|
|
--- |
|
|
title: LongCePO Chatbot (Sambanova) |
|
|
emoji: 🤖 |
|
|
colorFrom: blue |
|
|
colorTo: green |
|
|
sdk: gradio |
|
|
sdk_version: 5.27.1 |
|
|
app_file: app.py |
|
|
pinned: false |
|
|
--- |
|
|
|
|
|
# LongCePO Chatbot with Sambanova Backend |
|
|
|
|
|
This is a simple chatbot interface demonstrating the LongCePO (Long-Context Planning and Optimization) method using a Sambanova model (`Llama-4-Maverick-17B-128E-Instruct`) as the backend LLM. |
|
|
|
|
|
## How it works |
|
|
|
|
|
The LongCePO method is designed to handle long contexts (potentially millions of tokens) by: |
|
|
1. **Planning:** Decomposing the initial query into sub-questions. |
|
|
2. **MapReduce:** Answering each sub-question by processing chunks of the long context, summarizing relevant information, and aggregating results. |
|
|
|
|
|
This application takes a long text context and a query based on that context. It then uses the modified `longcepo` plugin (originally from the `optillm` repository) to generate an answer using the Sambanova API. |
|
|
|
|
|
## How to use |
|
|
|
|
|
1. **(Optional)** Enter a system prompt to guide the chatbot's behavior. |
|
|
2. Paste the long text document into the **Context** box. |
|
|
3. Enter your question based on the provided context into the **Query** box. |
|
|
4. Click **Submit**. |
|
|
|
|
|
The chatbot will process the request using the LongCePO pipeline and display the final answer. |
|
|
|
|
|
**Note:** Processing long contexts can take some time depending on the length of the context and the complexity of the query. |
|
|
|
|
|
## API Key |
|
|
|
|
|
This application requires a Sambanova API key to function. The key should be stored as a Hugging Face Space Secret named `SAMBANOVA_API_KEY`. |
|
|
|
