mirror of
https://github.com/we-promise/sure.git
synced 2026-04-07 22:34:47 +00:00
* 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>
69 lines
1.9 KiB
Ruby
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
|