Integration of LLM
LLM selection
To effectively integrate a Large Language Model (LLM) for text analysis and triplet extraction in PrivateAI, it is crucial to select an appropriate model based on several criteria. We will evaluate open-source models with Transformer architecture, focusing on key aspects such as the Named Entity Recognition (NER) task, triplet extraction, and solution comparison.
Integration of LLM
Our criteria for selecting a suitable Large Language Model:
- Open-source models with Transformer architecture: These models offer flexibility and are typically well-supported by the community.
- NER task with explicit label specification: The model handles common categorized entities (e.g., person, location) effectively.
- NER task without label specification for PrivateAI: The model extracts all entities without requiring predefined labels.
- Subject-link-object approach: This method is essential for effectively extracting triplet components.
Solution comparison
Criteria for evaluation:
- Development time
- Development complexity
- Configurability
- Potential triplet quality
- Triplet processing capabilities
- Reproducibility
| Criteria | Spacy + Python Script (Current Solution) | Mistral/Mixtral | spaCy_llm |
|---|---|---|---|
| Development time | Low | High | Medium |
| Development complexity | Low | Medium | Low |
| Configurability | Medium | High | High |
| Potential triplet quality | Low | Medium | High |
| Triplet processing capabilities | Low | High | High |
| Reproducibility | High | Low | Medium |
| Overall evaluation | Demo-appropriate solution. Quick solution with low output quality. | Thorough and long solution with potential for perfect triplets. | Out-of-box solution with valuable functionalities for entity and relation extractions, bringing many possibilities. Most stable solution at current stage. |
Conclusion
Integrating knowledge graph case studies and AI model training into PrivateAI's development reveals promising enhancements in data analysis and representation. By selecting the appropriate LLM and leveraging spaCy_llm, we enhance the quality of triplet extraction and overall data insights for PrivateAI. This approach ensures a balance between development efficiency and the quality of output, paving the way for advanced data analysis capabilities.