Files
sure/app/models/vector_store/registry.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

71 lines
1.8 KiB
Ruby

class VectorStore::Registry
ADAPTERS = {
openai: "VectorStore::Openai",
pgvector: "VectorStore::Pgvector",
qdrant: "VectorStore::Qdrant"
}.freeze
class << self
# Returns the configured adapter instance.
# Reads from VECTOR_STORE_PROVIDER env var, falling back to :openai
# when OpenAI credentials are present.
def adapter
name = adapter_name
return nil unless name
build_adapter(name)
end
def configured?
adapter.present?
end
def adapter_name
explicit = ENV["VECTOR_STORE_PROVIDER"].presence
return explicit.to_sym if explicit && ADAPTERS.key?(explicit.to_sym)
# Default: use OpenAI when credentials are available
:openai if openai_access_token.present?
end
private
def build_adapter(name)
klass = ADAPTERS[name]&.safe_constantize
raise VectorStore::ConfigurationError, "Unknown vector store adapter: #{name}" unless klass
case name
when :openai then build_openai
when :pgvector then build_pgvector
when :qdrant then build_qdrant
else raise VectorStore::ConfigurationError, "No builder defined for adapter: #{name}"
end
end
def build_openai
token = openai_access_token
return nil unless token.present?
VectorStore::Openai.new(
access_token: token,
uri_base: ENV["OPENAI_URI_BASE"].presence || Setting.openai_uri_base
)
end
def build_pgvector
VectorStore::Pgvector.new
end
def build_qdrant
url = ENV.fetch("QDRANT_URL", "http://localhost:6333")
api_key = ENV["QDRANT_API_KEY"].presence
VectorStore::Qdrant.new(url: url, api_key: api_key)
end
def openai_access_token
ENV["OPENAI_ACCESS_TOKEN"].presence || Setting.openai_access_token
end
end
end