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feat(ollama): opt-in structured output via tool-calling + pydantic validation

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Adds structured_output/output_schema/max_retries inputs to
OllamaChatCompletion, fixing three real reliability problems: models
prepending commentary, wrapping responses in code fences, and occasionally
returning blank output. When enabled, requests go through Ollama's
OpenAI-compatible tool-calling endpoint (a single forced tool call matching
output_schema) instead of native /api/chat, and each response is validated
against a dynamically-built pydantic model — invalid/blank responses retry
with fresh network calls, exhausting into a clear RuntimeError rather than
silently passing bad data downstream. One additional ComfyUI output socket
is exposed per schema property, mirroring FormatString's existing
dynamic-output pattern. Fully backward compatible: structured_output
defaults off and non-structured behavior is untouched.

Ollama's native /api/chat "format" field (JSON-Schema-constrained decoding)
was tried first but proved unreliable against a real local model during
implementation — see ADR-006 for the full investigation and why
tool-calling was chosen over both native format and pydantic-ai (the latter
would reintroduce httpx/openai-sdk, reversing ADR-004).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
James Veitch
2026-07-09 12:50:54 +01:00
co-authored by Claude Sonnet 5
parent 23f8f55493
commit 40e851588d
7 changed files with 1160 additions and 21 deletions
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project-management/ADRs/ADR-006-structured-ollama-output-tool-calling-not-pydantic-ai.md
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@@ -0,0 +1,172 @@
# ADR-006: Structured Ollama output via OpenAI-compatible tool-calling + dynamic pydantic validation, not pydantic-ai
## Status
> Accepted
_Date:_ 2026-07-09
_Deciders:_ darth-veitcher
---
## Context
`OllamaChatCompletion` sends free-text prompts to Ollama's `/api/chat` and
returns `result["message"]["content"]` as-is, with no constraint on what the
model may emit. In practice this is unreliable in three concrete ways:
models prepend commentary ("Here you are:"), wrap responses in ` ``` ` code
fences, or occasionally return blank content — all things prompt wording
alone (e.g. a stricter `system` message) cannot reliably prevent, since it
only *asks* the model to behave, it doesn't constrain what tokens the
decoder is able to produce.
The obvious library to reach for is `pydantic-ai`, which offers structured,
validated LLM output as a first-class feature. Its Ollama support, however,
goes through an OpenAI-compatible client — which pulls in the `openai` SDK,
which depends on `httpx`. This directly reverses
[ADR-004](ADR-004-aiohttp-over-httpx-for-ollama.md), which explicitly
rejected `httpx` as "a new runtime dep for functionality aiohttp already
provides" (ComfyUI's own server is aiohttp-based, so aiohttp is a guaranteed
transitive dependency; httpx is not).
Two Ollama-native mechanisms can force structured output without a new HTTP
client, since both are reachable over plain JSON POST via the existing
`aiohttp`-based `_post_json`:
1. **Native `/api/chat` `"format"` field** — a JSON Schema that constrains
**decoding itself** (grammar-constrained sampling): the model's sampler
is restricted to only emit tokens matching the schema.
2. **OpenAI-compatible `/v1/chat/completions` tool-calling** — a `tools`
array plus `tool_choice` forcing a single named function call, relying on
the model's own trained function-calling behavior rather than a
grammar-to-token mapping.
Both were tried against a real local model
(`lukey03/qwen3.5-9b-abliterated-vision`) during implementation. The native
`format` field was silently ignored — the model returned plain unstructured
text (`"pong"`) despite the schema constraint, reproduced twice. Inspecting
`/api/show` revealed this model's `TEMPLATE` is a degenerate `{{ .Prompt }}`
with no role/message structure — consistent with a community "abliteration"
process having modified the tokenizer/vocab in a way that breaks Ollama's
grammar-to-token mapping, causing it to silently fall back to unconstrained
generation instead of erroring. Tool-calling doesn't depend on that mapping;
it succeeded on its first test against the same model. (A follow-up
tool-calling call with `options` included did also fail — this specific
model appears broadly unreliable, consistent with a degraded fine-tune, so
this evidence is suggestive rather than conclusive. No second generative
model was available locally to get a cleaner signal.)
## Decision
Use Ollama's OpenAI-compatible tool-calling (`/v1/chat/completions`,
`tools`/`tool_choice` forcing a single call) for `structured_output=True`
requests, sent through the existing `_post_json` helper — no new HTTP
client, no `pydantic-ai`, no `openai` SDK. Non-structured requests are
completely unaffected and keep using native `/api/chat`.
