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Agents
symfony/ai-store
claude-code
cursor
mongodb
postgres
elasticsearch
azure
neo4j
cloudflare
opensearch
mariadb
ai
clickhouse
meilisearch
typesense
supabase
surrealdb
weaviate
pinecone
qdrant
milvus
chromadb
Install
php artisan boost:add-skill symfony/ai-store

Save this content to: AGENTS.md

---
package: symfony/ai-store
source_path: AGENTS.md
repo: https://github.com/symfony/ai-store
---

# AGENTS.md

AI agent guidance for the Store component.

## Component Overview

Low-level abstraction for vector stores enabling RAG applications. Unified interfaces for various vector database implementations.

## Architecture

### Core Interfaces
- **StoreInterface**: Main interface with `add()` and `query()` methods
- **ManagedStoreInterface**: Extends with `setup()` and `drop()` lifecycle methods
- **IndexerInterface** (implementations in `src/Indexer/`): high-level services converting TextDocuments to VectorDocuments

### Bridge Pattern
Multiple vector store implementations:

**Database**: Postgres, MariaDB, ClickHouse, MongoDB, Neo4j, SurrealDB
**Cloud**: Azure AI Search, Pinecone
**Search**: Meilisearch, Typesense, Weaviate, Qdrant, Milvus
**Local**: InMemory (`src/InMemory/Store.php`, not a bridge), Cache bridge (`src/Bridge/Cache/Store.php`, PSR-6)
**External**: ChromaDb (requires codewithkyrian/chromadb-php)

### Document System
- **TextDocument**: Input documents with text and metadata
- **VectorDocument**: Documents with embedded vectors for storage
- **Vectorizer**: Converts TextDocuments using AI Platform
- **Transformers**: ChainTransformer, TextSplitTransformer, ChunkDelayTransformer

## Essential Commands

### Testing
```bash
vendor/bin/phpunit
vendor/bin/phpunit tests/InMemory/StoreTest.php
vendor/bin/phpunit --filter testMethodName
```

### Code Quality
```bash
vendor/bin/phpstan analyse
```

### Dependencies
```bash
composer install
```

## Key Dependencies

- **symfony/ai-platform**: AI model integration and vectorization
- **psr/log**: Logging throughout indexing process
- **symfony/http-client**: HTTP-based vector store communication

## Development Notes

- Bridge pattern architecture with corresponding test structure
- PHPUnit 11+ with strict configuration
- Document preprocessing with transformers
- Batch indexing for performance
- Unified interface across all vector store types

package: symfony/ai-store source_path: AGENTS.md repo: https://github.com/symfony/ai-store

AGENTS.md

AI agent guidance for the Store component.

Component Overview

Low-level abstraction for vector stores enabling RAG applications. Unified interfaces for various vector database implementations.

Architecture

Core Interfaces

  • StoreInterface: Main interface with add() and query() methods
  • ManagedStoreInterface: Extends with setup() and drop() lifecycle methods
  • IndexerInterface (implementations in src/Indexer/): high-level services converting TextDocuments to VectorDocuments

Bridge Pattern

Multiple vector store implementations:

Database: Postgres, MariaDB, ClickHouse, MongoDB, Neo4j, SurrealDB Cloud: Azure AI Search, Pinecone Search: Meilisearch, Typesense, Weaviate, Qdrant, Milvus Local: InMemory (src/InMemory/Store.php, not a bridge), Cache bridge (src/Bridge/Cache/Store.php, PSR-6) External: ChromaDb (requires codewithkyrian/chromadb-php)

Document System

  • TextDocument: Input documents with text and metadata
  • VectorDocument: Documents with embedded vectors for storage
  • Vectorizer: Converts TextDocuments using AI Platform
  • Transformers: ChainTransformer, TextSplitTransformer, ChunkDelayTransformer

Essential Commands

Testing

vendor/bin/phpunit
vendor/bin/phpunit tests/InMemory/StoreTest.php
vendor/bin/phpunit --filter testMethodName

Code Quality

vendor/bin/phpstan analyse

Dependencies

composer install

Key Dependencies

  • symfony/ai-platform: AI model integration and vectorization
  • psr/log: Logging throughout indexing process
  • symfony/http-client: HTTP-based vector store communication

Development Notes

  • Bridge pattern architecture with corresponding test structure
  • PHPUnit 11+ with strict configuration
  • Document preprocessing with transformers
  • Batch indexing for performance
  • Unified interface across all vector store types
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