai-c...

This video shows how to get embeddings using Qwen 2 locally! Using Ollama, Huggingface, Langchain and LlamaIndex so people can use whichever suits their use case.


What can you do with embeddings?
Text embeddings are a transformative technique in natural language processing (NLP) that convert text into numerical vectors, capturing the semantic meaning and enabling a variety of powerful applications. Here are some practical examples of what can be done with text embeddings:

1. *Semantic Search*:
- Enhance search engine results by converting queries and documents into embeddings, retrieving documents with the closest semantic match to the query. Ideal for search engines and e-commerce product searches.

2. *Text Classification*:
- Classify texts like emails into categories such as spam or not spam by converting the text into embeddings and feeding it into a classifier. Useful for spam detection, sentiment analysis, and topic categorization.

3. *Recommendation Systems*:
- Improve content recommendation by representing user preferences and item attributes as embeddings, generating recommendations based on similarity. Applicable in recommending movies, music, and articles.

4. *Clustering*:
- Group similar texts into clusters by converting them into embeddings and using clustering algorithms like K-means. Perfect for news aggregation and market basket analysis.

5. *Text Generation*:
- Generate human-like text based on a given prompt using models like GPT, which leverage embeddings to understand context and produce coherent content. Useful for chatbots, content creation, and automated report generation.

6. *Named Entity Recognition (NER)*:
- Identify entities such as names, dates, and locations in text by using embeddings to understand the context. Beneficial for information extraction and automated customer support.

7. *Machine Translation*:
- Translate text from one language to another by capturing the meaning of sentences through embeddings, enabling accurate translation. Essential for language translation services.

8. *Similarity Detection*:
- Detect plagiarism or similar content by comparing text embeddings to find high similarity scores. Used in academic integrity tools and content management.

9. *Question Answering*:
- Find relevant answers in a document by converting questions and potential answers into embeddings, identifying the closest matches. Used in virtual assistants and customer support bots.

10. *Contextual Advertising*:
- Place ads in relevant content by understanding the context of a webpage using embeddings, ensuring ads are shown in the most appropriate context. Key for online advertising and targeted marketing.

Detailed Example: In e-commerce, text embeddings can significantly enhance the search experience. When a user searches for "red running shoes," both the query and product descriptions are converted into embeddings. The system calculates the similarity between the query embedding and product embeddings, ranks the products based on relevance, and combines user behavior data for further personalization. This leads to more accurate and relevant search results, boosting user satisfaction and sales.

Text embeddings are versatile and powerful, solving various NLP challenges and enhancing user experiences across different applications.

What is Qwen 2?
Qwen 2 is a series of large language models developed by Alibaba Cloud, designed to excel in various AI tasks. The Qwen 2 models range in size from 0.5 billion to 72 billion parameters, making them versatile for applications such as language understanding, generation, multilingual tasks, coding, and mathematics.



Langchain or LlamaIndex?
LlamaIndex is your go-to solution for streamlined search and retrieval applications. Tailored for efficiency, it simplifies LLM querying and ensures relevant document retrieval based on user input. Ideal for focused applications demanding simplicity and speed.

On the flip side, LangChain offers a broader toolkit for crafting versatile LLM-powered applications. From data loading to customization, LangChain empowers developers with flexibility beyond search and retrieval. Perfect for projects requiring adaptability and extensibility.