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content/develop/ai/_index.md

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| Vector | RAG | RedisVL |
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| :-- | :-- | :-- |
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| {{<image filename="images/ai-cube.svg" alt="AI Redis icon.">}}[Redis vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) |{{<image filename="images/ai-brain.svg" alt="AI Redis icon.">}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/get-started/rag" >}}) | {{<image filename="images/ai-lib.svg" alt="AI Redis icon.">}}[Redis vector Python client library documentation]({{< relref "/integrate/redisvl/" >}}) |
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| {{<image filename="images/ai-cube.svg" alt="AI Redis icon.">}}[Redis vector database quick start guide]({{< relref "/develop/get-started/vector-database" >}}) |{{<image filename="images/ai-brain.svg" alt="AI Redis icon.">}} [Retrieval-Augmented Generation quick start guide]({{< relref "/develop/get-started/rag" >}}) | {{<image filename="images/ai-lib.svg" alt="AI Redis icon.">}}[Redis vector Python client library documentation]({{< relref "/develop/ai/redisvl/" >}}) |
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#### Overview
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content/develop/clients/_index.md

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| Language | Client name | Docs | Supported |
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| :-- | :-- | :-- | :-- |
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| [Python](https://www.python.org/) | [`redis-py`](https://github.com/redis/redis-py) |[`redis-py` guide]({{< relref "/develop/clients/redis-py" >}}) | Yes |
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| [Python](https://www.python.org/) | [`RedisVL`](https://github.com/redis/redis-vl-python) |[RedisVL guide]({{< relref "/integrate/redisvl" >}}) | Yes
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| [Python](https://www.python.org/) | [`RedisVL`](https://github.com/redis/redis-vl-python) |[RedisVL guide]({{< relref "/develop/ai/redisvl" >}}) | Yes
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| [C#/.NET](https://learn.microsoft.com/en-us/dotnet/csharp/) | [`NRedisStack`](https://github.com/redis/NRedisStack) |[`NRedisStack` guide]({{< relref "/develop/clients/dotnet" >}}) | Yes |
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| [JavaScript](https://nodejs.org/en) | [`node-redis`](https://github.com/redis/node-redis) | [`node-redis` guide]({{< relref "/develop/clients/nodejs" >}}) | Yes |
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| [Java](https://www.java.com/en/) | [`Jedis`](https://github.com/redis/jedis) | [`Jedis` guide]({{< relref "/develop/clients/jedis" >}}) | Yes |

content/develop/clients/redis-vl.md

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---
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See the [RedisVL Guide]({{< relref "/integrate/redisvl" >}}) for more information.
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See the [RedisVL Guide]({{< relref "/develop/ai/redisvl" >}}) for more information.

content/develop/get-started/redis-in-ai.md

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- **Session Management**: in-memory data structures simplify managing session states in conversational AI scenarios.
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- **Flexibility**: Redis supports diverse data structures (for example, strings, hashes, lists, sets), allowing tailored solutions for GenAI apps.
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[RedisVL]({{< relref "/integrate/redisvl" >}}) is a Python library with an integrated CLI, offering seamless integration with Redis to enhance GenAI applications.
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[RedisVL]({{< relref "/develop/ai/redisvl" >}}) is a Python library with an integrated CLI, offering seamless integration with Redis to enhance GenAI applications.
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---
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content/get-started/_index.md

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## Data integration tools, libraries, and frameworks
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- [Client API libraries]({{< relref "/develop/clients" >}})
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- [Redis Data Integration]({{< relref "/integrate/redis-data-integration/" >}})
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- [Redis vector library for Python]({{< relref "/integrate/redisvl/" >}})
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- [Redis vector library for Python]({{< relref "/develop/ai/redisvl/" >}})
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- [Redis Cloud with Amazon Bedrock]({{< relref "/integrate/amazon-bedrock/" >}})
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- [Object-mapping for .NET]({{< relref "/integrate/redisom-for-net/" >}})
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- [Spring Data Redis for Java]({{< relref "/integrate/spring-framework-cache/" >}})

content/integrate/redisvl/_index.md

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RedisVL is a powerful, dedicated Python client library for Redis that enables seamless integration and management of high-dimensional vector data.
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Built to support machine learning and artificial intelligence workflows, RedisVL simplifies the process of storing, searching, and analyzing vector embeddings, which are commonly used for tasks like recommendation systems, semantic search, and anomaly detection.
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Key features of RedisVL include:
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- Vector Similarity Search: Efficiently find nearest neighbors in high-dimensional spaces using algorithms like HNSW (Hierarchical Navigable Small World).
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- Integration with AI Frameworks: RedisVL works seamlessly with popular frameworks such as TensorFlow, PyTorch, and Hugging Face, making it easy to deploy AI models.
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- Scalable and Fast: Leveraging Redis's in-memory architecture, RedisVL provides low-latency access to vector data, even at scale.
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- By bridging the gap between data storage and AI model deployment, RedisVL empowers developers to build intelligent, real-time applications with minimal infrastructure complexity.
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This content has been [moved]({{< relref "/develop/ai/redisvl/" >}}).

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