The provider-agnostic vector store stack (VectorStore::Pgvector + the
Embeddable concern) already shipped to main. This PR closes the
Anthropic loop:
- VectorStore::Registry.adapter_name now returns :pgvector when
Setting.llm_provider == "anthropic" and no explicit
VECTOR_STORE_PROVIDER override is set. Anthropic has no hosted vector
store, so falling back to the local pgvector adapter is the only
correct default. Explicit VECTOR_STORE_PROVIDER still wins.
- SearchFamilyFiles surfaces a longer message when no adapter is wired
up — calling out pgvector + EMBEDDING_URI_BASE as the supported
Anthropic-only path so the user is not stuck with an "OpenAI required"
hint that is no longer accurate.
The Embeddable concern already pulls embeddings from
EMBEDDING_URI_BASE / EMBEDDING_ACCESS_TOKEN (with OpenAI as fallback),
so Anthropic installs point this at Voyage AI, a local Ollama instance,
or OpenAI embeddings — independent of the chat provider.
Tests cover the new default routing, the existing OpenAI default
staying intact, and explicit VECTOR_STORE_PROVIDER overriding the
Anthropic default.
Stacked on #1985 (PR 3/5). 5/5 settings UI + retention disclosure next.
* Add conditional migration for vector_store_chunks table
Creates the pgvector-backed chunks table when VECTOR_STORE_PROVIDER=pgvector.
Enables the vector extension, adds store_id/file_id indexes, and uses
vector(1024) column type for embeddings.
* Add VectorStore::Embeddable concern for text extraction and embedding
Shared concern providing extract_text (PDF via pdf-reader, plain-text as-is),
paragraph-boundary chunking (~2000 chars, ~200 overlap), and embed/embed_batch
via OpenAI-compatible /v1/embeddings endpoint using Faraday. Configurable via
EMBEDDING_MODEL, EMBEDDING_URI_BASE, with fallback to OPENAI_* env vars.
* Implement VectorStore::Pgvector adapter with raw SQL
Replaces the stub with a full implementation using
ActiveRecord::Base.connection with parameterized binds. Supports
create_store, delete_store, upload_file (extract+chunk+embed+insert),
remove_file, and cosine-similarity search via the <=> operator.
* Add registry test for pgvector adapter selection
* Configure pgvector in compose.example.ai.yml
Switch db image to pgvector/pgvector:pg16, add VECTOR_STORE_PROVIDER,
EMBEDDING_MODEL, and EMBEDDING_DIMENSIONS env vars, and include
nomic-embed-text in Ollama's pre-loaded models.
* Update pgvector docs from scaffolded to ready
Document env vars, embedding model setup, pgvector Docker image
requirement, and Ollama pull instructions.
* Address PR review feedback
- Migration: remove env guard, use pgvector_available? check so it runs
on plain Postgres (CI) but creates the table on pgvector-capable servers.
Add NOT NULL constraints on content/embedding/metadata, unique index on
(store_id, file_id, chunk_index).
- Pgvector adapter: wrap chunk inserts in a DB transaction to prevent
partial file writes. Override supported_extensions to match formats
that extract_text can actually parse.
- Embeddable: add hard_split fallback for paragraphs exceeding CHUNK_SIZE
to avoid overflowing embedding model token limits.
* Bump schema version to include vector_store_chunks migration
CI uses db:schema:load which checks the version — without this bump,
the migration is detected as pending and tests fail to start.
* Update 20260316120000_create_vector_store_chunks.rb
---------
Co-authored-by: sokiee <sokysrm@gmail.com>
* 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>
