{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-deepset-ai--haystack","slug":"deepset-ai--haystack","name":"haystack","type":"framework","url":"https://haystack.deepset.ai","page_url":"https://unfragile.ai/deepset-ai--haystack","categories":["frameworks-sdks","rag-knowledge","deployment-infra"],"tags":["agent","agents","ai","gemini","generative-ai","gpt-4","information-retrieval","large-language-models","llm","machine-learning","nlp","orchestration","python","pytorch","question-answering","rag","retrieval-augmented-generation","semantic-search","summarization","transformers"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-deepset-ai--haystack__cap_0","uri":"capability://automation.workflow.modular.component.based.pipeline.composition.with.explicit.data.flow","name":"modular component-based pipeline composition with explicit data flow","description":"Haystack uses a decorator-based component system (@component) where any Python class can be registered as a reusable building block with typed inputs/outputs. Components connect via a directed acyclic graph (DAG) pipeline that validates type compatibility at graph construction time, enabling explicit control over data routing between retrieval, ranking, and generation stages. The Pipeline class manages execution order, handles variadic type conversion, and supports both sync and async execution paths with automatic serialization of component state.","intents":["I want to build a RAG pipeline where I can explicitly control which documents flow to which ranker before hitting the LLM","I need to compose retrieval, reranking, and generation steps with type-safe connections and runtime validation","I want to reuse the same document processor component across multiple pipelines without code duplication","I need to visualize and debug the exact data flow through my LLM application"],"best_for":["teams building production RAG systems requiring explicit control over retrieval pipelines","developers migrating from monolithic LLM chains to modular, testable architectures","researchers prototyping multi-stage retrieval and ranking workflows"],"limitations":["DAG validation adds ~50-100ms overhead at pipeline initialization for large graphs (100+ components)","No built-in cycle detection for dynamic pipelines — circular dependencies cause runtime hangs","Component state serialization requires all inputs/outputs to be JSON-serializable; custom objects need manual serialization","Async components cannot be mixed with sync-only third-party libraries in the same pipeline without wrapper adapters"],"requires":["Python 3.10+","haystack-ai package installed via pip","Type hints on component methods (required for input/output validation)"],"input_types":["Python objects with type hints","Structured data (lists, dicts, dataclasses)","Document objects (Haystack's Document class)","Chat messages (ChatMessage objects)"],"output_types":["Python objects matching declared output types","Structured data (lists, dicts, dataclasses)","Document objects with metadata","Generation results (strings, structured outputs)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_1","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.with.multi.stage.document.ranking","name":"retrieval-augmented generation (rag) with multi-stage document ranking","description":"Haystack provides end-to-end RAG by combining document retrieval (via vector databases or BM25), optional reranking stages (using cross-encoders or LLM-based rankers), and generation. The architecture separates retrieval from ranking from generation as distinct pipeline stages, allowing developers to swap retrievers (Elasticsearch, Weaviate, Pinecone) and rankers (Cohere, ColBERT, LLM-based) independently. Document preprocessing (splitting, embedding, metadata extraction) is handled by pluggable converters and embedders that support batch processing and streaming.","intents":["I want to build a question-answering system that retrieves documents, reranks them by relevance, and generates answers","I need to compare different embedding models and rerankers without rewriting my pipeline","I want to index documents from multiple sources (PDFs, web pages, databases) and query them semantically","I need to control how many documents are retrieved and ranked before passing to the LLM to optimize latency/cost"],"best_for":["teams building production QA systems over proprietary documents","enterprises migrating from keyword search to semantic search","researchers evaluating different retrieval and ranking strategies"],"limitations":["Multi-stage ranking adds 200-500ms latency per query (depends on reranker model size)","Embedding generation requires external API calls or local model inference; no built-in caching of embeddings across pipeline runs","Document store integrations require separate SDK setup (e.g., Weaviate client, Pinecone API key) — Haystack doesn't abstract authentication","Batch document indexing is not distributed; large corpora (>1M documents) require external indexing infrastructure"],"requires":["Python 3.10+","Document store client (Weaviate, Pinecone, Elasticsearch, etc.)","Embedding model (OpenAI, Hugging Face, local)","Optional: reranker model (Cohere, ColBERT, or LLM-based)"],"input_types":["Document objects (with text, metadata, embedding vectors)","Query strings (natural language questions)","Document paths (PDF, HTML, DOCX files for