docs: add recommender system example (#1561)

before:
![Screenshot 2024-08-24
230216](https://github.com/user-attachments/assets/cc8a810a-b032-45d7-b086-b2ef0720dc16)

After:
![Screenshot 2024-08-24
230228](https://github.com/user-attachments/assets/eaa1dc31-ac7f-4b81-aa79-b4cf94f0cbd5)

---------

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

.github/workflows/docs_test.yml

@@ -30,9 +30,13 @@ jobs:
       uses: actions/checkout@v4
     - name: Print CPU capabilities
       run: cat /proc/cpuinfo
+    - name: Install protobuf
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler
     - name: Install dependecies needed for ubuntu
       run: |
-        sudo apt install -y protobuf-compiler libssl-dev
+        sudo apt install -y libssl-dev
         rustup update && rustup default
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -72,9 +76,13 @@ jobs:
       uses: actions/setup-node@v4
       with:
         node-version: 20
+    - name: Install protobuf
+      run: |
+        sudo apt update
+        sudo apt install -y protobuf-compiler
     - name: Install dependecies needed for ubuntu
       run: |
-        sudo apt install -y protobuf-compiler libssl-dev
+        sudo apt install -y libssl-dev
         rustup update && rustup default
     - name: Rust cache
       uses: swatinem/rust-cache@v2

docs/mkdocs.yml

@@ -150,6 +150,7 @@ nav:
               - Chatbot: examples/python_examples/chatbot.md
               - Evaluation: examples/python_examples/evaluations.md
               - AI Agent: examples/python_examples/aiagent.md
+              - Recommender System: examples/python_examples/recommendersystem.md
               - Miscellaneous: 
                   - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
                   - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
@@ -241,6 +242,7 @@ nav:
           - Chatbot: examples/python_examples/chatbot.md
           - Evaluation: examples/python_examples/evaluations.md
           - AI Agent: examples/python_examples/aiagent.md
+          - Recommender System: examples/python_examples/recommendersystem.md
           - Miscellaneous: 
               - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
               - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb

docs/src/examples/python_examples/rag.md

@@ -2,11 +2,11 @@
 **RAG: Revolutionize Information Retrieval with LanceDB 🔓🧐**
 ====================================================================
 
-Unlock the full potential of Retrieval-Augmented Generation (RAG) with LanceDB, a solution for efficient vector-based information retrieval 📊. 
+Build RAG (Retrieval-Augmented Generation) with  LanceDB, a powerful solution for efficient vector-based information retrieval 📊. 
 
 **Experience the Future of Search 🔄**
 
-RAG integrates large language models (LLMs) with scalable knowledge bases, enabling efficient information retrieval and answer generation 🤖. By applying RAG to industry-specific use cases, developers can optimize query processing 📊, reduce response latency ⏱️, and improve resource utilization 💻. LanceDB provides a robust framework for integrating LLMs with external knowledge sources, facilitating accurate and informative responses 📝.
+🤖 RAG enables AI to **retrieve** relevant information from external sources and use it to **generate** more accurate and context-specific responses. 💻 LanceDB provides a robust framework for integrating LLMs with external knowledge sources 📝.
 
 | **RAG**                                      | **Description**                                                                                                                                                  | **Links**                  |
 |----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|                                                                                                                                                     

docs/src/examples/python_examples/recommendersystem.md

@@ -0,0 +1,37 @@
+**Recommender Systems: Personalized Discovery🍿📺**
+==============================================================
+Deliver personalized experiences with Recommender Systems. 🎁
+
+**Technical Overview📜**
+
+🔍️ LanceDB's powerful vector database capabilities can efficiently store and query item embeddings. Recommender Systems can utilize it and provide personalized recommendations based on user preferences 🤝 and item features 📊 and therefore enhance the user experience.🗂️ 
+
+| **Recommender System** | **Description** | **Links** |
+| ---------------------- | --------------- | --------- |
+| **Movie Recommender System🎬** | 🤝 Use **collaborative filtering** to predict user preferences, assuming similar users will like similar movies, and leverage **Singular Value Decomposition** (SVD) from Numpy for precise matrix factorization and accurate recommendations📊 | [![Github](../../assets/github.svg)][movie_github] <br>[![Open In Collab](../../assets/colab.svg)][movie_colab] <br>[![Python](../../assets/python.svg)][movie_python] |
+| **🎥 Movie Recommendation with Genres** | 🔍 Creates movie embeddings using Doc2Vec, capturing genre and characteristic nuances, and leverages VectorDB for efficient storage and querying, enabling accurate genre classification and personalized movie recommendations through similarity searches🎥 | [![Github](../../assets/github.svg)][genre_github] <br>[![Open In Collab](../../assets/colab.svg)][genre_colab] <br>[![Ghost](../../assets/ghost.svg)][genre_ghost] |
+| **🛍️ Product Recommender using Collaborative Filtering and LanceDB** | 📈 Using **Collaborative Filtering** and **LanceDB** to analyze your past purchases, recommends products based on user's past purchases. Demonstrated with the Instacart dataset in our example🛒 | [![Github](../../assets/github.svg)][product_github] <br>[![Open In Collab](../../assets/colab.svg)][product_colab] <br>[![Python](../../assets/python.svg)][product_python] |
+| **🔍 Arxiv Search with OpenCLIP and LanceDB** | 💡 Build a semantic search engine for Arxiv papers using LanceDB, and benchmarks its performance against traditional keyword-based search on Nomic's Atlas, to demonstrate the power of semantic search in finding relevant research papers📚 | [![Github](../../assets/github.svg)][arxiv_github] <br>[![Open In Collab](../../assets/colab.svg)][arxiv_colab] <br>[![Python](../../assets/python.svg)][arxiv_python] |
+| **Food Recommendation System🍴** | 🍔 Build a food recommendation system with LanceDB, featuring vector-based recommendations, full-text search, hybrid search, and reranking model integration for personalized and accurate food suggestions👌 | [![Github](../../assets/github.svg)][food_github] <br>[![Open In Collab](../../assets/colab.svg)][food_colab] |
+
+[movie_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender
+[movie_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb
+[movie_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.py
+
+
+[genre_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/movie-recommendation-with-genres
+[genre_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb
+[genre_ghost]: https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/
+
+[product_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender
+[product_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/product-recommender/main.ipynb
+[product_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/product-recommender/main.py
+
+
+[arxiv_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender
+[arxiv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.ipynb
+[arxiv_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/arxiv-recommender/main.py
+ 
+
+[food_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Food_recommendation
+[food_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Food_recommendation/main.ipynb

docs/src/examples/python_examples/vector_search.md

@@ -1,7 +1,7 @@
 **Vector Search: Unlock Efficient Document Retrieval 🔓👀**
 ====================================================================
 
-Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊. 
+Vector search with LanceDB, is a solution for  efficient and accurate similarity searches in large datasets 📊. 
 
 **Vector Search Capabilities in LanceDB🔝**