# Self-Discover LlamaPack
This LlamaPack implements [Self-Discover: Large Language Models Self-Compose Reasoning Structures](https://arxiv.org/abs/2402.03620) paper.
It has two stages for the given task:
1. STAGE-1:
a. SELECT: Selects subset of reasoning Modules.
b. ADAPT: Adapts selected reasoning modules to the task.
c. IMPLEMENT: It gives reasoning structure for the task.
2. STAGE-2: Uses the generated reasoning structure for the task to generate an answer.
The implementation is inspired from the [codebase](https://github.com/catid/self-discover)
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack SelfDiscoverPack --download-dir ./self_discover_pack
```
You can then inspect the files at `./self_discover_pack` and use them as a template for your own project!
## Code Usage
There are two ways using LlamaPack:
1. Do `download_llama_pack` to load the Self-Discover LlamaPack.
2. Directly use `SelfDiscoverPack`
### Using `download_llama_pack`
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
SelfDiscoverPack = download_llama_pack(
"SelfDiscoverPack", "./self_discover_pack"
)
self_discover_pack = SelfDiscoverPack(verbose=True, llm=llm)
```
### Directly use `SelfRAGPack`
```python
from llama_index.packs.self_discover import SelfDiscoverPack
self_discover_pack = SelfRAGPack(llm=llm, verbose=True)
```
The run() function serves as a concise wrapper that implements the logic outlined in the "self-discover" paper, applying it to a sample task as illustrated below.
`Emma needs to prepare 50 invitations for her upcoming birthday party. She can handwrite 10 invitations in an hour. After working for 2 hours, she takes a break for 30 minutes. If she resumes writing at the same pace, how long will it take her to complete all 50 invitations?`
```python
output = pack.run("<task>")
```
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"description": "# Self-Discover LlamaPack\n\nThis LlamaPack implements [Self-Discover: Large Language Models Self-Compose Reasoning Structures](https://arxiv.org/abs/2402.03620) paper.\n\nIt has two stages for the given task:\n\n1. STAGE-1:\n\n a. SELECT: Selects subset of reasoning Modules.\n\n b. ADAPT: Adapts selected reasoning modules to the task.\n\n c. IMPLEMENT: It gives reasoning structure for the task.\n\n2. STAGE-2: Uses the generated reasoning structure for the task to generate an answer.\n\nThe implementation is inspired from the [codebase](https://github.com/catid/self-discover)\n\n## CLI Usage\n\nYou can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:\n\n```bash\nllamaindex-cli download-llamapack SelfDiscoverPack --download-dir ./self_discover_pack\n```\n\nYou can then inspect the files at `./self_discover_pack` and use them as a template for your own project!\n\n## Code Usage\n\nThere are two ways using LlamaPack:\n\n1. Do `download_llama_pack` to load the Self-Discover LlamaPack.\n2. Directly use `SelfDiscoverPack`\n\n### Using `download_llama_pack`\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nSelfDiscoverPack = download_llama_pack(\n \"SelfDiscoverPack\", \"./self_discover_pack\"\n)\n\nself_discover_pack = SelfDiscoverPack(verbose=True, llm=llm)\n```\n\n### Directly use `SelfRAGPack`\n\n```python\nfrom llama_index.packs.self_discover import SelfDiscoverPack\n\nself_discover_pack = SelfRAGPack(llm=llm, verbose=True)\n```\n\nThe run() function serves as a concise wrapper that implements the logic outlined in the \"self-discover\" paper, applying it to a sample task as illustrated below.\n\n`Emma needs to prepare 50 invitations for her upcoming birthday party. She can handwrite 10 invitations in an hour. After working for 2 hours, she takes a break for 30 minutes. If she resumes writing at the same pace, how long will it take her to complete all 50 invitations?`\n\n```python\noutput = pack.run(\"<task>\")\n```\n",
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