feat: add MiniMax as LLM provider (M2.7)

README.md

@@ -9,6 +9,22 @@ AIHawk's core architecture remains **open source**, allowing developers to inspe
 
 
 
+---
+
+## Supported LLM Providers
+
+AIHawk supports multiple LLM providers. Set `LLM_MODEL_TYPE` in `config.py` and provide the corresponding API key in `data_folder/secrets.yaml`:
+
+| Provider | `LLM_MODEL_TYPE` | Example Model |
+|----------|-------------------|---------------|
+| OpenAI | `openai` | `gpt-4o-mini` |
+| Anthropic Claude | `claude` | `claude-sonnet-4-20250514` |
+| Google Gemini | `gemini` | `gemini-pro` |
+| Ollama | `ollama` | `llama3` |
+| HuggingFace | `huggingface` | `meta-llama/Meta-Llama-3-8B-Instruct` |
+| Perplexity | `perplexity` | `llama-3.1-sonar-small-128k-online` |
+| [MiniMax](https://www.minimax.io) | `minimax` | `MiniMax-M2.7` |
+
 ---
 
 

config.py

@@ -16,6 +16,7 @@
 JOB_MAX_APPLICATIONS = 5
 JOB_MIN_APPLICATIONS = 1
 
+# Supported LLM providers: openai, claude, ollama, gemini, huggingface, perplexity, minimax
 LLM_MODEL_TYPE = 'openai'
 LLM_MODEL = 'gpt-4o-mini'
 # Only required for OLLAMA models

src/libs/llm_manager.py

@@ -31,6 +31,7 @@
     FINISH_REASON,
     GEMINI,
     HUGGINGFACE,
+    MINIMAX,
     ID,
     INPUT_TOKENS,
     INTERESTS,
@@ -181,6 +182,23 @@ def invoke(self, prompt: str) -> BaseMessage:
         return response
 
 
+class MiniMaxModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str):
+        from langchain_openai import ChatOpenAI
+
+        self.model = ChatOpenAI(
+            model_name=llm_model,
+            openai_api_key=api_key,
+            openai_api_base="https://api.minimax.io/v1",
+            temperature=0.4,
+        )
+
+    def invoke(self, prompt: str) -> BaseMessage:
+        logger.debug("Invoking MiniMax API")
+        response = self.model.invoke(prompt)
+        return response
+
+
 class AIAdapter:
     def __init__(self, config: dict, api_key: str):
         self.model = self._create_model(config, api_key)
@@ -205,6 +223,8 @@ def _create_model(self, config: dict, api_key: str) -> AIModel:
             return HuggingFaceModel(api_key, llm_model)
         elif llm_model_type == PERPLEXITY:
             return PerplexityModel(api_key, llm_model)
+        elif llm_model_type == MINIMAX:
+            return MiniMaxModel(api_key, llm_model)
         else:
             raise ValueError(f"Unsupported model type: {llm_model_type}")
 
@@ -213,7 +233,7 @@ def invoke(self, prompt: str) -> str:
 
 
 class LLMLogger:
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel, MiniMaxModel]):
         self.llm = llm
         logger.debug(f"LLMLogger successfully initialized with LLM: {llm}")
 
@@ -325,7 +345,7 @@ def log_request(prompts, parsed_reply: Dict[str, Dict]):
 
 
 class LoggerChatModel:
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel, MiniMaxModel]):
         self.llm = llm
         logger.debug(f"LoggerChatModel successfully initialized with LLM: {llm}")
 

src/utils/constants.py

@@ -75,3 +75,4 @@
 GEMINI = "gemini"
 HUGGINGFACE = "huggingface"
 PERPLEXITY = "perplexity"
+MINIMAX = "minimax"