fix links

This commit is contained in:
davidmyriel
2024-04-10 16:26:19 -07:00
parent b82c8b711d
commit e64e66e683
15 changed files with 20 additions and 15 deletions
@@ -29,3 +29,8 @@ These tutorials demonstrate different ways you can build vector search into your
| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
| [Implement custom connector for Cohere RAG](../tutorials/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
| [Chatbot for Interactive Learning](../tutorials/rag-chatbot-red-hat-openshift-haystack/) | Configure Qdrant collections for best resource use. | Qdrant |
| [Information Extraction Engine](../tutorials/rag-chatbot-vultr-dspy-ollama/) | Configure Qdrant collections for best resource use. | Qdrant |
| [System for Employee Onboarding](../tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/) | Configure Qdrant collections for best resource use. | Qdrant |
| [System for Contract Management](../tutorials/rag-contract-management-stackit-aleph-alpha/) | Configure Qdrant collections for best resource use. | Qdrant |
| [Q/A System for AI Customer Support](../tutorials/orag-customer-support-cohere-airbyte-aws/) | Configure Qdrant collections for best resource use. | Qdrant |
@@ -1,9 +1,9 @@
---
title: Speak to your website
title: RAG System for Employee Onboarding
weight: 30
---
# Speak to your website
# RAG System for Employee Onboarding
Public websites are a great way to share information with a wide audience. However, finding the right information can be
challenging, if you are not familiar with the website's structure or the terminology used. That's what the search bar is
@@ -1,9 +1,9 @@
---
title: Information extraction with DSPy and Ollama
title: Private RAG Information Extraction Engine
weight: 32
---
# Information extraction with DSPy and Ollama
# Private RAG Information Extraction Engine
| Time: 90 min | Level: Advanced | | |
|--------------|-----------------|--|----|
@@ -1,9 +1,9 @@
---
title: Automate customer support tasks
title: Question-Answering System for AI Customer Support
weight: 26
---
# Build a RAG system to answer customer support queries
# Question-Answering System for AI Customer Support
| Time: 120 min | Level: Advanced | |
| --- | ----------- | ----------- |----------- |