Welcome to your go-to guide for using ChatLLM, a powerful tool for leveraging large language models (LLMs) in various applications. Let’s walk through the steps to set it up, use it efficiently, and troubleshoot common issues!

Installation

To begin your journey with ChatLLM, you’ll need to install it first. Here’s how you can do it:

pip install -U chatllm

This command will download and install the ChatLLM library, ensuring you have the latest version available.

Usage

Now that you have ChatLLM installed, let’s see how to use it. You can employ it for different functionalities, including question answering and processing files.

1. Question Answering with ChatBase

Imagine ChatBase as your trusty librarian, ready to fetch the answers you seek from a vast library of knowledge. Here’s how to set it up:

from chatllm.applications import ChatBase

qa = ChatBase()
qa.load_llm(model_name_or_path='THUDM/chatglm-6b')

for i in qa(query='', knowledge_base=''):
    print(i, end='')

In this code, you initialize the ChatBase, load a model, and then you can query it for information based on a specific knowledge base. Think of it like asking your librarian a question and receiving answers!

2. Using OpenAI API

If you prefer using OpenAI’s APIs, here’s how to integrate them:

pip install chatllm[openai]
chatllm-run openai --SDK

import openai
openai.api_base = 'http://0.0.0.0:8000/v1'
openai.api_key = 'your_api_key'
prompt = 'Your prompt here'
completion = openai.Completion.create(prompt=prompt, stream=True, model='text-davinci-003')

for c in completion:
    print(c.choices[0].text, end='')

This setup allows you to create prompts and retrieve responses in a streaming manner, similar to having an interactive conversation with an AI assistant.

3. ChatOCR

ChatOCR acts like a magician that transforms images into text. To use it:

from meutils.pipe import *
from chatllm.llmchain.applications import ChatOCR

llm = ChatOCR()
file_path = 'data/invoice.jpg'
llm.display(file_path, 700)
llm.chat(file_path=file_path)

Simply provide it with an image file, and it will perform optical character recognition (OCR) to extract the text content.

4. ChatPDF

ChatPDF allows you to interact with PDF documents effortlessly, transforming static files into dynamic conversations. Here’s how it’s done:

from chatllm.applications.chatpdf import ChatPDF

qa = ChatPDF(encode_model='nghuyongernie-3.0-nano-zh')
qa.load_llm(model_name_or_path='THUDM/chatglm-6b')
qa.create_index('.pdf')

for i in qa(query=''):
    print(i, end='')

Again, just like asking your librarian, you can now query your PDFs and extract useful information with ease!

Troubleshooting

If you encounter any issues while using ChatLLM, consider the following troubleshooting steps:

• Ensure you are using the latest version of ChatLLM by rerunning the install command.
• Check your internet connection if you face issues downloading models or libraries.
• Review your API keys, ensuring they are correct and have the necessary permissions.
• Look for typos in your code. A single character can sometimes make all the difference!

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.