Support matrix

This page lists specific model variants that EDB has tested and confirmed to work with AIDB, split into Local models (run on your Postgres host) and Remote models (called over the network — NVIDIA NIM microservices, plus OpenAI and Anthropic model families for AIDB’s native agentic adapters). Each table follows the same shape: a model identifier, whether it’s a default, and a description of what the model is good for — use the description to pick the right variant for your use case.

Local models

Default models

AIDB registers these models automatically when the extension is created — no configuration needed to start using them. See Default models for how to use them, and Model providers for what each provider means.

Model name	Provider	Description
bert	bert_local (Candle)	General-purpose English text embeddings. Good default for most text search and RAG use cases.
clip	clip_local (Candle)	Joint text/image embeddings for multimodal search.
t5	t5_local (Candle)	Lightweight text-to-text generation — translation, summarization, question answering.
llama	llama_instruct_local (Candle)	Instruction-following local chat/completion model.
bge-small-en-v1.5-f16	llamacpp_embeddings (llama.cpp)	Compact, fast English text embeddings.
nomic-embed-text-v1.5-Q8_0	llamacpp_embeddings (llama.cpp)	General-purpose English embeddings with a larger context window (2048 tokens) than bge-small-en-v1.5-f16.
bge-m3-f16	llamacpp_embeddings (llama.cpp)	Multilingual embeddings with an 8192-token context window — good for long or non-English documents.
qwen3-embedding-0.6b-Q8_0	llamacpp_embeddings (llama.cpp)	Larger embedding model with a 16384-token context window.
qwen3-embedding-4b-Q8_0	llamacpp_embeddings (llama.cpp)	Largest pre-registered embedding model (20480-token context) — higher quality, more compute.
qwen3.5-0.8b-Q8_0	llamacpp_generate (llama.cpp)	Small, fast chat/completion model with a very large context window (262144 tokens).
llama-3.2-1b-instruct-Q8_0	llamacpp_generate (llama.cpp)	Compact Meta Llama 3.2 instruction model for lightweight local chat/completion.
lightonocr-2-1b-Q8_0	llamacpp_ocr (llama.cpp)	Compact vision-language model for OCR — extracts text from images.
dummy	dummy	Returns fake data — for testing pipelines without running real inference.

Other tested variants (Candle)

The following HuggingFace model variants have been tested and verified to work with AIDB’s Candle-based local providers, beyond the defaults above. Use any of these identifiers with the corresponding config helper when calling aidb.create_model() — see Custom models .

BERT (bert_local )

Model identifier	Default	Description
sentence-transformers/all-MiniLM-L6-v2	OK	General-purpose English sentence embeddings; fast and small. This is what the default bert model uses.
sentence-transformers/paraphrase-multilingual-mpnet-base-v2	—	Multilingual embeddings — use when your text isn't (only) English.
sentence-transformers/multi-qa-MiniLM-L6-cos-v1	—	Tuned for question-answering and retrieval, rather than general sentence similarity.
sentence-transformers/paraphrase-TinyBERT-L6-v2	—	Smaller and faster than the default, at some quality cost.

SELECT aidb.create_model(my_bert, bert_local,
    config => aidb.bert_config(sentence-transformers/paraphrase-multilingual-mpnet-base-v2));

All BERT variants support text embedding (aidb.encode_text() ).

CLIP (clip_local )

Model identifier	Default	Description
openai/clip-vit-base-patch32	OK	The only tested CLIP variant. Joint text/image embeddings. This is what the default clip model uses.

CLIP supports both text embedding (aidb.encode_text() ) and image embedding (aidb.encode_image() ).

T5 (t5_local )

Model identifier	Default	Description
t5-small	OK	Fastest, smallest T5 variant. This is what the default t5 model uses.
t5-base	—	Larger than the default — better quality, slower.
t5-large	—	Largest T5 variant — highest quality, most resource use.

T5 variants support both text encoding (aidb.encode_text() ) and text generation (aidb.generate_text() ).

Llama (llama_instruct_local )

Model identifier	Default	Description
TinyLlama/TinyLlama-1.1B-Chat-v1.0	OK	Small, fast instruction-following chat model. This is what the default llama model uses.
HuggingFaceTB/SmolLM2-135M-Instruct	—	Extremely small and fast — lower quality, lowest resource use of these variants.
HuggingFaceTB/SmolLM2-360M-Instruct	—	Small — a step up in quality from the 135M variant.
HuggingFaceTB/SmolLM2-1.7B-Instruct	—	Largest of these variants — better quality than the default, at higher resource use.

All Llama variants support text generation (aidb.generate_text() ).

!!!note These are the pre-vetted variants confirmed to work with AIDB. You aren’t limited to these — any compatible HuggingFace model can be used with the appropriate config helper, but only the variants listed here have been tested by EDB.

