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Provides a normalized representation layer so downstream embedding and retrieval components can operate on a consistent data model regardless of input source or chunking method.","intents":["Work with documents from multiple sources (PDF, HTML, Markdown, databases) through a single interface","Support multiple chunking strategies without changing embedding or retrieval code","Preserve metadata (source, page number, chunk position) through the entire RAG pipeline"],"best_for":["RAG systems ingesting heterogeneous document types (PDFs, web content, structured data)","Teams building pluggable chunking strategies that need to work with any document loader"],"limitations":["Abstraction may lose source-specific metadata if not explicitly preserved in the interface","Performance overhead from normalization layer when processing large document batches","Requires careful design to balance flexibility with usability — overly generic interfaces become hard to work with"],"requires":["TypeScript 4.5+","Understanding of RAG pipeline stages (load → chunk → embed → retrieve)"],"input_types":["Raw documents from various sources (PDF buffers, HTML strings, JSON records)","Chunking configuration objects"],"output_types":["Normalized Document objects with metadata","Chunk objects with position, source reference, and content"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-forge-shared__cap_2","uri":"capability://tool.use.integration.embedding.provider.interface.and.adapter.pattern","name":"embedding provider interface and adapter pattern","description":"Defines a standardized interface for embedding providers (OpenAI, Anthropic, local models, etc.) with an adapter pattern that allows swapping embedding backends without changing application code. Handles provider-specific API details (authentication, rate limiting, batch sizing, dimension handling) behind a unified abstraction layer.","intents":["Switch between embedding providers (OpenAI → Anthropic → local Ollama) without refactoring application code","Batch embed documents efficiently while respecting provider rate limits and token budgets","Handle provider-specific quirks (dimension mismatches, API response formats) transparently"],"best_for":["RAG systems that need flexibility to change embedding providers based on cost/latency tradeoffs","Teams building multi-provider RAG systems with fallback strategies"],"limitations":["Adapter pattern adds ~50-100ms latency per embedding call due to abstraction overhead","Dimension mismatches between providers require explicit handling or vector normalization","No built-in caching of embeddings — requires external vector store for deduplication","Batch sizing and rate limiting must be configured per provider; no automatic optimization"],"requires":["API keys for at least one embedding provider (OpenAI, Anthropic, Hugging Face, etc.)","Node.js 16+","Network access to embedding provider APIs or local model server"],"input_types":["Text strings or Document/Chunk objects","Embedding provider configuration (API key, model name, batch size)"],"output_types":["Embedding vectors (Float32Array or number[])","Metadata about embedding (model used, dimensions, tokens consumed)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-forge-shared__cap_3","uri":"capability://search.retrieval.vector.store.abstraction.and.retrieval.interface","name":"vector store abstraction and retrieval interface","description":"Defines a unified interface for vector stores (Pinecone, Weaviate, Milvus, in-memory) that abstracts over different storage backends and retrieval strategies. Handles similarity search, filtering, metadata queries, and result ranking through a consistent API, allowing applications to swap vector stores without changing retrieval logic.","intents":["Query documents by semantic similarity across different vector store backends","Filter retrieval results by metadata (source, date, category) without backend-specific syntax","Rank and rerank results using different similarity metrics or custom scoring functions"],"best_for":["RAG systems that need to support multiple vector store backends (cloud-hosted vs self-hosted)","Teams evaluating different vector stores and need to avoid vendor lock-in"],"limitations":["Abstraction may not expose backend-specific optimizations (e.g., Pinecone's sparse-dense hybrid search)","Filtering and metadata query syntax varies significantly across backends; unified interface may be lowest-common-denominator","No built-in support for incremental updates or real-time indexing — depends on backend capabilities","Retrieval latency depends heavily on backend choice; abstraction doesn't hide performance differences"],"requires":["Connection credentials for at least one vector store (Pinecone API key, Weaviate URL, etc.)","Node.js 16+","Pre-computed embeddings for documents to be stored"],"input_types":["Query embeddings (Float32Array or number[])","Metadata filter objects","Top-k parameter for result limiting"],"output_types":["Ranked list of Document/Chunk objects with similarity scores","Metadata about retrieval (backend used, query time, result count)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-forge-shared__cap_4","uri":"capability://automation.workflow.rag.pipeline.orchestration.and.composition","name":"rag pipeline orchestration and composition","description":"Provides utilities for composing RAG pipelines from discrete components (loaders, chunkers, embedders, retrievers) with explicit data flow and error handling. 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Handles secrets management (API keys, database credentials) with support for different environments (dev, staging, prod) and configuration validation against defined schemas.","intents":["Load RAG pipeline configuration from environment variables or config files without hardcoding","Validate configuration objects against schemas before passing to pipeline components","Manage secrets (API keys, database credentials) securely across different deployment environments"],"best_for":["Teams deploying RAG systems across multiple environments (dev, staging, production)","Applications that need to support different embedding providers or vector stores per environment"],"limitations":["Configuration validation is static — doesn't catch runtime issues like invalid API keys until first use","No built-in secrets rotation or expiration handling","Environment variable naming conventions must be documented and followed consistently","No support for dynamic configuration updates without restarting the application"],"requires":["Node.js 16+","Environment variables or config files in supported format (JSON, YAML, .env)"],"input_types":["Environment variables","Configuration files (JSON, YAML, .env)","Runtime configuration objects"],"output_types":["Validated configuration objects","Configuration validation errors","Resolved secrets and credentials"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-forge-shared__cap_6","uri":"capability://automation.workflow.logging.and.observability.utilities","name":"logging and observability utilities","description":"Provides structured logging and observability hooks for RAG pipelines, including timing information, error tracking, and metrics collection at each stage. 