preprocessing)"],"output_types":["Retrieved documents (ranked by relevance score)","Generated answers (strings or structured outputs)","Metadata (source, relevance scores, chunk boundaries)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_10","uri":"capability://automation.workflow.async.await.support.for.non.blocking.pipeline.execution","name":"async/await support for non-blocking pipeline execution","description":"Haystack supports both synchronous and asynchronous pipeline execution through AsyncPipeline, enabling non-blocking I/O for external API calls, database queries, and file operations. Components can be marked as async, and the pipeline automatically handles concurrent execution where possible. This is critical for production systems where blocking on I/O would waste resources.","intents":["I want my RAG pipeline to handle multiple concurrent queries without blocking","I need to call multiple external APIs (embedding service, vector database, LLM) in parallel","I want to build a real-time streaming application that processes queries concurrently","I need to optimize latency by parallelizing independent pipeline stages"],"best_for":["teams building high-throughput LLM services","developers optimizing latency-sensitive applications","systems requiring concurrent handling of multiple requests"],"limitations":["Async components require understanding async/await patterns; mixing sync and async code is error-prone","Async debugging is harder than sync; stack traces are less readable","Not all third-party libraries support async; sync-only libraries require wrapper adapters","Async overhead can be significant for low-latency operations (< 10ms); sync may be faster","Resource contention (database connections, API rate limits) still applies; async doesn't eliminate bottlenecks"],"requires":["Python 3.10+","Understanding of async/await patterns","Async-compatible libraries for external services (aiohttp, asyncpg, etc.)"],"input_types":["Async component definitions (Python async functions)","Queries (for concurrent processing)"],"output_types":["Results (same as sync, but processed concurrently)","Execution traces (showing concurrent execution)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_11","uri":"capability://memory.knowledge.document.store.abstraction.with.multiple.backend.support","name":"document store abstraction with multiple backend support","description":"Haystack abstracts document storage through a DocumentStore interface that supports multiple backends (Weaviate, Pinecone, Qdrant, Chroma, Elasticsearch, In-Memory). Developers write document indexing and retrieval code once and can swap backends by changing configuration. The framework handles backend-specific details (API calls, query syntax, authentication) internally, enabling easy migration between databases.","intents":["I want to use an in-memory document store for testing and Weaviate for production","I need to migrate from Elasticsearch to Pinecone without rewriting my indexing code","I want to support multiple document stores simultaneously for redundancy or sharding","I need to evaluate different vector databases without changing my application code"],"best_for":["teams avoiding vendor lock-in with document storage","developers testing with in-memory stores before deploying to production","enterprises evaluating multiple document store options"],"limitations":["Backend-specific features (advanced filtering, aggregations) are not uniformly supported","Performance characteristics vary significantly across backends; optimization requires backend-specific tuning","Authentication and connection management are manual; Haystack doesn't provide credential abstraction","Schema migrations are not automated; changing document structure requires manual updates","No built-in sharding or replication; scaling requires external infrastructure"],"requires":["Python 3.10+","Document store client (Weaviate, Pinecone, Qdrant, Chroma, Elasticsearch, etc.)","Database credentials and connection details"],"input_types":["Document objects (with text, metadata, embeddings)","Queries (for retrieval)"],"output_types":["Retrieved documents (ranked by relevance)","Metadata (source, relevance scores)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_12","uri":"capability://automation.workflow.serialization.and.deserialization.of.pipelines.for.reproducibility","name":"serialization and deserialization of pipelines for reproducibility","description":"Haystack supports serializing entire pipelines to YAML or JSON, enabling reproducible execution and version control of pipeline definitions. Developers can save a pipeline configuration, commit it to git, and recreate the exact same pipeline later. Component state (model weights, configuration) is also serializable, enabling checkpoint-and-restore workflows.","intents":["I want to version control my RAG pipeline configuration in git","I need to save a trained pipeline and load it later without retraining","I want to share pipeline definitions with team members","I need to reproduce a pipeline execution from a saved configuration"],"best_for":["teams practicing infrastructure-as-code for LLM pipelines","developers managing multiple pipeline variants","enterprises requiring reproducibility for compliance"],"limitations":["Serialization requires all components to be serializable; custom components need manual serialization logic","Large model weights (embeddings, LLM