Use plain `pydantic` (not `pydantic-ai`) purely as a validation layer:
given the user-supplied JSON Schema (`output_schema` input on
`OllamaChatCompletion`), dynamically build a `pydantic.BaseModel` via
`pydantic.create_model(...)` and validate/parse the tool call's `arguments`
JSON against it. Required *string* fields get `min_length=1` — JSON
Schema's `"required"` only checks presence, so a model could satisfy it
with `""`, silently reintroducing the "blank output" problem. On validation
failure (invalid JSON, missing/empty required field, or the model not
calling the tool at all — observed to happen even with `tool_choice`
forcing it), retry with fresh network calls (bounded by a `max_retries`
input, clamped to 0–5); if every attempt fails, raise a clear
`RuntimeError` naming the model, the attempt count, and a truncated
snippet of the last invalid response — never silently degrade to
unvalidated content.
This is opt-in: a new `structured_output: BOOLEAN` input on the existing
`OllamaChatCompletion` node, default `False`. When off, behavior is
unchanged — no `tools`/`tool_choice` sent, native `/api/chat` used, no
dynamic outputs, `RETURN_TYPES` stays the original fixed 3-tuple. When on,
one additional ComfyUI output socket is exposed per schema property
(mirroring `FormatString`'s existing dynamic-output-socket pattern via
`unique_id`/`RETURN_TYPES` mutation), so downstream nodes can consume
individually typed fields instead of parsing JSON themselves.
## Consequences
**Easier:**
- Fixes the three concrete unreliability problems without depending on a
model/tokenizer-sensitive grammar-constraint mechanism that was observed
to fail silently on at least one real model.
- No new HTTP stack: `pydantic` is a validation-only dependency, not a
client library. ADR-004's aiohttp-only stance is preserved.
- Fully backward compatible — `structured_output` defaults off, and
non-structured requests still use native `/api/chat` exactly as before.
**Harder / constrained:**
- Tool-calling depends on the model having usable trained function-calling
behavior. Models with no tool-calling training may perform worse here
than they would under grammar-constrained `format` decoding — this
repo's only local test model was itself too unreliable to fully confirm
either mechanism's ceiling. If well-behaved-model testing later shows
native `format` is meaningfully more reliable in the common case, this
decision should be revisited rather than treated as permanent.
- Only a flat `properties: {name: {type: ...}}` shape is interpreted into
typed ComfyUI sockets. Complex JSON Schema constructs (`$ref`,
`oneOf`/`anyOf`/`allOf`, `enum`, nested `object`/`array` item schemas) are
still forwarded to Ollama verbatim as the tool's `parameters`, but
comfydv's own type mapping falls back to `STRING` for anything it doesn't
recognize — no nested typed sockets.
- `RETURN_TYPES`/`RETURN_NAMES` are class-level state, shared across every
`OllamaChatCompletion` instance in a graph (same accepted limitation
`FormatString.update_widget` already ships with) — the first execution
after toggling `structured_output` or editing `output_schema` may show
stale downstream socket typing until it runs once.
- Neither mechanism is guaranteed 100% across all versions/models — hence
the retry-then-raise defense-in-depth, rather than trusting either
constraint blindly.
**Debt introduced:**
- None. `pydantic` is a widely-used, low-conflict-risk dependency; ComfyUI
itself is expected to already bundle it for its own API layer, though
this repo lists it explicitly in both `pyproject.toml` and
`requirements.txt` per ADR-003 rather than assume so.
## Considered Alternatives
### Alternative A: `pydantic-ai`
**Why rejected:** Its Ollama support goes through an OpenAI-compatible
client, reintroducing `httpx` + the `openai` SDK — the exact dependency
ADR-004 evaluated and rejected. The reliability benefit it offers is the
same tool-calling/validation mechanism this ADR adopts directly over plain
`aiohttp`, without the added dependency weight.
### Alternative B: Native `/api/chat` `"format"` field (JSON-Schema-constrained decoding)
**Why rejected as primary:** Theoretically the stronger guarantee — a
sampler-level constraint rather than learned behavior — and remains a
reasonable mechanism for well-behaved models. Rejected here because it
failed outright (silently ignored, not even erroring) against the one real
model available for testing, traced to that model's modified tokenizer
breaking Ollama's grammar-to-token mapping. Tool-calling succeeded where it
failed. See "Harder / constrained" above — this may be revisited if
broader testing shows native `format` is more reliable in the common case.
### Alternative C: Prompt-only enforcement (stricter `system` message)
**Why rejected:** `system` already exists as an input and users can already
try this — it's what led to the reported problem in the first place.
Wording can reduce commentary/fences/blank output but cannot guarantee
their absence, since nothing constrains the actual token stream.
