by RionDsilvaCS · Agent Tool · ★ 25
Contextual Retrieval with Llama-Index (Anthropic) This repository provides an implementation of contextual retrieval, a novel approach that enhances the performance of retrieval systems by incorporating chunk-specific explanatory context. By prepending contextual information to each chunk before embedding and indexing, this method improves the relevance and accuracy of retrieved results. Key Technologies Llama-Index: A powerful framework for building semantic search applications. Ollama: A local LLMs serving solution, using the gemma2:2b model.
| Stars | 25 |
| Forks | 4 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 66.6482162788947/100 |
| Open Issues | 1 |
| Last Updated | 2024-09-29 |
| Created | 2024-09-27 |
| Platforms | gemini, python |
| Est. Tokens | ~43k |
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contextual-retrieval-by-anthropic is Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).. It is categorized as a Agent Tool with 25 GitHub stars.
contextual-retrieval-by-anthropic is primarily written in Python. It covers topics such as anthropic, bm25, chromadb.
You can find installation instructions and usage details in the contextual-retrieval-by-anthropic GitHub repository at github.com/RionDsilvaCS/contextual-retrieval-by-anthropic. The project has 25 stars and 4 forks, indicating an active community.