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0007: Unified Embedding API #13
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GEP: 0007 | ||
Title: Unified Embedding API | ||
Discussion: | ||
Implementation: | ||
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# Unified Embedding API | ||
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## Abstract | ||
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This GEP discusses the unified embeddings api and its interactions with embedding model providers. | ||
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## Motivation | ||
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Many LLM applications have a semantic search feature within their architecture. To support these applications on Glide, a unified embedding api is necessary. | ||
This will allow applications to embed chat requests as part of a retrieval augmented generation (RAG) application flow. | ||
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### Requirements | ||
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- R1: Handles all provider specific logic | ||
- R2: Easily maintained | ||
- R3: API schemas must unify common request params (e.g. dimensions) | ||
- R4: API routes must unify common embedding endpoints/API | ||
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## Design | ||
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```yaml | ||
routes: | ||
chat: /v1/language/{pool-id}/chat/ | ||
transcribers: /v1/speech/transcribers/{pool-id}/ | ||
speech-synthesizer: /v1/speech/synthesizers/{pool-id}/ | ||
multi-modal: /v1/multi/{pool-id}/multimodal/ | ||
embedding: /v1/embeddings/{pool-id}/embed/ | ||
``` | ||
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#### User Request Schema for Embedding Route | ||
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TBU | ||
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#### Response Schema for Embedding Route | ||
TBU | ||
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## Alternatives Considered | ||
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[TBU, what other solutions were considered and why they were rejected] | ||
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## Future Work | ||
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- Could we abstract away the entire RAG architecture? A single endpoint that takes a chat message -> embeds -> text semantic search -> LLM -> response |
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Looking forward to see proposed unified requests/responses 👀 The remaining workflow should be fairly close to the chat API.