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Nov 24, 2025
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Recipes/redisvl for java semantic caching and routing #118

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5dfeb99
Adding recipes for RedisVL for Java:
raphaeldelio Nov 24, 2025
eca5e15
Adding recipes for RedisVL for Java:
raphaeldelio Nov 24, 2025
0dee33a
adding kotlin notebooks to the ignored notebooks for testing
raphaeldelio Nov 24, 2025
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13 changes: 10 additions & 3 deletions README.md
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Expand Up @@ -162,9 +162,16 @@ A set of Java recipes can be found under [/java-recipes](/java-recipes/README.md

#### Notebooks

| Notebook | Description |
|---------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
| [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 |
| Notebook | Description |
|------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
| [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 |
| [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 |
| [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 |
| [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 |
| [semantic-routing/3_semantic_guardrails.ipynb](./semantic-routing/3_semantic_guardrails.ipynb) | Demonstrates how to implement guardrails with vector search (RedisVL) |
| [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 |
| [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 |


#### Applications

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11 changes: 8 additions & 3 deletions java-recipes/README.md
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Expand Up @@ -26,9 +26,14 @@ There are two types of Java Recipes: Notebooks and Applications. Notebooks are i

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.

| Notebook | Description |
|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
| [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 |
| Notebook | Description |
|------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
| [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 |
| [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 |
| [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 |
| [semantic-routing/3_semantic_guardrails.ipynb](./semantic-routing/3_semantic_guardrails.ipynb) | Demonstrates how to implement guardrails with vector search (RedisVL) |
| [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 |
| [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 |

## Applications

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14 changes: 9 additions & 5 deletions java-recipes/notebooks/RAG/spring_ai_redis_rag.ipynb
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Expand Up @@ -30,18 +30,22 @@
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f0483426-9a2a-4fc1-a184-9ba3343d2bf9",
"metadata": {},
"outputs": [],
"metadata": {
"jupyter": {
"is_executing": true
}
},
"source": [
"%mavenRepo spring_milestones https://repo.spring.io/milestone/ \n",
"%maven \"org.springframework.ai:spring-ai-openai:1.0.0-M6\"\n",
"%maven \"org.springframework.ai:spring-ai-transformers:1.0.0-M6\"\n",
"%maven \"org.springframework.ai:spring-ai-redis-store:1.0.0-M6\"\n",
"%maven \"org.slf4j:slf4j-simple:2.0.17\" \n",
"%maven \"redis.clients:jedis:5.2.0\""
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -268,7 +272,7 @@
"String[] KEYS = { \"name\", \"abv\", \"ibu\", \"description\" };\n",
"\n",
"// Data path\n",
"String filePath = \"../resources/beers.json.gz\";\n",
"String filePath = \"./resources/beers.json.gz\";\n",
"\n",
"// Check if embeddings are already loaded\n",
"Map<String, Object> indexInfo = vectorStore.getJedis().ftInfo(\"beers\");\n",
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14 changes: 10 additions & 4 deletions java-recipes/notebooks/README.md
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Expand Up @@ -24,7 +24,7 @@

This project uses Docker Compose to set up a complete environment for running Java-based AI applications with Redis. The environment includes:

- A Jupyter Notebook server with Java kernel support
- A Jupyter Notebook server with Java kernel support (For notebooks that based on Java)
- Redis Stack (includes Redis and RedisInsight)
- Pre-installed dependencies for AI/ML workloads

Expand Down Expand Up @@ -79,9 +79,15 @@ OPENAI_API_KEY=your_openai_api_key_here

## Notebooks

| Notebook | Description |
| --- | --- |
| [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 |
| Notebook | Description |
|------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
| [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 |
| [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 |
| [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 |
| [semantic-routing/3_semantic_guardrails.ipynb](./semantic-routing/3_semantic_guardrails.ipynb) | Demonstrates how to implement guardrails with vector search (RedisVL) |
| [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 |
| [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 |


## Project Structure

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