What is Squirro?
Squirro is a versatile generative AI tool designed for enterprise search, insights, and automation. It is built with an advanced AI framework, Retrieval Augmented Generation (RAG), that enhances the accuracy of the responses generated by larger language models (LLMs). This enhancement is made possible by incorporating external sources of knowledge to support the model's internal understanding. The SquirroGPT component of the tool uses Semantic Search to query the LLM, facilitating a more efficient and informed data retrieval process. When a user enters a prompt, SquirroGPT searches the knowledge base, including the ingested data and documents. Relevant information is then sent to the LLM, and the response is verified against the knowledge base before being relayed to the user. This ensures each answer is supported with evidence, and the refining process reduces inaccurate responses. The tool also specializes in providing accessibility to complex organisational data, with the capacity to define data sources and permission rights to suit individual business units. It enables users to interact with data without needing to open documents, providing more precise results by analyzing relevant paragraphs instead of entire documents. This tool offers enterprise-grade security and can be embedded for wide audience accessibility.
Pros
- Enterprise search capability
- Enhances LLM accuracy
- Incorporates external knowledge
- Semantic search feature
- Efficient data retrieval
- Knowledge base verification
- Reduced inaccurate responses
- Access to organisational data
- Ability to define data sources
- Customisable permission rights
- Data interaction without document opening
- Precise results via paragraph analysis
- Enterprise-grade security
- Can be embedded
- Reference supported answers
- Data source and right definition
- Conversationally interact with documents
- Four types of summarisation
- Personalised summaries
- Combine search with context aware chat
- Interaction with structured data in chat
- Structured data analysis and visualisation
- Bridges data silos
- Context and intent based information sharing
- Semantic enterprise search
- No-code ML platform
Cons
- Complex data integration process
- Inferior document summarization
- Limited structured data analysis
- Dependent on relevant data ingestion
- Requires data source definition
- Potentially high permission management
- Unclear error validation process
Squirro FAQ
What is Squirro and its key features?
Squirro is a generative AI tool purposed for enterprise search, insights, and automation. Key features include: utilization of an advanced AI framework, Retrieval Augmented Generation (RAG), for enhanced response accuracy from larger language models (LLMs); the integration of external knowledge sources to support the model's understanding; and a component, SquirroGPT, that uses Semantic Search to make the data retrieval process efficient and informed. Furthermore, Squirro offers refined responses, capabilities to access complex organizational data, options to define data sources and permission rights to match individual business units, and enterprise-grade security. Additionally, Squirro lets users chat with all organizational data and every answer is supported by a reference source.
What is Retrieval Augmented Generation (RAG) in Squirro?
Retrieval Augmented Generation (RAG) in Squirro is an AI framework designed to enhance the accuracy of responses generated by LLMs. RAG uses external sources of knowledge to complement the model's internal understanding. Squirro uses RAG, with its Semantic Search, to query the LLM, a process that ensures more precise and informed responses.
How does Squirro use larger language models (LLMs)?
Squirro uses larger language models (LLMs) in its Retrieval Augmented Generation (RAG) technology. This process starts when a user enters a prompt, triggering Squirro's semantic search to query relevant data from the knowledge base, including the ingested data and documents. The relevant information is then sent along with the query to the LLM. To verify the accuracy, the response from the LLM is checked against the knowledge base before delivering the answer to the user.
What is the role of Semantic Search in the SquirroGPT component?
In the SquirroGPT component, Semantic Search is used to query the LLM, an action that facilitates a more efficient and informed data retrieval process. When a user enters a prompt, Semantic Search is the mechanism that scans the knowledge base, including the ingested data and documents. This search strategy facilitates in acquiring contextually relevant responses.
How does the data retrieval process work in Squirro?
In Squirro, the data retrieval process works by using Squirro's Semantic Search for querying the knowledge base once a user enters a prompt. The retrieval process involves searching through the data and documents ingested, sending relevant information to the LLM, and then validating the response against the knowledge base before giving the answer to the user.
What happens when a user enters a prompt in Squirro?
When a user enters a prompt in Squirro, the system's Semantic Search initiates a scan through the knowledge base which includes ingested data and documents. SquirroGPT then sends the relevant information gathered to the Large Language Model (LLM) for response generation.
How does Squirro ensure the accuracy of the responses?
Squirro ensures the accuracy of responses through its Retrieval Augmented Generation technology. More specifically, after the LLM generates a response, it undergoes an additional verification layer where it is validated against the knowledge base once more before it is relayed to the user. It utilizes Semantic Search and LLMs in conjunction to seek context-relevant data ensuring that each answer is supported by evidence, thus reducing the potential for inaccurate responses.
What capabilities does Squirro have in accessing complex organisational data?
Squirro has unique capabilities in accessing complex organizational data. It allows users to define the data sources and permission rights suitable for their specific business units. This accessibility allows users to interact with data without having to open the documents themselves, providing more precise results. Squirro can locate and analyze relevant paragraphs instead of entire documents.
