ContextGem

Getting structured data out of documents using Large Language Models (LLMs) often involves wrestling with significant boilerplate code. You might find yourself spending excessive time writing custom prompts, defining data models and validation logic from scratch, and implementing complex chaining or context management just to extract specific information accurately. This repetitive setup slows down development and shifts focus away from the core extraction task.

ContextGem offers a different path. It's an LLM framework specifically designed for structured data and insights extraction from individual documents, striking a deliberate balance between ease of use, customizability, and accuracy. ContextGem provides powerful, intuitive abstractions that handle the most time-consuming parts of building extraction workflows, letting you achieve precise results with significantly less code and effort.

Key Features: Streamlining Your Extraction Process

ContextGem simplifies development by handling complex tasks behind the scenes:

• 💎 Automate Dynamic Prompt Generation: Automatically constructs tailored, comprehensive prompts based on your specific extraction needs, eliminating manual prompt engineering and maintenance.

• 🔧 Generate Data Models & Validators: Creates the necessary Pydantic data models and validation logic automatically from your definitions, saving you from writing repetitive boilerplate.

• 🗺️ Map Extractions with Precision: Automatically maps extracted data back to its precise location (down to the paragraph or sentence level) in the source document, ensuring verifiable accuracy and traceability.

• 🔍 Provide Extraction Justifications: Automatically includes the reasoning or evidence from the text that supports each extracted piece of data, enhancing transparency and trust in the results.

•  Segment Documents Intelligently: Utilizes state-of-the-art Neural Segmentation (SaT) models to accurately divide documents into paragraphs and sentences, supporting numerous languages out-of-the-box.

• ⚙️ Define Unified Extraction Pipelines: Structure your entire extraction workflow—including nested contexts and role-specific LLMs—within a single, declarative, and reusable pipeline configuration that is fully serializable.

• 🎯 Manage Nested Context Extraction: Automatically handles hierarchical information extraction (e.g., document > sections > sub-sections > entities) based on your pipeline definition, simplifying analysis of complex documents.

• ⚡ Accelerate with Built-in Concurrency: Speed up demanding extraction workflows involving multiple LLM calls by enabling concurrent I/O processing with a simple use_concurrency=True switch.

• 📊 Track Usage and Costs Automatically: Monitors LLM calls, token usage, and associated costs across your workflows without requiring extra setup.

• 🔄 Integrate Fallback and Retry Logic: Comes with built-in retry mechanisms and allows easy configuration of fallback LLMs to improve resilience.

Practical Use Cases: Putting ContextGem to Work

1. Analyzing Legal Contracts: Imagine needing to extract key clauses (like termination conditions, payment terms, and governing law) from hundreds of software license agreements. Instead of writing complex prompts and parsers for each clause type, you define Aspects for "Termination," "Payment," etc., and Concepts for specific data points (e.g., NoticePeriod as a NumericalConcept, GoverningLaw as a StringConcept). ContextGem handles generating the prompts, extracting the data, validating it, and linking it back to the exact sentence in the contract, complete with justifications.

2. Processing Financial Reports: You need to extract specific figures and assess sentiment from quarterly earnings reports. You could set up a DocumentLLMGroup where a cost-effective model (extractor_text role) pulls out standard figures like revenue and profit (as NumericalConcepts attached to a "Financial Summary" Aspect). Simultaneously, a more powerful model (reasoner_text role) analyzes the "Management Discussion" Aspect to derive a SentimentRating (using a RatingConcept) based on nuanced language. ContextGem orchestrates this multi-LLM workflow seamlessly.

3. Screening CVs for Technical Roles: Tasked with identifying candidates matching specific criteria? Define Aspects for "Work Experience," "Education," and "Skills." Within "Skills," create Concepts like ProgrammingLanguages (a JsonObjectConcept perhaps, or multiple StringConcepts) and YearsOfExperienceWithPython (a NumericalConcept). ContextGem can process submitted CVs, extract this structured information, and even use a BooleanConcept to determine if a candidate meets a mandatory requirement (e.g., "HasCloudCertification").

Conclusion: Focus on Extraction, Not Framework Plumbing

ContextGem is intentionally optimized for deep, accurate analysis of individual documents by leveraging the expanding context windows and capabilities of modern LLMs. It provides a "batteries-included" experience, abstracting away common development hurdles like prompt engineering, data modeling, reference mapping, and concurrency management.

If your goal is to build reliable, maintainable, and precise structured data extraction workflows from documents without getting bogged down in repetitive setup code, ContextGem offers a powerful and efficient solution. It allows you to focus your efforts on defining what data you need, while it handles the how of extracting it accurately and efficiently.