run_analyzer()

This operation processes the input data and generates tokenized output.

Request Syntax

run_analyzer(
    texts: Union[str, List[str]],
    analyzer_params: Union[str, Dict, None] = None,
    with_hash: bool = False,
    with_detail: bool = False,
    timeout: Optional[float] = None,
)

PARAMETERS:

• texts (Union[str, List[str]]) -

  The input text or a list of texts to be analyzed.

• analyzer_params (Union[str, Dict, None]) -

  The parameters for the analyzer. If set to None, defaults to an empty dictionary.

• with_hash (bool) -

  Optional flag indicating whether to include hash-based processing.

• with_detail (bool) -

  Optional flag indicating whether to return detailed analysis output.

• timeout (float | None) -

  The timeout duration for this operation. Setting this to None indicates that this operation timeouts when any response or error occurs.

RETURN TYPE:

List[str], List[List[str]]

RETURNS:

A tuple containing:

• A list of strings representing the primary tokenized output.
• A list of lists of strings representing detailed token information (if detailed output is enabled).

EXCEPTIONS:

• MilvusException - Raised if any error occurs during this operation.

Examples

from pymilvus import MilvusClient

client = MilvusClient(
    uri="http://localhost:19530",
)

analyzer_params = {
    "type": "standard", # Uses the standard built-in analyzer
    "stop_words": ["a", "an", "for"] # Defines a list of common words (stop words) to exclude from tokenization
}

# Sample text to analyze
text = "An efficient system relies on a robust analyzer to correctly process text for various applications."

# Run analyzer
result = client.run_analyzer(
    text,
    analyzer_params
)

print(result)

# Expected output:
# ['efficient', 'system', 'relies', 'on', 'robust', 'analyzer', 'to', 'correctly', 'process', 'text', 'various', 'applications']