Open Source
BestRAG
A Python retrieval library combining dense, sparse, and late-interaction search.
View repository ↗THE CASE STUDY
Finding evidence through more than semantic similarity.
For document retrieval and rag applications.
The challenge
Document questions can depend on both meaning and exact technical terms. A retrieval approach based only on semantic similarity may miss useful matches when the wording or level of detail matters.
The approach
The Python library integrates Qdrant with dense, sparse, and late-interaction retrieval. Reusable storage and search interfaces, including PDF ingestion, make those retrieval approaches available to document applications.
WORKFLOW OVERVIEW
From input to outcome.
- Ingest documents
- Search three ways
- Retrieve evidence
What it does
- Dense semantic retrieval.
- Sparse keyword and technical-term retrieval.
- Late-interaction search and PDF ingestion.
Delivered in the workflow.
Capabilities and outputs.
Search by meaning
Dense semantic retrieval
Find exact terminology
Sparse keyword retrieval
Match at a finer level
Late-interaction retrieval
A closer look at the build.
Open a topic to explore the details.
01Dense retrieval for meaning
Dense representations support semantic matching between a question and document content. This provides one route to relevant material when the user and the source express similar ideas in different words.
02Sparse retrieval for exact terms
Sparse retrieval provides a complementary route for keywords and technical terminology. It is useful when a particular term contributes meaning that should not be lost in a purely semantic match.
03Late interaction for finer matching
Late-interaction retrieval adds another level of relevance matching. Together with document ingestion and Qdrant-backed storage, it forms a reusable retrieval layer rather than a single-purpose chat interface.
WORKFLOW IN PRACTICE
A typical path through the product.
Documents are ingested into the retrieval workflow. A query can use the library's supported search approaches to find relevant material for a downstream application or answer-generation step.
Built with
- Python
- Qdrant
- Hybrid search