# Simple self-RAG short form pack
This LlamaPack implements (\*in short form) the [self-RAG paper by Akari et al.](https://arxiv.org/pdf/2310.11511.pdf).
This paper presents a novel framework called Self-Reflective Retrieval-Augmented Generation (SELF-RAG). Which aims to enhance the quality and factuality of large language models (LLMs) by combining retrieval and self-reflection mechanisms.
The implementation is adapted from the author [implementation](https://github.com/AkariAsai/self-rag)
A full notebook guide can be found [here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/self_rag/self_rag.ipynb).
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack SelfRAGPack --download-dir ./self_rag_pack
```
You can then inspect the files at `./self_rag_pack` and use them as a template for your own project!
## Code Usage
We will show you how to import the agent from these files!
The implementation uses llama-cpp, to download the relevant models (be sure to replace DIR_PATH)
```bash
pip3 install -q huggingface-hub
huggingface-cli download m4r1/selfrag_llama2_7b-GGUF selfrag_llama2_7b.q4_k_m.gguf --local-dir "<DIR_PATH>" --local-dir-use-symlinks False
```
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
SelfRAGPack = download_llama_pack("SelfRAGPack", "./self_rag_pack")
```
From here, you can use the pack. You can import the relevant modules from the download folder (in the example below we assume it's a relative import or the directory
has been added to your system path).
```python
from self_rag_pack.base import SelfRAGQueryEngine
query_engine = SelfRAGQueryEngine(
model_path=model_path, retriever=retriever, verbose=True
)
response = query_engine.query(
"Who won best Director in the 1972 Academy Awards?"
)
```
You can also use/initialize the pack directly.
```python
from llm_compiler_agent_pack.base import SelfRAGPack
agent_pack = SelfRAGPack(
model_path=model_path, retriever=retriever, verbose=True
)
```
The `run()` function is a light wrapper around `agent.chat()`.
```python
response = pack.run("Who won best Director in the 1972 Academy Awards?")
```
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"description": "# Simple self-RAG short form pack\n\nThis LlamaPack implements (\\*in short form) the [self-RAG paper by Akari et al.](https://arxiv.org/pdf/2310.11511.pdf).\n\nThis paper presents a novel framework called Self-Reflective Retrieval-Augmented Generation (SELF-RAG). Which aims to enhance the quality and factuality of large language models (LLMs) by combining retrieval and self-reflection mechanisms.\n\nThe implementation is adapted from the author [implementation](https://github.com/AkariAsai/self-rag)\nA full notebook guide can be found [here](https://github.com/run-llama/llama-hub/blob/main/llama_hub/llama_packs/self_rag/self_rag.ipynb).\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 SelfRAGPack --download-dir ./self_rag_pack\n```\n\nYou can then inspect the files at `./self_rag_pack` and use them as a template for your own project!\n\n## Code Usage\n\nWe will show you how to import the agent from these files!\nThe implementation uses llama-cpp, to download the relevant models (be sure to replace DIR_PATH)\n\n```bash\npip3 install -q huggingface-hub\nhuggingface-cli download m4r1/selfrag_llama2_7b-GGUF selfrag_llama2_7b.q4_k_m.gguf --local-dir \"<DIR_PATH>\" --local-dir-use-symlinks False\n```\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nSelfRAGPack = download_llama_pack(\"SelfRAGPack\", \"./self_rag_pack\")\n```\n\nFrom here, you can use the pack. You can import the relevant modules from the download folder (in the example below we assume it's a relative import or the directory\nhas been added to your system path).\n\n```python\nfrom self_rag_pack.base import SelfRAGQueryEngine\n\nquery_engine = SelfRAGQueryEngine(\n model_path=model_path, retriever=retriever, verbose=True\n)\n\nresponse = query_engine.query(\n \"Who won best Director in the 1972 Academy Awards?\"\n)\n```\n\nYou can also use/initialize the pack directly.\n\n```python\nfrom llm_compiler_agent_pack.base import SelfRAGPack\n\nagent_pack = SelfRAGPack(\n model_path=model_path, retriever=retriever, verbose=True\n)\n```\n\nThe `run()` function is a light wrapper around `agent.chat()`.\n\n```python\nresponse = pack.run(\"Who won best Director in the 1972 Academy Awards?\")\n```\n",
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