SQL-native LLM functions, Cortex Search, Analyst, and Agents inside Snowflake
Snowflake documents Cortex as a suite of managed AI features in docs.snowflake.com: Cortex AI Functions (AI_COMPLETE, AI_EXTRACT, AI_SENTIMENT, AI_TRANSLATE, and related SQL/Python surfaces) run unstructured analytics on text and images with models from OpenAI, Anthropic, Meta, Mistral AI, and DeepSeek subject to CORTEX_USER privileges. Cortex Search provides hybrid retrieval for RAG, Cortex Analyst turns natural-language questions into SQL over semantic models, and Cortex Agents orchestrate Analyst, Search, custom UDFs, and optional web search via agent APIs. Cortex Code adds CLI and Snowsight assistance for data engineering workflows, and Snowflake documents MCP support within the Cortex Code tooling hierarchy.
Use cases
- Run governed LLM tasks directly on warehouse tables without exporting data
- Expose semantic-model-backed chat analytics to business users through Analyst APIs
- Build agentic apps that ground answers in Snowflake Search indexes and SQL tools
- Estimate token spend with AI_COUNT_TOKENS before promoting prompts to production
- Pair Cortex features with Snowflake-managed MCP servers for IDE assistants
Key features
- Cortex AI Functions documented for completions, extraction, sentiment, translation, and PII redaction via SQL
- Cortex Search hybrid engine for low-latency RAG over unstructured enterprise data
- Cortex Analyst REST API converting natural language to SQL using semantic model YAML
- Cortex Agents combining Analyst, Search, stored procedures, and LLM orchestration
- Model lifecycle stages (Preview, GA, Legacy, EOL) and role-gated model access per Snowflake AI docs
Who Is It For?
- Data engineers shipping AI features without separate vector stacks
- Analytics teams needing natural-language access to curated semantic models
- Enterprises requiring RBAC-aligned AI inside existing Snowflake governance
Frequently Asked Questions
- Which role grants Cortex access?
- Snowflake docs reference SNOWFLAKE.CORTEX_USER and feature-specific roles such as CORTEX_AGENT_USER for Agents.
- Can I call Cortex from SQL only?
- Docs list SQL functions and Python interfaces; Analyst and Agents also expose REST APIs documented separately.
- How are models selected?
- Snowflake documents multiple vendor models with regional availability and model-level RBAC; some functions support cross-region inference when a model is unavailable locally.
Related
Related
3 Indexed items
Chroma
Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.
Voyage AI
Voyage AI documents state-of-the-art embedding models and rerankers at docs.voyageai.com for retrieval-augmented generation and semantic search. The embeddings API at POST https://api.voyageai.com/v1/embeddings converts text into dense vectors; the Python `voyageai` package exposes `voyageai.Client.embed()` with models such as voyage-4-large, voyage-4, voyage-4-lite, voyage-code-3, and domain-specific variants per docs.voyageai.com/docs/embeddings. Docs describe `input_type` query vs document prompts for retrieval tasks, optional `output_dimension` (256–2048), and `output_dtype` quantization (float, int8, binary). Rerankers score query–document relevance to refine initial retrieval results. Voyage AI is part of MongoDB's AI stack (MongoDB docs reference Voyage embeddings) and integrates with vector stores and LLMs as modular RAG components.
AssemblyAI
AssemblyAI documents Voice AI APIs at assemblyai.com/docs where developers transcribe and analyze audio via REST at `https://api.assemblyai.com` and real-time WebSockets at `wss://streaming.assemblyai.com` (EU pre-recorded host `api.eu.assemblyai.com` per cloud residency docs). Pre-recorded transcription requires an explicit `speech_models` array on every `POST /v2/transcript` request—docs recommend `universal-3-pro` with `universal-2` fallback for 99-language coverage. The platform also publishes a Voice Agent API for speech-to-speech agents, Speech Understanding features (diarization, sentiment, summarization), Guardrails, and an LLM Gateway to run frontier models on transcripts.