fix(ollama): make native think configurable via extra_body

hindsight-api-slim/hindsight_api/engine/providers/openai_compatible_llm.py

@@ -1463,7 +1463,9 @@ async def _call_ollama_native(
             "model": self.model,
             "messages": messages,
             "stream": False,
-            "think": False,  # Disable thinking for reasoning models (qwen3.5, etc.)
+            # Disable thinking by default (qwen3.5, etc.). Override via
+            # extra_body, e.g. {"think": "low"} for gpt-oss models (see #3246).
+            "think": False,
         }
 
         # Add schema as format parameter for structured output
@@ -1480,6 +1482,16 @@ async def _call_ollama_native(
             options["num_predict"] = max_completion_tokens
         if temperature is not None:
             options["temperature"] = temperature
+
+        # Merge configured extra_body into the native payload. Ollama's native
+        # /api/chat body has two tiers, unlike the OpenAI-compatible endpoint
+        # where the SDK flattens everything to top-level: native top-level
+        # fields (think, keep_alive, ...) pass through directly, while an
+        # "options" sub-dict merges into Ollama's generation options
+        # (seed, top_p, num_ctx, ...). User values win over the defaults above.
+        extra_body = dict(self._config_extra_body)
+        options.update(extra_body.pop("options", {}))
+        payload.update(extra_body)
         payload["options"] = options
 
         last_exception = None

hindsight-api-slim/tests/test_ollama_native_think.py

@@ -0,0 +1,111 @@
+"""
+Regression tests for Ollama native API extra_body handling.
+
+The native /api/chat payload has two tiers: native top-level fields (``think``,
+``keep_alive``, ...) and a nested ``options`` object (``seed``, ``top_p``,
+``num_ctx``, ...). Configured ``extra_body`` must reach both, so operators can
+enable thinking for gpt-oss models (``{"think": "low"}``) or tune generation
+options without a code change (see #3246).
+"""
+
+import json
+from unittest.mock import AsyncMock, patch
+
+import httpx
+import pytest
+from pydantic import BaseModel
+
+from hindsight_api.engine.providers.openai_compatible_llm import OpenAICompatibleLLM
+
+
+class _SampleOutput(BaseModel):
+    summary: str
+
+
+def _make_ollama_llm(model: str, extra_body: dict | None = None) -> OpenAICompatibleLLM:
+    return OpenAICompatibleLLM(
+        provider="ollama",
+        api_key="",
+        base_url="http://localhost:11434/v1",
+        model=model,
+        extra_body=extra_body,
+    )
+
+
+def _mock_ollama_response(content: dict) -> httpx.Response:
+    body = {
+        "model": "test-model",
+        "message": {
+            "role": "assistant",
+            "content": json.dumps(content),
+        },
+        "done": True,
+    }
+    request = httpx.Request("POST", "http://localhost:11434/api/chat")
+    return httpx.Response(200, json=body, request=request)
+
+
+async def _capture_payload(llm: OpenAICompatibleLLM) -> dict:
+    mock_client = AsyncMock()
+    mock_client.post.return_value = _mock_ollama_response({"summary": "test"})
+    mock_client.__aenter__.return_value = mock_client
+
+    with patch(
+        "hindsight_api.engine.providers.openai_compatible_llm.httpx.AsyncClient",
+        return_value=mock_client,
+    ):
+        await llm._call_ollama_native(
+            messages=[{"role": "user", "content": "hello"}],
+            response_format=_SampleOutput,
+            max_completion_tokens=512,
+            temperature=0.1,
+            max_retries=0,
+            initial_backoff=1.0,
+            max_backoff=10.0,
+            skip_validation=True,
+        )
+
+    request = mock_client.post.call_args
+    assert request is not None
+    return request.kwargs["json"]
+
+
+@pytest.mark.asyncio
+async def test_ollama_native_think_defaults_false():
+    """Thinking is disabled by default and structured-output format is included."""
+    payload = await _capture_payload(_make_ollama_llm("qwen3.5:2b"))
+
+    assert payload["think"] is False
+    assert "format" in payload
+    assert payload["options"]["num_predict"] == 512
+    assert payload["options"]["temperature"] == 0.1
+
+
+@pytest.mark.asyncio
+async def test_ollama_native_think_override_via_extra_body():
+    """extra_body top-level field overrides the think default (gpt-oss path)."""
+    payload = await _capture_payload(_make_ollama_llm("gpt-oss:20b", extra_body={"think": "low"}))
+
+    assert payload["think"] == "low"
+    # Computed options are preserved alongside the top-level override.
+    assert payload["options"]["num_predict"] == 512
+    assert payload["options"]["temperature"] == 0.1
+
+
+@pytest.mark.asyncio
+async def test_ollama_native_options_merge_via_extra_body():
+    """An extra_body "options" sub-dict merges into native generation options."""
+    payload = await _capture_payload(
+        _make_ollama_llm(
+            "qwen3.5:2b",
+            extra_body={"options": {"seed": 42, "temperature": 0.9}},
+        )
+    )
+
+    # New option added, and a user value wins over the computed default.
+    assert payload["options"]["seed"] == 42
+    assert payload["options"]["temperature"] == 0.9
+    assert payload["options"]["num_predict"] == 512
+    # "options" is not leaked as a top-level payload field.
+    assert "options" in payload
+    assert payload["think"] is False