checkpoints) are not serialized; only references are saved","Deserialization requires all dependencies to be installed; no automatic dependency resolution","YAML/JSON serialization is human-readable but verbose for complex pipelines","No built-in schema validation; invalid configurations are caught at runtime"],"requires":["Python 3.10+","All pipeline components must be importable at deserialization time"],"input_types":["Pipeline objects (in-memory)"],"output_types":["YAML/JSON configuration files","Serialized component state"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_2","uri":"capability://planning.reasoning.agentic.workflow.orchestration.with.tool.invocation.and.iterative.reasoning","name":"agentic workflow orchestration with tool invocation and iterative reasoning","description":"Haystack's agent system enables autonomous agents that iteratively reason over tool outputs using a loop pattern: agent receives query → selects tool → invokes tool → observes result → repeats until task complete. Tools are registered as components with type-safe schemas, and the agent uses an LLM to decide which tool to invoke based on the current state. The framework supports both simple tool-calling (via OpenAI/Anthropic function-calling APIs) and complex multi-step reasoning with memory of previous tool invocations.","intents":["I want to build an agent that can search the web, retrieve documents, and synthesize answers autonomously","I need an agent that can invoke multiple tools in sequence based on reasoning about intermediate results","I want to implement a customer support agent that can look up account info, process refunds, and escalate to humans","I need to debug why my agent is making incorrect tool choices or getting stuck in loops"],"best_for":["teams building autonomous agents for customer support, research, or data analysis","developers implementing complex multi-step workflows that require reasoning","researchers experimenting with agent architectures and tool-use strategies"],"limitations":["Agent loops can be unpredictable; no built-in max-iteration limits prevent infinite loops without explicit timeout configuration","Tool selection depends entirely on LLM reasoning quality; weak models (GPT-3.5) often make suboptimal tool choices","No built-in memory persistence across agent runs; requires external state store for multi-turn conversations","Tool invocation errors are not automatically recovered; agents require explicit error-handling tools to gracefully degrade","Observability is limited to logging; no built-in tracing of agent decision trees or tool invocation chains"],"requires":["Python 3.10+","LLM with function-calling support (OpenAI, Anthropic, Cohere, or compatible)","Tool definitions with type-annotated parameters","Optional: external tools (web search API, database client, etc.)"],"input_types":["Natural language queries (user intent)","Tool definitions (Python functions with type hints)","Chat message history (for multi-turn agents)"],"output_types":["Final answer (string or structured output)","Tool invocation trace (which tools were called, in what order)","Intermediate reasoning steps (if captured via logging)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_3","uri":"capability://text.generation.language.multi.provider.llm.integration.with.unified.chat.message.interface","name":"multi-provider llm integration with unified chat message interface","description":"Haystack abstracts LLM provider differences through a unified ChatMessage interface and pluggable generator components. Developers write once against the Haystack API and can swap between OpenAI, Anthropic, Cohere, Hugging Face, Azure, AWS Bedrock, and local models without changing pipeline code. The framework handles provider-specific details (API authentication, request formatting, response parsing) internally, and supports streaming responses, function calling, and vision capabilities where available.","intents":["I want to build an LLM application that can work with multiple model providers without rewriting code","I need to compare outputs from GPT-4, Claude, and Gemini on the same task","I want to use a local open-source model in development and switch to a cloud API in production","I need to handle streaming responses from different providers with a unified interface"],"best_for":["teams avoiding vendor lock-in by supporting multiple LLM providers","developers building cost-optimized systems that can fall back to cheaper models","researchers comparing model outputs across providers"],"limitations":["Provider-specific features (vision, function-calling) are not uniformly supported; some models lack streaming or structured output","API key management is manual; Haystack doesn't provide centralized credential handling (requires environment variables or custom loaders)","Rate limiting and retry logic are basic; production systems need external rate-limiting middleware","Streaming responses require async/await; sync code cannot consume streams without blocking","Cost tracking and token counting are not built-in; requires manual logging or external monitoring"],"requires":["Python 3.10+","API keys for chosen providers (OpenAI, Anthropic, Cohere, etc.)","Optional: local model setup (Ollama, vLLM) for on-premise deployment"],"input_types":["Chat messages (ChatMessage objects with role, content, metadata)","Prompts (strings or structured prompt