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Yields partial structured results incrementally as the language model produces them during extraction.
Distinct from Structured Data Extraction: Distinct from Structured Data Extraction: focuses on the streaming aspect of extraction results, not the extraction itself.
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Yields partial structured results incrementally as the language model produces them during extraction.
AdalFlow is an autonomous AI agent framework and LLM application library designed for building modular workflows. It serves as a model-agnostic interface and RAG pipeline orchestrator, allowing users to develop ReAct agents that utilize iterative reasoning and external tool execution to solve complex tasks. The project distinguishes itself through a prompt optimization system that uses textual gradient descent to automatically refine prompt templates and few-shot examples. It treats model feedback as a differentiable signal, enabling a form of LLM backpropagation to iteratively improve output
Processes server-sent events to yield text fragments incrementally during model generation.