pplx-embed-v2-context-9b-preview
huggingface.co- Category
- AI Tools
- Rank
- No. 4086Tools index
Previous survey · No. 3888 ·
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Data, Retrieval & Knowledge
- Date
About
A contextual embedding model from Perplexity for RAG document chunks. A document is passed as a list of chunks that are encoded together, so each chunk's embedding reflects its surrounding context, and one embedding is returned per chunk. It is a preview release: weights and the interface may change without backward compatibility, so embeddings from it should not be mixed with those from a later version.
At a glance
Perplexity’s contextual embedding preview encodes document chunks together, then returns a vector for each chunk. It is worth evaluating when a passage depends on nearby text and standalone chunk embeddings retrieve the wrong section.
Good fit
Retrieval developers who can compare candidate embeddings on representative queries.
Consider something else when
Pass if you need a stable production model or cannot review and isolate the custom code used by the loader.
Start here
Use a synthetic document whose sections refer to the same term in different ways. Compare retrieval with your baseline using the documented query and document encoders.
Cost and requirements
The model card lists MIT licensing and a Transformers setup with custom code. Budget for model storage and inference compute.
Limits to know
This is a preview and its outputs may change. The card documents 1,024 and 2,048 dimensions, and evaluating it may require rebuilding an index.
Sources checked 2026-11-05. Source-based assessment.
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