Prompts Guide¶

Use these capability snippets inside your application’s native runtime. Installation is through PyFly Agentic extras.

The Prompts module provides a Jinja2-based template engine with versioning, composition strategies, validation, and file/string loaders. Templates split a prompt into a system and a user part, and rendering yields a structured Prompt object.

Concepts¶

A prompt template pairs a Jinja2 system template with a Jinja2 user template. Both are rendered at runtime with concrete variables, producing a Prompt object whose .system and .user fields carry the rendered text. Templates can be composed, versioned, and validated.

The public surface of pyfly.agentic.prompts is: PromptTemplate, PromptInfo, PromptRegistry, prompt_registry, SequentialComposer, ConditionalComposer, MergeComposer, PromptValidator, ValidationResult, and PromptLoader.

classDiagram
    class PromptTemplate {
        +name: str
        +version: str
        +system_template: str
        +user_template: str
        +required_variables: list~str~
        +render(**kwargs) Prompt
        +validate_variables(kwargs)
        +info() PromptInfo
    }

    class Prompt {
        +system: str
        +user: str
        +estimate_tokens() int
    }

    class PromptInfo {
        +name: str
        +version: str
        +variable_names: list~str~
        +description: str
    }

    class PromptRegistry {
        +register(template)
        +get(name, version) PromptTemplate
        +list_templates() list~PromptInfo~
        +has(name) bool
        +clear()
    }

    class PromptValidator {
        +validate(rendered: str) ValidationResult
    }

    class PromptLoader {
        +from_string(name, system, user)$ PromptTemplate
        +from_file(path)$ PromptTemplate
        +from_directory(directory)$ list~PromptTemplate~
    }

    PromptTemplate --> Prompt : render()
    PromptTemplate --> PromptInfo : info()
    PromptRegistry --> PromptTemplate
    PromptLoader --> PromptTemplate

Creating a Template¶

PromptTemplate takes three positional arguments — the template name, the system_template, and the user_template — followed by keyword-only metadata (version, description, required_variables, metadata). Either template may be an empty string if you only need one of the two parts.

from pyfly.agentic.prompts import PromptTemplate

template = PromptTemplate(
    "greeting",
    "You are a helpful {{ role }}.",          # system_template
    "Hello, {{ name }}! How can I help?",      # user_template
    version="1.0.0",
    required_variables=["name", "role"],
)

prompt = template.render(name="Alice", role="developer")
print(prompt.system)  # "You are a helpful developer."
print(prompt.user)    # "Hello, Alice! How can I help?"
print(prompt.estimate_tokens())  # rough heuristic: words / 0.75

render(**kwargs) returns a Prompt object (.system, .user), not a string. Before rendering, it calls validate_variables(kwargs), which raises PromptValidationError if any name listed in required_variables is missing from the supplied kwargs.

template.info() returns a PromptInfo summary (name, version, variable_names, description) suitable for serialisation or listings.

Versioning¶

The PromptRegistry stores templates under (name, version) keys and tracks the latest registered version per name. When you get a template without a version, the registry returns the most recently registered version for that name.

from pyfly.agentic.prompts import PromptRegistry

registry = PromptRegistry()
registry.register(template_v1)  # version="1.0.0"
registry.register(template_v2)  # version="2.0.0"

latest = registry.get("greeting")          # returns v2 (latest registered)
specific = registry.get("greeting", "1.0.0")  # returns v1

registry.has("greeting")        # True
registry.list_templates()       # list[PromptInfo]
len(registry)                   # total (name, version) entries

get raises PromptNotFoundError when no matching template is registered. The registry also supports clear(), __len__, and name in registry membership checks.

The module-level prompt_registry resolves the active runtime’s registry:

from pyfly.agentic.prompts import prompt_registry

prompt_registry.register(template)
shared = prompt_registry.get("greeting")

Composition¶

Templates can be composed using three strategies. Each composer exposes a render(**kwargs) method that returns a Prompt object, composing the .system and .user parts of its member templates independently.

flowchart TD
    subgraph Sequential
        S1[Template A] --> SR[Joined by separator]
        S2[Template B] --> SR
    end

    subgraph Conditional
        C[condition_fn kwargs] -->|returns key| K{template_map}
        K --> C1[Template for key]
    end

    subgraph Merge
        M1[Template A] --> MR[merge_fn list str]
        M2[Template B] --> MR
    end

SequentialComposer(templates, *, separator="\n\n") — renders each template and joins the system parts (and the user parts) with separator.
ConditionalComposer(condition_fn, template_map) — condition_fn(**kwargs) returns a string key; the matching template in template_map (dict[str, PromptTemplate]) is rendered. An unknown key raises KeyError.
MergeComposer(templates, merge_fn) — renders all templates, then applies merge_fn(list[str]) -> str to the system parts and to the user parts separately.

from pyfly.agentic.prompts import (
    SequentialComposer,
    ConditionalComposer,
    MergeComposer,
)

seq = SequentialComposer([base, context], separator="\n---\n")
prompt = seq.render(name="Alice", role="developer")

cond = ConditionalComposer(
    lambda *, tier, **_: "detailed" if tier == "pro" else "brief",
    {"detailed": detailed_tpl, "brief": brief_tpl},
)
prompt = cond.render(tier="pro", name="Alice")

merged = MergeComposer([a, b], merge_fn=lambda parts: " | ".join(parts))
prompt = merged.render(name="Alice")

Validation¶

The PromptValidator inspects an already-rendered string and reports issues via a ValidationResult (valid: bool, errors: list[str]). It performs two checks:

Token limit — if max_tokens > 0, the estimated token count (word_count / 0.75) must not exceed it. max_tokens=0 (the default) disables the check.
Required sections — every substring in required_sections must appear in the rendered text.

It does not validate variable presence or types — that is enforced by PromptTemplate.validate_variables() during render(). Because validate() takes a string, pass a specific part of the rendered Prompt (.system or .user):

from pyfly.agentic.prompts import PromptValidator

validator = PromptValidator(max_tokens=4000, required_sections=["helpful"])
prompt = template.render(name="Alice", role="developer")
result = validator.validate(prompt.system)
if not result.valid:
    print(result.errors)

Loading from Files¶

PromptLoader is a collection of static factory methods — there is nothing to instantiate.

PromptLoader.from_string(name, system_template, user_template, *, version="1.0.0", description="") — build a template from inline strings.
PromptLoader.from_file(path, *, name=None, version="1.0.0", description="") — read a YAML file and construct PromptTemplate(**data). The file must contain the template fields as YAML keys (name, system_template, user_template, and any of version/description/required_variables/metadata). If name is omitted, it defaults to the file stem.
PromptLoader.from_directory(directory, *, glob_pattern="*.j2", version="1.0.0") — load every YAML file matching glob_pattern in directory, returning list[PromptTemplate].

from pyfly.agentic.prompts import PromptLoader

inline = PromptLoader.from_string(
    "greeting",
    "You are a helpful {{ role }}.",
    "Hello, {{ name }}!",
)

template = PromptLoader.from_file("prompts/greeting.j2")
all_templates = PromptLoader.from_directory("prompts/")

A loadable file looks like:

# prompts/greeting.j2
name: greeting
version: "1.0.0"
system_template: "You are a helpful {{ role }}."
user_template: "Hello, {{ name }}!"
required_variables: ["name", "role"]