normal-computing/outlines
Outlines
Outlines is a guided generation framework designed to enforce structural constraints on large language model output in real time. It serves as a structured output generator that ensures model responses adhere to predefined JSON schemas, regular expressions, or fixed sets of choices to produce predictable and parsable results.
The project provides an interface for tool calling by extracting structured function parameters from natural language prompts for programmatic execution. It also includes a prompt templating engine that decouples prompt logic from application code through reusable templates and few-shot learning strategies.
The framework manages output through a combination of JSON schema validation, regular expression mapping, and context-free grammar enforcement. These capabilities allow for precise text pattern enforcement and consistent model categorization.
Features
- Real-time Generation Constraints - Forces model responses to match specified types by constraining token generation in real time.
- Function Parameter Extraction - Provides the ability to translate natural language requests into structured arguments based on predefined function signatures.
- LLM Tool Calling - Translates natural language requests into structured function arguments for automated tool and API execution.
- Structured Output Generators - Forces language models to produce valid, strictly typed JSON that adheres to a specific schema.
- Prompt Engineering - Implements a system for decoupling prompt structures from code using reusable templates and few-shot strategies.
- Prompt Templates - Decouples natural language instructions from application logic using a dedicated templating system.
- Structured Output Enforcements - Enforces data models and schemas on model outputs to ensure predictable and reliable processing.
- Grammar-Constrained Samplers - Restricts token generation based on regular expressions or formal grammar rules to enforce output patterns.
- Schema Validators - Maps structural schemas to token constraints to guarantee that generated output is valid JSON.
- LLM JSON Constraints - Forces large language models to generate valid JSON that adheres to a predefined schema.
- Generative Schema Enforcement Frameworks - Provides a framework for applying programmatic constraints during token sampling to ensure predictable and parsable output.
- Predictable Model Categorization - Limits generation to a fixed set of choices or literal types to ensure consistent classification and labeling.
- Token Bias Adjustments - Modifies token probabilities during sampling to ensure only tokens satisfying specific constraints are selected.
- Regex-to-State Mapping - Converts regular expression patterns into a state machine that filters allowed tokens for each generation step.
- Token Sequence Guidance - Uses state machines to track valid token sequences and prune the vocabulary in real time during generation.
- Application Development - Library for simplifying prompting and constraining generation.
Star history
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