{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"haystack","slug":"haystack","name":"Haystack","type":"framework","url":"https://github.com/deepset-ai/haystack","page_url":"https://unfragile.ai/haystack","categories":["rag-knowledge","deployment-infra","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"haystack__cap_0","uri":"capability://automation.workflow.declarative.pipeline.dag.composition.with.component.based.orchestration","name":"declarative pipeline dag composition with component-based orchestration","description":"Haystack provides a decorator-based component system (@component) where any Python class can be registered as a reusable pipeline node with typed inputs/outputs. Pipelines are constructed as directed acyclic graphs (DAGs) where components connect via socket-based routing, enabling explicit control flow definition. The Pipeline class validates component compatibility at graph construction time, performs type checking across connections, and supports both synchronous and asynchronous execution paths through separate Pipeline and AsyncPipeline implementations.","intents":["I want to build a reusable RAG pipeline by connecting retrieval, ranking, and generation components without writing orchestration boilerplate","I need to define complex multi-step workflows where output from one component feeds into multiple downstream components with type safety","I want to visualize and debug the execution flow of my LLM pipeline to understand data routing and component dependencies"],"best_for":["Teams building production RAG systems requiring explicit control over retrieval and generation stages","Developers migrating from monolithic LLM scripts to modular, testable component architectures","Organizations needing transparent, auditable pipelines for compliance and debugging"],"limitations":["DAG structure prevents cyclic dependencies — feedback loops require explicit component design (e.g., agent loops implemented via component state, not graph cycles)","Type validation happens at pipeline construction time, not runtime — dynamic type changes require component redesign","AsyncPipeline and Pipeline are separate implementations, requiring duplicate pipeline definitions for async support or manual conversion"],"requires":["Python 3.10+","haystack-ai package installed via pip","Understanding of Python type hints for component input/output specification"],"input_types":["Python objects with type hints","Structured data (dicts, lists, dataclasses)","Serialized pipeline definitions (YAML/JSON)"],"output_types":["Pipeline execution results as typed dictionaries","Component output routed to downstream nodes","Execution traces with component-level timing and data flow"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_1","uri":"capability://memory.knowledge.multi.backend.document.store.abstraction.with.vector.and.keyword.search","name":"multi-backend document store abstraction with vector and keyword search","description":"Haystack abstracts document persistence through a DocumentStore interface supporting Elasticsearch, Pinecone, Weaviate, and in-memory implementations. Documents are stored with both dense embeddings (for semantic search) and sparse keyword indices, enabling hybrid retrieval strategies. The abstraction layer handles backend-specific query translation, filtering, and result ranking without exposing provider APIs to pipeline code, allowing seamless backend swapping via configuration.","intents":["I want to switch from Elasticsearch to Pinecone without rewriting my retrieval pipeline code","I need to perform hybrid search combining keyword matching and semantic similarity on the same document corpus","I want to store documents with metadata filters and retrieve them based on both vector similarity and structured field matching"],"best_for":["Teams evaluating multiple vector database vendors and needing vendor-agnostic code","Organizations with existing Elasticsearch deployments looking to add semantic search capabilities","Developers building RAG systems requiring both keyword and semantic retrieval for different query types"],"limitations":["Backend-specific features (e.g., Pinecone's namespaces, Weaviate's GraphQL) are not exposed through the abstraction — advanced features require direct backend access","Metadata filtering syntax varies by backend; complex filters may require backend-specific query syntax","In-memory document store is suitable only for prototyping; production deployments require external backends"],"requires":["Python 3.10+","Document store backend (Elasticsearch 7.0+, Pinecone API key, Weaviate instance, or in-memory for dev)","Documents with embeddings pre-computed or generated via Haystack embedders"],"input_types":["Document objects with content, metadata, and embeddings","Query strings and embedding vectors","Metadata filter dictionaries"],"output_types":["Retrieved documents ranked by relevance score","Document metadata and content","Similarity scores from vector search"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"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 provides AsyncPipeline as a parallel implementation to Pipeline, enabling non-blocking execution of components with async/await syntax. Async components can perform I/O-bound