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Technologies Involved:
PYTHON
Area Of Work: Generative AI
Project Description

The client specializes in legal technology solutions, focusing on streamlining the retrieval and analysis of Turkish legal documents. They approached Oodles to develop a system that leverages advanced NLP techniques and a fine-tuned large language model (LLM) for Turkish law. Oodles implemented a scalable solution, incorporating Elasticsearch for enhanced search functionality and fine-tuned the LLM for domain-specific queries.
Scope Of Work

The client sought a system to streamline the retrieval and analysis of Turkish legal documents. Facing challenges with accuracy and efficiency in handling vast amounts of legal texts, they approached Oodles for a tailored solution. Oodles developed a scalable infrastructure using advanced NLP techniques and a fine-tuned large language model (LLM) optimized for Turkish law. The project covered areas such as implementing Elasticsearch for enhanced search capabilities and fine-tuning the LLM to handle domain-specific queries, ultimately improving document retrieval accuracy and speeding up legal research.
Our Solution

Our solution to address the challenges of processing Turkish legal documents with improved accuracy and efficiency involved the following key steps:

  1. Infrastructure Setup: Established a robust API project and configured the repository, selecting the optimal large language model (LLM) tailored for Turkish legal language.
  2. Elasticsearch Integration: Integrated Elasticsearch with VectorDB to enable efficient indexing and search functionalities, experimenting with Turkish-supported embedding models for precision.
  3. RAG (Retrieval-Augmented Generation) Integration: Implemented a RAG architecture to enable advanced document retrieval and semantic search, improving query relevance using LLM and embeddings.
  4. LLM Fine-Tuning: Fine-tuned the LLM with domain-specific Turkish legal data to enhance search relevance and overall system accuracy.
  5. Prompt Optimization: Refined prompts to further boost the accuracy of search results and improve user interaction with the system.
  6. Deployment: Successfully deployed the solution in a scalable production environment, ready for real-world legal document retrieval.

Tech Stack: LLM, Elasticsearch, VectorDB, and custom embeddings were selected for their ability to handle domain-specific queries and scale efficiently.

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