Denodo AI SDK

Denodo AI SDK helps you quickly build AI chatbots and agents that answer questions using your enterprise data, combining search + generative AI for accurate, context-aware results.

It connects to the Denodo Platform, works with popular LLMs and vector stores, and ships with a ready-to-run sample chatbot and simple APIs to get started fast.

The complete user manual for the Denodo AI SDK is available here.

DeepQuery

Requirements to use Denodo DeepQuery

• A thinking model from either OpenAI/AWS Bedrock/Google Vertex (Not Ollama).
• An minimum allowance of minimum 50RPM OpenAI/AWS Bedrock/Google Vertex.
• Powerful thinking model with over 128k context length.

Installation

1. Delete any previous vector store and virtual environment.
2. Create a new virtual environment (python -m venv venv), activate it (source venv/bin/activate or .\venv\Scripts\activate) and install the requirements.txt (python -m pip install -r requirements.txt)

Configuration

Depending on your LLM provider, here's a guide on how to configure Denodo DeepQuery:

OpenAI (recommended model: o4-mini)

THINKING_PROVIDER=openai
THINKING_MODEL=o4-mini

AWS Bedrock (recommended model: claude-4-sonnet)

THINKING_PROVIDER = bedrock
THINKING_MODEL = us.anthropic.claude-sonnet-4-20250514-v1:0

AWS_CLAUDE_THINKING = 1
AWS_CLAUDE_THINKING_TOKENS = 2048

Please note that AWS Bedrock requires the previously mentioned extra env variables in sdk_config.env to activate thinking.

Google Vertex (recommended model: gemini-2.5-pro)

THINKING_PROVIDER = google
THINKING_MODEL = gemini-2.5-pro

GOOGLE_THINKING = 1
GOOGLE_THINKING_TOKENS = 2048

Please note that Google requires the previously mentioned extra env variables in sdk_config.env to activate thinking.

AI SDK Benchmarks

We test our query-to-SQL pipeline on our propietary benchmark across the whole range of LLMs that we support. The benchmark dataset consists of 20+ questions in the finance sector. You may use this benchmark as reference to choose an LLM model.

Latest update: 03/31/2025 on AI SDK version 0.7

LLM Provider	Model	🎯 Accuracy	🕒 LLM execution time (s)	🔢 Input Tokens	🔡 Output Tokens	💰 Cost per Query
OpenAI	GPT-4o	🟢	3.20	4,230	398	$0.015
OpenAI	GPT-4o Mini	🟡	4.30	4,607	445	$0.001
OpenAI	o1	🟢	18.60	5,110	5,438	$0.403
OpenAI	o1-high	🟢	28.21	3,755	6,220	$0.429
OpenAI	o1-low	🟢	15.75	3,746	2,512	$0.207
OpenAI	o3-mini	🟢	16.61	3,756	2,750	$0.016
OpenAI	o3-mini-high	🟢	28.68	3,764	8,237	$0.040
OpenAI	o3-mini-low	🟢	8.66	3,811	1,080	$0.009
OpenRouter	Amazon Nova Lite	🟡	1.34	4,572	431	<$0.001
OpenRouter	Amazon Nova Micro	🔴	1.29	5,788	668	<$0.001
OpenRouter	Amazon Nova Pro	🟢	2.53	4,522	424	$0.005
OpenRouter	Claude 3.5 Haiku	🟢	4.38	4,946	495	$0.006
OpenRouter	Claude 3.5 Sonnet	🟢	5.02	4,569	435	$0.020
OpenRouter	Claude 3.7 Sonnet	🟢	5.46	4,695	465	$0.021
OpenRouter	Deepseek R1 671b	🟢	40.28	4,138	3,041	$0.011
OpenRouter	Deepseek v3 671b	🟢	13.50	4,042	424	$0.005
OpenRouter	Deepseek v3.1 671b	🟡	12.46	4,910	435	$0.006
OpenRouter	Llama 3.1 8b	🔴	2.98	6,024	752	<$0.001
OpenRouter	Llama 3.1 Nemotron 70b	🟡	9.76	5,110	892	$0.001
OpenRouter	Llama 3.3 70b	🟡	10.46	4,681	402	$0.001
OpenRouter	Microsoft Phi-4 14b	🟢	6.75	4,348	728	<$0.001
OpenRouter	Mistral Small 24b	🟢	5.52	5,537	563	<$0.001
OpenRouter	Qwen 2.5 72b	🟢	6.30	4,874	463	$0.004
Google	Gemini 1.5 Flash	🟡	2.18	4,230	398	<$0.001
Google	Gemini 1.5 Pro	🟢	5.44	4,230	398	$0.007
Google	Gemini 2.0 Flash	🟢	2.42	4,230	398	$0.001

Please note that "Input Tokens" and "Output Tokens" is the average input/output tokens per query. Also, each color corresponds to the following range in terms of accuracy:

• 🟢 = 90%+
• 🟡 = 80–90%
• 🔴 = <80%

Finally, any model with its size in the name, i.e.: Llama 3.1 8b, represents an open-source model.

List of supported LLM providers

The Denodo AI SDK supports the following LLM providers:

• OpenAI
• AzureOpenAI
• Bedrock
• Google
• GoogleAIStudio
• Anthropic
• NVIDIA
• Groq
• Ollama
• Mistral
• SambaNova
• OpenRouter

Where Bedrock refers to AWS Bedrock, NVIDIA refers to NVIDIA NIM and Google refers to Google Vertex AI.

List of supported embedding providers + recommended

• OpenAI (text-embedding-3-large)
• AzureOpenAI (text-embedding-3-large)
• Bedrock (amazon.titan-embed-text-v2:0)
• Google (text-multilingual-embedding-002)
• Ollama (bge-m3)
• Mistral (mistral-embed)
• NVIDIA (baai/bge-m3)
• GoogleAIStudio (gemini-embedding-exp-03-07)

Where Bedrock refers to AWS Bedrock, NVIDIA refers to NVIDIA NIM and Google refers to Google Vertex AI.

Licensing

Please see the file called LICENSE.