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fix blog arrangement

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davidmyriel
2024-04-15 16:34:28 -07:00
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commit 944fa97e5a
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qdrant-landing/content/documentation/examples/rag-chatbot-red-hat-openshift-haystack.md
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@@ -21,7 +21,7 @@ With a simple RAG pipeline, you can build a private chatbot. In this tutorial, y
## Components
To maintain complete data isolation, we need to limit ourselves to open-source tools and use them in a private environment, such as [Red Hat OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift). The pipeline will run internally and will be inaccessible from the internet.
- **Dataset:** [Red Hat Interactive Learning Portal](https://developers.redhat.com/learn), an online library of RedHat course materials.
- **Dataset:** [Red Hat Interactive Learning Portal](https://developers.redhat.com/learn), an online library of Red Hat course materials.
- **LLM:** `mistralai/Mistral-7B-Instruct-v0.1`, deployed as a standalone service on OpenShift.
- **Embedding Model:** `BAAI/bge-base-en-v1.5`, lightweight embedding model deployed from within the Haystack pipeline
with [FastEmbed](https://github.com/qdrant/fastembed)
@@ -56,7 +56,7 @@ os.environ["INFERENCE_ENDPOINT_URL"] = "http://mistral-service.default.svc.clust
### Launch Qdrant Hybrid Cloud
Complete **How to Set Up Qdrant on RedHat OpenShift**. When in Hybrid Cloud, your Qdrant instance is private and and its nodes run on the same OpenShift infrastructure as your other components.
Complete **How to Set Up Qdrant on Red Hat OpenShift**. When in Hybrid Cloud, your Qdrant instance is private and and its nodes run on the same OpenShift infrastructure as your other components.
Retrieve your Qdrant URL and API key and store them as environment variables:
@@ -453,7 +453,7 @@ The response should be similar to the one we got in the Python before:
## Next steps
- In this example, [RedHat OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is the infrastructure of choice for proprietary chatbots. [Read more](https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.8) about how to host AI projects in their [extensive documentation](https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.8).
- In this example, [Red Hat OpenShift](https://www.redhat.com/en/technologies/cloud-computing/openshift) is the infrastructure of choice for proprietary chatbots. [Read more](https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.8) about how to host AI projects in their [extensive documentation](https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.8).
- [Haystack's documentation](https://docs.haystack.deepset.ai/docs/kubernetes) describes [how to deploy the Hayhooks service in a Kubernetes
environment](https://docs.haystack.deepset.ai/docs/kubernetes), so you can easily move it to your own OpenShift infrastructure.
qdrant-landing/content/documentation/examples/rag-chatbot-vultr-dspy-ollama.md
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@@ -47,11 +47,7 @@ pip install dspy-ai[qdrant]
### Qdrant Hybrid Cloud
Our documentation contains a comprehensive guide on how to set up Qdrant in the Hybrid Cloud mode on Vultr. Please
follow it carefully to get your Qdrant instance up and running. Once it's done, we need to store the Qdrant URL and the
API key in the environment variables. You can do it by running the following commands:
[//]: # (TODO: add a link to the Qdrant Hybrid Cloud documentation above)
Our [documentation](/documentation/hybrid-cloud/) contains a comprehensive guide on how to set up Qdrant in the Hybrid Cloud mode on Vultr. Please follow it carefully to get your Qdrant instance up and running. Once it's done, we need to store the Qdrant URL and the API key in the environment variables. You can do it by running the following commands:
```shell
export QDRANT_URL="https://qdrant.example.com"
qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md
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@@ -16,8 +16,6 @@ Your support team's expertise is typically kept private, but you can still use A
In this tutorial we will setup a private AI service that answers customer support queries with high accuracy and effectiveness. By leveraging Cohere's powerful models (deployed to [AWS](https://cohere.com/deployment-options/aws)) with Qdrant Hybrid Cloud, you can create a fully private customer support system. Data synchronization, facilitated by [Airbyte](https://airbyte.com/), will complete the setup.
[//]: # (TODO: add a link to the corresponding Qdrant Hybrid Cloud documentation: deployment on AWS)
![Architecture diagram](/documentation/examples/customer-support-cohere-airbyte/architecture-diagram.png)
## System design
@@ -37,9 +35,7 @@ an ingestion pipeline and then a Retrieval Augmented Generation application that
- **RAG:** Cohere [RAG](https://docs.cohere.com/docs/retrieval-augmented-generation-rag) using our knowledge base
through a custom connector
All the selected components are compatible with the [AWS](https://aws.amazon.com/) infrastructure. Thanks to Cohere
models' availability, you can build a fully private customer support system completely isolates data within your
infrastructure. Also, if you have AWS credits, you can now use them without spending additional money on the models or
All the selected components are compatible with the [AWS](https://aws.amazon.com/) infrastructure. Thanks to Cohere models' availability, you can build a fully private customer support system completely isolates data within your infrastructure. Also, if you have AWS credits, you can now use them without spending additional money on the models or
semantic search layer.
### Data ingestion