Files
sure/app/models/vector_store/base.rb
Juan José Mata 9e57954a99 Add Family vector search function call / support for document vault (#961)
* Add SearchFamilyImportedFiles assistant function with vector store support

Implement per-Family document search using OpenAI vector stores, allowing
the AI assistant to search through uploaded financial documents (tax returns,
statements, contracts, etc.). The architecture is modular with a provider-
agnostic VectorStoreConcept interface so other RAG backends can be added.

Key components:
- Assistant::Function::SearchFamilyImportedFiles - tool callable from any LLM
- Provider::VectorStoreConcept - abstract vector store interface
- Provider::Openai vector store methods (create, upload, search, delete)
- Family::VectorSearchable concern with document management
- FamilyDocument model for tracking uploaded files
- Migration adding vector_store_id to families and family_documents table

https://claude.ai/code/session_01TSkKc7a9Yu2ugm1RvSf4dh

* Extract VectorStore adapter layer for swappable backends

Replace the Provider::VectorStoreConcept mixin with a standalone adapter
architecture under VectorStore::. This cleanly separates vector store
concerns from the LLM provider and makes it trivial to swap backends.

Components:
- VectorStore::Base — abstract interface (create/delete/upload/remove/search)
- VectorStore::Openai — uses ruby-openai gem's native vector_stores.search
- VectorStore::Pgvector — skeleton for local pgvector + embedding model
- VectorStore::Qdrant — skeleton for Qdrant vector DB
- VectorStore::Registry — resolves adapter from VECTOR_STORE_PROVIDER env
- VectorStore::Response — success/failure wrapper (like Provider::Response)

Consumers updated to go through VectorStore.adapter:
- Family::VectorSearchable
- Assistant::Function::SearchFamilyImportedFiles
- FamilyDocument

Removed: Provider::VectorStoreConcept, vector store methods from Provider::Openai

https://claude.ai/code/session_01TSkKc7a9Yu2ugm1RvSf4dh

* Add Vector Store configuration docs to ai.md

Documents how to configure the document search feature, covering all
three supported backends (OpenAI, pgvector, Qdrant), environment
variables, Docker Compose examples, supported file types, and privacy
considerations.

https://claude.ai/code/session_01TSkKc7a9Yu2ugm1RvSf4dh

* No need to specify `imported` in code

* Missed a couple more places

* Tiny reordering for the human OCD

* Update app/models/assistant/function/search_family_files.rb

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Signed-off-by: Juan José Mata <jjmata@jjmata.com>

* PR comments

* More PR comments

---------

Signed-off-by: Juan José Mata <jjmata@jjmata.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
2026-02-11 15:22:56 +01:00

69 lines
1.9 KiB
Ruby

class VectorStore::Base
SUPPORTED_EXTENSIONS = %w[
.c .cpp .css .csv .docx .gif .go .html .java .jpeg .jpg .js .json
.md .pdf .php .png .pptx .py .rb .sh .tar .tex .ts .txt .xlsx .xml .zip
].freeze
# Create a new vector store / collection / namespace
# @param name [String] human-readable name
# @return [Hash] { id: "store-identifier" }
def create_store(name:)
raise NotImplementedError
end
# Delete a vector store and all its files
# @param store_id [String]
def delete_store(store_id:)
raise NotImplementedError
end
# Upload and index a file
# @param store_id [String]
# @param file_content [String] raw file bytes
# @param filename [String] original filename with extension
# @return [Hash] { file_id: "file-identifier" }
def upload_file(store_id:, file_content:, filename:)
raise NotImplementedError
end
# Remove a previously uploaded file
# @param store_id [String]
# @param file_id [String]
def remove_file(store_id:, file_id:)
raise NotImplementedError
end
# Semantic search across indexed files
# @param store_id [String]
# @param query [String] natural-language search query
# @param max_results [Integer]
# @return [Array<Hash>] each { content:, filename:, score:, file_id: }
def search(store_id:, query:, max_results: 10)
raise NotImplementedError
end
# Which file extensions this adapter can ingest
def supported_extensions
SUPPORTED_EXTENSIONS
end
private
def success(data)
VectorStore::Response.new(success?: true, data: data, error: nil)
end
def failure(error)
wrapped = error.is_a?(VectorStore::Error) ? error : VectorStore::Error.new(error.message)
VectorStore::Response.new(success?: false, data: nil, error: wrapped)
end
def with_response(&block)
data = yield
success(data)
rescue => e
Rails.logger.error("#{self.class.name} error: #{e.class} - #{e.message}")
failure(e)
end
end