Add MiniMax LLM adapter#2166
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WalkthroughAdds MiniMax LLM adapter support with provider-aware model routing, thinking and service-tier validation, adapter metadata, configuration schema, exports, and comprehensive branded adapter tests. ChangesMiniMax adapter integration
Estimated code review effort: 3 (Moderate) | ~20 minutes Sequence Diagram(s)sequenceDiagram
participant AdapterConfiguration
participant MiniMaxLLMAdapter
participant MiniMaxLLMParameters
AdapterConfiguration->>MiniMaxLLMAdapter: provide api_key, model, and options
MiniMaxLLMAdapter->>MiniMaxLLMParameters: validate configuration
MiniMaxLLMParameters-->>MiniMaxLLMAdapter: return routed model and validated parameters
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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| Filename | Overview |
|---|---|
| unstract/sdk1/src/unstract/sdk1/adapters/base1.py | Adds MiniMax endpoint detection, model routing, parameter validation, and usage-cost model selection. |
| unstract/sdk1/src/unstract/sdk1/adapters/llm1/init.py | Exports the MiniMax adapter through the LLM adapter package. |
| unstract/sdk1/src/unstract/sdk1/adapters/llm1/minimax.py | Defines the MiniMax adapter identity, metadata, provider, icon, and adapter type. |
| unstract/sdk1/src/unstract/sdk1/adapters/llm1/static/minimax.json | Adds the MiniMax configuration schema for models, endpoints, request controls, and thinking. |
| unstract/sdk1/tests/test_branded_openai_adapters.py | Covers registration, routing, validation, revalidation, pricing, service tiers, and schema metadata. |
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🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py`:
- Line 539: Update the MiniMax subclass’s temperature declaration to retain the
base class’s Pydantic Field validation, including default 0.1 and ge=0/le=2
constraints, rather than redeclaring it as an unconstrained scalar. Preserve the
existing nullable float type.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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⛔ Files ignored due to path filters (1)
frontend/public/icons/adapter-icons/MiniMax.pngis excluded by!**/*.png
📒 Files selected for processing (5)
unstract/sdk1/src/unstract/sdk1/adapters/base1.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/__init__.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/minimax.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/static/minimax.jsonunstract/sdk1/tests/test_branded_openai_adapters.py
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⚠️ Outside diff range comments (1)
unstract/sdk1/src/unstract/sdk1/adapters/base1.py (1)
564-583: 🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win"Thinking always enabled for MiniMax-M2.7" is only partially enforced.
The validation blocks explicitly disabling thinking for
minimax-m2*models (line 579-583), but if neitherenable_thinkingnorthinkingis present inadapter_metadataat all,thinkingstays at the class default (None) and nothinkingparam is sent — relying entirely on the JSON-schema UI default (enable_thinking: true) and/or the provider's own default to actually enable it. Any caller that invokesMiniMaxLLMParameters.validate()/LLM.__init__directly (bypassing the schema-populated defaults — e.g. persisted adapter configs created before this default existed, or non-UI programmatic callers) would silently omitthinkingfor M2.7, which doesn't guarantee "always enabled" as stated in the PR objectives.Consider enforcing the default at the validation layer itself, e.g.:
💡 Proposed fix to force adaptive thinking as the default for the M2 family
thinking = adapter_metadata.get("thinking") + if thinking is None and model_id.lower().startswith("minimax-m2"): + thinking = {"type": "adaptive"} + adapter_metadata["thinking"] = thinking if thinking is not None:🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py` around lines 564 - 583, Update the adapter metadata validation around the existing thinking handling so minimax-m2* models default to {"type": "adaptive"} when neither enable_thinking nor thinking is provided. Preserve explicit disabled-thinking rejection and existing validation for supplied values, ensuring direct validation and LLM initialization always send adaptive thinking for MiniMax-M2 models.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Outside diff comments:
In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py`:
- Around line 564-583: Update the adapter metadata validation around the
existing thinking handling so minimax-m2* models default to {"type": "adaptive"}
when neither enable_thinking nor thinking is provided. Preserve explicit
disabled-thinking rejection and existing validation for supplied values,
ensuring direct validation and LLM initialization always send adaptive thinking
for MiniMax-M2 models.