operations (API calls, database queries) without blocking the event loop, improving throughput in high-concurrency scenarios. The AsyncPipeline validates component compatibility with async execution and manages event loop lifecycle, allowing developers to write async pipelines with the same component-based architecture as synchronous pipelines.","intents":["I want to build a high-throughput RAG pipeline that handles multiple concurrent requests without blocking","I need to call external APIs (LLM providers, document stores) asynchronously to maximize throughput","I want to integrate my pipeline with async web frameworks (FastAPI, aiohttp) without blocking the event loop"],"best_for":["Teams building production RAG APIs requiring high concurrency and low latency","Organizations deploying pipelines in async environments (FastAPI, async workers)","Developers optimizing throughput by eliminating blocking I/O in pipelines"],"limitations":["AsyncPipeline is a separate implementation from Pipeline — requires duplicate pipeline definitions or manual conversion","Not all components support async execution — components must explicitly implement async methods","Async debugging is more complex than synchronous execution — requires understanding of event loops and async/await","Async components may have different performance characteristics than synchronous versions — requires profiling"],"requires":["Python 3.10+","Components with async support (async def run() methods)","Understanding of Python async/await and event loops"],"input_types":["Pipeline inputs (same as synchronous pipelines)","Async component definitions"],"output_types":["Pipeline execution results (same as synchronous pipelines)","Coroutines for async execution"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_11","uri":"capability://code.generation.editing.type.safe.component.composition.with.runtime.validation","name":"type-safe component composition with runtime validation","description":"Haystack enforces type safety at multiple levels: component input/output types are specified via Python type hints, pipeline connections are validated at graph construction time to ensure type compatibility, and runtime type conversion is performed automatically for compatible types (e.g., str to List[str]). The component system uses socket-based routing (haystack/core/component/sockets.py) where each output socket has a declared type, and connections are validated before pipeline execution. This prevents type mismatches that would cause runtime errors.","intents":["I want the framework to catch type errors in my pipeline at construction time, not at runtime","I need automatic type conversion between components (e.g., single string to list of strings) without manual casting","I want IDE autocomplete and type checking for component inputs and outputs"],"best_for":["Teams building complex pipelines requiring early error detection","Developers using type-aware IDEs (PyCharm, VS Code with Pylance) for pipeline development","Organizations implementing strict code quality standards with type checking (mypy, pyright)"],"limitations":["Type hints are optional in Python — developers can bypass type safety by omitting hints","Runtime type conversion is limited to compatible types — complex conversions require custom components","Type validation adds overhead to pipeline construction — significant for large pipelines","Generic types (List[T], Dict[K, V]) are supported but may be complex to specify correctly"],"requires":["Python 3.10+","Type hints on component input/output methods","Optional: type checker (mypy, pyright) for static analysis"],"input_types":["Component definitions with type hints","Pipeline connections with type validation"],"output_types":["Type-validated pipeline graphs","Runtime type conversion results","Type error messages at construction time"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_12","uri":"capability://code.generation.editing.custom.component.development.with.type.safe.input.output.contracts","name":"custom component development with type-safe input/output contracts","description":"Haystack enables developers to create custom components by decorating Python classes with @component, defining typed inputs and outputs via method signatures. The framework validates component contracts at pipeline construction time, ensuring type compatibility with connected components. Custom components can be stateful (holding model instances), async, and integrated seamlessly into pipelines without special handling.","intents":["I want to create a custom retriever that queries my proprietary database","I need to implement a domain-specific ranker with custom scoring logic","I want to add a preprocessing step that cleans text in a specific way","I need to integrate a third-party model or service as a pipeline component"],"best_for":["teams with custom business logic that doesn't fit standard components","organizations integrating proprietary systems into Haystack pipelines","developers who want to extend Haystack with domain-specific components"],"limitations":["component development requires understanding Haystack's component protocol — learning curve for new developers","type hints are required for input/output contracts — no duck typing