prompt_orchestrator

Python module for structured prompt orchestration with:

• static/semi-stable/dynamic prompt layout
• configurable summary LLM with provider selection
• TTL cache backends
• optional RAG providers
• safety checks (config-driven grouped threats, weighted groups, bilingual patterns, contradiction pairs)
• prompt efficiency analyzer
• token counting with tiktoken
• centralized mutable config (Pydantic)
• one-call orchestrator bootstrap from config store

Install

pip install -e .

For development and tests:

pip install -e .[dev]

Install with optional OpenTelemetry support:

pip install -e .[otel]

Optional OpenTelemetry + SigNoz

OpenTelemetry is optional. If not installed or not enabled, PromptOrchestrator works as before.

SigNoz is expected to run separately (for example, official SigNoz Docker deployment on http://localhost:8080).

Enable OTel (host runtime):

ENABLE_OTEL=true
OTEL_EXPORTER_OTLP_ENDPOINT=localhost:4317
OTEL_SERVICE_NAME=prompt-orchestrator
OTEL_SERVICE_NAMESPACE=prompt-stack
OTEL_DEPLOYMENT_ENVIRONMENT=dev

Required/optional flags summary:

• Start telemetry export (required): set ENABLE_OTEL=true
• Stop telemetry export (required): set ENABLE_OTEL=false
• OTLP destination (optional, used when enabled): OTEL_EXPORTER_OTLP_ENDPOINT
• Resource labels (optional): OTEL_SERVICE_NAME, OTEL_SERVICE_NAMESPACE, OTEL_DEPLOYMENT_ENVIRONMENT, OTEL_SERVICE_VERSION

Run local OTel Collector (1 additional container):

docker compose -f docker-compose.otel.yml up -d

Disable OTel (host runtime):

ENABLE_OTEL=false

Stop local OTel Collector:

docker compose -f docker-compose.otel.yml down

Files used:

• docker-compose.otel.yml
• observability/otel-collector-config.yaml

Default endpoints:

• SigNoz UI (external): http://localhost:8080
• OTLP gRPC ingest (local collector): http://localhost:4317
• OTLP HTTP ingest (local collector): http://localhost:4318

Exposed telemetry (when enabled):

Dashboard template blueprint:

• observability/signoz-dashboard-prompt-orchestrator.yaml

Use it as a panel/query blueprint in SigNoz to create a dashboard for prompt build latency, token pressure, RAG payload size, safety events, summary latency, logs, and traces.

Configuration Models

• PromptConfig: static prompt structure
• OrchestratorSettings: runtime limits and behavior
• SummaryLLMConfig: summary provider and model settings
• SafetyLLMConfig: optional LLM-based safety analysis (provider/model selectable)
• ModuleConfig: full module config in one object
• ConfigStore: mutable config holder (get, set_config, as_dict)

Enterprise Prompt Controls

PromptConfig now includes enterprise-level controls for answer governance:

• response_language: ru | en | auto (default: ru)
• output_contract: strict output contract configuration
  • default mode="json_markdown"
  • default strict=True
  • default schema hint for enterprise review payload
• tool_calling_policy: tool usage policy for downstream tool-aware agents
  • mode: allow | deny | allowlist (default: allow)
  • max_calls (default: 8)
  • allowed_tools for allowlist mode
  • JSON args/result acknowledgement guard flags

Example:

from prompt_orchestrator import PromptConfig, OutputContractConfig, ToolCallingPolicyConfig

cfg = PromptConfig(
        system_prompt="Вы корпоративный ассистент.",
        role="Ревьюер документации",
        task="Сформируйте проверяемый ответ на русском.",
        constraints=["Не придумывайте факты", "Всегда давайте ссылки/цитаты"],
        output_format="Markdown",
        examples=[],
        response_language="ru",
        output_contract=OutputContractConfig(
                mode="json_markdown",
                strict=True,
                schema_hint='{"summary":"str","findings":["str"],"risks":["str"],"actions":["str"],"citations":["str"]}',
        ),
        tool_calling_policy=ToolCallingPolicyConfig(
                mode="allow",
                max_calls=8,
        ),
)

Safety Engine

The safety layer is configured from prompt_orchestrator/safety/threats.json. The catalog is grouped by threat family, and each family has its own weight so the final severity is still computed by the maximum matched threat score.

