@convex-dev/rag vs Supabase — Comparison | Unfragile

@convex-dev/rag vs Supabase

Supabase ranks higher at 42/100 vs @convex-dev/rag at 32/100. Capability-level comparison backed by match graph evidence from real search data.

@convex-dev/rag

Framework

/ 100

Free

Supabase

MCP Server

/ 100

Free

Feature	@convex-dev/rag	Supabase
Type	Framework	MCP Server
UnfragileRank	32/100	42/100
Adoption	0	0
Quality	0	0

@convex-dev/rag Capabilities

semantic document embedding and vector storage

Automatically converts documents into dense vector embeddings using configurable embedding models (OpenAI, Anthropic, or local alternatives) and persists them in Convex's serverless database with metadata indexing. The system handles chunking strategies, batch processing, and incremental updates without requiring external vector databases like Pinecone or Weaviate.

Unique: Integrates embedding generation and vector storage directly into Convex's serverless database layer, eliminating the need for external vector DBs and enabling co-location of documents, embeddings, and application state in a single ACID-compliant database

vs alternatives: Simpler than Pinecone/Weaviate for Convex users (no separate infrastructure), but slower than specialized vector DBs for large-scale similarity search due to lack of ANN indexing

semantic similarity search with configurable distance metrics

Executes vector similarity queries against stored embeddings using cosine distance, dot product, or Euclidean distance metrics. Queries are performed via Convex functions that compute similarity scores between a query embedding and all stored document embeddings, returning ranked results with configurable result limits and filtering predicates applied before or after similarity computation.

Unique: Performs similarity search within Convex's transactional database context, allowing atomic combination of vector search with document updates, metadata filtering, and application logic in a single function call without network round-trips to external services

vs alternatives: More integrated with application state than Pinecone (no sync delays), but significantly slower than specialized vector DBs with HNSW/IVF indexing for large-scale searches

document chunking and recursive text splitting

Automatically splits long documents into semantically coherent chunks using configurable strategies (character-based, token-based, or recursive with overlap). The framework handles chunk size limits, overlap windows to preserve context, and metadata propagation so each chunk retains references to the original document and its position, enabling retrieval of full context during RAG synthesis.

Unique: Integrates chunking directly into the Convex RAG pipeline with automatic metadata propagation, so chunks are stored with full lineage information enabling direct retrieval of source documents without separate lookup queries

vs alternatives: Simpler than LangChain's text splitters (no external dependencies), but less sophisticated than semantic chunking approaches that use embeddings to identify natural boundaries

embedding model provider abstraction and switching

Provides a pluggable interface for embedding generation supporting OpenAI, Anthropic, and local/self-hosted models through a unified API. The framework abstracts provider-specific details (API endpoints, authentication, request/response formats) so developers can switch embedding models without changing application code, and handles retries, rate limiting, and error recovery transparently.

Unique: Abstracts embedding provider selection at the Convex function level, allowing different documents or batches to use different embedding models within the same application without architectural changes, and storing provider metadata with embeddings for future re-embedding decisions

vs alternatives: More flexible than LangChain's embedding wrappers (supports Convex-native batching), but requires manual re-embedding when switching models unlike some managed RAG platforms that handle this automatically

rag context retrieval and synthesis integration

Provides utilities to retrieve relevant documents from semantic search results and format them as context for LLM prompts, handling token budgeting, context window management, and integration with LLM APIs (OpenAI, Anthropic, etc.). The framework manages the retrieval-augmented generation loop: query → embed → search → retrieve → format context → call LLM → return answer.

Unique: Orchestrates the complete RAG loop within Convex functions, maintaining document/embedding/LLM state in a single transactional context and enabling atomic updates to conversation history and retrieved context without external workflow engines

vs alternatives: More integrated than LangChain's RAG chains (no separate orchestration layer), but less flexible than frameworks like LlamaIndex for complex retrieval strategies or multi-stage reasoning

incremental document indexing and update handling

Automatically detects document changes and re-embeds only modified documents rather than rebuilding the entire index. The system tracks document versions, timestamps, and change hashes to identify which documents need re-embedding, and handles concurrent updates safely within Convex's transactional guarantees without requiring manual index invalidation or rebuild triggers.

Unique: Leverages Convex's transactional database to track document versions and automatically trigger re-embedding on updates, eliminating the need for external change data capture (CDC) systems or manual index invalidation

vs alternatives: More seamless than Pinecone's upsert operations (automatic change detection), but less sophisticated than specialized search engines with incremental indexing strategies optimized for massive document collections

batch embedding generation with error handling and retries

Processes multiple documents in batches through the embedding API, handling rate limiting, transient failures, and partial failures gracefully. The framework groups documents into optimal batch sizes for the embedding provider, implements exponential backoff retry logic, and tracks which documents succeeded/failed so applications can retry failed embeddings without re-processing successful ones.

Unique: Integrates batch processing directly into Convex functions with automatic retry and error tracking, allowing failed embeddings to be persisted and retried without re-processing the entire batch or losing application state

vs alternatives: Simpler than managing batch jobs with external task queues (no separate infrastructure), but less sophisticated than specialized ETL tools with checkpoint/resume capabilities for massive-scale embedding operations

metadata filtering and hybrid search (semantic + keyword)

Combines semantic similarity search with metadata-based filtering and optional keyword matching to refine results. The framework applies metadata predicates (e.g., 'category=finance AND date>2024') before or after similarity computation, and can optionally incorporate keyword/BM25 scoring alongside vector similarity for hybrid ranking that balances semantic relevance with exact term matches.

Unique: Performs metadata filtering within Convex's query engine before similarity computation, reducing the number of documents to score and enabling efficient combination of structured filtering with semantic ranking in a single database query

vs alternatives: More integrated than Elasticsearch hybrid search (no separate index), but less flexible than Pinecone's metadata filtering for complex boolean queries on high-cardinality fields

Supabase Capabilities

postgresql database query execution via mcp protocol

Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.

Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents

vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration

supabase authentication state inspection and user management

Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.

Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows

vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system

edge function invocation and result streaming

Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.

@convex-dev/rag vs Supabase

@convex-dev/rag Capabilities

Supabase Capabilities

Verdict

Company