Add different models for litellm, resolve conflicts

[madina1203]

PR Type

enhancement

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Description

• Introduced new models for litellm integration.

• Enhanced workflow creation with model management.

• Improved API key handling for various providers.

• Updated configuration for litellm models.

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Changes walkthrough 📝

	Relevant files
Enhancement	evaluation.py Update evaluation workflow with new model handling              app/core/evaluation.py Added link_kg_database and llm_creation imports. Updated workflow creation to use models. Changed variable names for clarity. +12/-6    main.py Enhance main module with API key management                            app/core/main.py Added get_api_key and create_litellm_model functions. Improved API key retrieval for multiple providers. Updated llm_creation to support litellm models. +142/-72 langraph_workflow.py Refactor workflow creation for model integration                  app/core/workflow/langraph_workflow.py Modified create_workflow to accept models. Removed redundant API key parameter. Streamlined workflow creation process. +5/-7      params.ini Update configuration for litellm models                                    app/config/params.ini Added configurations for multiple litellm models. Updated model IDs and parameters. +12/-3
Dependencies	environment.yml Update dependencies for compatibility                                        environment.yml Updated tiktoken dependency version. +1/-1

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[github-actions]

PR Reviewer Guide 🔍

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⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🔒 No security concerns identified

⚡ Recommended focus areas for review Possible Issue The function create_litellm_model raises a ValueError if the model id is not present in the configuration. This could potentially cause the application to crash if the configuration is not properly set. Consider adding error handling or default values to prevent this. def create_litellm_model(config: configparser.SectionProxy) -> ChatLiteLLM: """ Create a ChatLiteLLM instance based on the model id and configuration. Only uses parameters that are explicitly specified in the configuration. Args: config (configparser.SectionProxy): The configuration section Returns: ChatLiteLLM: Configured ChatLiteLLM instance """ if "id" not in config: raise ValueError("Model id is required in configuration") Configuration Handling The llm_creation function reads from a configuration file but does not handle cases where expected configuration sections or keys are missing. This could lead to runtime errors. Consider adding validation for the configuration file. def llm_creation(api_key=None, params_file=None): """ Reads the parameters from the configuration file (default is params.ini) and initializes the language models. Args: api_key (str, optional): The API key for the OpenAI API. params_file (str, optional): Path to an alternate configuration file. Returns: dict: A dictionary containing the language models. """ config = configparser.ConfigParser() if params_file: config.read(params_file) else: config.read(params_path) models = {} # Get the OpenAI API key from the configuration file or the environment variables if none is passed. openai_api_key = api_key if api_key else os.getenv("OPENAI_API_KEY") for section in config.sections(): if section.startswith("llm_litellm"): models[section] = create_litellm_model(config[section]) continue temperature = config[section]["temperature"] model_id = config[section]["id"] max_retries = config[section]["max_retries"] provider = "openai" if section.startswith("deepseek"): provider = "deepseek" elif section.startswith("ovh"): provider = "ovh" api_key = get_api_key(provider) model_params = { "temperature": float(temperature), "model": model_id, "max_retries": int(max_retries), "verbose": True } if "base_url" in config[section]: base_url = config[section]["base_url"] if provider == "deepseek": model_params["openai_api_base"] = base_url model_params["openai_api_key"] = api_key else: model_params["base_url"] = base_url model_params["api_key"] = api_key else: model_params["openai_api_key"] = api_key llm = ChatOpenAI(**model_params) models[section] = llm return models

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
General	Validate configuration values' ranges Validate that the temperature and max_retries values in the configuration are within acceptable ranges to prevent unexpected behavior. app/core/main.py [179-180] -temperature = config[section]["temperature"] -max_retries = config[section]["max_retries"] +temperature = float(config[section]["temperature"]) +if not (0.0 <= temperature <= 1.0): + raise ValueError("Temperature must be between 0.0 and 1.0") +max_retries = int(config[section]["max_retries"]) +if max_retries < 0: + raise ValueError("Max retries must be non-negative") Suggestion importance[1-10]: 9 __ Why: Validating the ranges of temperature and max_retries ensures that the application behaves as expected and prevents potential errors due to invalid configuration values. This enhances the reliability and stability of the application.	High
	Add error handling for config read Add error handling for the config.read() method to handle cases where the configuration file might not be found or is unreadable. app/core/main.py [164] -config.read(params_file) +if not config.read(params_file): + raise FileNotFoundError(f"Configuration file {params_file} not found or unreadable.") Suggestion importance[1-10]: 8 __ Why: Adding error handling for the config.read() method is important to handle cases where the configuration file might be missing or unreadable, which can prevent the application from crashing unexpectedly.	Medium
Possible issue	Ensure params_path is defined Ensure that the params_path variable is defined before it is used in the llm_creation function to avoid potential runtime errors. app/core/main.py [166] -config.read(params_path) +config.read(params_path) # Ensure params_path is defined Suggestion importance[1-10]: 7 __ Why: Ensuring that params_path is defined before use is crucial to prevent runtime errors, especially since the code relies on this variable to read configuration files. This suggestion helps improve the robustness of the function.	Medium