Develop GenAI apps with RAG

Source

  • Choose from OpenAI,  Vertex AI, AWS Bedrock or a fine-tuned LLM
  • ETL for LLMs to make data ready for AI.
  • Deliver easy no-code preparation to create chunks and vectors for RAG search and retrieval.

Input Guardrail

  • Detect toxic input and off topic questions
  • Respond to questions that adhere to a “use policy”
  • Extract entities to detect PII

Query Rewriting

  • Understand intent even with ambiguous questions
  • Extract key concepts to overcome limitations in searches
  • Split long questions into sub parts, use classifiers to categorize for better answers

Embedding

  • Capture semantic representation of your data
  • Choose any embedding from Hugging Face MTEB leaderboard
  • Customize an embedding model for your data

Query Retriever

  • Role based access and metadata based filtering for enterprise search
  • Combination of lexical and semantic search, enhanced with a knowledge graph
  • Out of the box implementations for context re-ranking strategies such as MMR

Encoder and Chunker

  • Choose your vector database - Aerospike,  AstraDB, Couchbase, MongoDB, Pinecone 
  • Choose a recommended chunking strategy with flexibility to change
  • Retain ownership of your data and your chunks 

Caching & External Memory​

  • Maintain adaptive cache for answers with high ratings to save on round trips 
  • Detect changes in the knowledge source to regenerate answers
  • Improve performance and avoid unnecessary costs

Generation

  • Pick your model from OpenAI, Google Vertex AI or any open-source model 
  • Switch between foundation models with the click of a button
  • Attach policies to monitor costs

RLHF/Feedback

  • Get user feedback on answers
  • Enable domain experts to modify LLM response to create golden answers
  • Detect gaps in your knowledge sources

Response Evaluation

  • Evaluate LLM response for retrieval and generation
  • Use RAGAS framework for evaluating RAG as a Service
  • Customize metrics for your use case

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