### Alternative D: Response-side post-processing (regex-strip fences/preamble)
**Why rejected as the primary fix:** Cheap, but fundamentally reactive —
it can strip a fence wrapper after the fact but can't recover genuinely
blank output, and heuristics for "commentary" are unreliable across models.
Not pursued as a fallback either, to keep this change minimal and avoid two
competing "make output clean" mechanisms with unclear precedence.
---
## Links
- Related ADRs: [ADR-003](ADR-003-requirements-txt-authoring-policy.md), [ADR-004](ADR-004-aiohttp-over-httpx-for-ollama.md), [ADR-005](ADR-005-ollama-host-config-via-client-node.md)
- Originating epic (archived, scope predates this decision): `project-management/Roadmap/epics/archive/ollama-integration.md`
pyproject.toml
+1
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@@ -10,6 +10,7 @@ authors = [
dependencies = [
"aiohttp>=3.9.0",
"jinja2>=3.1.6",
"pydantic>=2.0",
]
[project.urls]
requirements.txt
+1
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@@ -2,3 +2,4 @@
# Lists only deps NOT already provided by ComfyUI's own environment.
# See ADR-003: never auto-generate this file with `uv export`.
jinja2>=3.1.6
pydantic>=2.0
src/comfydv/ollama.py
+278 -14
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@@ -7,6 +7,9 @@ chat completion, composable inference options, and history utilities.
ADR-004: aiohttp (already in ComfyUI dep tree) for all Ollama HTTP calls.
ADR-005: OllamaClient node is the single source of the host URL (and, per
US7, of any auth headers — every downstream node reaches the same server).
ADR-006: structured_output uses Ollama's OpenAI-compatible tool-calling
endpoint + a dynamically-built pydantic model for validation, not
pydantic-ai — still plain aiohttp, no new HTTP client.
"""
import asyncio
@@ -17,6 +20,8 @@ import sys
import threading
import time
from pydantic import ValidationError
logger = logging.getLogger(__name__)
@@ -460,9 +465,152 @@ def _history_preview(messages: list[dict]) -> str:
return "\n".join(lines)
# ---------------------------------------------------------------------------
# Structured output — Ollama's OpenAI-compatible /v1/chat/completions
# tool-calling, with a single forced tool matching output_schema
# ---------------------------------------------------------------------------
#
# ADR-006: forces the model to emit its answer as a single tool call whose
# arguments match output_schema, instead of relying on prompt wording —
# fixes commentary, code-fence wrapping, and blank output at the source.
# Ollama's native /api/chat "format" (JSON-Schema-constrained decoding) was
# tried first but proved unreliable against at least one real model in
# testing (a modified-tokenizer quantization silently ignored the schema
# rather than erroring); tool-calling relies on the model's own trained
# function-calling behavior instead of grammar-to-token mapping, and is
# still reached over the existing aiohttp-based _post_json — no new HTTP
# client. pydantic is used purely to dynamically build a validation model
# from the user's schema, not as an HTTP client.
_JSON_SCHEMA_TO_PY_TYPE: dict = {
"string": str,
"integer": int,
"number": float,
"boolean": bool,
"array": list,
"object": dict,
}
_JSON_SCHEMA_TO_COMFY_TYPE: dict = {
"string": "STRING",
"integer": "INT",
"number": "FLOAT",
"boolean": "BOOLEAN",
"array": "STRING",
"object": "STRING",
}
_DEFAULT_OUTPUT_SCHEMA = (
'{"type": "object", "properties": '
'{"output": {"type": "string"}}, "required": ["output"]}'
)
def _parse_output_schema(output_schema: str) -> dict:
"""Parse+validate output_schema JSON, fail fast before any network call."""
try:
schema = json.loads(output_schema)
except json.JSONDecodeError as exc:
raise ValueError(f"output_schema is not valid JSON: {exc}") from exc
if not isinstance(schema, dict) or schema.get("type") != "object":
raise ValueError(
'output_schema must be a JSON Schema object with "type": "object" '
'at the root, e.g. {"type": "object", "properties": {...}}'
)
properties = schema.get("properties")
if not isinstance(properties, dict) or not properties:
raise ValueError(
"output_schema.properties must be a non-empty object — "
"structured_output requires at least one field to extract"
)
return schema
def _comfy_types_for_schema(schema: dict) -> tuple:
return tuple(
_JSON_SCHEMA_TO_COMFY_TYPE.get(prop.get("type"), "STRING")
for prop in schema["properties"].values()
)
def _build_structured_model(schema: dict):
"""Dynamically build a pydantic BaseModel from schema properties/required.