What changed:

• threat families are defined in threats.json and loaded at runtime
• regular lexical rules live under patterns
• contradiction rules live under contradictions and are matched as pairs
• each family can include English and Russian analogs for the same threat family
• duplicate patterns were removed from the catalog
• each matched rule keeps its threat code in the report

SafetyReport now includes:

• issues: flat list of matched safety issues
• threat_groups: grouped report by threat family
• severity: overall severity (none, low, medium, high)
• threat_score: weighted maximum score used for the final severity
• sanitized_prompt: optional rewritten prompt when auto rewrite is enabled
• llm_used: whether LLM safety analyzer was applied
• llm_provider / llm_model: provider/model used for LLM safety pass
• llm_score / llm_severity: raw LLM risk output before final merge
• llm_reasoning: short textual explanation returned by LLM checker

Optional LLM Safety Layer

You can enable an additional LLM-based safety pass on top of lexical rules.

Default config:

• security_checks_llm_enabled=False (opt-in)
• provider="ollama"
• model="qwen2.5:3b" (multilingual, works with Russian prompts)
• security_checks_llm_merge_strategy="max" (take max risk between lexical + LLM)
• security_checks_llm_fail_mode="open" (fallback to lexical-only if LLM check fails)
• security_checks_llm_auto_pull_ollama_model=True (if model is missing, it is pulled from Ollama)

Important behavior:

• LLM provider clients are initialized lazily.
• If security_checks_llm_enabled=False, no LLM provider client is created.

Example:

from prompt_orchestrator import ModuleConfig, SafetyLLMConfig

cfg = ModuleConfig(
    prompt=...,  # PromptConfig
    safety_llm=SafetyLLMConfig(
        security_checks_llm_enabled=True,
        provider="ollama",  # or "openai" / "custom" / "none"
        model="qwen2.5:3b",
        security_checks_llm_merge_strategy="max",  # max | llm_only | heuristic_only
        security_checks_llm_fail_mode="open",  # open | closed
    ),
)

Each grouped report includes the threat family name, the number of matches, the matched codes, and the family weight. Use result.safety.grouped_summary or result.safety.model_dump() to inspect the grouped output.

OrchestratorSettings.debug_mode

By default, section headers (=== STATIC PART (CACHE-FRIENDLY) ===, etc.) are excluded from the final prompt sent to LLMs to save tokens.

Enable debug_mode=True to include section headers for:

• Debugging and development
• Understanding prompt structure during testing
• Console/log output inspection

settings = OrchestratorSettings(
    debug_mode=True,  # Enables section headers in output
)

In simulations, use --debug flag:

python simulations/console_pipeline_test.py  # Prompts for debug mode
python simulations/conversation_simulation_test.py --debug  # Enable debug headers

Security rewrite toggle in OrchestratorSettings:

• security_checks_auto_rewrite=True: rewrite prompt when safety severity is medium or high
• Legacy alias safety_auto_rewrite is still accepted for backward compatibility

Supported Summary Providers

• none: deterministic local fallback summarization
• openai: OpenAI via openai SDK
• ollama: local Ollama endpoint via /api/generate
• custom: bring your own client implementing generate(prompt, model, max_tokens, temperature)

Integration with RagflowOrchestrator

PromptOrchestrator can work directly with RagflowOrchestrator as a retrieval backend.

