

Skypilot RAG
Overview :
SkyPilot RAG is a retrieval-augmented generation system that combines vector search and large language models. It provides legal professionals with efficient information retrieval and analysis tools through semantic search and intelligent question answering. Built on SkyPilot, the system manages infrastructure and efficiently utilizes computing resources, supporting deployment on any cloud environment or Kubernetes. Its main advantages include high accuracy, context awareness, and traceability, significantly improving the efficiency and reliability of legal document processing.
Target Users :
This product primarily targets legal professionals, including lawyers, legal personnel, and legal researchers. It helps them quickly find relevant legal precedents, analyze complex legal scenarios, and extract useful information from a large number of documents, thereby improving work efficiency and decision-making quality.
Use Cases
Legal professionals can use SkyPilot RAG to quickly find relevant legal cases.
Users can extract key information from a large number of legal documents using the semantic search function.
The system supports multi-cloud deployment to meet the needs of different users.
Features
Manage infrastructure using SkyPilot, simplifying the running of large-scale jobs.
Achieve semantic search of legal documents through vector embeddings.
Generate intelligent question-answering responses using the DeepSeek R1 model.
Support multi-cloud environments and Kubernetes deployment for increased flexibility.
Automatically handle job state management and fault recovery to ensure efficient operation.
How to Use
1. Install SkyPilot and set the bucket name.
2. Use the batch_compute_embeddings.py script to compute document embeddings.
3. Use build_rag.yaml to build the vector database.
4. Use serve_rag.yaml to deploy the RAG service.
5. Obtain the service endpoint through Sky Serve and start querying.
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