aws-samples / generating-synthetic-datasets-for-evaluating-retrieval-augmented-generation-systemsLinks
The repository guides you through generating a synthetic dataset for a QA-RAG application using the Bedrock API, Python and Langchain.
☆19Updated last year
Alternatives and similar repositories for generating-synthetic-datasets-for-evaluating-retrieval-augmented-generation-systems
Users that are interested in generating-synthetic-datasets-for-evaluating-retrieval-augmented-generation-systems are comparing it to the libraries listed below
Sorting:
- ☆23Updated last week
- ☆17Updated last year
- ☆14Updated last year
- ☆45Updated 10 months ago
- Amazon Q Business enables querying structured data using natural language, leveraging schemas and metadata. This example demonstrates an …☆19Updated last year
- ☆10Updated 9 months ago
- ☆25Updated 2 years ago
- ☆24Updated last year
- ☆54Updated last year
- ☆14Updated 2 years ago
- ☆24Updated last year
- Learn to build custom prompts and tools for LangChain agents☆40Updated last year
- ☆49Updated last year
- ☆16Updated 2 years ago
- ☆45Updated 5 months ago
- ☆32Updated 2 years ago
- ☆47Updated 8 months ago
- 'Talk to your slide deck' (Multimodal RAG) using foundation models (FMs) hosted on Amazon Bedrock and Amazon SageMaker☆44Updated 11 months ago
- ☆15Updated last year
- ☆18Updated 2 years ago
- Multi-modal Assistant With Advanced RAG And Amazon Bedrock Claude 3☆20Updated 11 months ago
- ☆22Updated 2 years ago
- ☆13Updated 6 months ago
- Operational Data Processing Framework developed using AWS Glue and Apache Hudi. This framework is suitable for Data Lake and Modern Data …☆24Updated 2 years ago
- ☆27Updated 2 years ago
- End to end example of a Retail Agent implemented with agents for Amazon Bedrock☆37Updated last year
- Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Service☆28Updated last year
- ☆15Updated 8 months ago
- ☆83Updated 2 years ago
- Use LLMs for building real-world apps☆112Updated 11 months ago