ContextGem
ContextGem is a free, open-source LLM framework that simplifies structured data and insights extraction from documents with minimal code through powerful built-in abstractions and automated features.
https://github.com/shcherbak-ai/contextgem?ref=aipure
Product Information
Updated:May 9, 2025
What is ContextGem
ContextGem is an innovative open-source framework designed to streamline the process of extracting structured data from documents using Large Language Models (LLMs). Created by Shcherbak AI AS, it addresses the common challenge of requiring extensive boilerplate code in document analysis by providing an intuitive, flexible framework that significantly reduces development complexity. The framework supports both cloud-based and local LLMs through LiteLLM integration, including providers like OpenAI, Anthropic, Google, and Azure OpenAI, while offering built-in converters for various file formats, particularly excelling in DOCX conversion.
Key Features of ContextGem
ContextGem is an open-source LLM framework that simplifies the extraction of structured data and insights from documents with minimal code. It offers powerful built-in abstractions including automated dynamic prompts, data modeling, reference mapping, and multilingual support. The framework excels at focused document analysis, leveraging LLMs' long context windows for superior extraction accuracy while supporting both cloud-based and local LLMs through LiteLLM integration.
Automated Dynamic Prompts & Data Modeling: Eliminates boilerplate code through automated prompt generation and data validation, significantly reducing development overhead
Precise Reference Mapping: Provides granular reference mapping at paragraph and sentence levels with built-in justifications for extraction reasoning
Multi-LLM Pipeline Support: Enables creation of complex extraction workflows using multiple LLMs with role-specific tasks and unified serializable results storage
Document Format Conversion: Built-in converters for various document formats including DOCX, preserving document structure and rich metadata for improved LLM analysis
Use Cases of ContextGem
Legal Document Analysis: Extract key clauses, terms, and anomalies from contracts and legal documents with precise reference tracking
Financial Documentation Processing: Analyze financial reports and documents to extract structured data, insights, and key metrics with justifications
Research Document Analysis: Extract concepts, themes, and insights from academic papers and research documents with hierarchical aspect analysis
Multilingual Document Processing: Process documents in multiple languages without requiring specific prompting, enabling global document analysis workflows
Pros
Minimal code required for complex document analysis tasks
Comprehensive built-in abstractions reducing development time
Flexible support for both cloud and local LLMs
Cons
Focused on single-document analysis rather than cross-document querying
Does not currently support corpus-wide retrieval capabilities
How to Use ContextGem
Install ContextGem: Install the package using pip: pip install -U contextgem
Import required modules: Import necessary classes: from contextgem import Document, DocumentLLM, StringConcept
Create a Document object: Create a Document object with your text content using Document(raw_text='your text here')
Define concepts to extract: Attach concepts to the document using doc.concepts = [StringConcept(name='concept_name', description='concept_description', add_references=True, reference_depth='sentences', add_justifications=True, justification_depth='brief')]
Configure LLM: Set up DocumentLLM with your preferred model and API key: llm = DocumentLLM(model='openai/gpt-4o-mini', api_key='your_api_key')
Extract information: Use the LLM to extract information from the document: doc = llm.extract_all(doc) or use async version with await llm.extract_all_async(doc)
Access results: Access extracted information through doc.concepts[0].extracted_items or doc.get_concept_by_name('concept_name').extracted_items
Optional: Convert DOCX files: For DOCX files, use DocxConverter: converter = DocxConverter(); document = converter.convert('path/to/document.docx')
Optional: Save results: Use built-in serialization methods to save processed documents and avoid repeating LLM calls
ContextGem FAQs
ContextGem is a free, open-source LLM framework that makes it radically easier to extract structured data and insights from documents with minimal code. It provides flexible, intuitive abstractions that simplify document analysis and eliminates the need for extensive boilerplate code.
ContextGem Video
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