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📒 Files selected for processing (6)
unstract/sdk1/src/unstract/sdk1/adapters/base1.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/minimax.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/static/minimax.jsonunstract/sdk1/src/unstract/sdk1/llm.pyunstract/sdk1/tests/test_branded_openai_adapters.pyunstract/sdk1/tests/test_openai_compatible_adapter.py
🚧 Files skipped from review as they are similar to previous changes (1)
- unstract/sdk1/src/unstract/sdk1/adapters/llm1/static/minimax.json
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Thanks for the rework — routing through One structural request on the cost side: could we drop the bespoke pricing engine ( The only reason that engine is needed today is that our pinned Once #2169 lands, the standard path covers M3 with zero local pricing code — which avoids the drift/maintenance of a MiniMax-only cost table and keeps this adapter consistent with the rest. Two follow-ups:
Happy to help coordinate sequencing with #2169. |
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@octo-patch TLDR from this comment
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…r-add-recvozNE1fKYRA-clean
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Thanks for the review. I removed the adapter-specific model registration, pricing calculator, and shared billing hook; the adapter now uses the standard LiteLLM cost path from the merged dependency update, while keeping the model field free-text. I also added protocol-specific temperature validation and MiniMax-M2.x thinking coverage, and ran 25 focused tests plus four endpoint request captures. |
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unstract/sdk1/src/unstract/sdk1/adapters/base1.py (1)
622-624: 🎯 Functional Correctness | 🟡 Minor | ⚡ Quick winFix literal
"None"coercion for missing models.If a caller explicitly sets
{"model": None}inadapter_metadata,adapter_metadata.get("model", "")evaluates toNone. The subsequentstr(None)call coerces it into the literal string"None". Because"None"is truthy, it bypasses thenot modelcheck and sends a malformed model ID to the upstream API.Update the extraction to safely handle
Nonevalues, mirroring the robust pattern used in the Mistral adapter below.🐛 Proposed fix
`@staticmethod` def validate_model(adapter_metadata: dict[str, "Any"]) -> str: - model = str(adapter_metadata.get("model", "")).strip() + raw_model = adapter_metadata.get("model") + model = str(raw_model).strip() if raw_model is not None else "" if not model: raise ValueError("model is required for the MiniMax adapter.")🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py` around lines 622 - 624, Update the model extraction in the MiniMax adapter to handle an explicit None as missing before string conversion, matching the robust handling used by the Mistral adapter. Ensure None, empty, and whitespace-only values still trigger the existing “model is required” ValueError, while valid model identifiers remain stripped and accepted.
🧹 Nitpick comments (1)
unstract/sdk1/src/unstract/sdk1/adapters/base1.py (1)
591-604: 📐 Maintainability & Code Quality | 🔵 Trivial | 💤 Low valueFuture-proof the
MiniMax-M2prefix check.The
model_id.lower().startswith("minimax-m2")check correctly identifies current models likeMiniMax-M2.7, but it will also unintentionally match future models likeMiniMax-M20orMiniMax-M200.Consider tightening the check (e.g., matching
"minimax-m2.","minimax-m2-", or using a regex boundary) to ensure future model families don't accidentally inherit M2-specific thinking constraints.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py` around lines 591 - 604, Update both MiniMax-M2 checks in the thinking logic to require a model-family boundary after “minimax-m2” (such as a dot, hyphen, or equivalent regex boundary), so models like MiniMax-M20 and MiniMax-M200 do not match. Preserve automatic adaptive thinking and the disabled-thinking validation for valid MiniMax-M2 variants.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Outside diff comments:
In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py`:
- Around line 622-624: Update the model extraction in the MiniMax adapter to
handle an explicit None as missing before string conversion, matching the robust
handling used by the Mistral adapter. Ensure None, empty, and whitespace-only
values still trigger the existing “model is required” ValueError, while valid
model identifiers remain stripped and accepted.
---
Nitpick comments:
In `@unstract/sdk1/src/unstract/sdk1/adapters/base1.py`:
- Around line 591-604: Update both MiniMax-M2 checks in the thinking logic to
require a model-family boundary after “minimax-m2” (such as a dot, hyphen, or
equivalent regex boundary), so models like MiniMax-M20 and MiniMax-M200 do not
match. Preserve automatic adaptive thinking and the disabled-thinking validation
for valid MiniMax-M2 variants.
ℹ️ Review info
⚙️ Run configuration
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
Run ID: 77326b44-0569-40ac-8939-f863ea0f799e
📒 Files selected for processing (4)
unstract/sdk1/src/unstract/sdk1/adapters/base1.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/minimax.pyunstract/sdk1/src/unstract/sdk1/adapters/llm1/static/minimax.jsonunstract/sdk1/tests/test_branded_openai_adapters.py
💤 Files with no reviewable changes (1)
- unstract/sdk1/src/unstract/sdk1/adapters/llm1/minimax.py
🚧 Files skipped from review as they are similar to previous changes (1)
- unstract/sdk1/src/unstract/sdk1/adapters/llm1/static/minimax.json
2 participants
What
Why
How
minimax/routing for OpenAI-compatible bases andanthropic/routing for bases ending in/anthropic.thinking.type, and forwardsservice_tier.Can this PR break any existing features. If yes, please list possible items. If no, please explain why. (PS: Admins do not merge the PR without this section filled)
Database Migrations
Env Config
Relevant Docs
Related Issues or PRs
Dependencies Versions
litellm==1.90.3.Notes on Testing
/v1/chat/completionspaths and both Anthropic-compatible/anthropic/v1/messagespaths, including thinking and priority-tier fields.Screenshots
Checklist