support","stateful components (e.g., holding model instances) require careful memory management","testing custom components requires mocking Haystack's component infrastructure"],"requires":["Python >= 3.10","understanding of type hints and decorators","familiarity with Haystack's component protocol"],"input_types":["Python class with @component decorator","method with typed inputs and outputs"],"output_types":["registered component usable in pipelines","component with validated input/output contracts"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_2","uri":"capability://text.generation.language.multi.model.llm.integration.with.provider.agnostic.prompt.templating","name":"multi-model llm integration with provider-agnostic prompt templating","description":"Haystack provides a unified GenerativeModel interface supporting OpenAI, Azure OpenAI, Anthropic, Cohere, Hugging Face (API and local), and AWS Bedrock. Prompts are built using a ChatMessage-based abstraction (haystack/lazy_imports.py, chat message classes) that normalizes role/content across providers, and a PromptBuilder component enables Jinja2-based templating with variable injection. The framework handles provider-specific serialization (e.g., OpenAI's function_call vs Anthropic's tool_use), token counting, and error handling without exposing provider APIs.","intents":["I want to swap from OpenAI GPT-4 to Anthropic Claude without rewriting my prompt and generation logic","I need to build dynamic prompts that inject retrieved documents, user queries, and context variables at runtime","I want to use local open-source models (Llama, Mistral) alongside cloud APIs in the same pipeline"],"best_for":["Teams building multi-model applications to compare LLM performance across providers","Organizations with cost constraints needing to route requests to cheaper models (e.g., local Llama for simple tasks, GPT-4 for complex reasoning)","Developers building RAG systems requiring dynamic prompt construction with retrieved context"],"limitations":["Advanced provider features (e.g., OpenAI's vision, Anthropic's extended thinking) require custom component implementations — not all provider capabilities are abstracted","Token counting is approximate and provider-specific; actual token usage may differ from estimates","Streaming responses are supported but require separate streaming component implementations per provider"],"requires":["Python 3.10+","API keys for chosen providers (OpenAI, Anthropic, Cohere, etc.) or local model setup (Ollama, vLLM)","Jinja2 for prompt templating (included as dependency)"],"input_types":["Chat message lists with role/content","Prompt templates with variable placeholders","Generation parameters (temperature, max_tokens, top_p)"],"output_types":["Generated text responses","Chat message objects with assistant role","Token usage metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_3","uri":"capability://planning.reasoning.agentic.reasoning.with.iterative.tool.invocation.and.state.management","name":"agentic reasoning with iterative tool invocation and state management","description":"Haystack's Agent system (AGENTS.md, Advanced Features) implements autonomous agents that iteratively reason about tasks, invoke tools, and update state based on results. Agents use an Agent component that wraps an LLM with a tool registry, manages conversation history, and implements a loop that continues until a termination condition is met (e.g., max iterations, tool returns final answer). Tool invocation is handled through a schema-based function registry that converts tool definitions to LLM-compatible formats (OpenAI function_call, Anthropic tool_use) and executes them with error handling.","intents":["I want to build an autonomous agent that can search documents, perform calculations, and call APIs to answer complex user queries","I need an agent that maintains conversation history and can reason over multiple tool invocations to solve multi-step problems","I want to define custom tools and have the agent automatically learn when and how to invoke them based on task context"],"best_for":["Teams building autonomous AI assistants that need to interact with multiple data sources and APIs","Organizations implementing customer support bots requiring multi-step reasoning and tool access","Developers prototyping agentic workflows before deploying to production orchestration systems"],"limitations":["Agent loops are not guaranteed to terminate — max iteration limits are required to prevent infinite loops","Tool definitions must be manually specified; no automatic tool discovery from codebase","State management is in-memory only — distributed agents require custom persistence layer","Agent reasoning quality depends heavily on LLM capability; weaker models may fail to invoke correct tools"],"requires":["Python 3.10+","LLM with function calling support (OpenAI, Anthropic, Cohere, etc.)","Tool definitions with type hints and docstrings for LLM interpretation"],"input_types":["User query or task description","Tool definitions with input/output schemas","Conversation history (optional)"],"output_types":["Final answer from agent reasoning","Tool invocation history with results","Conversation messages with tool calls and