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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 | |
``` | |