Documents

Documents enable you to expand an agent’s knowledge by adding information from attached files or HTTP/HTTPS URLs.

• Improved accuracy: By grounding responses in real-time retrieved data, this approach reduces hallucinations and enhances factual reliability.

• Dynamic adaptability: The model adjusts its responses based on the latest information from the knowledge source.

• Domain-specific knowledge: Ideal for use cases that demand specialized or frequently updated knowledge, such as customer support, research, or product documentation.

AI Agents framework supports the following methods of working with documents

• RAG in every query – implements classic Retrieval-Augmented Generation (RAG) technique, which provides document extracts relevant to user’s query.

• RAG via doc_search tool – implements agentic RAG pipeline, where LLM can decide when it needs document extracts.

• Full content in prompt – includes complete content of attached documents in the prompt. This mode is suitable for relatively short documents.

• Content via doc_get tool – implements agentic pipeline, where LLM can request content from specific documents or their sections.

For more details, see Using documents.