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, …)
LangGraph `SaidexToolNode`	Drop-in `ToolNode` replacement (`pip install "saidex[langgraph]"`) that repairs, validates and corrects tool calls before execution

License

Metadata

Release files for saidex 0.8.0

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Source distribution (sdist)

Source distribution for saidex 0.8.0
File	Size	Uploaded
saidex-0.8.0.tar.gz	571.4 kB	Oct 12, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for saidex 0.8.0
File	Interpreter	ABI	Platform	Reset
saidex-0.8.0-py3-none-any.whl 115.9 kB Oct 12, 2026	Python 3	none	any	Details

Total release size: 687.3 kB

Release files / saidex-0.8.0.tar.gz

Download URL	saidex-0.8.0.tar.gz
Size	571.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`80dbf183aa0e297edd3001f1e7cef7eb9f1e0085cba231609fd784c69bbf0838`
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Upload date	Oct 12, 2026
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Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/mlauf-labs/SAIDEX
Publishing commit: github.com/mlauf-labs/SAIDEX/tree/2bc777668074a196d013b163d93178341a370fb5

Workflow

Publishing configuration: github.com/mlauf-labs/SAIDEX/blob/2bc777668074a196d013b163d93178341a370fb5/.github/workflows/release.yml
Publishing logs: github.com/mlauf-labs/SAIDEX/actions/runs/38224395120/attempts/1

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Subject: saidex-0.8.0.tar.gz
SHA-256 checksum: 80dbf183aa0e297edd3001f1e7cef7eb9f1e0085cba231609fd784c69bbf0838 Verifying attestations

Release files / saidex-0.8.0-py3-none-any.whl

Download URL	saidex-0.8.0-py3-none-any.whl
Size	115.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`92ed36fc5d3108d7dcd334ebeff24deef8893e4335bffe8e0c35d16d96e7757d`
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Upload date	Oct 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/mlauf-labs/SAIDEX
Publishing commit: github.com/mlauf-labs/SAIDEX/tree/2bc777668074a196d013b163d93178341a370fb5

Workflow

Publishing configuration: github.com/mlauf-labs/SAIDEX/blob/2bc777668074a196d013b163d93178341a370fb5/.github/workflows/release.yml
Publishing logs: github.com/mlauf-labs/SAIDEX/actions/runs/38224395120/attempts/1

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Subject: saidex-0.8.0-py3-none-any.whl
SHA-256 checksum: 92ed36fc5d3108d7dcd334ebeff24deef8893e4335bffe8e0c35d16d96e7757d Verifying attestations