--- name: guidance-faf8c6f4d045 description: >- Guidance is Microsoft's open-source Python library for controlling LLM output with constrained generation: you interleave text, control flow, regex, select() choices and JSON schemas, and the model can only emit tokens that fit. Use when a user needs guaranteed-valid JSON, classification into fixed labels, regex-shaped fields or a custom grammar from a local model (Transformers, llama.cpp), or asks for "guidance", "constrained decoding" or "structured output" without retry loops. license: Apache-2.0 compatibility: "Python 3.10+; pip install guidance; full constraint support needs a local backend (Transformers, llama.cpp, ONNX Runtime GenAI)" metadata: author: terminal-skills version: "1.1.0" repository: "https://github.com/guidance-ai/guidance" category: data-ai tags: "llm, constrained-generation, grammar, structured-output, json-schema" --- # Guidance — Constrained LLM Generation ## Overview Guidance (`guidance-ai/guidance`, version 0.3.x) constrains the token sampling itself, so output always matches a regex, a fixed list of options, a JSON schema or a context-free grammar. Model objects are immutable: `lm += ...` returns a new model with the text and any named captures. Backends: `Transformers`, `LlamaCpp`, `OnnxRuntimeGenAI`, `OpenAI`, Azure helpers (`create_azure_openai_model`, `create_azure_aifoundry_model`) and `Mock` for tests. Constraints are fully enforced only where the backend gives token-level control, i.e. local models; hosted APIs restrict what can be expressed. ## Instructions ### Install ```bash pip install guidance # backend packages (transformers, torch, llama-cpp-python) are installed separately ``` ### Chat models: roles, capture, regex, select ```python from guidance import system, user, assistant, gen, select from guidance.models import Transformers base = Transformers("microsoft/Phi-4-mini-instruct") # downloads the model on first use lm = base with system(): lm += "You classify product reviews." with user(): lm += 'Review: "The product arrived damaged but customer service was great"' with assistant(): lm += select(["positive", "negative", "mixed", "neutral"], name="sentiment") print(lm["sentiment"]) # one of the four labels, nothing else is possible ``` `gen(regex=r"0\.\d{2}", name="confidence")` constrains a free-text field to a pattern; `gen(max_tokens=100, stop="\n")` bounds length. Read captures with `lm["name"]`. ### Guaranteed JSON from a Pydantic model or schema ```python import json from pydantic import BaseModel from guidance import json as gen_json class ProductReview(BaseModel): product_name: str rating: int pros: list[str] cons: list[str] recommendation: bool lm = base with user(): lm += "Extract data: The XPS 15 has an amazing display and battery life, but runs hot. Would buy again." with assistant(): lm += gen_json(name="review", schema=ProductReview) review = ProductReview.model_validate_json(lm["review"]) ``` `schema` accepts a Pydantic class or a JSON-schema dict. Use `model_config = dict(extra="forbid")` on the Pydantic class for strict objects. ### Control flow and lists Python `if`/`for` run between generations; f-strings can embed grammar pieces. `one_or_more` and `zero_or_more` (from `guidance`) add repetition to a grammar: ```python lm = base with user(): lm += "Classify this message: 'Please restart the staging database'" with assistant(): lm += f"Type: {select(['question', 'command', 'chitchat'], name='kind')}" if lm["kind"] == "command": with user(): lm += "Write the shell command only." with assistant(): lm += gen(name="command", stop="\n", max_tokens=80) ``` ### Reusable grammar functions and offline testing Mark functions with `@guidance` (add `stateless=True` to compose them into a grammar). Debug constraints without a model: ```python from guidance import gen, select from guidance.models import Mock grammar = "Sentiment: " + select(["positive", "negative", "mixed"], name="sentiment") + "\nScore: " + gen(regex=r"0\.\d{2}", name="score") assert grammar.match("Sentiment: mixed\nScore: 0.82") is not None assert grammar.match("Sentiment: great\nScore: 0.82") is None lm = Mock(b"Sentiment: mixed\nScore: 0.82") + grammar print(lm["sentiment"], lm["score"]) # mixed 0.82 ``` ## Examples ### "Tag support tickets with a priority and a due-date format" ```python lm = base with user(): lm += "Ticket: 'Checkout page returns 500 for all EU customers since 14:00'" with assistant(): lm += "Priority: " + select(["P1", "P2", "P3"], name="priority") lm += "\nDue: " + gen(regex=r"20\d{2}-\d{2}-\d{2}", name="due") ``` Result: `lm["priority"]` is `P1`, `P2` or `P3`, and `lm["due"]` always looks like `2026-10-21`; no parsing or retry is needed. ### "Extract an invoice into typed JSON" ```python class Invoice(BaseModel): vendor: str total_eur: float paid: bool with user(): lm += "Invoice text: Hetzner Online GmbH, total 41.65 EUR, status: paid" with assistant(): lm += gen_json(name="invoice", schema=Invoice) ``` Result: `{"vendor": "Hetzner Online GmbH", "total_eur": 41.65, "paid": true}`, valid against `Invoice` by construction (content correctness still depends on the model). ## Guidelines - Valid format does not mean correct content: constraints guarantee syntax, not facts. Validate semantics separately. - Use `select()` for classification and `regex=` for IDs, dates and numbers; always give `gen()` a `max_tokens` or `stop` when the field is free text. - Prefer a local backend for hard guarantees; with `models.OpenAI` only the constraints the API can honor are available, so test your grammar before relying on it. - Use the chat roles (`system()`, `user()`, `assistant()`) with instruct models so the chat template is applied correctly; raw f-string prompts suit base models. - Models are immutable: assign the result (`lm = lm + ...` or `lm += ...`) and start each independent request from the base model object. - Test grammars offline with `grammar.match(...)` and `Mock`, which needs no GPU or API key. - Overly tight constraints (a regex the model finds very unlikely) can produce odd text; loosen them or improve the prompt. --- Source attribution: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/guidance/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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