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Routes embedding requests through Bedrock's API with automatic model selection and response normalization, enabling seamless swapping between AWS and other embedding providers without changing application code.","intents":["I want to use AWS Bedrock embeddings in my Backstage RAG pipeline without vendor lock-in","I need to switch embedding providers (AWS → OpenAI → local) without rewriting my RAG logic","I want to leverage Bedrock's managed embedding models for cost-effective vector generation at scale"],"best_for":["Backstage plugin developers building RAG systems on AWS infrastructure","Teams already invested in AWS Bedrock for LLM access seeking unified embedding strategy","Organizations requiring provider-agnostic RAG backends for multi-cloud deployments"],"limitations":["Bedrock API rate limits apply — no built-in request queuing or backoff strategy beyond AWS SDK defaults","Embedding dimensions and model capabilities vary by Bedrock model; no automatic normalization across model families","Requires AWS credentials and Bedrock service access in target region; no fallback to alternative providers if Bedrock unavailable","No local caching of embeddings — every request hits Bedrock API, increasing latency and cost for repeated documents"],"requires":["AWS account with Bedrock service enabled in target region","AWS credentials (IAM role or access keys) with bedrock:InvokeModel permission","@roadiehq/rag-ai plugin installed in Backstage instance","Node.js 16+ runtime","Valid Bedrock model ID (e.g., amazon.titan-embed-text-v1)"],"input_types":["text (raw strings or document chunks)","structured metadata (optional, for filtering)"],"output_types":["vector embeddings (float32 arrays, typically 384-1536 dimensions depending on model)","embedding metadata (model ID, token count, generation timestamp)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-roadiehq-rag-ai-backend-embeddings-aws__cap_1","uri":"capability://tool.use.integration.backstage.plugin.backend.module.registration.and.configuration","name":"backstage plugin backend module registration and configuration","description":"Registers the AWS Bedrock embedding backend as a pluggable module within Backstage's backend plugin architecture, exposing configuration hooks and dependency injection points for seamless integration into existing Backstage instances. 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Abstracts model-specific API differences (input/output formats, token limits, dimension counts) behind a unified embedding interface.","intents":["I want to use Cohere embeddings but fall back to Titan if Cohere is unavailable or rate-limited","I need to compare embedding quality across Bedrock models without changing application code","I want to optimize cost by using cheaper models for non-critical embeddings and premium models for important queries"],"best_for":["Teams evaluating multiple Bedrock embedding models for quality/cost tradeoffs","High-availability RAG systems requiring graceful degradation under Bedrock service disruptions","Cost-conscious deployments needing dynamic model selection based on request priority or budget"],"limitations":["Fallback routing adds latency — retry attempts can add 1-5 seconds per embedding if primary model fails","Model-specific dimension counts and token limits require manual configuration; no automatic detection or normalization","No built-in cost tracking or billing attribution per model — difficult to measure cost impact of model selection decisions","Fallback logic is sequential, not parallel — doesn't attempt multiple models concurrently to minimize latency"],"requires":["AWS Bedrock access to multiple embedding models (Titan, Cohere, or others)","Configuration specifying primary and fallback model IDs","Understanding of each model's input/output specifications (dimensions, token limits)"],"input_types":["text (document chunks or queries)","model selection metadata (priority level, cost budget)"],"output_types":["vector embeddings from selected model","model ID and fallback status in response metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-roadiehq-rag-ai-backend-embeddings-aws__cap_3","uri":"capability://data.processing.analysis.rag.pipeline.integration.with.document.chunking.and.batch.embedding","name":"rag pipeline integration with document chunking and batch embedding","description":"Integrates AWS Bedrock embeddings into the @roadiehq/rag-ai document processing pipeline, supporting batch embedding of document chunks with configurable batch sizes and concurrency limits. Handles document preprocessing (chunking, metadata extraction) and coordinates embedding generation with vector storage ingestion. 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Implements secure credential injection without exposing keys in logs or configuration files, leveraging AWS SDK's built-in credential chain and Backstage's secrets management integration.","intents":["I want to use IAM roles for Bedrock access without managing long-lived credentials","I need to rotate AWS credentials securely without restarting Backstage","I want to assume cross-account roles for Bedrock access in multi-account AWS environments"],"best_for":["Enterprise deployments with strict credential management policies","Teams running Backstage on AWS (EC2, ECS, Lambda) with IAM role support","Multi-account AWS organizations requiring cross-account Bedrock access"],"limitations":["IAM role support requires Backstage to run on AWS infrastructure (EC2, ECS, EKS); not available for on-premises or non-AWS cloud deployments","STS assume-role requires additional IAM permissions and cross-account trust setup; misconfiguration can cause silent auth failures","No built-in credential rotation — relies on AWS SDK's credential refresh; manual intervention needed for emergency credential revocation","Credential caching in AWS SDK can mask credential expiration issues; no explicit credential validation at startup"],"requires":["AWS credentials accessible to Backstage process (IAM role, env vars, or ~/.aws/credentials)","IAM permissions for bedrock:InvokeModel on Bedrock models","Backstage backend with AWS SDK integration","Optional: STS assume-role permissions for cross-account access"],"input_types":["AWS credential sources (IAM role, access keys, STS tokens)","credential configuration (region, role ARN, external ID)"],"output_types":["authenticated Bedrock API client","credential metadata (expiration, assumed role)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-roadiehq-rag-ai-backend-embeddings-aws__cap_5","uri":"capability://memory.knowledge.vector.storage.backend.abstraction.and.metadata.persistence","name":"vector storage backend abstraction and metadata persistence","description":"Abstracts vector storage operations (insert, search, delete) behind a provider-agnostic interface, enabling integration with multiple vector databases (Postgres pgvector, Pinecone, Weaviate, etc.) without changing embedding code. Handles metadata persistence alongside vectors (document source, chunk ID, timestamps) and implements filtering/retrieval logic for RAG context assembly. Coordinates embedding generation with vector storage writes to maintain consistency.","intents":["I want to store Bedrock embeddings in my existing vector database without custom integration code","I need to search embeddings by metadata (document source, tags) in addition to semantic similarity","I want to switch vector storage backends (Postgres → Pinecone) without rewriting RAG queries"],"best_for":["Teams with existing vector database infrastructure seeking Bedrock embedding integration","Organizations evaluating multiple vector storage options (managed vs self-hosted)","RAG systems requiring rich metadata filtering alongside semantic search"],"limitations":["Vector storage abstraction adds latency — each operation goes through abstraction layer (typically 10-50ms overhead)","Metadata filtering capabilities vary by backend; complex filters may not be portable across storage providers","No built-in consistency guarantees between embedding generation and vector storage writes — failures can leave orphaned vectors or metadata","Search result ranking and filtering logic must be implemented per-backend; 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