Microsoft Data Formulator Data Formulator

name: language-injection description: LLM Agent 多语言注入规范。在修改 Agent 提示词、添加新的 Agent 端点、处理用户可见的后端消息（message_code）时使用。

Language Injection for Agent Prompts

Authoritative developer guide: docs/dev-guides/6-i18n-language-injection.md.

Prerequisites: Read docs/dev-guides/6-i18n-language-injection.md before changing Agent prompts, Agent routes, backend user-visible messages, or frontend i18n strings. If your work introduces new language injection patterns or conventions, update this file and related dev-guides accordingly.

Architecture

Frontend i18n.language  →  Accept-Language header  →  get_language_instruction()
                                                           │
                                                   build_language_instruction()
                                                   (agents/agent_language.py)
                                                           │
                                              ┌────────────┴────────────┐
                                              ▼                         ▼
                                        mode="full"               mode="compact"
                                    (text-heavy agents)        (code-gen agents)

Core Modules

Code Examples

Route handler — inject language

# In a Flask route handler:
lang_instruction = get_language_instruction(mode="compact")
lang_suffix = f"\n\n{lang_instruction}" if lang_instruction else ""

messages = [
    {"role": "system", "content": "You are a helpful assistant." + lang_suffix},
    {"role": "user", "content": user_input},
]

Agent constructor — use inject_language_instruction()

from data_formulator.agents.agent_language import inject_language_instruction

# Simple append (most agents)
system_prompt = inject_language_instruction(system_prompt, language_instruction)

# Insert before a marker (complex prompts)
system_prompt = inject_language_instruction(
    system_prompt, language_instruction,
    marker="**About the execution environment:**"
)

Python-side user-visible messages — message_code pattern

For fixed strings in Python that appear in the UI, do NOT translate in Python. Return a message_code and let the frontend translate:

# In an Agent or route handler:
yield {
    "type": "error",
    "message": "Output DataFrame is empty (0 rows).",  # English fallback
    "message_code": "agent.emptyDataframe",             # frontend i18n key
}

# With parameters:
result = {
    "status": "error",
    "content": f"Fields not found: {missing}",
    "content_code": "agent.fieldsNotFound",
    "content_params": {"missing": missing, "available": available},
}

Frontend consumption:

import { translateBackend } from '../app/utils';
const msg = translateBackend(event.message, event.message_code, event.message_params);

Translation keys go in src/i18n/locales/{en,zh}/messages.json under messages.agent.*.

Anti-Patterns (with explanations)

Adding a New Language

1. Add language code + display name to LANGUAGE_DISPLAY_NAMES in agents/agent_language.py.
2. Optionally add extra rules to LANGUAGE_EXTRA_RULES (e.g. simplified vs traditional Chinese).
3. Add frontend translations in src/i18n/locales/<lang>/ — copy an existing locale folder as template.
4. No Agent code changes needed — the existing flow picks up new languages automatically.

Source: microsoft/data-formulator — distributed by TomeVault.