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Handles transient failures (API rate limits, network timeouts) differently from permanent failures (invalid API keys, unsupported document formats) with appropriate recovery strategies.","intents":["Automatically retry failed API calls (embedding, retrieval) with exponential backoff","Handle rate limiting from embedding providers gracefully without losing data","Distinguish between transient and permanent failures and respond appropriately"],"best_for":["RAG systems calling external APIs (OpenAI, Anthropic, Pinecone) that may fail transiently","Production deployments that need resilience to network issues and API rate limiting"],"limitations":["Retry logic adds latency — exponential backoff can cause significant delays for heavily rate-limited APIs","No built-in circuit breaker pattern; repeated failures will eventually exhaust retry budgets","Fallback strategies must be configured explicitly; no automatic provider failover","Retries consume additional API quota; may increase costs for metered APIs"],"requires":["Node.js 16+","Configuration of retry parameters (max attempts, backoff strategy, timeout)"],"input_types":["Function calls that may fail (API requests, file operations)","Error objects with type information (transient vs permanent)","Retry configuration (max attempts, backoff multiplier, timeout)"],"output_types":["Successful result after retries, or final error if all retries exhausted","Metadata about retry attempts (count, delays, final error)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-forge-shared__cap_8","uri":"capability://data.processing.analysis.utility.functions.for.text.processing.and.normalization","name":"utility functions for text processing and normalization","description":"Provides helper functions for common text processing tasks in RAG pipelines: tokenization, text normalization (lowercasing, removing punctuation), whitespace handling, and encoding/decoding. These utilities ensure consistent text preprocessing across different document loaders and chunking strategies.","intents":["Normalize text consistently across different document sources and formats","Count tokens accurately for embedding and retrieval operations","Handle encoding issues (UTF-8, special characters) transparently"],"best_for":["RAG systems processing documents from multiple sources with inconsistent formatting","Teams that need consistent text preprocessing without reimplementing utilities in each component"],"limitations":["Tokenization is model-specific; generic tokenizers may not match embedding model tokenization exactly","Text normalization can lose information (e.g., lowercasing removes proper nouns)","No support for language-specific processing (stemming, lemmatization); requires external libraries","Performance overhead for large text batches; no built-in parallelization"],"requires":["Node.js 16+","Optional: tokenizer library (js-tiktoken for OpenAI models) for accurate token counting"],"input_types":["Text strings","Document objects with raw content","Encoding specifications (UTF-8, ASCII, etc.)"],"output_types":["Normalized text strings","Token counts","Processed text with metadata (original length, normalized length)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["TypeScript 4.5+","Node.js 16+ for runtime validation if using schema validators","npm or yarn workspace support for monorepo consumption","Understanding of RAG pipeline stages (load → chunk → embed → retrieve)","API keys for at least one embedding provider (OpenAI, Anthropic, Hugging Face, etc.)","Node.js 16+","Network access to embedding provider APIs or local model server","Connection credentials for at least one vector store (Pinecone API key, Weaviate URL, etc.)","Pre-computed embeddings for documents to be stored","All required RAG components (loaders, chunkers, embedders, retrievers) configured and available"],"failure_modes":["Type definitions are TypeScript-only; non-TS consumers must rely on runtime validation or manual type mapping","Schema changes require coordinated updates across all dependent packages in the monorepo","No automatic migration path for breaking schema changes in production deployments","Abstraction may lose source-specific metadata if not explicitly preserved in the interface","Performance overhead from normalization layer when processing large document batches","Requires careful design to balance flexibility with usability — overly generic interfaces become hard to work with","Adapter pattern adds ~50-100ms latency per embedding call due to abstraction overhead","Dimension mismatches between providers require explicit handling or vector normalization","No built-in caching of embeddings — requires external vector store for deduplication","Batch sizing and rate limiting must be configured per provider; no automatic optimization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.08075350280688533,"quality":0.28,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.653Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":321,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rag-forge-shared","compare_url":"https://unfragile.ai/compare?artifact=rag-forge-shared"}},"signature":"XyFs3lY6nxtKmw4g+g9qv9u0Mwtc3a/f7kVoR6tiK9jmAU1ryVJzDZAvmRPDr7HAYrWv7ukQXe3zF+RE/Nu7Bg==","signedAt":"2026-06-22T02:20:16.788Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rag-forge-shared","artifact":"https://unfragile.ai/rag-forge-shared","verify":"https://unfragile.ai/api/v1/verify?slug=rag-forge-shared","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}