Why this pairing works well:

• PromptOrchestrator controls prompt layout, context compaction, safety checks, and token budgets.
• RagflowOrchestrator handles indexing, embedding, and retrieval from vector storage.
• Both projects use a compatible DocChunk shape (id, content, score, metadata).

Option 1: Use RagflowOrchestrator compatibility adapter (recommended)

RagflowOrchestrator includes PromptStyleRAGProviderAdapter, which exposes the exact interface PromptOrchestrator expects (retrieve(query, limit)).

from prompt_orchestrator import (
    LocalTTLCacheBackend,
    OrchestratorSettings,
    PromptConfig,
    PromptContextManager,
    PromptOrchestrator,
    SummaryLLM,
)

from ragflow_orchestrator import HashEmbedder, create_provider
from ragflow_orchestrator.rag import PromptStyleRAGProviderAdapter

# RagflowOrchestrator side: provider + embedder
provider = create_provider(kind="sqlite", db_path="rag.db", table="chunks")
embedder = HashEmbedder(dimensions=256)

# Adapter gives PromptOrchestrator-compatible retrieve(query, limit)
rag_provider = PromptStyleRAGProviderAdapter(provider=provider, embedder=embedder)

config = PromptConfig(
    system_prompt="You are a grounded assistant.",
    role="Engineer",
    task="Answer using retrieved context.",
    constraints=["Cite retrieved facts", "Avoid unsupported claims"],
    output_format="Markdown",
    examples=[],
)

settings = OrchestratorSettings(use_rag_default=True, rag_limit=4)
cache = LocalTTLCacheBackend(default_ttl_seconds=settings.cache_ttl_seconds)
context_manager = PromptContextManager(cache, settings, SummaryLLM())

orchestrator = PromptOrchestrator(
    config=config,
    context_manager=context_manager,
    rag_provider=rag_provider,
    settings=settings,
)

result = orchestrator.build_for_request(
    session_id="rag-integration-demo",
    user_message="How does deduplication work in our retrieval pipeline?",
    use_rag=True,
)

print(result.prompt)

Option 2: Wrap RAGOrchestrator.search(...) in a thin adapter

If you already use a full RAGOrchestrator pipeline (ingest + search), expose it as a RAGProvider for PromptOrchestrator:

from prompt_orchestrator.rag.base import RAGProvider
from prompt_orchestrator.context.state import DocChunk

from rag_orchestrator import RAGOrchestrator


class RagOrchestratorProvider(RAGProvider):
    def __init__(self, orchestrator: RAGOrchestrator) -> None:
        self._orchestrator = orchestrator

    def retrieve(self, query: str, limit: int) -> list[DocChunk]:
        rows = self._orchestrator.search(query_text=query, top_k=limit)
        return [
            DocChunk(
                id=row.chunk.id,
                content=row.chunk.text,
                score=row.score,
                metadata={str(k): str(v) for k, v in row.chunk.metadata.items()},
            )
            for row in rows
        ]

Use this adapter as rag_provider in PromptOrchestrator(...) and set use_rag=True when building requests.

Simulations Folder

Simulation assets are located in simulations:

• simulations/console_pipeline_test.py: interactive console runner for manual checks
• simulations/conversation_simulation_test.py: scripted multi-turn simulation with prompt/STATS/SAFETY output
• simulations/test_turns.json: regular conversation turns for context-window and compaction checks
• simulations/safety_injection_turns.json: unsafe/injection turns for SAFETY trigger checks
• simulations/conversation_simulation_test.log: output log from last simulation run (overwritten on each run)

How to work with simulations:

