OpenJarvis/docs/architecture/intelligence.md

Intelligence Pillar

The Intelligence pillar handles model management and query routing. It maintains a catalog of known models with detailed metadata and provides a heuristic router that selects the best model for a given query based on its characteristics.

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Purpose

When a user sends a query to OpenJarvis, the system needs to decide which model should handle it. A short, simple question like "What time is it?" does not need a 70B parameter model, while a complex multi-step math problem benefits from the largest available model. The Intelligence pillar encapsulates this decision-making logic.

The pillar provides three key capabilities:

1. Model catalog -- a registry of well-known models with metadata (parameter count, context length, VRAM requirements, supported engines)
2. Query routing -- analyzing query characteristics and selecting the optimal model
3. Auto-discovery -- merging models discovered from running engines into the catalog

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ModelSpec

Every model in the system is described by a ModelSpec dataclass, defined in core/types.py:

@dataclass(slots=True)
class ModelSpec:
    model_id: str                              # Unique identifier (e.g., "qwen3:8b")
    name: str                                  # Human-readable name
    parameter_count_b: float                   # Total parameters in billions
    context_length: int                        # Maximum context window (tokens)
    active_parameter_count_b: Optional[float]  # MoE active params (None for dense)
    quantization: Quantization                 # Quantization format (none, fp8, int4, etc.)
    min_vram_gb: float                         # Minimum VRAM required
    supported_engines: Sequence[str]           # Which engines can run this model
    provider: str                              # Model provider (e.g., "alibaba", "meta")
    requires_api_key: bool                     # Whether cloud API key is needed
    metadata: Dict[str, Any]                   # Additional metadata (pricing, architecture)

Models are registered in the ModelRegistry:

from openjarvis.core.registry import ModelRegistry

# Register a model
ModelRegistry.register_value("qwen3:8b", ModelSpec(
    model_id="qwen3:8b",
    name="Qwen3 8B",
    parameter_count_b=8.2,
    context_length=32768,
    supported_engines=("vllm", "ollama", "llamacpp", "sglang"),
    provider="alibaba",
))

---

Model Catalog

The built-in model catalog is defined in intelligence/model_catalog.py as the BUILTIN_MODELS list. It includes models across three categories:

Local Models -- Dense

Model ID	Name	Parameters	Context	Supported Engines
qwen3:8b	Qwen3 8B	8.2B	32K	vLLM, Ollama, llama.cpp, SGLang
qwen3:32b	Qwen3 32B	32B	32K	Ollama, vLLM
llama3.3:70b	Llama 3.3 70B	70B	128K	Ollama, vLLM
llama3.2:3b	Llama 3.2 3B	3B	128K	Ollama, vLLM, llama.cpp
deepseek-coder-v2:16b	DeepSeek Coder V2 16B	16B	128K	Ollama, vLLM
mistral:7b	Mistral 7B	7B	32K	Ollama, vLLM, llama.cpp

Local Models -- Mixture of Experts (MoE)

Model ID	Name	Total / Active Params	Context	Min VRAM
gpt-oss:120b	GPT-OSS 120B	117B / 5.1B	128K	12 GB
glm-4.7-flash	GLM 4.7 Flash	30B / 3B	128K	8 GB
trinity-mini	Trinity Mini	26B / 3B	128K	8 GB

Cloud Models

Model ID	Provider	Context	Pricing (input/output per 1M tokens)
gpt-4o	OpenAI	128K	$2.50 / $10.00
gpt-4o-mini	OpenAI	128K	$0.15 / $0.60
gpt-5-mini	OpenAI	400K	$0.25 / $2.00
claude-sonnet-4-20250514	Anthropic	200K	$3.00 / $15.00
claude-opus-4-20250514	Anthropic	200K	$15.00 / $75.00
claude-opus-4-6	Anthropic	200K	$5.00 / $25.00
gemini-2.5-pro	Google	1M	$1.25 / $10.00
gemini-2.5-flash	Google	1M	$0.30 / $2.50

