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* 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>
119 lines
2.9 KiB
Ruby
119 lines
2.9 KiB
Ruby
class Assistant::Function::SearchFamilyFiles < Assistant::Function
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class << self
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def name
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"search_family_files"
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end
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def description
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<<~DESC
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Search through documents that the family has uploaded to their financial document store.
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Use this when the user asks questions about their uploaded financial documents such as
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tax returns, bank statements, contracts, insurance policies, investment reports, or any
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other files they've imported.
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Returns relevant excerpts from matching documents along with the source filename and
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a relevance score.
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Supported file types include: PDF, DOCX, XLSX, PPTX, TXT, CSV, JSON, XML, HTML, MD,
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and common source code formats.
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Example:
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```
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search_family_files({
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query: "What was the total income on my 2024 tax return?"
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})
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```
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DESC
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end
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end
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def strict_mode?
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false
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end
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def params_schema
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build_schema(
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required: [ "query" ],
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properties: {
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query: {
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type: "string",
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description: "The search query to find relevant information in the family's uploaded documents"
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},
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max_results: {
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type: "integer",
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description: "Maximum number of results to return (default: 10, max: 20)"
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}
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}
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)
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end
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def call(params = {})
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query = params["query"]
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max_results = (params["max_results"] || 10).to_i.clamp(1, 20)
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unless family.vector_store_id.present?
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return {
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success: false,
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error: "no_documents",
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message: "No documents have been uploaded to the family document store yet."
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}
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end
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adapter = VectorStore.adapter
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unless adapter
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return {
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success: false,
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error: "provider_not_configured",
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message: "No vector store is configured. Set VECTOR_STORE_PROVIDER or configure OpenAI."
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}
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end
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response = adapter.search(
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store_id: family.vector_store_id,
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query: query,
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max_results: max_results
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)
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unless response.success?
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return {
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success: false,
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error: "search_failed",
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message: "Failed to search documents: #{response.error&.message}"
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}
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end
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results = response.data
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if results.empty?
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return {
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success: true,
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results: [],
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message: "No matching documents found for the query."
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}
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end
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{
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success: true,
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query: query,
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result_count: results.size,
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results: results.map do |result|
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{
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content: result[:content],
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filename: result[:filename],
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score: result[:score]
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}
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end
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}
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rescue => e
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Rails.logger.error("SearchFamilyFiles error: #{e.class.name} - #{e.message}")
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{
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success: false,
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error: "search_failed",
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message: "An error occurred while searching documents: #{e.message.truncate(200)}"
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}
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end
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end
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