> For a complete index of all SignalWire documentation pages, fetch https://signalwire.com/docs/llms.txt # Helper Functions & Constants > Query preprocessing, document preprocessing, model alias resolution, and constants. [searchservice]: /docs/server-sdks/reference/python/agents/search/search-service [searchengine]: /docs/server-sdks/reference/python/agents/search/search-engine [searchengine-search]: /docs/server-sdks/reference/python/agents/search/search-engine/search [indexbuilder]: /docs/server-sdks/reference/python/agents/search/index-builder The Search module provides standalone helper functions for query preprocessing, document content preprocessing, and embedding model alias resolution, along with constants for model configuration. ```python from signalwire.search import ( preprocess_query, preprocess_document_content, resolve_model_alias, MODEL_ALIASES, DEFAULT_MODEL, ) ``` These functions require search dependencies. Install with `pip install signalwire-sdk[search]`. --- ## Functions ### preprocess\_query **preprocess\_query**(`query`, `language="en"`, `pos_to_expand=None`, `max_synonyms=5`, `debug=False`, `vector=False`, `query_nlp_backend="nltk"`, `model_name=None`, `preserve_original=True`) -> `dict[str, Any]` Preprocess a search query with language detection, tokenization, stop word removal, POS tagging, synonym expansion, stemming, and optional vectorization. This function is used internally by [`SearchService`][searchservice] and [`SearchEngine`][searchengine] but can also be called directly for custom search pipelines. #### Parameters **`query`** `str` — required Input query string. --- **`language`** `str` — default: en Language code (e.g., `"en"`, `"es"`, `"fr"`) or `"auto"` for automatic detection. --- **`pos_to_expand`** `Optional[list[str]]` POS tags to expand with synonyms. Defaults to `["NOUN", "VERB", "ADJ"]`. --- **`max_synonyms`** `int` — default: 5 Maximum number of synonyms to add per word. --- **`debug`** `bool` — default: false Enable debug logging output. --- **`vector`** `bool` — default: false Include a vector embedding of the query in the output. Set to `True` when passing the result to [`SearchEngine.search()`][searchengine-search]. --- **`query_nlp_backend`** `str` — default: nltk NLP backend for query processing. Valid values: * `"nltk"` -- fast, lightweight (default) * `"spacy"` -- better quality, requires spaCy models --- **`model_name`** `Optional[str]` Sentence transformer model name for vectorization. Must match the model used to build the index being searched. If not specified, uses the default model. --- **`preserve_original`** `bool` — default: true Keep the original query terms in the enhanced text alongside expanded synonyms and stems. --- #### Returns `dict[str, Any]` -- A dictionary containing: * `input` (str) -- the original query string as passed in * `enhanced_text` (str) -- the preprocessed query text with synonyms and stems * `language` (str) -- detected or specified language code * `POS` (dict) -- POS tag analysis results * `vector` (list\[float]) -- embedding vector (only when `vector=True`) #### Example ```python from signalwire.search import preprocess_query # Basic preprocessing result = preprocess_query("How do I configure voice agents?") print(result["enhanced_text"]) # With vectorization for search result = preprocess_query( "How do I configure voice agents?", vector=True, language="auto", ) query_vector = result["vector"] enhanced_text = result["enhanced_text"] ``` --- ### preprocess\_document\_content **preprocess\_document\_content**(`content`, `language="en"`, `index_nlp_backend="nltk"`) -> `dict[str, Any]` Preprocess document content for indexing. Uses less aggressive synonym expansion than query preprocessing to keep document representations focused. This function is called internally by [`IndexBuilder`][indexbuilder] during index construction. #### Parameters **`content`** `str` — required Document text content to preprocess. --- **`language`** `str` — default: en Language code for processing. --- **`index_nlp_backend`** `str` — default: nltk NLP backend for processing. `"nltk"` or `"spacy"`. --- #### Returns `dict[str, Any]` -- A dictionary containing: * `enhanced_text` (str) -- the preprocessed document text * `keywords` (list\[str]) -- up to 20 extracted keywords (stop words removed) * `language` (str) -- the language used for processing * `pos_analysis` (dict) -- POS tag analysis #### Example ```python from signalwire.search import preprocess_document_content result = preprocess_document_content( "SignalWire agents can be configured with custom prompts and tools.", language="en", ) print(result["keywords"]) # ['signalwire', 'agents', 'configured', 'custom', 'prompts', 'tools'] ``` --- ### resolve\_model\_alias **resolve\_model\_alias**(`model_name`) -> `str` Resolve a short model alias to its full model name. If the input is not a known alias, it is returned unchanged. #### Parameters **`model_name`** `str` — required A model alias or full model name. Known aliases: * `"mini"` -- `sentence-transformers/all-MiniLM-L6-v2` (384 dims, fastest) * `"base"` -- `sentence-transformers/all-mpnet-base-v2` (768 dims, balanced) * `"large"` -- `sentence-transformers/all-mpnet-base-v2` (768 dims, same as base) --- #### Returns `str` -- The full sentence transformer model name. #### Example ```python from signalwire.search import resolve_model_alias print(resolve_model_alias("mini")) # "sentence-transformers/all-MiniLM-L6-v2" print(resolve_model_alias("sentence-transformers/all-mpnet-base-v2")) # "sentence-transformers/all-mpnet-base-v2" (unchanged) ``` --- ## Constants ### MODEL\_ALIASES ```python from signalwire.search import MODEL_ALIASES print(MODEL_ALIASES) # dict[str, str] ``` Dictionary mapping short model aliases to full sentence transformer model names. | Alias | Full Model Name | Dimensions | | --------- | ----------------------------------------- | ---------- | | `"mini"` | `sentence-transformers/all-MiniLM-L6-v2` | 384 | | `"base"` | `sentence-transformers/all-mpnet-base-v2` | 768 | | `"large"` | `sentence-transformers/all-mpnet-base-v2` | 768 | --- ### DEFAULT\_MODEL ```python from signalwire.search import DEFAULT_MODEL print(DEFAULT_MODEL) # "sentence-transformers/all-MiniLM-L6-v2" ``` The default embedding model used for new indexes. This is the `"mini"` model, chosen for its smaller size and faster inference. Use the `"base"` alias or specify a full model name when higher embedding quality is needed.