Rebuilt every call — create_model() on a handful of fields is cheap, and
OllamaChatCompletion already re-executes every queue run (OUTPUT_NODE=True).
Required *string* fields get `min_length=1`: JSON Schema's "required"
only checks presence, so a model could satisfy it with `""` — which is
exactly the "blank output" problem this feature exists to eliminate. An
empty required string now fails validation and triggers a retry like any
other malformed response, rather than silently passing through.
"""
from pydantic import Field, create_model
required = set(schema.get("required", []))
fields = {}
for name, prop in schema["properties"].items():
py_type = _JSON_SCHEMA_TO_PY_TYPE.get(prop.get("type"), str)
if name not in required:
fields[name] = (py_type, None)
elif py_type is str:
fields[name] = (py_type, Field(..., min_length=1))
else:
fields[name] = (py_type, ...)
return create_model("OllamaStructuredOutput", **fields)
def _coerce_structured_value(value, comfy_type: str):
if comfy_type == "STRING" and isinstance(value, (list, dict)):
return json.dumps(value)
if comfy_type == "STRING" and value is None:
return ""
return value
_STRUCTURED_TOOL_NAME = "emit_structured_output"
def _build_tool_call_payload(schema: dict) -> tuple:
"""tools/tool_choice extras forcing a single call matching schema."""
tools = [
{
"type": "function",
"function": {
"name": _STRUCTURED_TOOL_NAME,
"description": "Emit the result matching the required schema.",
"parameters": schema,
},
}
]
tool_choice = {"type": "function", "function": {"name": _STRUCTURED_TOOL_NAME}}
return tools, tool_choice
def _extract_tool_call_arguments(result: dict) -> str:
"""Pull the forced tool call's arguments JSON string out of an OpenAI-
compatible /v1/chat/completions response. Empty string (which will fail
pydantic validation and trigger a retry) if the model didn't call the
tool at all — this happens on occasion even with tool_choice forcing it.
"""
choices = result.get("choices") or []
if not choices:
return ""
message = choices[0].get("message", {})
tool_calls = message.get("tool_calls") or []
if not tool_calls:
return ""
return tool_calls[0].get("function", {}).get("arguments", "") or ""
class OllamaChatCompletion:
OUTPUT_NODE = True
_BASE_RETURN_TYPES = ("STRING", "OLLAMA_HISTORY", "STRING")
_BASE_RETURN_NAMES = ("response", "updated_history", "model_name")
# Per-node-instance structured-output config, keyed by unique_id — same
# pattern as FormatString.node_configs.
node_configs: dict = {}
@classmethod
def INPUT_TYPES(s):
return {
@@ -478,14 +626,45 @@ class OllamaChatCompletion:
"history": ("OLLAMA_HISTORY",),
"options": ("OLLAMA_OPTIONS",),
"timeout_secs": ("INT", {"default": 300, "min": 30, "max": 3600}),
"structured_output": ("BOOLEAN", {"default": False}),
"output_schema": (
"STRING",
{"multiline": True, "default": _DEFAULT_OUTPUT_SCHEMA},
),
"max_retries": ("INT", {"default": 2, "min": 0, "max": 5}),
},
"hidden": {"unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ("STRING", "OLLAMA_HISTORY", "STRING")
RETURN_NAMES = ("response", "updated_history", "model_name")
RETURN_TYPES = _BASE_RETURN_TYPES
RETURN_NAMES = _BASE_RETURN_NAMES
FUNCTION = "chat"
CATEGORY = "dv/ollama"
@classmethod
def update_outputs(
cls, unique_id: str, structured_output: bool, schema: dict | None
) -> None:
"""Mutate class-level RETURN_TYPES/RETURN_NAMES for structured_output mode.
Same class-level-shared-state pattern (and limitation) as
FormatString.update_widget: RETURN_TYPES/RETURN_NAMES are shared
across all instances of this node type in a graph, so the very first
execution after toggling structured_output or editing output_schema
may show stale downstream socket typing until it runs once.
