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fix: Internal link checker, Use abs links (#1220)

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* fix: links in hybrid-queries.md

* refactor: Use abs links

* fix: Check internal links

* ci: Rename job
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2024-10-05 00:09:41 +02:00
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qdrant-landing/content/documentation/examples/_index.md
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| End-to-End Code Samples | Description | Stack |
|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
| [Multitenancy with LlamaIndex](../examples/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
| [Implement custom connector for Cohere RAG](../examples/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
| [Chatbot for Interactive Learning](../examples/rag-chatbot-red-hat-openshift-haystack/) | Build a Private RAG Chatbot for Interactive Learning | Qdrant, Haystack, OpenShift |
| [Information Extraction Engine](../examples/rag-chatbot-vultr-dspy-ollama/) | Build a Private RAG Information Extraction Engine | Qdrant, Vultr, DSPy, Ollama |
| [System for Employee Onboarding](../examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/) | Build a RAG System for Employee Onboarding | Qdrant, Cohere, LangChain |
| [System for Contract Management](../examples/rag-contract-management-stackit-aleph-alpha/) | Build a Region-Specific RAG System for Contract Management | Qdrant, Aleph Alpha, STACKIT |
| [Question-Answering System for Customer Support](../examples/rag-customer-support-cohere-airbyte-aws/) | Build a RAG System for AI Customer Support | Qdrant, Cohere, Airbyte, AWS |
| [Hybrid Search on PDF Documents](../examples/hybrid-search-llamaindex-jinaai/) | Develop a Hybrid Search System for Product PDF Manuals | Qdrant, LlamaIndex, Jina AI
| [Blog-Reading RAG Chatbot](../examples/rag-chatbot-scaleway) | Develop a RAG-based Chatbot on Scaleway and with LangChain | Qdrant, LangChain, GPT-4o
| [Movie Recommendation System](../examples/recommendation-system-ovhcloud/) | Build a Movie Recommendation System with LlamaIndex and With JinaAI | Qdrant |
| [Multitenancy with LlamaIndex](/documentation/examples/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
| [Implement custom connector for Cohere RAG](/documentation/examples/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
| [Chatbot for Interactive Learning](/documentation/examples/rag-chatbot-red-hat-openshift-haystack/) | Build a Private RAG Chatbot for Interactive Learning | Qdrant, Haystack, OpenShift |
| [Information Extraction Engine](/documentation/examples/rag-chatbot-vultr-dspy-ollama/) | Build a Private RAG Information Extraction Engine | Qdrant, Vultr, DSPy, Ollama |
| [System for Employee Onboarding](/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/) | Build a RAG System for Employee Onboarding | Qdrant, Cohere, LangChain |
| [System for Contract Management](/documentation/examples/rag-contract-management-stackit-aleph-alpha/) | Build a Region-Specific RAG System for Contract Management | Qdrant, Aleph Alpha, STACKIT |
| [Question-Answering System for Customer Support](/documentation/examples/rag-customer-support-cohere-airbyte-aws/) | Build a RAG System for AI Customer Support | Qdrant, Cohere, Airbyte, AWS |
| [Hybrid Search on PDF Documents](/documentation/examples/hybrid-search-llamaindex-jinaai/) | Develop a Hybrid Search System for Product PDF Manuals | Qdrant, LlamaIndex, Jina AI
| [Blog-Reading RAG Chatbot](/documentation/examples/rag-chatbot-scaleway/) | Develop a RAG-based Chatbot on Scaleway and with LangChain | Qdrant, LangChain, GPT-4o
| [Movie Recommendation System](/documentation/examples/recommendation-system-ovhcloud/) | Build a Movie Recommendation System with LlamaIndex and With JinaAI | Qdrant |
## Notebooks
qdrant-landing/content/documentation/examples/mighty.md
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}
```
For Qdrant, follow our [cloud documentation](../../cloud/cloud-quick-start/) to spin up a [free tier](https://cloud.qdrant.io/). Make sure to retrieve an API key.
For Qdrant, follow our [cloud documentation](/documentation/cloud/cloud-quick-start/) to spin up a [free tier](https://cloud.qdrant.io/). Make sure to retrieve an API key.
## Implement model API
qdrant-landing/content/documentation/examples/rag-contract-management-stackit-aleph-alpha.md
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Then, we can glue the components together and build the search process. `RetrievalQA` is a class that takes implements
the Question Retrieval process, with a specified retriever and Large Language Model. The instance of `Qdrant` might be
converted into a retriever, with additional filter that will be passed to the `similarity_search` method. The filter
is created as [in a regular Qdrant query](../../../documentation/concepts/filtering/), with the `roles` field set to the
is created as [in a regular Qdrant query](/documentation/concepts/filtering/), with the `roles` field set to the
user's roles.
```python
qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md
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## RAG connector
One of our previous tutorials, guides you step-by-step on [implementing custom connector for Cohere
RAG](../cohere-rag-connector/) with Cohere Embed v3 and Qdrant. You can just point it to use your Hybrid Cloud
RAG](documentation/examples/cohere-rag-connector/) with Cohere Embed v3 and Qdrant. You can just point it to use your Hybrid Cloud
Qdrant instance running on AWS. Created connector might be deployed to Amazon Web Services in various ways, even in a
[Serverless](https://aws.amazon.com/serverless/) manner using [AWS
Lambda](https://aws.amazon.com/lambda/?c=ser&sec=srv).
In general, RAG connector has to expose a single endpoint that will accept POST requests with `query` parameter and
return the matching documents as JSON document with a specific structure. Our FastAPI implementation created [in the
related tutorial](../cohere-rag-connector/) is a perfect fit for this task. The only difference is that you
related tutorial](documentation/examples/cohere-rag-connector/) is a perfect fit for this task. The only difference is that you
should point it to the Cohere models and Qdrant running on AWS infrastructure.
> Our connector is a lightweight web service that exposes a single endpoint and glues the Cohere embedding model with