Search

Disaster Recovery of Vector Databases

Generative AI (GenAI) Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) can deliver good results for Question/Answer applications.

Self-hosting production LLM + RAG models offers the following advantages.
  • Cost: less expensive than public APIs
  • Scaling: eliminate rate limits
  • Performance: eliminate noisy neighbors
  • Control: eliminate outage risks
  • Data Privacy: eliminate data leak risks

Let us use a sample GenAI Pipeline to learn how to self-host.

Sample GenAI Pipeline: Drug Research Q&A Service

Drug Research Q&A Service would involve below workflow steps.
  1. Data Team produces Document Data Source
  2. Document Data Source triggers Message Queue (ex: AWS SQS)
  3. Message Queue triggers KEDA job
  4. KEDA job reads input text data, converts to data embedding (ex: using LangChain)
  5. Data Embedding gets stored in VectorDB (ex: FAISS, Weaviate, AstraDB)
  6. End User Query sent to RAG Service endpoint
  7. RAG composes query response from VectorDB and LLM Model (ex: MosaicML 7B from HuggingFace)

Self-hosting Requirements
​

Infrastructure Requirements
​
​GPU price and availability sensitivity​
​
  • GPU Availability based scheduling across on-prem and cloud providers
  • LLM and Data Embedding Jobs need GPU
  • VectorDB can run on CPU or GPU
Data Requirements
​
​Data Gravity
​
  • Data ingestion jobs with PII data need to be scheduled to run at source (ex: on-prem)
  • Workloads need to dynamically migrate across cloud clusters to shadow availability of non-PII data​
Operational Requirements
​
​Separation of Concern between App and Platform teams​
  • AI workload manifests should require zero changes by the app developer
  • Workload schedule policies should be owned by Platform team​
Elotl Nova satisfies all of the above requirements!​

Pipeline blueprint
​

All components except Document Datastore and Message Queue land on Kubernetes clusters.​

  • ​Pipeline Rollout on Day 0 (green): Choose primary deploy location (ex: on-prem) for hosting KEDA, Message Queue Scaler, KEDA Scaled Job, RAG Query Service, LLM model and Service. Rollout Pipeline components into Kubernetes namespace(s) across multiple clusters using Nova.
  • Pipeline Maintenance on Day 2 (red): New data set emerges on secondary cloud. Automagically migrate subset of Pipeline components to secondary cloud using Nova.​
  • Pipeline Teardown: One step Pipeline teardown - just delete its Kubernetes namespace(s)!

Nova In Action!
​

Watch Nova schedule and dynamically reschedule Q&A Service Pipeline components across a fleet of 10 GPU+CPU Clusters spanning 2 clouds.
​


​Takeaways​

​
  • Self-hosting GenAI Pipeline offers cost, performance, and privacy gains
  • GenAI Pipeline Rollout
    • Day 0 cost-effective scheduling of Pipeline components in a hybrid-cloud environment can be automated using Nova
  • GenAI Pipeline Maintenance
    • Day 2 mobility of Pipeline components across clouds can be automated using Nova​
  • GenAI Pipeline Teardown
    • Teardown of Pipeline components can be automated using Nova by simply removing namespace(s)
  • ​Optionally, Elotl Luna can be used for dynamic resource allocation on each of the workload clusters managed by Nova
​© 2024 Elotl, Inc.