hindsight-docs/docs/developer/configuration.md

@@ -190,7 +190,7 @@ For non-English banks (especially CJK) and the language/extraction-language trad
 | `HINDSIGHT_API_LLM_OPENAI_SERVICE_TIER` | OpenAI service tier: `flex` for 50% cost savings (OpenAI Flex Processing) | None (default) |
 | `HINDSIGHT_API_LLM_BEDROCK_SERVICE_TIER` | Bedrock service tier: `flex` for 50% cost savings (best-effort inference), `priority` (guaranteed throughput), or `reserved` (provisioned capacity) | Unset (default tier) |
 | `HINDSIGHT_API_LLM_GEMINI_SERVICE_TIER` | Gemini service tier: `flex` for 50% cost savings (best-effort inference) | Unset (default tier) |
-| `HINDSIGHT_API_LLM_EXTRA_BODY` | JSON dict of extra request-body params (e.g. `temperature`, `top_p`, `max_tokens`) merged into every LLM call. Applied across the OpenAI-compatible, Fireworks, Anthropic, Gemini/VertexAI and LiteLLM (incl. Bedrock/Router) providers. Each provider merges them in its own native parameter space, so use that provider's field names (e.g. `max_tokens` for OpenAI/Anthropic vs `max_output_tokens` for Gemini). Also useful for custom model servers (e.g. vLLM `chat_template_kwargs`). | `null` |
+| `HINDSIGHT_API_LLM_EXTRA_BODY` | JSON dict of extra request-body params (e.g. `temperature`, `top_p`, `max_tokens`) merged into every LLM call. Applied across the OpenAI-compatible, Fireworks, Anthropic, Gemini/VertexAI and LiteLLM (incl. Bedrock/Router) providers. Each provider merges them in its own native parameter space, so use that provider's field names (e.g. `max_tokens` for OpenAI/Anthropic vs `max_output_tokens` for Gemini). The native Ollama structured-output path is a special case — see the note below. Also useful for custom model servers (e.g. vLLM `chat_template_kwargs`). | `null` |
 | `HINDSIGHT_API_LLM_DEFAULT_HEADERS` | JSON dict passed as `default_headers` to provider SDK clients. Used by operators routing through proxies / request-tracing middleware (e.g. Cloudflare AI Gateway, Helicone, corporate proxies). Currently wired into the Anthropic provider; other providers can opt in. | `null` |
 | `HINDSIGHT_API_LLM_STRICT_SCHEMA` | Grammar-enforce structured output via `json_schema` `strict: true` instead of the soft "schema-in-prompt + `json_object`" path. Typed Pydantic response models are serialized directly into the OpenAI strict subset: every object rejects additional properties, every declared property is required, and nullable fields remain nullable. Use it with weaker self-hosted models that return prose preambles, markdown ` ```json ` fences, or invalid JSON — which otherwise fail to parse and wedge retain/consolidation. Applies to OpenAI-compatible backends (OpenAI, llama.cpp, vLLM), Codex, and LiteLLM; Gemini already enforces its native `response_schema` regardless, and providers without a strict mode ignore it. | `false` |
 | `HINDSIGHT_API_LLM_STRICT_SCHEMA_RETAIN` | Override `HINDSIGHT_API_LLM_STRICT_SCHEMA` for retain (fact extraction) only. Applies to both the streaming and batch extraction paths. | Inherits global |
@@ -205,6 +205,11 @@ For non-English banks (especially CJK) and the language/extraction-language trad
 
 When `HINDSIGHT_API_LLM_PROVIDER=ollama`, Hindsight no longer sends the previous native API default `num_ctx=16384` unless you set it explicitly. To keep the old request behavior, set `HINDSIGHT_API_LLM_OLLAMA_NUM_CTX=16384`; otherwise Ollama uses the model Modelfile or server default.
 