responses"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_4","uri":"capability://data.processing.analysis.document.processing.pipeline.with.format.conversion.and.chunking","name":"document processing pipeline with format conversion and chunking","description":"Haystack provides a modular document processing stack (Document Converters, Document Preprocessing and Retrieval) supporting multiple input formats (PDF, HTML, DOCX, Markdown, etc.) through format-specific converters. Documents are converted to a unified Document object, then processed through a pipeline of cleaning, splitting, and embedding stages. The DocumentSplitter component implements multiple strategies (sliding window, recursive character splitting, semantic splitting) with configurable chunk size and overlap, enabling fine-grained control over document segmentation for retrieval.","intents":["I want to ingest PDFs, Word documents, and web pages into a unified document format for RAG without writing format-specific parsing code","I need to split large documents into chunks optimized for my retriever's context window while preserving semantic coherence","I want to clean and normalize documents (remove headers/footers, fix encoding) before embedding them for search"],"best_for":["Teams building document ingestion pipelines for RAG systems handling diverse file formats","Organizations with large document repositories needing automated preprocessing before embedding","Developers optimizing retrieval quality by tuning document chunk size and overlap strategies"],"limitations":["PDF parsing quality depends on document structure — scanned PDFs or complex layouts may require OCR (not built-in)","Semantic splitting requires embeddings, adding latency to preprocessing pipeline","Format converters are best-effort; complex document structures (nested tables, multi-column layouts) may lose formatting","No built-in deduplication — duplicate documents must be handled by downstream components"],"requires":["Python 3.10+","Format-specific libraries (pypdf for PDFs, python-docx for DOCX, etc.)","Embedder component for semantic splitting (optional but recommended)"],"input_types":["File paths or file objects (PDF, DOCX, HTML, Markdown, TXT)","Raw document text","Document metadata (title, author, source URL)"],"output_types":["Unified Document objects with content and metadata","Chunked documents with chunk boundaries and overlap","Cleaned and normalized text"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_5","uri":"capability://memory.knowledge.embedding.generation.and.semantic.ranking.with.multi.provider.support","name":"embedding generation and semantic ranking with multi-provider support","description":"Haystack provides Embedder components that generate dense vector representations using OpenAI, Hugging Face, Cohere, and local models. Embedders are integrated into document processing pipelines to embed documents at ingestion time and queries at retrieval time. Ranker components (e.g., SentenceTransformerRanker, LLMRanker) re-rank retrieved documents using semantic similarity or LLM-based scoring, improving retrieval quality. The abstraction allows swapping embedding models without changing pipeline code, enabling experimentation with different embedding strategies.","intents":["I want to embed documents using a specific embedding model (OpenAI, Hugging Face) and store embeddings in my vector database","I need to re-rank retrieved documents using semantic similarity or LLM-based scoring to improve answer quality","I want to experiment with different embedding models to optimize retrieval quality without rewriting my pipeline"],"best_for":["Teams building RAG systems requiring high-quality semantic retrieval","Organizations optimizing retrieval quality through multi-stage ranking (BM25 + semantic + LLM)","Developers evaluating different embedding models for domain-specific retrieval tasks"],"limitations":["Embedding generation adds latency to document ingestion — large document collections may require batch processing","LLM-based ranking is expensive (requires LLM call per document) — practical only for top-k re-ranking","Embedding model selection significantly impacts retrieval quality — no automatic model selection","Embeddings are not updated when documents change — requires re-embedding on document updates"],"requires":["Python 3.10+","Embedding model provider (OpenAI API key, Hugging Face API key, or local model setup)","Vector database supporting embedding storage (Pinecone, Weaviate, Elasticsearch, etc.)"],"input_types":["Document text or chunks","Query strings","Embedding dimension specifications"],"output_types":["Dense vector embeddings (float arrays)","Re-ranked document lists with relevance scores","Embedding metadata (model name, dimension, timestamp)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_6","uri":"capability://data.processing.analysis.evaluation.framework.for.retrieval.and.generation.quality.assessment","name":"evaluation framework for retrieval and generation quality assessment","description":"Haystack includes built-in evaluation components (Evaluation Components) for assessing RAG pipeline quality. Evaluators measure retrieval metrics (recall, precision, NDCG) by comparing retrieved documents against ground truth, and generation metrics (BLEU, ROUGE, semantic similarity) by comparing generated answers against reference answers. Evaluators are implemented as pipeline components, enabling evaluation to be integrated into training and validation workflows. The framework supports custom evaluators through a standard interface.","intents":["I want to measure how well my retriever is finding relevant documents compared to a ground truth dataset","I need to evaluate answer quality from my RAG pipeline using standard metrics (BLEU, ROUGE, semantic similarity)","I want to run evaluation as part of my CI/CD pipeline to detect regressions in retrieval or generation quality"],"best_for":["Teams building RAG systems requiring quantitative quality metrics for production monitoring","Organizations implementing evaluation-driven development for LLM applications","Developers tuning retrieval and generation parameters based on evaluation results"],"limitations":["Ground truth datasets are required for meaningful evaluation — creating ground truth is manual and expensive","Automatic metrics (BLEU, ROUGE) correlate imperfectly with human judgment — human evaluation is still necessary","Evaluation components add latency to pipelines — typically run offline on test sets, not in production","Custom metrics require implementing the Evaluator interface — no automatic metric discovery"],"requires":["Python 3.10+","Ground truth dataset with expected documents or answers","Evaluation metrics library (NLTK for BLEU/ROUGE, sentence-transformers for semantic similarity)"],"input_types":["Retrieved documents with ground truth labels","Generated answers with reference answers","Evaluation parameters (metric type, threshold)"],"output_types":["Evaluation scores (recall, precision, NDCG, BLEU, ROUGE)","Per-example evaluation results","Aggregated metrics across test set"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_7","uri":"capability://automation.workflow.observability.and.execution.tracing.with.component.level.instrumentation","name":"observability and execution tracing with component-level instrumentation","description":"Haystack provides built-in observability through component-level tracing (Observability and Tracing) that captures execution flow, timing, and data at each pipeline step. Traces include component inputs/outputs, execution duration, and error information, enabling debugging and performance analysis. The framework integrates with external observability platforms (e.g., Datadog, New Relic) through a pluggable tracer interface, allowing production deployments to send traces to centralized monitoring systems without code changes.","intents":["I want to understand which components in my pipeline are slow and optimize bottlenecks","I need to debug why my RAG pipeline is returning incorrect answers by inspecting component inputs and outputs","I want to send pipeline execution traces to a monitoring platform for production observability and alerting"],"best_for":["Teams deploying RAG pipelines to production requiring performance monitoring and debugging","Organizations implementing observability best practices with centralized trace collection","Developers optimizing pipeline latency by identifying slow components"],"limitations":["Tracing adds overhead to pipeline execution — significant for high-throughput systems","Trace storage can be expensive for long-running pipelines with many components — requires sampling or filtering","Custom components must implement tracing hooks — not automatic for user-defined components","Trace format is Haystack-specific — integration with external systems requires custom adapters"],"requires":["Python 3.10+","Optional: observability platform (Datadog, New Relic, etc.) for centralized trace collection"],"input_types":["Pipeline execution with component instrumentation","Tracer configuration (sampling rate, export destination)"],"output_types":["Execution traces with component timing and data flow","Performance metrics (latency, throughput)","Error logs with component context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_8","uri":"capability://planning.reasoning.human.in.the.loop.workflows.with.feedback.collection.and.model.improvement","name":"human-in-the-loop workflows with feedback collection and model improvement","description":"Haystack supports human-in-the-loop (HITL) patterns through components that pause pipeline execution for human feedback, collect user ratings or corrections, and use feedback to improve model performance. HITL components integrate with evaluation frameworks to measure impact of feedback on pipeline quality. The framework enables workflows where humans review and correct LLM outputs, with corrections fed back into training or fine-tuning pipelines.","intents":["I want to collect user feedback on generated answers and use it to improve my RAG pipeline","I need to implement a workflow where humans review and correct LLM outputs before they reach end users","I want to measure how feedback impacts pipeline quality and identify which components need improvement"],"best_for":["Teams building production RAG systems requiring human oversight for quality assurance","Organizations implementing continuous improvement loops with user feedback","Developers fine-tuning models based on