feat(ai): add valkey_uses field and improve library descriptions

[jbrinkman]

Add valkey_uses field and improve AI library descriptions

Description

This PR updates the AI libraries JSON schema to include a new valkey_uses field that explains how each library integrates with Valkey (vector store, caching, memory storage, etc.). Additionally, all 14 AI library descriptions have been improved based on GitHub project research to be more accurate and concise.

Related Issue

Fixes valkey-io/valkey-io.github.io#595

Changes

Schema Update

• Added new valkey_uses field to document how each library uses Valkey
• Improved description field to be concise 1-2 sentences explaining what the library does and why someone would use it
• Updated ai/README.md to document the new schema
• Reorganized field order for better presentation:
  • name, description, valkey_uses, repo, installation, version, version_released, language, license

Libraries Updated (14 total)

Python (10):

• cognee - Vector database adapter for AI memory systems
• haystack - Document store with vector similarity search for RAG
• langchain-aws - Cache store and vector store for AWS integrations
• openmemory - Memory storage backend for cognitive memory sectors
• praisonai - Caching layer for multi-agent workflows
• open-webui - Session storage and caching for chat histories
• vllm - Caching backend for KV cache and vector storage
• cocoindex - Vector store for semantic code search
• db-gpt - Vector store and cache store for database queries
• kserve - Caching layer for model predictions

JavaScript (2):

• recall - Memory storage backend for AI agent sessions
• openmemory - Memory storage backend for cognitive memory sectors

Java (1):

• langchain4j - Embedding store for vector search

Lua (1):

• kong - Backend for rate limiting, caching, and session storage

Example Changes

Before:

{
    "name": "LangChain AWS",
    "description": "AWS integrations for LangChain including Valkey cache and vector store support for building LLM applications on AWS infrastructure.",
    "repo": "https://github.com/langchain-ai/langchain-aws",
    ...
}

After:

{
    "name": "LangChain AWS",
    "description": "Build LLM applications on AWS infrastructure with LangChain integrations for Bedrock, SageMaker, and other AWS services.",
    "valkey_uses": "Cache store for LLM responses and vector store for semantic search with AWS ElastiCache for Valkey or MemoryDB",
    "repo": "https://github.com/langchain-ai/langchain-aws",
    ...
}

Testing

• All 15 files validated
• Schema documented in ai/README.md
• Descriptions verified against GitHub project pages
• Field ordering consistent across all files

Checklist

• All commit messages follow conventional commits format
• Commit is signed-off (DCO)
• Changes are limited to AI library metadata
• No breaking changes to existing fields

Notes

This PR is part 1 of 2 for implementing the AI Libraries page. The companion PR for valkey-io.github.io (https://github.com/valkey-io/valkey-io.github.io/pull/XXX) depends on these changes and updates the website template to display the new fields.

The new valkey_uses field will appear on the website between "Repo" and "Installation" to provide clear context about Valkey integration use cases.

[jbrinkman]

Required by valkey-io/valkey-io.github.io#602

[edlng]

A lot of these JSON files had incorrect version dates or ambiguous versions since they don't directly integrate Valkey (e.g., using LMCache for Valkey support instead). Some of them I've left room for discussion as to how we want to approach certain versions or repos. I've tested some of the installation commands too and they're OK.

That being said, this is just an initial review. After we finish the discussions I want to run the installations one more time to verify.

Also, In the PR description, we need to update the XXX at the bottom to match the companion PR's number.