lexoid 0.1.20.post1

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Lexoid is an efficient document parsing library that supports both LLM-based and non-LLM-based (static) PDF document parsing.

Documentation

Motivation:

• Use the multi-modal advancement of LLMs
• Enable convenience for users
• Collaborate with a permissive license

Installation

Installing with pip

pip install lexoid

To use LLM-based parsing, define the following environment variables or create a .env file with the following definitions

OPENAI_API_KEY=""
GOOGLE_API_KEY=""

Optionally, to use Playwright for retrieving web content (instead of the requests library):

playwright install --with-deps --only-shell chromium

Building .whl from source

[!NOTE] Installing the package from within the virtual environment could cause unexpected behavior, as Lexoid creates and activates its own environment in order to build the wheel.

make build

Creating a local installation

To install dependencies:

make install

or, to install with dev-dependencies:

make dev

To activate virtual environment:

source .venv/bin/activate

Usage

Example Notebook

Example Colab Notebook

Here's a quick example to parse documents using Lexoid:

from lexoid.api import parse
from lexoid.api import ParserType

parsed_md = parse("https://www.justice.gov/eoir/immigration-law-advisor", parser_type="AUTO")["raw"]
# or
pdf_path = "path/to/immigration-law-advisor.pdf"
parsed_md = parse(pdf_path, parser_type="LLM_PARSE")["raw"]
# or
pdf_path = "path/to/immigration-law-advisor.pdf"
parsed_md = parse(pdf_path, parser_type="STATIC_PARSE")["raw"]

print(parsed_md)

Parameters

• path (str): The file path or URL.
• parser_type (str, optional): The type of parser to use ("LLM_PARSE" or "STATIC_PARSE"). Defaults to "AUTO".
• pages_per_split (int, optional): Number of pages per split for chunking. Defaults to 4.
• max_threads (int, optional): Maximum number of threads for parallel processing. Defaults to 4.
• **kwargs: Additional arguments for the parser.

Supported API Providers

• Google
• OpenAI
• Hugging Face
• Together AI
• OpenRouter
• Fireworks

Benchmark

Results aggregated across 14 documents.

Note: Benchmarks are currently done in the zero-shot setting.

Rank	Model	SequenceMatcher Similarity	TFIDF Similarity	Time (s)	Cost ($)
1	gemini-3-pro-preview	0.917 (±0.127)	0.943 (±0.159)	46.92	0.06288
2	AUTO (with auto-selected model)	0.899 (±0.131)	0.960 (±0.066)	21.17	0.00066
3	AUTO	0.895 (±0.112)	0.973 (±0.046)	9.29	0.00063
4	gpt-5.2	0.890 (±0.193)	0.975 (±0.036)	33.32	0.03959
5	gemini-2.5-flash	0.886 (±0.164)	0.986 (±0.027)	52.55	0.01226
6	mistral-ocr-latest	0.882 (±0.106)	0.932 (±0.091)	5.75	0.00121
7	gemini-2.5-pro	0.876 (±0.195)	0.976 (±0.049)	22.65	0.02408
8	gemini-2.0-flash	0.875 (±0.148)	0.977 (±0.037)	11.96	0.00079
9	claude-3-5-sonnet-20241022	0.858 (±0.184)	0.930 (±0.098)	17.32	0.01804
10	gemini-1.5-flash	0.842 (±0.214)	0.969 (±0.037)	15.58	0.00043
11	gpt-5-mini	0.819 (±0.201)	0.917 (±0.104)	52.84	0.00811
12	gpt-5	0.807 (±0.215)	0.919 (±0.088)	98.12	0.05505
13	claude-sonnet-4-20250514	0.801 (±0.188)	0.905 (±0.136)	22.02	0.02056
14	claude-opus-4-20250514	0.789 (±0.220)	0.886 (±0.148)	29.55	0.09513
15	accounts/fireworks/models/llama4-maverick-instruct-basic	0.772 (±0.203)	0.930 (±0.117)	16.02	0.00147
16	gemini-1.5-pro	0.767 (±0.309)	0.865 (±0.230)	24.77	0.01139
17	gemini-3-flash-preview	0.766 (±0.293)	0.858 (±0.210)	39.38	0.00969
18	gpt-4.1-mini	0.754 (±0.249)	0.803 (±0.193)	23.28	0.00347
19	accounts/fireworks/models/llama4-scout-instruct-basic	0.754 (±0.243)	0.942 (±0.063)	13.36	0.00087
20	gpt-4o	0.752 (±0.269)	0.896 (±0.123)	28.87	0.01469
21	gpt-4o-mini	0.728 (±0.241)	0.850 (±0.128)	18.96	0.00609
22	claude-3-7-sonnet-20250219	0.646 (±0.397)	0.758 (±0.297)	57.96	0.01730
23	gpt-4.1	0.637 (±0.301)	0.787 (±0.185)	35.37	0.01498
24	google/gemma-3-27b-it	0.604 (±0.342)	0.788 (±0.297)	23.16	0.00020
25	ds4sd/SmolDocling-256M-preview	0.603 (±0.292)	0.705 (±0.262)	507.74	0.00000
26	microsoft/phi-4-multimodal-instruct	0.589 (±0.273)	0.820 (±0.197)	14.00	0.00045
27	qwen/qwen-2.5-vl-7b-instruct	0.498 (±0.378)	0.630 (±0.445)	14.73	0.00056

Citation

If you use Lexoid in production or publications, please cite accordingly and acknowledge usage. We appreciate the support 🙏