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