---

name: generation-and-model-components description: "Choose and configure Haystack prompt builders, generators, embedders, classifiers, samplers, validators, and provider/model-backed components." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Generation and Model Components

Use this sub-skill when the task is about model-facing Haystack components: prompt construction, text or chat generation, embeddings, local/API provider setup, model-backed classification, answer formatting, sampling, JSON validation, credentials, streaming, or optional model dependencies.

Route Here

• Build prompts with PromptBuilder or ChatPromptBuilder, including Jinja variables, chat-message templates, runtime template overrides, and required variables.
• Select generators or chat generators such as OpenAIChatGenerator, AzureOpenAIChatGenerator, HuggingFaceAPIChatGenerator, HuggingFaceLocalChatGenerator, OpenAIGenerator, AzureOpenAIGenerator, and Hugging Face local/API text generators.
• Configure embedders for model-backed query/document embeddings, including OpenAI, Azure OpenAI, Hugging Face API, Sentence Transformers, sparse, dense, local, and async variants.
• Add model-backed classifiers, TopPSampler, AnswerBuilder, JsonSchemaValidator, Secret, structured output, and streaming callbacks.
• Debug optional dependency, credential, backend, JSON schema, generation parameter, device, or provider-specific failures.

Reroute Elsewhere

• For pipeline graph construction, component sockets, custom components, loops, serialization, and breakpoints, use ../pipelines-and-components/SKILL.md.
• For document stores, retrievers, rankers, RAG retrieval strategy, indexing, and hybrid search, use ../retrieval-and-rag/SKILL.md.
• For agents, tools, tool invocation, human-in-the-loop, and multi-step orchestration, use ../agents-tools-and-hitl/SKILL.md.
• For evaluators, tracing, metrics, experiment comparison, and observability, use ../evaluation-and-observability/SKILL.md.

Fast Workflow

1. Identify whether the model slot expects str, list[ChatMessage], Document, or list[Document]; choose text generators for prompt -> replies, chat generators for messages -> replies, embedders for vectors, and classifiers/samplers/validators for post-processing.
2. Use PromptBuilder for plain string prompts and ChatPromptBuilder for chat models; set required_variables="*" or a concrete list when missing variables must fail instead of rendering as empty strings.
3. Use Secret.from_env_var(...) for provider credentials and avoid hard-coded tokens; pass model parameters through generation_kwargs, huggingface_pipeline_kwargs, embedder constructor options, or runtime run(..., generation_kwargs=...) overrides.
4. Add JsonSchemaValidator or provider structured-output response_format when the downstream task needs machine-readable JSON; route validation_error back into a correction loop at the pipeline layer.
5. Run scripts/model_component_smoke_check.py to verify public imports, prompt rendering, answer extraction, sampling, and JSON validation without network credentials.

References

• references/api-reference.md lists the core component imports, inputs, outputs, and configuration knobs.
• references/model-and-provider-guide.md explains provider selection, credentials, optional dependencies, local devices, streaming, and structured outputs.
• references/troubleshooting.md maps common install/import, credential/backend, API misuse, data/config, and workflow failures to fixes.
• scripts/model_component_smoke_check.py is a deterministic smoke check for importable public APIs and non-network component behavior.