templates)","Function schemas (for function-calling models)"],"output_types":["Generated text (strings)","Structured outputs (JSON, if model supports it)","Function calls (tool invocations with arguments)","Streaming tokens (for real-time response generation)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_4","uri":"capability://data.processing.analysis.document.preprocessing.and.embedding.with.pluggable.converters.and.embedders","name":"document preprocessing and embedding with pluggable converters and embedders","description":"Haystack provides a modular document processing pipeline that converts raw files (PDF, DOCX, HTML, Markdown) into structured Document objects, splits them into chunks, extracts metadata, and generates embeddings. Converters handle file format parsing, splitters implement various chunking strategies (fixed-size, semantic, recursive), and embedders integrate with external APIs (OpenAI, Hugging Face) or local models. The entire pipeline is composable — developers can chain converters, splitters, and embedders in custom sequences and apply them at scale.","intents":["I want to ingest a folder of PDFs, extract text, split into chunks, and embed them for semantic search","I need to preserve document structure (headings, tables) while splitting for better retrieval","I want to extract metadata (author, date, source URL) from documents during preprocessing","I need to batch-process 100k documents efficiently without loading everything into memory"],"best_for":["teams building document ingestion pipelines for RAG systems","enterprises migrating from keyword search to semantic indexing","developers handling diverse document formats (PDFs, web pages, databases)"],"limitations":["PDF parsing is fragile for complex layouts (scanned PDFs, multi-column documents); requires external OCR tools for image-based PDFs","Embedding generation requires external API calls or local model inference; no built-in caching across runs","Metadata extraction is manual; no automatic field detection (requires custom converters for domain-specific metadata)","Batch processing is single-threaded by default; parallel processing requires manual thread/process management","No built-in deduplication; duplicate documents must be detected and removed externally"],"requires":["Python 3.10+","File format libraries (pypdf for PDFs, python-docx for DOCX, etc.)","Embedding model (OpenAI, Hugging Face, or local)","Optional: OCR library (Tesseract) for scanned documents"],"input_types":["File paths (PDF, DOCX, HTML, Markdown, TXT)","Raw file bytes","Document objects (for re-processing)"],"output_types":["Document objects (with text, metadata, embeddings)","Chunks (split documents with boundaries)","Embedding vectors (numerical representations)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_5","uri":"capability://search.retrieval.semantic.search.and.vector.database.integration","name":"semantic search and vector database integration","description":"Haystack integrates with multiple vector databases (Weaviate, Pinecone, Qdrant, Chroma, Elasticsearch) through pluggable DocumentStore implementations. The framework handles embedding generation, vector indexing, and similarity search with configurable distance metrics (cosine, dot product, Euclidean). Developers define retrieval strategies (top-k, threshold-based, hybrid BM25+vector) and the pipeline automatically handles batching, filtering by metadata, and result ranking.","intents":["I want to search a large document corpus semantically and retrieve the most relevant chunks","I need to filter search results by metadata (date, source, category) before ranking","I want to use hybrid search combining keyword (BM25) and semantic (vector) matching","I need to scale semantic search to millions of documents across multiple vector databases"],"best_for":["teams building semantic search features for knowledge bases or customer support","enterprises with large document corpora requiring efficient retrieval","developers evaluating different vector databases without rewriting search logic"],"limitations":["Vector database setup and authentication are manual; Haystack doesn't provide managed hosting","Similarity search quality depends entirely on embedding model quality; no built-in evaluation of retrieval performance","Metadata filtering is database-specific; complex filters may not be portable across databases","No built-in approximate nearest neighbor optimization; large-scale searches can be slow without proper indexing","Embedding updates require re-indexing; no incremental update mechanism for changed documents"],"requires":["Python 3.10+","Vector database (Weaviate, Pinecone, Qdrant, Chroma, Elasticsearch, etc.)","Embedding model (OpenAI, Hugging Face, or local)","Database credentials and connection details"],"input_types":["Query strings (natural language questions)","Query embeddings (pre-computed vectors)","Metadata filters (dict-based filter expressions)"],"output_types":["Retrieved documents (ranked by similarity score)","Similarity scores (numerical relevance values)","Metadata (source, chunk boundaries, timestamps)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_6","uri":"capability://text.generation.language.prompt.templating.and.chat.message.construction","name":"prompt templating and chat message construction","description":"Haystack provides a PromptBuilder component that constructs prompts from templates