SAIDEX — Structured AI Data Extraction
Extract validated Pydantic models from LLM responses — with automatic retry, field-level error feedback, an optional fallback model, and an agentic tool loop.
Works with any LangChain-compatible model: GPT-4o, Claude, Gemini, and local models via Ollama or vLLM. No tool-calling support required — use ExtractionMode.JSON for any chat model.
Installation
pip install saidex
With automatic OpenAI rate-limit handling:
pip install "saidex[openai]"
Quick start
import asyncio
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
from saidex import extract_data_from_text
class PersonInfo(BaseModel):
name: str
age: int
occupation: str
async def main():
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
person, stats = await extract_data_from_text(
llm,
PersonInfo,
"Alice Müller, 34, works as a software engineer in Munich.",
)
print(person) # name='Alice Müller' age=34 occupation='software engineer'
print(stats.total_retries) # 0 — first attempt was valid
asyncio.run(main())
Key features
| Feature | Detail |
|---|---|
| Validated output | Pydantic validation with field-level error feedback fed back to the LLM for self-correction |
| Auto-retry | Configurable retries with exponential back-off for transient errors and rate limits |
| Fallback model | Pair a cheap primary with a powerful fallback — pay for the big model only when needed |
| No tool-calling required | ExtractionMode.JSON works with any chat model including local models via Ollama / vLLM |
| Agentic tool loop | Give the LLM your own tools (lookups, API calls) — it calls them freely, then delivers a validated final answer |
| Multimodal | Pass images via standard LangChain messages to any vision-capable model |
| LangChain-native | Plugs into any LangChain chat model; supports callbacks (Langfuse, LangSmith, …) |
Links
License
Copyright 2026 Martin Lauff. Licensed under the Apache License, Version 2.0.
1 maintainer
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