feat: add multi-provider LLM support with MiniMax integration

Add get_langchain_chat_model() factory function to helper_functions.py
that enables switching between OpenAI, Groq, Anthropic, Amazon Bedrock,
and MiniMax LLM providers. This complements the existing
get_langchain_embedding_provider() factory and makes the ModelProvider
enum functional.

Also update README.md with documentation and usage examples for the
multi-provider support.

Author: octo-patch

README.md

@@ -558,6 +558,33 @@ To begin implementing these advanced RAG techniques in your projects:
    ```
 3. Follow the detailed implementation guide in each technique's directory.
 
+## Multi-Provider LLM Support
+
+The `helper_functions.py` module includes a `get_langchain_chat_model()` factory function that lets you easily switch between different LLM providers:
+
+| Provider | Enum Value | Default Model | Required Environment Variable |
+|----------|------------|---------------|-------------------------------|
+| OpenAI | `ModelProvider.OPENAI` | `gpt-4o` | `OPENAI_API_KEY` |
+| Groq | `ModelProvider.GROQ` | `llama-3.3-70b-versatile` | `GROQ_API_KEY` |
+| Anthropic | `ModelProvider.ANTHROPIC` | `claude-sonnet-4-20250514` | `ANTHROPIC_API_KEY` |
+| Amazon Bedrock | `ModelProvider.AMAZON_BEDROCK` | `anthropic.claude-3-sonnet-20240229-v1:0` | AWS credentials |
+| MiniMax | `ModelProvider.MINIMAX` | `MiniMax-M1` | `MINIMAX_API_KEY` |
+
+**Example usage:**
+
+```python
+from helper_functions import ModelProvider, get_langchain_chat_model
+
+# Use OpenAI (default)
+llm = get_langchain_chat_model(ModelProvider.OPENAI)
+
+# Use MiniMax
+llm = get_langchain_chat_model(ModelProvider.MINIMAX, model="MiniMax-M1")
+
+# Use Anthropic with custom settings
+llm = get_langchain_chat_model(ModelProvider.ANTHROPIC, temperature=0.7, max_tokens=2000)
+```
+
 ## Contributing
 
 We welcome contributions from the community! If you have a new technique or improvement to suggest:

helper_functions.py

@@ -333,6 +333,64 @@ class ModelProvider(Enum):
     GROQ = "groq"
     ANTHROPIC = "anthropic"
     AMAZON_BEDROCK = "bedrock"
+    MINIMAX = "minimax"
+
+
+def get_langchain_chat_model(provider: ModelProvider, model: str = None, temperature: float = 0, max_tokens: int = 4000):
+    """
+    Returns a LangChain chat model based on the specified provider.
+
+    Args:
+        provider (ModelProvider): The model provider to use.
+        model (str): Optional - The specific model ID to use.
+        temperature (float): The temperature for generation (default: 0).
+        max_tokens (int): Maximum tokens for generation (default: 4000).
+
+    Returns:
+        A LangChain chat model instance.
+
+    Raises:
+        ValueError: If the specified provider is not supported.
+    """
+    if provider == ModelProvider.OPENAI:
+        from langchain_openai import ChatOpenAI
+        return ChatOpenAI(
+            model=model or "gpt-4o",
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+    elif provider == ModelProvider.GROQ:
+        from langchain_groq import ChatGroq
+        return ChatGroq(
+            model=model or "llama-3.3-70b-versatile",
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+    elif provider == ModelProvider.ANTHROPIC:
+        from langchain_anthropic import ChatAnthropic
+        return ChatAnthropic(
+            model=model or "claude-sonnet-4-20250514",
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+    elif provider == ModelProvider.AMAZON_BEDROCK:
+        from langchain_aws import ChatBedrock
+        return ChatBedrock(
+            model_id=model or "anthropic.claude-3-sonnet-20240229-v1:0",
+            model_kwargs={"temperature": temperature, "max_tokens": max_tokens},
+        )
+    elif provider == ModelProvider.MINIMAX:
+        from langchain_openai import ChatOpenAI
+        import os
+        return ChatOpenAI(
+            model=model or "MiniMax-M1",
+            temperature=temperature if temperature > 0 else 0.1,
+            max_tokens=max_tokens,
+            openai_api_key=os.environ.get("MINIMAX_API_KEY"),
+            openai_api_base="https://api.minimax.io/v1",
+        )
+    else:
+        raise ValueError(f"Unsupported model provider: {provider}")
 
 
 def get_langchain_embedding_provider(provider: EmbeddingProvider, model_id: str = None):