with variable substitution and chat message formatting. Templates support Jinja2 syntax for conditional logic and loops, and the builder automatically formats messages according to the target LLM's requirements (OpenAI's message format, Anthropic's format, etc.). Developers can define reusable prompt templates and compose them in pipelines, with support for few-shot examples and dynamic prompt engineering.","intents":["I want to build a prompt template that inserts retrieved documents and user queries dynamically","I need to format chat messages correctly for different LLM providers without manual string manipulation","I want to experiment with different prompt variations (few-shot examples, system instructions) without changing code","I need to construct complex prompts with conditional logic (e.g., different instructions for different document types)"],"best_for":["teams building prompt-driven LLM applications","researchers experimenting with prompt engineering and few-shot learning","developers managing multiple prompt variants for A/B testing"],"limitations":["Jinja2 templating adds minimal overhead but can be confusing for non-technical users","No built-in prompt optimization or automatic few-shot example selection","Template variables must be manually passed from pipeline components; no automatic variable discovery","No version control or A/B testing framework for prompt variants","Token counting is not built-in; developers must manually track prompt length"],"requires":["Python 3.10+","Jinja2 library (included with Haystack)"],"input_types":["Template strings (Jinja2 format)","Template variables (dicts with key-value pairs)","Chat messages (for message formatting)"],"output_types":["Formatted prompts (strings)","Chat messages (ChatMessage objects with proper formatting)","Prompt metadata (token count, template name, variables used)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_7","uri":"capability://data.processing.analysis.evaluation.and.metrics.for.retrieval.and.generation.quality","name":"evaluation and metrics for retrieval and generation quality","description":"Haystack includes built-in evaluation components for assessing retrieval quality (precision, recall, MRR, NDCG) and generation quality (BLEU, ROUGE, semantic similarity). Developers can define evaluation pipelines that run queries against a gold standard dataset, compare retrieved documents to expected results, and score generated answers. The framework supports custom metrics and integrates with external evaluation libraries (e.g., RAGAS for RAG evaluation).","intents":["I want to measure how well my retrieval pipeline is working compared to a baseline","I need to evaluate if my LLM-generated answers match expected outputs","I want to run A/B tests comparing different retrieval strategies or ranking models","I need to track evaluation metrics over time to detect regressions in my RAG system"],"best_for":["teams building production RAG systems requiring quality assurance","researchers evaluating retrieval and generation models","developers implementing continuous evaluation pipelines"],"limitations":["Evaluation metrics require gold standard datasets; no automatic ground truth generation","Semantic similarity metrics depend on embedding model quality; no built-in metric validation","Evaluation is single-threaded; large-scale evaluation (1000+ queries) can be slow","No built-in statistical significance testing; results require manual interpretation","Custom metrics require implementing Haystack's Evaluator interface; no simple metric registration"],"requires":["Python 3.10+","Gold standard dataset (queries with expected documents/answers)","Optional: external evaluation libraries (RAGAS, evaluate, etc.)"],"input_types":["Query-document pairs (for retrieval evaluation)","Query-answer pairs (for generation evaluation)","Predicted results (from retrieval or generation pipelines)"],"output_types":["Evaluation metrics (precision, recall, BLEU, ROUGE, etc.)","Metric aggregates (mean, std dev across dataset)","Per-sample scores (individual query/document scores)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_8","uri":"capability://planning.reasoning.human.in.the.loop.workflows.with.explicit.approval.gates","name":"human-in-the-loop workflows with explicit approval gates","description":"Haystack supports human-in-the-loop (HITL) patterns where agents or pipelines pause for human review and approval before proceeding. Developers can insert approval components that collect human feedback, validate decisions, or request clarification. The framework handles state persistence across human interactions and supports both synchronous (blocking) and asynchronous (non-blocking) approval patterns.","intents":["I want my agent to ask for human approval before executing high-risk actions (e.g., refunds, data deletion)","I need to collect human feedback on generated answers to improve my RAG system","I want to implement a review workflow where documents are validated by humans before indexing","I need to escalate to human support when my agent is uncertain about the right action"],"best_for":["teams building customer-facing agents requiring human oversight","enterprises with compliance requirements for automated decisions","developers implementing feedback loops for model improvement"],"limitations":["No built-in UI for human approval; requires custom frontend or integration with