Squirro Glossary

Squirro Glossary#

This glossary explains Squirro-specific terminology, that is used in Squirro’s documentation.

Also see the Glossary of Industry Terms for explanations of common industry terminology.

AI Studio#: AI Studio is Squirro’s no-code AI platform. It allows users to train and deploy AI models without writing any code. Learn more at AI Studio.
Agents#: Agents combine a Large Language Model (LLM) and specialized tools to achieve a specific goal or complete a task. The agent leverages the capabilities of the LLM and enhances it by routing specific tasks to dedicated tools, which can perform functions such as content creation, analysis, enrichment, classification, fact-checking, and transformation.
Chat Queries#: Term that refers to the inputs Squirro Chat users submit. Also known as user prompts, user questions, or user queries.
Cognitive search#: Cognitive search is a the Squirro product that refers to the combination of traditional keyword search and AI technologies to provide a more intelligent search experience. Learn more at Squirro Search.
Communities#: Built on the idea of user preferences, Communities is a feature that allows end users to personalize their Squirro experience by following topics of interest. See Communities.
Community Augmentation#: Squirro feature that allows you to add additional data to a community through an external API. See Communities Augmentation.
Copilot#: Copilot refers to a Squirro Chat setting that activates the assistant mode, allowing users to receive answers in the context of the currently applied filters. See Chat Widget.
Dashboard#: The main user interface for Squirro end users. Using widgets and layers, and utilizing visibility conditions, dashboards allow developers to create complex user experiences. See Dashboards.
Hybrid Search#: Hybrid search refers to the combination of traditional keyword search and other search technologies, such as semantic search, using scoring profiles. Learn more at Semantic and Hybrid Search.
libNLP#: libNLP is a Squirro-specific term for the Natural Language Processing library used in Squirro. Learn more at libNLP.
LLM Prompt#: The input data provided by Squirro to the LLM includes the user’s query and system-level instructions, which the LLM uses to generate a response for the user.
KEE#: Known Entity Extraction, commonly abbreviated as KEE, is Squirro’s technology to enrich unstructured data by linking it to structured information such as company names or products. Learn more at Known Entity Extraction.
Pipelet#: A pipelet is a Squirro-specific term for a custom plugin used in a Squirro pipeline. Pipelets are written in Python and can be used to enrich data, classify data, or perform other custom tasks. Learn more at Pipelets.
Pipeline#: In Squirro, the term pipeline generally refers to a project’s primary data processing pipeline which enriches, classifies, indexes, and otherwise processes ingested data. See Data Processing Pipeline.
Project Templates#: Project templates are a Squirro-specific term for a pre-configured project that can be used as a starting point for a new project. Project templates can be exported and imported within the Squirro UI to quickly duplicate, back up, or recreate projects. Learn more at Project Templates Overview.
Semantic Search#: A search technique that uses natural language processing (NLP) to understand the intent behind a query and the context in which it is being used. It is a core part of Squirro Hybrid Search. Learn more at Learn more at Semantic and Hybrid Search.
Scoring Profile#: A scoring profile is a configuration that allows you to customize the relevancy scoring of documents in Squirro. It can be applied to either all matching documents or a subset of the most relevant documents. Scoring Profiles can be used to incorporate custom scoring algorithms, boost document scores, and combine different scoring signals. They are maintained within the Configuration Service and can be configured for each project individually. To learn more, see How to Use Scoring Profiles to Customize Document Relevancy Scoring.
Sources#: In the context of Squirro Chat, the documents that answers were extracted from. The LLM indicates to Squirro which documents were used, then Squirro attempts to highlight relevant passages related to the answer. Outside of a Squirro Chat context, Squirro often refers to connected data sources simply as sources.
Source Candidates#: In the context of Squirro Chat, source candidates are a list of documents that Squirro retrieves using a combination of keyword and vector search that serve as potential reference sources for digital assistant chat responses. Up to 10 candidates are retrieved.
Squirro Chat#: Generative AI application that uses Retrieval Augmented Generation (RAG) to allow users to safely and securely chat with their own data or website content. It is offered as two products: Squirro Chat Data and Squirro Chat Web and can be embedded on any website as a digital assistant. Learn more by visiting Squirro Chat.