"""
cls.node_configs[unique_id] = {
"structured_output": structured_output,
"schema": schema,
}
if not structured_output or not schema:
cls.RETURN_TYPES = cls._BASE_RETURN_TYPES
cls.RETURN_NAMES = cls._BASE_RETURN_NAMES
return
names = tuple(schema["properties"].keys())
cls.RETURN_TYPES = cls._BASE_RETURN_TYPES + _comfy_types_for_schema(schema)
cls.RETURN_NAMES = cls._BASE_RETURN_NAMES + names
def chat(
self,
client,
@@ -495,10 +674,24 @@ class OllamaChatCompletion:
history=None,
options=None,
timeout_secs=300,
structured_output=False,
output_schema=_DEFAULT_OUTPUT_SCHEMA,
max_retries=2,
unique_id="",
):
effective_model = model.strip()
if not effective_model:
raise ValueError("model cannot be empty — type a model name or wire one in")
schema = None
pydantic_model = None
if structured_output:
schema = _parse_output_schema(output_schema) # fail fast, no network call
pydantic_model = _build_structured_model(schema)
if unique_id:
type(self).update_outputs(unique_id, structured_output, schema)
if history is None:
history = []
messages = list(history)
@@ -512,25 +705,85 @@ class OllamaChatCompletion:
}
if options:
payload["options"] = options
if structured_output:
# ADR-006: OpenAI-compatible tool-calling, not native /api/chat
# "format" — forces a single call whose arguments match schema.
assert schema is not None # structured_output implies this was parsed
tools, tool_choice = _build_tool_call_payload(schema)
payload["tools"] = tools
payload["tool_choice"] = tool_choice
headers = _client_headers(client)
cache_key = _cache_key(
"chat", client, headers or {}, effective_model, messages, options or {}
"chat",
client,
headers or {},
effective_model,
messages,
options or {},
schema or {},
)
cached, hit = _CHAT_RESPONSE_CACHE.get(cache_key)
if hit:
url = (
f"{client}/v1/chat/completions"
if structured_output
else f"{client}/api/chat"
)
parsed = None
if not structured_output:
# Unchanged from pre-structured-output behavior.
if hit:
response_text = cached
else:
result = _run_async(
_post_json(
url, payload, timeout=float(timeout_secs), headers=headers
)
)
response_text = result.get("message", {}).get("content", "")
_CHAT_RESPONSE_CACHE.set(cache_key, response_text)
elif hit:
# schema is part of the cache key, so a hit was necessarily
# validated against this exact schema when it was written —
# re-parsing here is guaranteed-successful deserialization, not
# a fallible re-check.
assert (
pydantic_model is not None
) # structured_output implies this was built
response_text = cached
parsed = pydantic_model.model_validate_json(response_text)
else:
result = _run_async(
_post_json(
f"{client}/api/chat",
payload,
timeout=float(timeout_secs),
headers=headers,
assert (
pydantic_model is not None
) # structured_output implies this was built
total_attempts = max(0, min(int(max_retries), 5)) + 1
response_text = None
last_invalid_text = ""
last_error = None
for _attempt in range(1, total_attempts + 1):
result = _run_async(
_post_json(
url, payload, timeout=float(timeout_secs), headers=headers
)
)
candidate = _extract_tool_call_arguments(result)
try:
parsed = pydantic_model.model_validate_json(candidate)
response_text = candidate
_CHAT_RESPONSE_CACHE.set(cache_key, response_text)
break
except ValidationError as exc:
last_invalid_text = candidate
last_error = exc
else:
raise RuntimeError(
f"OllamaChatCompletion: structured_output response failed "
f"validation against output_schema after {total_attempts} "
f"attempt(s) (model={effective_model!r}). Last error: "
f"{last_error}. Last response (truncated): "
f"{last_invalid_text[:300]!r}"
)
)
response_text = result.get("message", {}).get("content", "")
_CHAT_RESPONSE_CACHE.set(cache_key, response_text)
updated = list(history)
updated.append({"role": "user", "content": prompt})
@@ -541,9 +794,20 @@ class OllamaChatCompletion:
if n > 2
else response_text
)
result_tuple = (response_text, updated, effective_model)
if structured_output:
assert schema is not None # structured_output implies this was parsed
comfy_types = _comfy_types_for_schema(schema)
extra = tuple(
_coerce_structured_value(getattr(parsed, name), ctype)
for name, ctype in zip(schema["properties"].keys(), comfy_types)
)
result_tuple += extra
return {
"ui": {"text": [ui_text]},
"result": (response_text, updated, effective_model),
"result": result_tuple,
}
tests/conftest.py
+21 -7
View File
@@ -68,20 +68,34 @@ def pytest_configure(config):
@pytest.fixture(autouse=True)
def _clear_ollama_caches():
"""Reset comfydv.ollama's module-level LRU caches around every test.
"""Reset comfydv.ollama's module-level LRU caches and OllamaChatCompletion's
dynamic RETURN_TYPES/RETURN_NAMES around every test.