external tools","State persistence across human interactions requires external storage (database, message queue)","Timeout handling is manual; no built-in escalation if humans don't respond within a deadline","Feedback collection is not standardized; each use case requires custom feedback schema","No built-in analytics on human decisions; requires manual logging for audit trails"],"requires":["Python 3.10+","External state store (database, message queue) for persistence","Optional: UI framework for approval interface (React, Vue, etc.)"],"input_types":["Agent decisions (action, parameters, confidence)","Human feedback (approval, rejection, alternative action)","Context (documents, reasoning, previous decisions)"],"output_types":["Approval status (approved, rejected, modified)","Human feedback (comments, alternative actions)","Audit trail (who approved, when, why)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-deepset-ai--haystack__cap_9","uri":"capability://automation.workflow.observability.and.tracing.with.structured.logging","name":"observability and tracing with structured logging","description":"Haystack provides structured logging and tracing capabilities that capture component execution, LLM API calls, and pipeline state at each step. The framework integrates with OpenTelemetry for distributed tracing and supports custom instrumentation. Developers can trace execution flows, measure latency at each pipeline stage, and debug failures by inspecting intermediate results and error logs.","intents":["I want to trace why my RAG pipeline is returning irrelevant documents","I need to measure latency at each pipeline stage to identify bottlenecks","I want to log all LLM API calls for compliance and cost tracking","I need to debug agent decision-making by inspecting intermediate reasoning steps"],"best_for":["teams operating production LLM systems requiring observability","developers debugging complex multi-stage pipelines","enterprises with compliance requirements for audit trails"],"limitations":["Structured logging adds ~5-10% overhead per pipeline execution","OpenTelemetry integration requires external tracing backend (Jaeger, Datadog, etc.)","Log volume can be high for large pipelines; requires filtering or sampling for production","No built-in alerting on anomalies (e.g., high latency, low relevance scores)","Custom instrumentation requires understanding Haystack's logging API"],"requires":["Python 3.10+","Optional: OpenTelemetry libraries and tracing backend (Jaeger, Datadog, etc.)"],"input_types":["Pipeline execution events (component start, end, error)","LLM API calls (request, response, latency)","Custom metrics (relevance scores, token counts, etc.)"],"output_types":["Structured logs (JSON format with timestamps, component names, metrics)","Traces (distributed traces showing execution flow)","Metrics (latency, error rates, token usage)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":62,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","haystack-ai package installed via pip","Type hints on component methods (required for input/output validation)","Document store client (Weaviate, Pinecone, Elasticsearch, etc.)","Embedding model (OpenAI, Hugging Face, local)","Optional: reranker model (Cohere, ColBERT, or LLM-based)","Understanding of async/await patterns","Async-compatible libraries for external services (aiohttp, asyncpg, etc.)","Document store client (Weaviate, Pinecone, Qdrant, Chroma, Elasticsearch, etc.)","Database credentials and connection details"],"failure_modes":["DAG validation adds ~50-100ms overhead at pipeline initialization for large graphs (100+ components)","No built-in cycle detection for dynamic pipelines — circular dependencies cause runtime hangs","Component state serialization requires all inputs/outputs to be JSON-serializable; custom objects need manual serialization","Async components cannot be mixed with sync-only third-party libraries in the same pipeline without wrapper adapters","Multi-stage ranking adds 200-500ms latency per query (depends on reranker model size)","Embedding generation requires external API calls or local model inference; no built-in caching of embeddings across pipeline runs","Document store integrations require separate SDK setup (e.g., Weaviate client, Pinecone API key) — Haystack doesn't abstract authentication","Batch document indexing is not distributed; large corpora (>1M documents) require external indexing infrastructure","Async components require understanding async/await patterns; mixing sync and async code is error-prone","Async debugging is harder than sync; stack traces are less readable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7588495234919252,"quality":0.6,"ecosystem":0.85,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:21.997Z","last_commit":"2026-04-30T11:36:44Z"},"community":{"stars":25065,"forks":2760,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepset-ai--haystack","compare_url":"https://unfragile.ai/compare?artifact=deepset-ai--haystack"}},"signature":"4cbNneAr8zJrjdVGSHfxJufWlUe/lj42x8EBtQ1LW7Fzhd1zoj6Gx2S/4O0x2Om7PZN7RlRtO4uym1V/Ak98Cg==","signedAt":"2026-06-20T04:17:50.088Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepset-ai--haystack","artifact":"https://unfragile.ai/deepset-ai--haystack","verify":"https://unfragile.ai/api/v1/verify?slug=deepset-ai--haystack","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"}}