Other tested variants (llama.cpp)

Reranking (llamacpp_reranking )

Model identifier	Default	Architecture	Description
gpustack/bge-reranker-v2-m3-GGUF	—	Cross-encoder	Multilingual cross-encoder reranker. Scores each query/document pair directly via a classification head.
ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF	—	Decoder-only	Qwen3-Reranker family. Scores each pair by reading the model's yes/no verdict-token logits — see llamacpp_reranking .

Neither variant is pre-registered as a default model. Use either identifier with jsonb_build_object() when calling aidb.create_model() — see llamacpp_reranking .

Remote models

Remote models are called over the network rather than run on your Postgres host: NVIDIA NIM microservices and chat/agent models from OpenAI and Anthropic — reachable through several providers, including the native openai_responses and anthropic_messages adapters.

NVIDIA NIM

AIDB supports NVIDIA NIM microservices via several providers — see NVIDIA NIM for how to register one. Models not pre-deployed by Hybrid Manager’s Model Serving can be self-hosted using KServe and registered with AIDB using aidb.create_model() .

For the full list of supported NIM models per provider (completions, embeddings, CLIP, OCR, and reranking), including which are pre-deployed by EDB Hybrid Manager’s Model Serving, see GPU recommendations for default NIM models .

OpenAI and Anthropic models

Each model identifier below can be registered under more than one AIDB provider — which one to pick depends on how you’re using the model, not which model it is:

• ``openai_responses`` (+ openai_responses_azure ) and ``anthropic_messages`` (+ anthropic_messages_azure /anthropic_messages_bedrock ) — AIDB’s native, agent-ready providers. Real tools /tool_choice /response_format support, both for direct calls and for agents created with Managing agents .

• ``openai_completions`` — OpenAI’s older Chat Completions API. Works with the same OpenAI models, and is a perfectly good choice for plain text generation (aidb.generate_text() , summarization, pipeline steps), but rejects tools /tool_choice /response_format directly, and only simulates tool calling (via prompt injection) when backing an agent.

• ``openrouter_chat`` — a single gateway to OpenAI, Anthropic, and other vendors’ models (including everything below) via OpenRouter’s own model slugs, for example openai/gpt-4o or anthropic/claude-opus-4-6 . Same simulated, not native, tool calling as openai_completions .

See OpenAI Responses API and Anthropic Messages API for the full comparison and registration examples. The tables below aren’t an exhaustive catalog of every model each vendor offers; they call out commonly used families as a starting point, covering both current flagship/efficient tiers and a longer-established variant for reference — check the vendor’s own model documentation for the full, current lineup and exact capabilities.

OpenAI

Model identifier	Native provider(s)	Also usable via	Cloud host support (native)	Description
gpt-5.6-sol / -terra / -luna	openai_responses, openai_responses_azure	openai_completions, openrouter_chat	Direct, Azure AI Foundry	The GPT-5.6 family, offered in three tiers trading off capability, latency, and cost. Check OpenAI's docs for how the tiers compare.
gpt-5.4-mini	openai_responses, openai_responses_azure	openai_completions, openrouter_chat	Direct, Azure AI Foundry	Smaller, cost-efficient tier of the GPT-5.4 generation — solid tool-calling accuracy at a fraction of flagship cost.
gpt-4o	openai_responses, openai_responses_azure	openai_completions, openrouter_chat	Direct, Azure AI Foundry	Long-established multimodal model. A stable, well-known anchor when you don't need the newest GPT-5.x tier.

Anthropic

Model identifier	Native provider(s)	Also usable via	Cloud host support (native)	Description
claude-opus-4-6	anthropic_messages, anthropic_messages_azure, anthropic_messages_bedrock	openrouter_chat	Direct, Azure AI Foundry, AWS Bedrock	Flagship Claude model for the most demanding reasoning and agentic tool-calling tasks.
claude-sonnet-5	anthropic_messages, anthropic_messages_azure, anthropic_messages_bedrock	openrouter_chat	Direct, Azure AI Foundry, AWS Bedrock	Balanced mid-tier model — strong general-purpose choice for agents and everyday chat/completion.
claude-haiku-4-5	anthropic_messages, anthropic_messages_azure, anthropic_messages_bedrock	openrouter_chat	Direct, Azure AI Foundry, AWS Bedrock	Fastest, most cost-efficient current Claude tier — good for high-volume or latency-sensitive tool calls.
claude-3-5-haiku	anthropic_messages, anthropic_messages_azure, anthropic_messages_bedrock	openrouter_chat	Direct, Azure AI Foundry, AWS Bedrock	Long-established, widely used variant. A stable, well-known anchor when you don't need the newest Claude tier.

Anthropic has no older completions-style provider the way OpenAI has openai_completions — anthropic_messages is Claude’s only direct AIDB provider, alongside the openrouter_chat gateway option.

!!!note A model’s availability on Azure AI Foundry or AWS Bedrock can lag behind its release on the vendor’s own direct API, especially for very recently released tiers. If openai_responses_azure or anthropic_messages_bedrock rejects a model you expect to work, confirm current availability with the respective cloud provider.