FEATURE: AI Bot RAG support. (#537)
This PR lets you associate uploads to an AI persona, which we'll split and generate embeddings from. When building the system prompt to get a bot reply, we'll do a similarity search followed by a re-ranking (if available). This will let us find the most relevant fragments from the body of knowledge you associated with the persona, resulting in better, more informed responses.
For now, we'll only allow plain-text files, but this will change in the future.
Commits:
* FEATURE: RAG embeddings for the AI Bot
This first commit introduces a UI where admins can upload text files, which we'll store, split into fragments,
and generate embeddings of. In a next commit, we'll use those to give the bot additional information during
conversations.
* Basic asymmetric similarity search to provide guidance in system prompt
* Fix tests and lint
* Apply reranker to fragments
* Uploads filter, css adjustments and file validations
* Add placeholder for rag fragments
* Update annotations
2024-04-01 12:43:34 -04:00
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# frozen_string_literal: true
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class RagDocumentFragment < ActiveRecord::Base
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belongs_to :upload
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belongs_to :ai_persona
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class << self
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def link_persona_and_uploads(persona, upload_ids)
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return if persona.blank?
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return if upload_ids.blank?
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return if !SiteSetting.ai_embeddings_enabled?
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UploadReference.ensure_exist!(upload_ids: upload_ids, target: persona)
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upload_ids.each do |upload_id|
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Jobs.enqueue(:digest_rag_upload, ai_persona_id: persona.id, upload_id: upload_id)
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end
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end
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def update_persona_uploads(persona, upload_ids)
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return if persona.blank?
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return if !SiteSetting.ai_embeddings_enabled?
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if upload_ids.blank?
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RagDocumentFragment.where(ai_persona: persona).destroy_all
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UploadReference.where(target: persona).destroy_all
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else
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RagDocumentFragment.where(ai_persona: persona).where.not(upload_id: upload_ids).destroy_all
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link_persona_and_uploads(persona, upload_ids)
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end
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end
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2024-04-09 10:03:07 -04:00
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def indexing_status(persona, uploads)
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truncation = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep =
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(truncation)
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embeddings_table = vector_rep.rag_fragments_table_name
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results = DB.query(<<~SQL, persona_id: persona.id, upload_ids: uploads.map(&:id))
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SELECT
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uploads.id,
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SUM(CASE WHEN (rdf.upload_id IS NOT NULL) THEN 1 ELSE 0 END) AS total,
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SUM(CASE WHEN (eft.rag_document_fragment_id IS NOT NULL) THEN 1 ELSE 0 END) as indexed,
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SUM(CASE WHEN (rdf.upload_id IS NOT NULL AND eft.rag_document_fragment_id IS NULL) THEN 1 ELSE 0 END) as left
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FROM uploads
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LEFT OUTER JOIN rag_document_fragments rdf ON uploads.id = rdf.upload_id AND rdf.ai_persona_id = :persona_id
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LEFT OUTER JOIN #{embeddings_table} eft ON rdf.id = eft.rag_document_fragment_id
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WHERE uploads.id IN (:upload_ids)
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GROUP BY uploads.id
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SQL
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results.reduce({}) do |acc, r|
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acc[r.id] = { total: r.total, indexed: r.indexed, left: r.left }
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acc
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end
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end
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def publish_status(upload, status)
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MessageBus.publish(
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"/discourse-ai/ai-persona-rag/#{upload.id}",
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status,
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user_ids: [upload.user_id],
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)
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end
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FEATURE: AI Bot RAG support. (#537)
This PR lets you associate uploads to an AI persona, which we'll split and generate embeddings from. When building the system prompt to get a bot reply, we'll do a similarity search followed by a re-ranking (if available). This will let us find the most relevant fragments from the body of knowledge you associated with the persona, resulting in better, more informed responses.
For now, we'll only allow plain-text files, but this will change in the future.
Commits:
* FEATURE: RAG embeddings for the AI Bot
This first commit introduces a UI where admins can upload text files, which we'll store, split into fragments,
and generate embeddings of. In a next commit, we'll use those to give the bot additional information during
conversations.
* Basic asymmetric similarity search to provide guidance in system prompt
* Fix tests and lint
* Apply reranker to fragments
* Uploads filter, css adjustments and file validations
* Add placeholder for rag fragments
* Update annotations
2024-04-01 12:43:34 -04:00
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end
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end
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# == Schema Information
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#
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# Table name: rag_document_fragments
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#
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# id :bigint not null, primary key
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# fragment :text not null
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# upload_id :integer not null
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2024-04-04 10:02:16 -04:00
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# ai_persona_id :integer not null
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FEATURE: AI Bot RAG support. (#537)
This PR lets you associate uploads to an AI persona, which we'll split and generate embeddings from. When building the system prompt to get a bot reply, we'll do a similarity search followed by a re-ranking (if available). This will let us find the most relevant fragments from the body of knowledge you associated with the persona, resulting in better, more informed responses.
For now, we'll only allow plain-text files, but this will change in the future.
Commits:
* FEATURE: RAG embeddings for the AI Bot
This first commit introduces a UI where admins can upload text files, which we'll store, split into fragments,
and generate embeddings of. In a next commit, we'll use those to give the bot additional information during
conversations.
* Basic asymmetric similarity search to provide guidance in system prompt
* Fix tests and lint
* Apply reranker to fragments
* Uploads filter, css adjustments and file validations
* Add placeholder for rag fragments
* Update annotations
2024-04-01 12:43:34 -04:00
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# fragment_number :integer not null
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# created_at :datetime not null
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# updated_at :datetime not null
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2024-04-04 10:02:16 -04:00
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# metadata :text
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FEATURE: AI Bot RAG support. (#537)
This PR lets you associate uploads to an AI persona, which we'll split and generate embeddings from. When building the system prompt to get a bot reply, we'll do a similarity search followed by a re-ranking (if available). This will let us find the most relevant fragments from the body of knowledge you associated with the persona, resulting in better, more informed responses.
For now, we'll only allow plain-text files, but this will change in the future.
Commits:
* FEATURE: RAG embeddings for the AI Bot
This first commit introduces a UI where admins can upload text files, which we'll store, split into fragments,
and generate embeddings of. In a next commit, we'll use those to give the bot additional information during
conversations.
* Basic asymmetric similarity search to provide guidance in system prompt
* Fix tests and lint
* Apply reranker to fragments
* Uploads filter, css adjustments and file validations
* Add placeholder for rag fragments
* Update annotations
2024-04-01 12:43:34 -04:00
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#
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