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Commitff02bb7

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Merge branch 'master' into dan-product-notifications
2 parentsd6c598c +cabbfa5 commitff02bb7

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‎pgml-cms/blog/SUMMARY.md

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#Table of contents
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*[Home](README.md)
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*[Unified RAG](unified-rag.md)
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*[Announcing the Release of our Rust SDK](announcing-the-release-of-our-rust-sdk.md)
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*[Serverless LLMs are dead; Long live Serverless LLMs](serverless-llms-are-dead-long-live-serverless-llms.md)
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*[Speeding up vector recall 5x with HNSW](speeding-up-vector-recall-5x-with-hnsw.md)

‎pgml-cms/blog/unified-rag.md

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‎pgml-cms/docs/SUMMARY.md

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*[Chatbots](guides/chatbots/README.md)
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*[Example Application](use-cases/chatbots.md)
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*[Supervised Learning](guides/supervised-learning.md)
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*[Unified RAG](guides/unified-rag.md)
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*[OpenSourceAI](guides/opensourceai.md)
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*[Natural Language Processing](guides/natural-language-processing.md)
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---
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description:Rank documents against a piece of text using the specified ranking model.
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---
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#pgml.rank()
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The`pgml.rank()` function is used to compute a relevance score between documents and some text. This function is primarily used as the last step in a search system where the results returned from the initial search are re-ranked by relevance before being used.
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##API
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```postgresql
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pgml.rank(
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transformer TEXT, -- transformer name
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query TEXT, -- text to rank against
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documents TEXT[], -- documents to rank
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kwargs JSON -- optional arguments (see below)
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)
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```
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##Example
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Ranking documents is as simple as calling the the function with the documents you want to rank, and text you want to rank against:
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```postgresql
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SELECT pgml.rank('mixedbread-ai/mxbai-rerank-base-v1', 'test', ARRAY['doc1', 'doc2']);
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```
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By default the`pgml.rank()` function will return and rank all of the documents. The function can be configured to only return the relevance score and index of the top k documents by setting`return_documents` to`false` and`top_k` to the number of documents you want returned.
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```postgresql
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SELECT pgml.rank('mixedbread-ai/mxbai-rerank-base-v1', 'test', ARRAY['doc1', 'doc2'], '{"return_documents": false, "top_k": 10}'::JSONB);
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```
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##Supported ranking models
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We currently support cross-encoders for re-ranking. Check out[Sentence Transformer's documentation](https://sbert.net/examples/applications/cross-encoder/README.html) for more information on how cross-encoders work.
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By default we provide the following ranking models:
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*`mixedbread-ai/mxbai-rerank-base-v1`

‎pgml-cms/docs/guides/unified-rag.md

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‎pgml-extension/pgml.control

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comment = 'pgml: Created by the PostgresML team'
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comment = 'Machine Learning and AI functions from postgresml.org'
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default_version = '@CARGO_VERSION@'
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module_pathname = '$libdir/pgml'
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relocatable = false

‎pgml-sdks/pgml/Cargo.toml

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[package]
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name ="pgml"
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version ="1.0.4"
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version ="1.1.0"
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edition ="2021"
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authors = ["PosgresML <team@postgresml.org>"]
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homepage ="https://postgresml.org/"

‎pgml-sdks/pgml/javascript/package.json

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{
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"name":"pgml",
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"version":"1.0.3",
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"version":"1.1.0",
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"description":"Open Source Alternative for Building End-to-End Vector Search Applications without OpenAI & Pinecone",
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"keywords": [
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"postgres",

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