# LLMs

***

A large language model, LLM for short, is a AI system trained on massive amounts of text data. This allows them to communicate and generate human-like text in response to a wide range of prompts and questions. In essence, they can understand and respond to complex language.

### LLM Nodes:

* [AWS Bedrock](https://docs.flowiseai.com/integrations/langchain/llms/aws-bedrock)
* [Azure OpenAI](https://docs.flowiseai.com/integrations/langchain/llms/azure-openai)
* [NIBittensorLLM](https://github.com/FlowiseAI/FlowiseDocs/blob/main/en/integrations/langchain/llms/broken-reference/README.md)
* [Cohere](https://docs.flowiseai.com/integrations/langchain/llms/cohere)
* [GooglePaLM](https://github.com/FlowiseAI/FlowiseDocs/blob/main/en/integrations/langchain/llms/broken-reference/README.md)
* [GoogleVertex AI](https://docs.flowiseai.com/integrations/langchain/llms/googlevertex-ai)
* [HuggingFace Inference](https://docs.flowiseai.com/integrations/langchain/llms/huggingface-inference)
* [Ollama](https://docs.flowiseai.com/integrations/langchain/llms/ollama)
* [OpenAI](https://docs.flowiseai.com/integrations/langchain/llms/openai)
* [Replicate](https://docs.flowiseai.com/integrations/langchain/llms/replicate)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.flowiseai.com/integrations/langchain/llms.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.