Several tests reuse identical client/model/prompt inputs across cases
with different monkeypatched responses — without this, a later test would
silently get an earlier test's cached result instead of exercising its
own fake.
own fake. RETURN_TYPES/RETURN_NAMES are class-level mutable state (set by
OllamaChatCompletion.update_outputs for structured_output mode) shared
across every test in the module — without resetting them, a structured-
output test would leak its dynamic outputs into unrelated tests that
assert the fixed 3-tuple.
"""
from comfydv.ollama import _CHAT_RESPONSE_CACHE, _MODEL_LIST_CACHE
from comfydv.ollama import (
_CHAT_RESPONSE_CACHE,
_MODEL_LIST_CACHE,
OllamaChatCompletion,
)
_MODEL_LIST_CACHE.clear()
_CHAT_RESPONSE_CACHE.clear()
def _reset():
_MODEL_LIST_CACHE.clear()
_CHAT_RESPONSE_CACHE.clear()
OllamaChatCompletion.RETURN_TYPES = OllamaChatCompletion._BASE_RETURN_TYPES
OllamaChatCompletion.RETURN_NAMES = OllamaChatCompletion._BASE_RETURN_NAMES
OllamaChatCompletion.node_configs.clear()
_reset()
yield
_MODEL_LIST_CACHE.clear()
_CHAT_RESPONSE_CACHE.clear()
_reset()
@pytest.fixture(scope="session")
tests/test_ollama.py
+547
View File
@@ -626,6 +626,478 @@ class TestUS4ChatCompletion:
)["result"]
assert len(h2) == 4
@pytest.mark.integration
def test_structured_output_retries_then_raises_against_live_server(
self, ollama_host, skip_if_no_ollama
):
"""Scenario: structured_output's retry-then-raise fallback against a
real server, using an unreliable model that empirically cannot be
made to call the forced tool consistently.
This intentionally does NOT assert a happy-path clean result: the
only generative model available in CI/dev environments running this
(a community "abliterated" quantization) was found during
implementation to unreliably invoke tool calls regardless of
mechanism — see ADR-006. What IS worth proving against a live
server: the retry loop makes genuine repeated network calls (not
just replaying a mock) and produces a well-formed, diagnostic error
rather than hanging, crashing uninformatively, or silently returning
bad data. The happy path (clean structured extraction) is covered
thoroughly by the mocked TestStructuredOutput suite.
"""
with pytest.raises(RuntimeError, match="failed validation") as exc_info:
OllamaChatCompletion().chat(
client=ollama_host,
model=_CHAT_MODEL,
prompt="Say exactly: pong",
options={"think": False},
structured_output=True,
output_schema=(
'{"type":"object","properties":{"output":{"type":"string"}},'
'"required":["output"]}'
),
max_retries=1,
unique_id="smoke-test",
)
assert "2 attempt" in str(exc_info.value)
assert _CHAT_MODEL in str(exc_info.value)
# ---------------------------------------------------------------------------
# US8 — Structured output (OpenAI-compatible tool-calling + dynamic pydantic
# model — see ADR-006 for why tool-calling rather than native `format`)
# ---------------------------------------------------------------------------
_SINGLE_FIELD_SCHEMA = (
'{"type": "object", "properties": {"output": {"type": "string"}}, '
'"required": ["output"]}'
)
_MULTI_FIELD_SCHEMA = (
'{"type": "object", "properties": {'
'"summary": {"type": "string"}, '
'"score": {"type": "integer"}, '
'"is_positive": {"type": "boolean"}}, '
'"required": ["summary", "score", "is_positive"]}'
)
def _tool_response(arguments_json: str) -> dict:
"""Build an OpenAI-compatible /v1/chat/completions response with a single
forced tool call, matching what Ollama's compat layer returns."""
return {
"choices": [
{
"message": {
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {
"name": "emit_structured_output",
"arguments": arguments_json,
},
}
],
},
"finish_reason": "tool_calls",
}
]
}
def _no_tool_call_response() -> dict:
"""Model didn't invoke the forced tool at all — happens even with
tool_choice forcing it, on some models. Empty arguments must fail
validation and trigger a retry like any other malformed response."""
