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RAG use-case implementation using Llamaindex
Supercharge your RAG pipeline with the following:
- Framework -> [Llamaindex](https://docs.llamaindex.ai/en/stable/index.html)
- Loader -> [SimpleDirectoryLoader](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html)
- Chunking -> [Semantic Chunking](https://docs.llamaindex.ai/en/stable/examples/node_parsers/semantic_chunking.html)
- Embeddings -> [Gemini Embeddings](https://docs.llamaindex.ai/en/stable/examples/node_parsers/semantic_chunking.html)
- Reranking -> [Cohere Rerank Model](https://docs.llamaindex.ai/en/stable/examples/node_postprocessor/CohereRerank.html)
- LLM -> [Groq Mistral](https://docs.llamaindex.ai/en/stable/examples/llm/groq.html#groq)
For index creation follow notebook file ```rag.ipynb```
To run application:
```
pip install -r requirements.txt
chainlit run app.py
```
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