- Minimal example
- A minimal example with the least amount of code and no comments
- Uses OpenAI for creating the embeddings
- RAG Wikipedia Ollama
- This example shows a retrieval augmented generation (RAG) application, using
chromem-goas knowledge base for finding relevant info for a question. More specifically the app is doing question answering. - The underlying data is 200 Wikipedia articles (or rather their lead section / introduction).
- Runs the embeddings model and LLM in Ollama, to showcase how a RAG application can run entirely offline, without relying on OpenAI or other third party APIs.
- This example shows a retrieval augmented generation (RAG) application, using
- Semantic search arXiv OpenAI
- This example shows a semantic search application, using
chromem-goas vector database for finding semantically relevant search results. - Loads and searches across ~5,000 arXiv papers in the "Computer Science - Computation and Language" category, which is the relevant one for Natural Language Processing (NLP) related papers.
- Uses OpenAI for creating the embeddings
- This example shows a semantic search application, using
- WebAssembly
- This example shows how
chromem-gocan be compiled to WebAssembly and then used from JavaScript in a browser
- This example shows how
- S3 Export/Import
- This example shows how to export the DB to and import it from any S3-compatible blob storage service
examples
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