return {
"choices": [
{"message": {"role": "assistant", "content": ""}, "finish_reason": "stop"}
]
}
class TestStructuredOutput:
def test_structured_output_defaults_to_false(self):
input_types = OllamaChatCompletion.INPUT_TYPES()
assert input_types["optional"]["structured_output"] == (
"BOOLEAN",
{"default": False},
)
def test_structured_output_false_payload_has_no_tools_key(self, monkeypatch):
captured = {}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
captured["url"] = url
captured["payload"] = payload
return {"message": {"content": "hello"}}
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=False
)
assert "tools" not in captured["payload"]
assert "tool_choice" not in captured["payload"]
assert captured["url"].endswith("/api/chat")
def test_structured_output_true_sends_tool_call_payload(self, monkeypatch):
captured = {}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
captured["url"] = url
captured["payload"] = payload
return _tool_response('{"output": "hi there"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
output_schema=_SINGLE_FIELD_SCHEMA,
unique_id="n1",
)
assert captured["url"].endswith("/v1/chat/completions")
tool = captured["payload"]["tools"][0]
assert tool["type"] == "function"
assert tool["function"]["parameters"] == json.loads(_SINGLE_FIELD_SCHEMA)
assert captured["payload"]["tool_choice"] == {
"type": "function",
"function": {"name": tool["function"]["name"]},
}
def test_default_schema_produces_single_output_field(self, monkeypatch):
async def fake_post(url, payload, *, timeout=120.0, headers=None):
return _tool_response('{"output": "clean text"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
ret = OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
unique_id="n2",
)
assert OllamaChatCompletion.RETURN_NAMES == (
"response",
"updated_history",
"model_name",
"output",
)
assert len(ret["result"]) == 4
assert ret["result"][3] == "clean text"
assert ret["result"][0] == '{"output": "clean text"}'
def test_multi_field_schema_sets_return_types_and_values(self, monkeypatch):
async def fake_post(url, payload, *, timeout=120.0, headers=None):
return _tool_response(
'{"summary": "great", "score": 9, "is_positive": true}'
)
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
ret = OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
output_schema=_MULTI_FIELD_SCHEMA,
unique_id="n3",
)
assert OllamaChatCompletion.RETURN_TYPES == (
"STRING",
"OLLAMA_HISTORY",
"STRING",
"STRING",
"INT",
"BOOLEAN",
)
assert OllamaChatCompletion.RETURN_NAMES == (
"response",
"updated_history",
"model_name",
"summary",
"score",
"is_positive",
)
_, _, _, summary, score, is_positive = ret["result"]
assert summary == "great"
assert score == 9
assert isinstance(score, int)
assert is_positive is True
def test_array_object_property_json_dumped_into_string_slot(self, monkeypatch):
schema = (
'{"type": "object", "properties": {'
'"tags": {"type": "array"}}, "required": ["tags"]}'
)
async def fake_post(url, payload, *, timeout=120.0, headers=None):
return _tool_response('{"tags": ["a", "b", "c"]}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
ret = OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
output_schema=schema,
unique_id="n4",
)
assert ret["result"][3] == json.dumps(["a", "b", "c"])
def test_return_types_reset_when_toggled_off(self, monkeypatch):
async def fake_post(url, payload, *, timeout=120.0, headers=None):
return _tool_response('{"output": "x"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
unique_id="n5",
)
assert len(OllamaChatCompletion.RETURN_TYPES) == 4
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=False,
unique_id="n5",
)
assert (
OllamaChatCompletion.RETURN_TYPES == OllamaChatCompletion._BASE_RETURN_TYPES
)
assert (
OllamaChatCompletion.RETURN_NAMES == OllamaChatCompletion._BASE_RETURN_NAMES
)
def test_invalid_output_schema_json_raises_before_network_call(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
return _tool_response("{}")
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
with pytest.raises(ValueError, match="not valid JSON"):
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
output_schema="not json",
)
assert calls["n"] == 0, "invalid schema must fail before touching the network"
def test_non_object_root_schema_raises(self):
with pytest.raises(ValueError, match='"type": "object"'):
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
output_schema='{"type": "string"}',
)
def test_empty_properties_raises(self):
with pytest.raises(ValueError, match="non-empty object"):
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
output_schema='{"type": "object", "properties": {}}',
)
def test_valid_json_first_attempt_no_retry(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
return _tool_response('{"output": "ok"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=True
)
assert calls["n"] == 1
def test_retries_on_invalid_json_then_succeeds(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
if calls["n"] == 1:
return _tool_response("not json at all")
return _tool_response('{"output": "ok on retry"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
ret = OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=True
)
assert calls["n"] == 2
assert ret["result"][3] == "ok on retry"
def test_retries_when_model_never_calls_the_tool(self, monkeypatch):
"""The model can ignore tool_choice and just respond normally —
happens on occasion even with a forced tool. Must retry, not crash."""
