0cacd97950
- Migrate to Hugging Face Inference API for LLM - Add comprehensive workflow examples with multi-step and branching logic - Implement workflow drawing functionality using pyvis - Update notebook with state management and multi-agent workflow demonstrations - Enhance code with more detailed comments and type hinting
47 lines
1.7 KiB
Plaintext
47 lines
1.7 KiB
Plaintext
# Introduction to the LlamaHub
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**LlamaHub is a registry of hundreds of integrations, agents and tools that you can use within LlamaIndex.**
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We will be using various integrations in this course, so let's first look at the LlamaHub and how it can help us.
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Let's see how to find and install the dependencies for the components we need.
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## Installation
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LlamaIndex installation instructions are available as a well-structured **overview on [LlamaHub](https://llamahub.ai/)**.
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This might be a bit overwhelming at first, but most of the **installation commands generally follow an easy-to-remember format**:
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```bash
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pip install llama-index-{component-type}-{framework-name}
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```
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Let's try to install the dependencies for an LLM component using the [Hugging Face inference API integration](https://llamahub.ai/l/llms/llama-index-llms-huggingface-api?from=llms).
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```bash
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pip install llama-index-llms-huggingface-api
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```
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## Usage
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Once installed, we can see the usage patterns. You'll notice that the import paths follow the install command!
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Underneath, we can see an example of the usage of **the Hugging Face inference API for an LLM component**.
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```python
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from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
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llm = HuggingFaceInferenceAPI(
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model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
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temperature=0.7,
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max_tokens=100,
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token="hf_xxx",
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)
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llm.complete("Hello, how are you?")
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# I am good, how can I help you today?
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```
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Wonderful, we now know how to find, install and use the integrations for the components we need.
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**Let's dive deeper into the components** and see how we can use them to build our own agents.
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