Registering Built-in Models

The register_builtin_models() function populates the ModelRegistry with all built-in models. It skips models that are already registered, making it safe to call multiple times:

from openjarvis.intelligence import register_builtin_models

register_builtin_models()
# All BUILTIN_MODELS are now in ModelRegistry

---

Auto-Discovery: Merging Runtime Models

When engines are discovered at runtime, they report models that may not be in the built-in catalog. The merge_discovered_models() function creates minimal ModelSpec entries for these:

from openjarvis.intelligence import merge_discovered_models

# Models reported by Ollama that aren't in the catalog
merge_discovered_models("ollama", ["phi3:3.8b", "codellama:7b"])

For each model ID not already in the registry, a ModelSpec is created with the model ID as both the model_id and name, with zero-value defaults for unknown fields. This ensures the routing system can still select from all available models, even ones it has no metadata for.

---

HeuristicRouter

The HeuristicRouter is a rule-based model router that selects the best model based on query characteristics. It applies six priority rules in order:

Routing Rules

Priority	Rule	Condition	Action
1	Code detection	Query contains code patterns (backticks, def, class, import, function, =>, etc.)	Prefer model with "code" or "coder" in name; fall back to largest model
2	Math detection	Query contains math keywords (solve, integral, equation, calculate, compute, etc.)	Select the largest available model
3	Short query	Query length < 50 characters, no code/math	Select the smallest available model (faster response)
4	Long/complex query	Query length > 500 characters OR contains reasoning keywords (explain, analyze, compare, step-by-step, etc.)	Select the largest available model
5	High urgency	urgency > 0.8	Override to smallest model (fastest response)
6	Default fallback	None of the above match	Use default_model, then fallback_model, then first available

!!! note "Priority 5 overrides all others" The urgency check (rule 5) is actually evaluated first in the code -- if urgency exceeds 0.8, the router immediately returns the smallest model regardless of query content.

Usage

from openjarvis.intelligence import HeuristicRouter, build_routing_context

router = HeuristicRouter(
    available_models=["qwen3:8b", "llama3.2:3b", "deepseek-coder-v2:16b"],
    default_model="qwen3:8b",
    fallback_model="llama3.2:3b",
)

ctx = build_routing_context("Write a Python function to sort a list")
model = router.select_model(ctx)  # Returns "deepseek-coder-v2:16b" (has "coder")

---

build_routing_context()

The build_routing_context() function analyzes a raw query string and produces a RoutingContext dataclass:

@dataclass(slots=True)
class RoutingContext:
    query: str = ""
    query_length: int = 0
    has_code: bool = False
    has_math: bool = False
    language: str = "en"
    urgency: float = 0.5  # 0 = low priority, 1 = real-time
    metadata: Dict[str, Any] = field(default_factory=dict)

Code detection uses regex patterns matching:

• Backtick code blocks (``` or `inline`)
• Language keywords (def, class, import, function, const, var, let)
• Syntax patterns (if (, ->, =>, { }, for x in, #include, System.out)

Math detection uses regex patterns matching:

• Mathematical terms (solve, integral, equation, proof, derivative, matrix)
• Computational keywords (calculate, compute, sigma, sum, limit, probability)

from openjarvis.intelligence import build_routing_context

ctx = build_routing_context("Solve the integral of x^2 dx")
# ctx.has_math = True, ctx.has_code = False, ctx.query_length = 32

ctx = build_routing_context("```python\ndef hello():\n    pass\n```")
# ctx.has_code = True, ctx.has_math = False

---

Integration with Learning

The HeuristicRouter implements the RouterPolicy ABC from the Learning pillar, which means it can be swapped out for a TraceDrivenPolicy or any other policy via the RouterPolicyRegistry. See the Learning & Traces documentation for details on how trace-driven routing works.

The router is registered as "heuristic" in the RouterPolicyRegistry and is the default routing policy. Users can switch policies via the --router CLI flag or the learning.default_policy config setting.