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
if calls["n"] == 1:
return _no_tool_call_response()
return _tool_response('{"output": "ok on retry"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
ret = OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=True
)
assert calls["n"] == 2
assert ret["result"][3] == "ok on retry"
def test_retries_on_missing_required_field(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
if calls["n"] == 1:
return _tool_response("{}") # missing required "output"
return _tool_response('{"output": "ok"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=True
)
assert calls["n"] == 2
def test_retries_on_empty_required_string(self, monkeypatch):
"""A required string field satisfied by "" must still be rejected —
JSON Schema "required" only checks presence, not non-emptiness, so
without this an empty answer would silently pass validation (exactly
the "blank output" problem structured_output exists to eliminate)."""
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
if calls["n"] == 1:
return _tool_response('{"output": ""}')
return _tool_response('{"output": "ok"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
ret = OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=True
)
assert calls["n"] == 2
assert ret["result"][3] == "ok"
def test_exhausts_retries_raises_runtime_error(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
return _tool_response("always broken")
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
with pytest.raises(RuntimeError, match="failed validation") as exc_info:
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
max_retries=2,
)
assert calls["n"] == 3, "max_retries=2 must allow exactly 3 total attempts"
assert "3 attempt" in str(exc_info.value)
assert "always broken" in str(exc_info.value)
def test_max_retries_clamped_upper_bound(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
return _tool_response("always broken")
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
with pytest.raises(RuntimeError):
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
max_retries=99,
)
assert calls["n"] == 6, "max_retries must be clamped to 5 (6 total attempts)"
def test_max_retries_clamped_lower_bound(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
return _tool_response("always broken")
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
with pytest.raises(RuntimeError):
OllamaChatCompletion().chat(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
max_retries=-5,
)
assert calls["n"] == 1, (
"even a negative max_retries must allow at least 1 attempt"
)
# ---------------------------------------------------------------------------
# US5 — Composable Options (us5_composable_options.feature)
@@ -1094,6 +1566,81 @@ class TestResponseCache:
"a cache entry"
)
def test_structured_output_cache_hit_skips_network(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
return _tool_response('{"output": "cached value"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
kwargs = dict(
client="http://x",
model="m",
prompt="hi",
structured_output=True,
unique_id="cache-1",
)
ret1 = OllamaChatCompletion().chat(**kwargs)
ret2 = OllamaChatCompletion().chat(**kwargs)
assert calls["n"] == 1, "second identical structured call must hit the cache"
assert ret1["result"] == ret2["result"], (
"dynamic outputs must be correctly re-extracted from the cached text"
)
assert ret2["result"][3] == "cached value"
def test_structured_and_non_structured_do_not_collide_in_cache(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
if "tools" in payload:
return _tool_response('{"output": "structured"}')
return {"message": {"content": "plain text"}}
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=False
)
OllamaChatCompletion().chat(
client="http://x", model="m", prompt="hi", structured_output=True
)
assert calls["n"] == 2, (
"identical client/model/prompt with structured_output True vs False "
"must not share a cache entry"
)
def test_structured_output_only_caches_after_validation_passes(self, monkeypatch):
calls = {"n": 0}
async def fake_post(url, payload, *, timeout=120.0, headers=None):
calls["n"] += 1
if calls["n"] == 1:
return _tool_response("invalid json")
return _tool_response('{"output": "good"}')
import comfydv.ollama as ollama_mod
monkeypatch.setattr(ollama_mod, "_post_json", fake_post)
kwargs = dict(client="http://x", model="m", prompt="hi", structured_output=True)
OllamaChatCompletion().chat(
**kwargs
) # attempt 1 invalid, attempt 2 caches "good"
assert calls["n"] == 2
ret = OllamaChatCompletion().chat(**kwargs) # must hit cache, not re-call
assert calls["n"] == 2, "the invalid first attempt must never be cached"
assert ret["result"][3] == "good"
# ---------------------------------------------------------------------------
# Node contract sanity checks (ComfyUI registration requirements)
uv.lock
Generated
+140
View File
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[[package]]
name = "annotated-types"
version = "0.7.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
wheels = [
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]
[[package]]
name = "attrs"
version = "25.4.0"
@@ -245,6 +254,7 @@ source = { editable = "." }
dependencies = [
{ name = "aiohttp" },
{ name = "jinja2" },
{ name = "pydantic" },
]
[package.dev-dependencies]
@@ -271,6 +281,7 @@ docs = [
requires-dist = [
{ name = "aiohttp", specifier = ">=3.9.0" },
{ name = "jinja2", specifier = ">=3.1.6" },
{ name = "pydantic", specifier = ">=2.0" },
]
[package.metadata.requires-dev]
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]
[[package]]
name = "pydantic"
version = "2.13.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "annotated-types" },
{ name = "pydantic-core" },
{ name = "typing-extensions" },
{ name = "typing-inspection" },
]
sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775, upload-time = "2026-05-06T13:43:05.343Z" }
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