how to use LLM's with LlamaIndex|Tutorial:6

### How to Use LLM Standalone with Llama Index

Welcome back to Total Technology Zone! This is Ronnie, and today we're diving into tutorial 6: "How to Use LLM with Llama Index." In this tutorial, we'll explore how to leverage large language models (LLMs) within the Llama Index framework for standalone tasks such as text completion and chat. We'll demonstrate using an open-source or closed-source LLM for these purposes.

### Objectives

1. **Text Completion:** Using an LLM to complete a given text.
2. **Chat:** Implementing chat functionality with an LLM within Llama Index.

### Steps to Achieve the Objectives

#### Text Completion

1. **Import Necessary Libraries:**
- Import `openAI` module from Llama Index.

2. **Set Up LLM:**
- Configure the LLM (e.g., GPT-4) by setting the model type.

3. **Generate Non-Streaming Response:**
- Use the `complete` method to generate a text completion for a given prompt.

4. **Generate Streaming Response:**
- Implement streaming by using the `stream_complete` method.
- Use a loop to print responses progressively.

#### Chat Functionality

1. **Import Required Modules:**
- Import `chat_message` and `openAI` modules from Llama Index.

2. **Set Up LLM for Chat:**
- Configure the LLM (e.g., GPT-4) for chat purposes.

3. **Define Chat Messages:**
- Create a list of messages defining roles (system and user) and their respective contents.

4. **Generate Chat Response:**
- Use the `chat` method to generate a response to the user message.

### Detailed Steps

#### Text Completion - Non-Streaming

1. **Import Libraries:**

2. **Set Up LLM:**
- `llm = openAI(model='gpt-4')`

3. **Generate Response:**
- `print(response)`

#### Text Completion - Streaming

1. **Set Up LLM with Streaming:**
- `llm = openAI(model='gpt-4')`

2. **Generate Streaming Response:**

#### Chat with LLM

1. **Import Libraries:**

2. **Set Up LLM:**
- `llm = openAI(model='gpt-4')`

3. **Define Messages:**
- `messages = [chat_message(role='system', content='You are a character from Greek mythology'), chat_message(role='user', content='What is your name?')]`

4. **Generate Chat Response:**
- `print(response)`

### Conclusion

In this tutorial, we demonstrated how to use Llama Index to perform text completion and chat using a large language model (LLM) such as GPT-4. We covered both non-streaming and streaming responses, providing a comprehensive understanding of how to integrate and utilize LLMs for standalone tasks.

### Final Notes

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