Analyzing Documents with an Assistant Mate (RAG)

When to Use an Assistant Mate (RAG)?

Assistant-type Mates specialize in document analysis and information extraction using RAG (Retrieval-Augmented Generation) capabilities. They are particularly useful for processing large volumes of text, extracting key information, and providing detailed summaries or analyses. By using an Assistant Mate, you can optimize document processing and improve the quality of information available for decision-making.

"Mate Knowledge" further amplifies these benefits by providing persistent, secure, and private access to contextual information like glossaries, internal guidelines, or related documents, resulting in more accurate and insightful analyses.

Enhanced Security and Privacy with "Mate Knowledge":

By attaching sensitive documents directly to the Mate via "Mate Knowledge," you ensure that only the designated Mate has access to this information. This granular control enhances security and privacy, as the documents are not shared with other Mates or users in the conversation, minimizing the risk of unauthorized access or data leaks. This is particularly crucial when dealing with confidential information, such as legal documents, financial reports, or internal strategies.

Important Note: Currently, Assistant-type Mates necessarily use OpenAI LLM models. Gemini, Claude 3.x, and Mistral models will be available soon.

Steps to Follow

Upload Documents:
- Click on the "attachment" button in the message to upload documents (PDFs, Word files, etc.).
- The documents will appear as attachments to the message.
Leverage "Mate Knowledge" for Sensitive Data:
- For confidential or frequently used resources, attach them directly to the Mate via "Mate Knowledge." This ensures private access for the Mate and optimizes token usage.
Send a Request to the Assistant Mate:
- Send a message to the Assistant Mate, providing clear instructions on what you want to extract or analyze from the document. For example:
  - "@Assistant, can you extract the key points from this report?"
  - "@Assistant, analyze this document and provide a summary of the important sections."
Analyze the Results:
- The Assistant Mate will use its RAG capabilities to analyze the document content uploaded combined with relevant information from "Mate Knowledge,” and provide a summary or extraction of the necessary information.
- Examine the results and use the information to support your discussions or decisions.

Importance of the Action

Analyzing documents via an Assistant Mate allows you to effectively leverage RAG capabilities to extract relevant information from large volumes of text. This improves the quality and speed of document analysis, provides key information for decision-making, and optimizes the processing of complex textual data. By using this feature, you can benefit from detailed and accurate analyses that enrich your discussions and decisions.

Example using "Mate Knowledge" with enhanced security:

A legal team can attach confidential legal precedents and internal guidelines to a specialized Mate Assistant. When analyzing new cases, the Mate leverages this private knowledge base, ensuring data confidentiality while delivering precise and contextually relevant legal analyses.

💡 Better Understand Generative AI

What is RAG (Retrieval-Augmented Generation)?

RAG, or "Retrieval-Augmented Generation," is an advanced technique used by language models to enhance their responses by retrieving relevant information from external databases or documents. Unlike traditional language models that generate responses solely from their internal knowledge, RAG combines text generation with information retrieval to provide more accurate and contextually relevant answers.

How RAG Works

Information Retrieval:
- When a question or request is posed, the RAG language model begins by searching for relevant information in an external database or set of documents.
- This search helps find passages or text extracts that contain useful information.
Response Generation:
- After retrieving the relevant information, the model uses these extracts to generate a complete and coherent response.
- The generation process considers both the model's internal knowledge and the retrieved information to produce an enriched answer.
Advantages of RAG:
- Increased Accuracy: By leveraging external information, RAG improves the accuracy of responses, especially for specific questions or niche subjects.
- Dynamic Updates: Responses can incorporate up-to-date information, even if the language model has not been recently trained on these data.
- Contextualization: Information retrieval allows better contextualization of responses, taking into account documents or databases specific to an organization.

RAG is particularly useful in contexts where access to precise and up-to-date information is crucial, such as document analysis, technical information retrieval, or providing detailed summaries. By using RAG, Mates can offer more comprehensive and relevant answers, enriching interactions and collaborations.