A new embedding model cuts vector DB costs by ~200x.
It also outperforms OpenAI and Cohere models.
Here's a complete breakdown (with visuals):
RAG is 80% retrieval and 20% generation.
So if RAG isn't working, most likely, it's a retrieval issue, which further originates from chunking and embedding.
Contextualized chunk embedding models solve this.
Let's dive in to learn more!
So if RAG isn't working, most likely, it's a retrieval issue, which further originates from chunking and embedding.
Contextualized chunk embedding models solve this.
Let's dive in to learn more!
In RAG:
- No chunking drives up token costs
- Large chunks lose fine-grained context
- Small chunks lose global/neighbourhood context
In fact, chunking also involves determining chunk overlap, generating summaries, etc., which are tedious.
There's another problem!
- No chunking drives up token costs
- Large chunks lose fine-grained context
- Small chunks lose global/neighbourhood context
In fact, chunking also involves determining chunk overlap, generating summaries, etc., which are tedious.
There's another problem!
Despite tuning and balancing tradeoffs, the final chunk embeddings are generated independently with no interaction with each other.
This isn't true with real-world docs, which have long-range dependencies.
Check this 👇
This isn't true with real-world docs, which have long-range dependencies.
Check this 👇
voyage-context-3 embedding model by @MongoDB solves this.
It is a contextualized chunk embedding model that produces vectors for chunks that capture the full document context without any manual metadata and context augmentation.
Check this visual 👇
It is a contextualized chunk embedding model that produces vectors for chunks that capture the full document context without any manual metadata and context augmentation.
Check this visual 👇
Technically, unlike traditional chunk embedding, the model processes the entire doc in a single pass to embed each chunk.
This way, it sees all the chunks at the same time to generate global document-aware chunk embeddings.
This gives semantically aware retrieval in RAG.
This way, it sees all the chunks at the same time to generate global document-aware chunk embeddings.
This gives semantically aware retrieval in RAG.
Across 93 retrieval datasets, spanning nine domains (web reviews, law, medical, long documents, etc.):
voyage-context-3 outperforms:
- all models across all domains
- OpenAI-v3-large by 14.2%
- Cohere-v4 by 7.89%
- Jina-v3 by 23.66%
Check this 👇
voyage-context-3 outperforms:
- all models across all domains
- OpenAI-v3-large by 14.2%
- Cohere-v4 by 7.89%
- Jina-v3 by 23.66%
Check this 👇
voyage-context-3 supports 2048, 1024, 512, and 256 dimensions with quantization.
Compared to OpenAI-v3-large (float, 3072d), voyage-context-3 (int8, 2048):
- delivers 83% lower vector DB costs
- provides 8.60% better retrieval quality
Check this 👇
Compared to OpenAI-v3-large (float, 3072d), voyage-context-3 (int8, 2048):
- delivers 83% lower vector DB costs
- provides 8.60% better retrieval quality
Check this 👇
Compared to OpenAI-v3-large (float, 3072d). voyage-context-3 (binary, 512):
- 99.48% lower vector DB costs.
- 0.73% better retrieval quality.
Check this 👇
- 99.48% lower vector DB costs.
- 0.73% better retrieval quality.
Check this 👇
In terms of practical usage...
voyage-context-3 is a drop-in replacement for standard embeddings without downstream workflow changes.
So you can start using it by just changing the model name.
Find the docs here: fnf.dev/4m4bW1H
voyage-context-3 is a drop-in replacement for standard embeddings without downstream workflow changes.
So you can start using it by just changing the model name.
Find the docs here: fnf.dev/4m4bW1H
To recap, instead of producing independent chunk embeddings, contextualized chunk embedding models like voyage-context-3 process the entire doc in a single pass to embed each chunk.
This leads to document-aware chunk embeddings that generate semantically aware retrieval in RAG.
Check the visual below 👇
Thanks to the #MongoDB team for working with me on this thread!
This leads to document-aware chunk embeddings that generate semantically aware retrieval in RAG.
Check the visual below 👇
Thanks to the #MongoDB team for working with me on this thread!
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Find me → @_avichawla
Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.
Find me → @_avichawla
Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.
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