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fix links
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@@ -29,3 +29,8 @@ These tutorials demonstrate different ways you can build vector search into your
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| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
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| [Implement custom connector for Cohere RAG](../tutorials/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
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| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
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| [Chatbot for Interactive Learning](../tutorials/rag-chatbot-red-hat-openshift-haystack/) | Configure Qdrant collections for best resource use. | Qdrant |
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| [Information Extraction Engine](../tutorials/rag-chatbot-vultr-dspy-ollama/) | Configure Qdrant collections for best resource use. | Qdrant |
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| [System for Employee Onboarding](../tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/) | Configure Qdrant collections for best resource use. | Qdrant |
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| [System for Contract Management](../tutorials/rag-contract-management-stackit-aleph-alpha/) | Configure Qdrant collections for best resource use. | Qdrant |
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| [Q/A System for AI Customer Support](../tutorials/orag-customer-support-cohere-airbyte-aws/) | Configure Qdrant collections for best resource use. | Qdrant |
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---
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title: Speak to your website
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title: RAG System for Employee Onboarding
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weight: 30
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---
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# Speak to your website
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# RAG System for Employee Onboarding
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Public websites are a great way to share information with a wide audience. However, finding the right information can be
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challenging, if you are not familiar with the website's structure or the terminology used. That's what the search bar is
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---
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title: Information extraction with DSPy and Ollama
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title: Private RAG Information Extraction Engine
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weight: 32
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---
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# Information extraction with DSPy and Ollama
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# Private RAG Information Extraction Engine
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| Time: 90 min | Level: Advanced | | |
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|--------------|-----------------|--|----|
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---
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title: Automate customer support tasks
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title: Question-Answering System for AI Customer Support
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weight: 26
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---
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# Build a RAG system to answer customer support queries
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# Question-Answering System for AI Customer Support
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| Time: 120 min | Level: Advanced | |
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| --- | ----------- | ----------- |----------- |
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