Updates the evaluation results. Args: **kwargs: Arbitrary keyword arguments for updating the evaluation results.

Extract specific components from a string based on provided tags. This function searches for specific tags in the provided content a

get the whole SOP configuration in dict format Returns: dict

Convert a sequence of messages to strings and encode them into one string.

return LLM's response

r"""Saves a batch of records to the key-value storage system. Args: records (List[Dict[str, Any]]): A list of dictionaries, where

Get the variables needed in the prompt configuration. This function retrieves the variables required by the prompt templates specified in th

r"""Removes all records from the key-value storage system.

Formulate a prompt using the provided configuration and data dictionary. This function generates a prompt by extracting necessary variables

Executes forward pass for each case in parallel and saves the results. Args: case_list (list[Case]): List of cases to be

Train solution.json Returns: None: This function does not return any value.

r"""Adds a list of vectors to the specified collection. Args: vectors (List[VectorRecord]): List of vectors to be added.

Save the solution configuration to a file.

Load data from a JSON file. Args: file_path (str): The path to the JSON file. Returns: List[Any]: The data

Optimizes the SOP for the given list of cases. Args: case_list (list[Case]): List of cases to be optimized.

Create a dictionary representing a case at a specific index. Args: idx (int): The index of the case. Returns:

Get the dict for the optimizer. only get the info needed for the optimizer.

get the SOP configuration in dict format for SOP optimizer. 会删除掉一些与SOP优化无关的字段 Returns:

Gets the information needed for the trainer. Allowed keys: - prompt_template: Components used to generate the last prompt, e

trans to a dict, it will cover all the information of the node. the dict can be used to initialize a node.

Returns a JSON dict representation of the case. It covers all the information of the case. Returns: dict: A dictionary c

used for serialization in state

r"""Creates a new collection in the database. Args: collection_name (str): Name of the collection to be created. size

r"""Creates a new collection in the database. Args: collection_name (str): Name of the collection to be created. **kw

r"""Deletes an existing collection from the database. Args: collection (str): Name of the collection to be deleted. *

r"""Retrieves details of an existing collection. Args: collection_name (str): Name of the collection to be checked. Retu

r"""Retrieves details of an existing collection. Args: collection_name (str): Name of the collection to be checked. Retu

Apply the padding to the prompt template.

Check for variables needed in a prompt template. This function identifies all variables wrapped in `{}` but not `{{}}` within a given prompt

r"""A class method to construct a `VectorDBQueryResult` instance.

r"""Generates an embedding for the given text. Args: obj (T): The object for which to generate the embedding. **kwarg

r"""Generates embeddings for the given texts. Args: objs (List[T]): The objects for which to generate the embeddings.

extract the content between <type></type> Args: text (str): complete sentence type (str): tag Returns: str: content

Format a string template with the provided data dictionary, ensuring no errors occur due to missing keys. This function cannot handle cases

Abstract method to get a case-specific dict by index. Args: idx (int): The index of the case-specific dict. Returns

r"""Returns the output dimension of the embeddings. Returns: int: The dimensionality of the embedding for the current model.

r"""Loads all stored records from the key-value storage system. Returns: List[Dict[str, Any]]: A list of dictionaries, where each

Load the node prompts from the padding in the config or the case.

r"""Executes the automatic vector retriever process using vector storage. Args: query (str): Query string for information retriev

Convert the dataset to a dictionary using task IDs as keys. Returns: Dict[str, Dict[str, Any]]: A dictionary mapping tas

Initializes the LossConfig object. Args: config_path (str): The path to the configuration file.

Initializes the Optimizer object. Args: config (OptimizerConfig): The optimizer configuration.

Initializes the NodeOptimizer object. Args: config (NodeOptimizerConfig): The node optimizer configuration.

Init the trainer Args: config (TrainerConfig): The TrainerConfig used to initialize the Trainer dataset (Bas

r"""Checks if the specified collection exists in Milvus and creates it if it doesn't, ensuring it matches the specified vector dimensionality.

r"""Returns wether the collection exists in the database

r"""Checks whether a collection with the specified name exists in the database. Args: collection_name (str): The name of

r"""Generates a valid collection name from a given file path or URL. Args: content_input_path: str. The input URL or file path from w

r"""Initializes the Milvus client with the provided connection details. Args: url_and_api_key (Tuple[str, str]): The URL and API

r"""Deletes an existing collection from the database. Args: collection (str): Name of the collection to be deleted.

r"""Validates that the installed 'Unstructured' library version satisfies the specified minimum version requirement. This function is

r"""Generates a collection name if user doesn't provide

r"""Generates a unique name for a new collection based on the current timestamp. Milvus collection names can only contain alphanumeric

r"""Retrieves the last modified date and time of a given file. This function takes a file path as input and returns the last modified date

r"""Retrieves the last modified date and time of a given file. This function takes vector storage instance as input and returns the last

r"""Sets up and returns a vector storage instance with specified parameters. Args: collection_name (Optional[str]): Name of the c

Loads data from all files in the given directory that match the expected format. Args: root_dir (str): The root director

https://www.weatherbit.io/api/historical-weather-daily

r"""Validates and converts VectorRecord instances to the format expected by Milvus. Args: records (List[VectorRecord]): L