https://github.com/run-llama/create_llama_projects

https://github.com/run-llama/create_llama_projects

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/run-llama/create_llama_projects
• Owner: run-llama
• Created: 2023-11-15T08:33:51.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-19T17:15:21.000Z (8 months ago)
• Last Synced: 2024-07-31T08:18:42.109Z (4 months ago)
• Language: HTML
• Size: 45.5 MB
• Stars: 425
• Watchers: 7
• Forks: 80
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

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• awesome - run-llama/create\_llama\_projects - (HTML)

README

        
# create-llama Projects

Here's some fun projects created with the `create-llama` command.

- [`embedded-tables`](embedded-tables/README.md): This project builds an agent that can analyze different data items within the 2021+2020 Tesla 10Q document, including embedded tables.

- [`multi-document-agent`](multi-document-agent/README.md): This project builds an agent that can do advanced analysis/comparisons across multiple documents. It streams intermediate results as they come in.

- [`nextjs-multi-modal`](nextjs-multi-modal/README.md): This example allows you to have a chat using the [GPT4 Vision model](https://platform.openai.com/docs/guides/vision) from OpenAI. You can upload files and ask the model to describe them.

- [`llamaindex-docs-agent`](llamaindex-docs-agent/README.md): This project builds an advanced chatbot over the LlamaIndex TS documentation.

- [`chat-llamaindex`](chat-llamaindex/README.md): Create and share LLM chatbots that know your data (PDF or text documents)

- [`homeai`](homeai/README.md): The AI real estate agent

- [`sec-insights`](sec-insights/README.md): Answer questions about SEC 10-K & 10-Q documents

- [`nextjs-edge-llamaparse`](nextjs-edge-llamaparse/README.md): This is a NextJs example that use Edge Runtime and allows you to parse documents using the LlamaParse

- [`rag-stream-intermediate-events-tutorial`](rag-stream-intermediate-events-tutorial/README.md): Send intermediate steps in a Python RAG pipeline to the frontend for an intuitive UX

- [`nextjs-llama3`](nextjs-llama3/README.md): Use Llama3 with LlamaIndex to create a RAG application