real-world performance and user corrections"],"limitations":["Human feedback collection requires external infrastructure (UI, database) — not provided by Haystack","Feedback loops are manual — no automatic model retraining based on feedback","Scaling HITL workflows requires careful workflow design to avoid bottlenecks","Feedback quality depends on annotator expertise — requires clear annotation guidelines"],"requires":["Python 3.10+","External feedback collection system (web UI, annotation platform, etc.)","Mechanism for storing and retrieving feedback (database, file storage)"],"input_types":["Pipeline outputs (generated answers, retrieved documents)","Human feedback (ratings, corrections, annotations)"],"output_types":["Feedback-annotated pipeline outputs","Feedback statistics and quality metrics","Training data for model fine-tuning"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__cap_9","uri":"capability://automation.workflow.serialization.and.deployment.of.pipelines.as.reproducible.artifacts","name":"serialization and deployment of pipelines as reproducible artifacts","description":"Haystack pipelines can be serialized to YAML/JSON format, capturing component definitions, connections, and parameters. Serialized pipelines are reproducible — they can be loaded and executed in different environments without code changes. The serialization format is human-readable, enabling version control and code review of pipeline definitions. Deserialization reconstructs the pipeline graph and component instances, enabling deployment of pipelines as configuration files rather than Python code.","intents":["I want to version control my RAG pipeline definition and track changes over time","I need to deploy the same pipeline across multiple environments (dev, staging, prod) without code duplication","I want to share pipeline definitions with team members who can load and run them without understanding the underlying code"],"best_for":["Teams implementing MLOps practices with pipeline versioning and reproducibility","Organizations deploying pipelines across multiple environments with configuration management","Developers building pipeline templates for reuse across projects"],"limitations":["Serialization captures only component definitions and connections — custom component logic must be in code","Secrets (API keys, database credentials) cannot be safely stored in serialized pipelines — require external secret management","Complex component configurations may be difficult to express in YAML/JSON — custom components may require code","Deserialization requires all component classes to be importable — breaks if component code is removed or moved"],"requires":["Python 3.10+","YAML or JSON serialization of pipeline definition","All component classes available in Python environment"],"input_types":["Pipeline objects (Python instances)","YAML/JSON pipeline definitions"],"output_types":["Serialized pipeline definitions (YAML/JSON)","Deserialized pipeline objects ready for execution"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"haystack__headline","uri":"capability://data.processing.analysis.rag.framework.for.production.ready.nlp.applications","name":"rag framework for production-ready nlp applications","description":"Haystack is an open-source framework designed for building production-ready NLP applications using Retrieval-Augmented Generation (RAG) techniques, enabling efficient search and document processing workflows.","intents":["best RAG framework","RAG framework for NLP applications","production-ready NLP solutions","open-source frameworks for document retrieval","AI orchestration tools for LLMs"],"best_for":["developers building NLP applications","teams needing scalable search solutions"],"limitations":["requires Python knowledge","may need integration with external document stores"],"requires":["Python 3.10 or higher"],"input_types":["text documents","query inputs"],"output_types":["retrieved documents","generated responses"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","haystack-ai package installed via pip","Understanding of Python type hints for component input/output specification","Document store backend (Elasticsearch 7.0+, Pinecone API key, Weaviate instance, or in-memory for dev)","Documents with embeddings pre-computed or generated via Haystack embedders","Components with async support (async def run() methods)","Understanding of Python async/await and event loops","Type hints on component input/output methods","Optional: type checker (mypy, pyright) for static analysis","Python >= 3.10"],"failure_modes":["DAG structure prevents cyclic dependencies — feedback loops require explicit component design (e.g., agent loops implemented via component state, not graph cycles)","Type validation happens at pipeline construction time, not runtime — dynamic type changes require component redesign","AsyncPipeline and Pipeline are separate implementations, requiring duplicate pipeline definitions for async support or manual conversion","Backend-specific features (e.g., Pinecone's namespaces, Weaviate's GraphQL) are not exposed through the abstraction — advanced features require direct backend access","Metadata filtering syntax varies by backend; 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