+**`HINDSIGHT_API_LLM_EXTRA_BODY` on the native Ollama path.** For structured-output calls, Ollama uses its native `/api/chat` API, whose request body has *two tiers* — this differs from the OpenAI-compatible endpoint, where the SDK flattens everything to top-level. On the native path `extra_body` is split accordingly:
+
+- **Top-level native fields** (`think`, `keep_alive`, ...) pass through directly. For example, gpt-oss models require a thinking level for structured extraction, so set `HINDSIGHT_API_LLM_EXTRA_BODY='{"think": "low"}'` (thinking is disabled by default).
+- **Generation parameters** (`seed`, `top_p`, `top_k`, `num_ctx`, `temperature`, ...) live under Ollama's `options` object, so nest them: `HINDSIGHT_API_LLM_EXTRA_BODY='{"options": {"seed": 42, "top_p": 0.9}}'`. On the OpenAI-compatible endpoints these same params are top-level instead.
+
 **Provider Examples**
 
 ```bash

skills/hindsight-docs/references/developer/configuration.md

@@ -190,7 +190,7 @@ For non-English banks (especially CJK) and the language/extraction-language trad
 | `HINDSIGHT_API_LLM_OPENAI_SERVICE_TIER` | OpenAI service tier: `flex` for 50% cost savings (OpenAI Flex Processing) | None (default) |
 | `HINDSIGHT_API_LLM_BEDROCK_SERVICE_TIER` | Bedrock service tier: `flex` for 50% cost savings (best-effort inference), `priority` (guaranteed throughput), or `reserved` (provisioned capacity) | Unset (default tier) |
 | `HINDSIGHT_API_LLM_GEMINI_SERVICE_TIER` | Gemini service tier: `flex` for 50% cost savings (best-effort inference) | Unset (default tier) |
-| `HINDSIGHT_API_LLM_EXTRA_BODY` | JSON dict of extra request-body params (e.g. `temperature`, `top_p`, `max_tokens`) merged into every LLM call. Applied across the OpenAI-compatible, Fireworks, Anthropic, Gemini/VertexAI and LiteLLM (incl. Bedrock/Router) providers. Each provider merges them in its own native parameter space, so use that provider's field names (e.g. `max_tokens` for OpenAI/Anthropic vs `max_output_tokens` for Gemini). Also useful for custom model servers (e.g. vLLM `chat_template_kwargs`). | `null` |
+| `HINDSIGHT_API_LLM_EXTRA_BODY` | JSON dict of extra request-body params (e.g. `temperature`, `top_p`, `max_tokens`) merged into every LLM call. Applied across the OpenAI-compatible, Fireworks, Anthropic, Gemini/VertexAI and LiteLLM (incl. Bedrock/Router) providers. Each provider merges them in its own native parameter space, so use that provider's field names (e.g. `max_tokens` for OpenAI/Anthropic vs `max_output_tokens` for Gemini). The native Ollama structured-output path is a special case — see the note below. Also useful for custom model servers (e.g. vLLM `chat_template_kwargs`). | `null` |
 | `HINDSIGHT_API_LLM_DEFAULT_HEADERS` | JSON dict passed as `default_headers` to provider SDK clients. Used by operators routing through proxies / request-tracing middleware (e.g. Cloudflare AI Gateway, Helicone, corporate proxies). Currently wired into the Anthropic provider; other providers can opt in. | `null` |
 | `HINDSIGHT_API_LLM_STRICT_SCHEMA` | Grammar-enforce structured output via `json_schema` `strict: true` instead of the soft "schema-in-prompt + `json_object`" path. Typed Pydantic response models are serialized directly into the OpenAI strict subset: every object rejects additional properties, every declared property is required, and nullable fields remain nullable. Use it with weaker self-hosted models that return prose preambles, markdown ` ```json ` fences, or invalid JSON — which otherwise fail to parse and wedge retain/consolidation. Applies to OpenAI-compatible backends (OpenAI, llama.cpp, vLLM), Codex, and LiteLLM; Gemini already enforces its native `response_schema` regardless, and providers without a strict mode ignore it. | `false` |
 | `HINDSIGHT_API_LLM_STRICT_SCHEMA_RETAIN` | Override `HINDSIGHT_API_LLM_STRICT_SCHEMA` for retain (fact extraction) only. Applies to both the streaming and batch extraction paths. | Inherits global |
@@ -205,6 +205,11 @@ For non-English banks (especially CJK) and the language/extraction-language trad
 
 When `HINDSIGHT_API_LLM_PROVIDER=ollama`, Hindsight no longer sends the previous native API default `num_ctx=16384` unless you set it explicitly. To keep the old request behavior, set `HINDSIGHT_API_LLM_OLLAMA_NUM_CTX=16384`; otherwise Ollama uses the model Modelfile or server default.
 
+**`HINDSIGHT_API_LLM_EXTRA_BODY` on the native Ollama path.** For structured-output calls, Ollama uses its native `/api/chat` API, whose request body has *two tiers* — this differs from the OpenAI-compatible endpoint, where the SDK flattens everything to top-level. On the native path `extra_body` is split accordingly:
+
+- **Top-level native fields** (`think`, `keep_alive`, ...) pass through directly. For example, gpt-oss models require a thinking level for structured extraction, so set `HINDSIGHT_API_LLM_EXTRA_BODY='{"think": "low"}'` (thinking is disabled by default).
+- **Generation parameters** (`seed`, `top_p`, `top_k`, `num_ctx`, `temperature`, ...) live under Ollama's `options` object, so nest them: `HINDSIGHT_API_LLM_EXTRA_BODY='{"options": {"seed": 42, "top_p": 0.9}}'`. On the OpenAI-compatible endpoints these same params are top-level instead.
+
 **Provider Examples**
 
 ```bash