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Recipes/redisvl for java semantic caching and routing (#118)
* Adding recipes for RedisVL for Java: - Semantic Classification - Semantic Tool Calling - Semantic Guardrails - Pre generated Semantic Caching - Semantic Caching with LangCache
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‎.github/ignore-notebooks.txt‎

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00_litellm_proxy_redis.ipynb
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04_redisvl_benchmarking_basics.ipynb
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06_hnsw_to_svs_vamana_migration.ipynb
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07_flat_to_svs_vamana_migration.ipynb
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07_flat_to_svs_vamana_migration.ipynb
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1_pre_generated_semantic_caching.ipynb
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2_semantic_caching_with_langcache.ipynb
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1_semantic_classification.ipynb
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2_semantic_tool_calling.ipynb
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3_semantic_guardrails.ipynb

‎README.md‎

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#### Notebooks
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| Notebook | Description |
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|---------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
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| [notebooks/RAG/spring_ai_redis_rag.ipynb](./java-recipes/notebooks/RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-ba sed beer recommendation chatbot using Spring AI and Redis as the vector store |
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| Notebook | Description |
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|------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
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| [notebooks/RAG/spring_ai_redis_rag.ipynb](./java-recipes/notebooks/RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-ba sed beer recommendation chatbot using Spring AI and Redis as the vector store |
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| [RAG/spring_ai_redis_rag.ipynb](./RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store |
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| [semantic-routing/1_semantic_classification.ipynb](./semantic-routing/1_semantic_classification.ipynb) | Demonstrates how to perform text classification with vector search (RedisVL) instead of LLMs |
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| [semantic-routing/2_semantic_tool_calling.ipynb](./semantic-routing/2_semantic_tool_calling.ipynb) | Demonstrates how to perform tool selection with vector search (RedisVL) instead of LLMs |
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| [semantic-routing/3_semantic_guardrails.ipynb](./semantic-routing/3_semantic_guardrails.ipynb) | Demonstrates how to implement guardrails with vector search (RedisVL) |
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| [semantic-caching/1_pre_generated_semantic_caching.ipynb](./semantic-caching/1_pre_generated_semantic_caching.ipynb) | Demonstrates how to perform pre generated semantic caching with RedisVL |
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| [semantic-caching/2_semantic_caching_with_langcache.ipynb](./semantic-caching/2_semantic_caching_with_langcache.ipynb) | Demonstrates how to perform pre generated semantic caching with LangCache |
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#### Applications
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‎java-recipes/README.md‎

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Notebooks require a Jupyter Notebook environment to run. Check out the [Setup Instructions & Implementation Details](./notebooks/README.md) for more details on how to set up your environment.
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| Notebook | Description |
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|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
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| [notebooks/RAG/spring_ai_redis_rag.ipynb](./notebooks/RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-ba sed beer recommendation chatbot using Spring AI and Redis as the vector store |
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| Notebook | Description |
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|------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
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| [RAG/spring_ai_redis_rag.ipynb](./RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store |
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| [semantic-routing/1_semantic_classification.ipynb](./semantic-routing/1_semantic_classification.ipynb) | Demonstrates how to perform text classification with vector search (RedisVL) instead of LLMs |
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| [semantic-routing/2_semantic_tool_calling.ipynb](./semantic-routing/2_semantic_tool_calling.ipynb) | Demonstrates how to perform tool selection with vector search (RedisVL) instead of LLMs |
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| [semantic-routing/3_semantic_guardrails.ipynb](./semantic-routing/3_semantic_guardrails.ipynb) | Demonstrates how to implement guardrails with vector search (RedisVL) |
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| [semantic-caching/1_pre_generated_semantic_caching.ipynb](./semantic-caching/1_pre_generated_semantic_caching.ipynb) | Demonstrates how to perform pre generated semantic caching with RedisVL |
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| [semantic-caching/2_semantic_caching_with_langcache.ipynb](./semantic-caching/2_semantic_caching_with_langcache.ipynb) | Demonstrates how to perform pre generated semantic caching with LangCache |
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## Applications
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‎java-recipes/notebooks/RAG/resources/beers.json.gz‎

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‎java-recipes/notebooks/RAG/spring_ai_redis_rag.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f0483426-9a2a-4fc1-a184-9ba3343d2bf9",
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"metadata": {},
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"outputs": [],
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"metadata": {
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"jupyter": {
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"is_executing": true
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}
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},
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"source": [
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"%mavenRepo spring_milestones https://repo.spring.io/milestone/ \n",
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"%maven \"org.springframework.ai:spring-ai-openai:1.0.0-M6\"\n",
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"%maven \"org.springframework.ai:spring-ai-transformers:1.0.0-M6\"\n",
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"%maven \"org.springframework.ai:spring-ai-redis-store:1.0.0-M6\"\n",
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"%maven \"org.slf4j:slf4j-simple:2.0.17\" \n",
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"%maven \"redis.clients:jedis:5.2.0\""
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]
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"String[] KEYS = { \"name\", \"abv\", \"ibu\", \"description\" };\n",
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"// Data path\n",
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"String filePath = \"../resources/beers.json.gz\";\n",
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"String filePath = \"./resources/beers.json.gz\";\n",
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"// Check if embeddings are already loaded\n",
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"Map<String, Object> indexInfo = vectorStore.getJedis().ftInfo(\"beers\");\n",

‎java-recipes/notebooks/README.md‎

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This project uses Docker Compose to set up a complete environment for running Java-based AI applications with Redis. The environment includes:
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- A Jupyter Notebook server with Java kernel support
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- A Jupyter Notebook server with Java kernel support (For notebooks that based on Java)
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- Redis Stack (includes Redis and RedisInsight)
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- Pre-installed dependencies for AI/ML workloads
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## Notebooks
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| Notebook | Description |
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| --- | --- |
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| [RAG/spring_ai_redis_rag.ipynb](./RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store |
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| Notebook | Description |
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|------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
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| [RAG/spring_ai_redis_rag.ipynb](./RAG/spring_ai_redis_rag.ipynb) | Demonstrates building a RAG-based beer recommendation chatbot using Spring AI and Redis as the vector store |
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| [semantic-routing/1_semantic_classification.ipynb](./semantic-routing/1_semantic_classification.ipynb) | Demonstrates how to perform text classification with vector search (RedisVL) instead of LLMs |
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| [semantic-routing/2_semantic_tool_calling.ipynb](./semantic-routing/2_semantic_tool_calling.ipynb) | Demonstrates how to perform tool selection with vector search (RedisVL) instead of LLMs |
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| [semantic-routing/3_semantic_guardrails.ipynb](./semantic-routing/3_semantic_guardrails.ipynb) | Demonstrates how to implement guardrails with vector search (RedisVL) |
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| [semantic-caching/1_pre_generated_semantic_caching.ipynb](./semantic-caching/1_pre_generated_semantic_caching.ipynb) | Demonstrates how to perform pre generated semantic caching with RedisVL |
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| [semantic-caching/2_semantic_caching_with_langcache.ipynb](./semantic-caching/2_semantic_caching_with_langcache.ipynb) | Demonstrates how to perform pre generated semantic caching with LangCache |
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## Project Structure
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‎java-recipes/notebooks/resources/beers.json.gz‎

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