# Examples

Here you can find a wide variety of examples that demonstrate how to use the Vespa Python API. These examples cover different use cases and functionalities, providing a practical understanding of how to interact with Vespa using Python.

## Vespa Cloud

To create a free Vespa Cloud account, visit [Vespa Cloud](https://vespa.ai/free-trial/).

- [ANN parameter tuning](https://vespa-engine.github.io/pyvespa/examples/ann-parameter-tuning-vespa-cloud.md)
- [BGE-M3 - The Mother of all embedding models](https://vespa-engine.github.io/pyvespa/examples/mother-of-all-embedding-models-cloud.md)
- [Billion-scale vector search with Cohere binary embeddings in Vespa](https://vespa-engine.github.io/pyvespa/examples/billion-scale-vector-search-with-cohere-embeddings-cloud.md)
- [Building cost-efficient retrieval-augmented personal AI assistants](https://vespa-engine.github.io/pyvespa/examples/scaling-personal-ai-assistants-with-streaming-mode-cloud.md)
- [Chat with your pdfs with ColBERT, langchain, and Vespa](https://vespa-engine.github.io/pyvespa/examples/chat_with_your_pdfs_using_colbert_langchain_and_Vespa-cloud.md)
- [ColPali Ranking Experiments on DocVQA](https://vespa-engine.github.io/pyvespa/examples/colpali-benchmark-vqa-vlm_Vespa-cloud.md)
- [Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa](https://vespa-engine.github.io/pyvespa/examples/Matryoshka_embeddings_in_Vespa-cloud.md)
- [Feeding to Vespa Cloud](https://vespa-engine.github.io/pyvespa/examples/feed_performance_cloud.md)
- [Multilingual Hybrid Search with Cohere binary embeddings and Vespa](https://vespa-engine.github.io/pyvespa/examples/multilingual-multi-vector-reps-with-cohere-cloud.md)
- [PDF-Retrieval using ColQWen2 (ColPali) with Vespa](https://vespa-engine.github.io/pyvespa/examples/pdf-retrieval-with-ColQwen2-vlm_Vespa-cloud.md)
- [RAG Blueprint tutorial](https://vespa-engine.github.io/pyvespa/examples/rag-blueprint-vespa-cloud.md)
- [Scaling ColPALI (VLM) Retrieval](https://vespa-engine.github.io/pyvespa/examples/simplified-retrieval-with-colpali-vlm_Vespa-cloud.md)
- [Standalone ColBERT + Vespa for long-context ranking](https://vespa-engine.github.io/pyvespa/examples/colbert_standalone_long_context_Vespa-cloud.md)
- [Standalone ColBERT with Vespa for end-to-end retrieval and ranking](https://vespa-engine.github.io/pyvespa/examples/colbert_standalone_Vespa-cloud.md)
- [Turbocharge RAG with LangChain and Vespa Streaming Mode for Partitioned Data](https://vespa-engine.github.io/pyvespa/examples/turbocharge-rag-with-langchain-and-vespa-streaming-mode-cloud.md)
- [Using Cohere Binary Embeddings in Vespa](https://vespa-engine.github.io/pyvespa/examples/cohere-binary-vectors-in-vespa-cloud.md)
- [Using Mixedbread.ai embedding model with support for binary vectors](https://vespa-engine.github.io/pyvespa/examples/mixedbread-binary-embeddings-with-sentence-transformers-cloud.md)
- [Vespa 🤝 ColPali: Efficient Document Retrieval with Vision Language Models](https://vespa-engine.github.io/pyvespa/examples/colpali-document-retrieval-vision-language-models-cloud.md)
- [Video Search and Retrieval with Vespa and TwelveLabs](https://vespa-engine.github.io/pyvespa/examples/video_search_twelvelabs_cloud.md)
- [Visual PDF RAG with Vespa - ColPali demo application](https://vespa-engine.github.io/pyvespa/examples/visual_pdf_rag_with_vespa_colpali_cloud.md)

## Local deployment (docker/podman)

- [Evaluating retrieval with Snowflake arctic embed](https://vespa-engine.github.io/pyvespa/examples/evaluating-with-snowflake-arctic-embed.md)
- [Feeding performance](https://vespa-engine.github.io/pyvespa/examples/feed_performance.md)
- [LightGBM: Mapping model features to Vespa features](https://vespa-engine.github.io/pyvespa/examples/lightgbm-with-categorical-mapping.md)
- [LightGBM: Training the model with Vespa features](https://vespa-engine.github.io/pyvespa/examples/lightgbm-with-categorical.md)
- [Multi-vector indexing with HNSW](https://vespa-engine.github.io/pyvespa/examples/multi-vector-indexing.md)
- [Pyvespa examples](https://vespa-engine.github.io/pyvespa/examples/pyvespa-examples.md)
- [Using Mixedbread.ai cross-encoder for reranking in Vespa.ai](https://vespa-engine.github.io/pyvespa/examples/cross-encoders-for-global-reranking.md)