# Interactive pipeline (manual typing)
python simulations/console_pipeline_test.py

# Scripted simulation from JSON turns
python simulations/conversation_simulation_test.py

# Include unsafe/injection scenarios
python simulations/conversation_simulation_test.py --include-safety

# Run without RAG and cap turns
python simulations/conversation_simulation_test.py --no-rag --max-turns 5

Example 1: Manual Wiring (Local, No RAG)

from prompt_orchestrator import (
    LocalTTLCacheBackend,
    NoRAGProvider,
    OrchestratorSettings,
    PromptConfig,
    PromptContextManager,
    PromptOrchestrator,
    SummaryLLM,
)

config = PromptConfig(
    system_prompt="You are a helpful assistant.",
    role="Senior Analyst",
    task="Answer user questions precisely.",
    constraints=["Do not hallucinate", "Use concise style"],
    output_format="Markdown",
    examples=["Q: 2+2? A: 4"],
)

settings = OrchestratorSettings(
    max_prompt_chars=12000,
    max_prompt_tokens=3000,
    recent_messages_limit=10,
    cache_ttl_seconds=900,
    rag_limit=3,
)

cache = LocalTTLCacheBackend(default_ttl_seconds=settings.cache_ttl_seconds)
summary_llm = SummaryLLM()
context_manager = PromptContextManager(cache, settings, summary_llm)

orchestrator = PromptOrchestrator(
    config=config,
    context_manager=context_manager,
    rag_provider=NoRAGProvider(),
    settings=settings,
)

result = orchestrator.build_for_request(
    session_id="demo-session",
    user_message="Explain how TTL helps prompt caching",
    use_rag=False,
)

print(result.prompt)
print(result.stats.model_dump())
print(result.safety.model_dump())

Example 2: Centralized Config + Factory (One-Call Bootstrap)

from prompt_orchestrator import (
    ConfigStore,
    ModuleConfig,
    OrchestratorSettings,
    PromptConfig,
    SummaryLLMConfig,
    PromptOrchestratorFactory,
)

full_config = ModuleConfig(
    prompt=PromptConfig(
        system_prompt="You are a helpful assistant.",
        role="Engineer",
        task="Answer clearly",
        constraints=["No hallucinations"],
        output_format="Markdown",
        examples=[],
    ),
    settings=OrchestratorSettings(max_prompt_tokens=3000),
    summary_llm=SummaryLLMConfig(provider="openai", model="gpt-4o-mini"),
)

store = ConfigStore(full_config)
model_name = store.get("summary_llm.model")

orchestrator = PromptOrchestratorFactory.from_config_store(store)
result = orchestrator.build_for_request(
    session_id="factory-demo",
    user_message="What is TTL cache?",
    use_rag=False,
)

Example 3: OpenAI Summary Provider

from prompt_orchestrator import (
    ConfigStore,
    ModuleConfig,
    OpenAIConfig,
    OrchestratorSettings,
    PromptConfig,
    PromptOrchestratorFactory,
    SummaryLLMConfig,
)

cfg = ModuleConfig(
    prompt=PromptConfig(
        system_prompt="You are a concise assistant.",
        role="Tech Writer",
        task="Summarize conversation state and answer user request.",
        constraints=["No speculative claims"],
        output_format="Markdown",
        examples=[],
    ),
    settings=OrchestratorSettings(
        max_prompt_tokens=2500,
        token_model="gpt-4o-mini",
    ),
    summary_llm=SummaryLLMConfig(
        provider="openai",
        model="gpt-4o-mini",
        openai=OpenAIConfig(
            api_key="YOUR_OPENAI_API_KEY",
            base_url=None,
            organization=None,
        ),
    ),
)

store = ConfigStore(cfg)
orchestrator = PromptOrchestratorFactory.from_config_store(store)
response = orchestrator.build_for_request(
    session_id="openai-summary",
    user_message="Please summarize previous decisions and next actions",
    use_rag=False,
)
print(response.stats.total_tokens)

Token Counting (tiktoken)

• Prompt length checks use tiktoken-based counting
• Configure tokenizer via OrchestratorSettings.token_model and OrchestratorSettings.token_encoding
• Limit fitting in PromptContextManager.ensure_fits_limit trims sections to satisfy both char and token budgets

Running Tests

pytest -q