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Fix calling the embedding model an LLM

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Kacper Łukawski
2024-04-10 21:16:15 +02:00
parent b97e9644d4
commit 9683c4ad14
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qdrant-landing/content/documentation/tutorials/student-rag-haystack-red-hat-openshift-hc.md
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@@ -20,7 +20,7 @@ To maintain complete data isolation, we need to limit ourselves to open-source t
- **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 RedHat course materials.
- **LLM:** `mistralai/Mistral-7B-Instruct-v0.1`, deployed as a standalone service on OpenShift. - **LLM:** `mistralai/Mistral-7B-Instruct-v0.1`, deployed as a standalone service on OpenShift.
- **Embedding Model:** `BAAI/bge-m3`, lightweight LLM deployed from within the Haystack pipeline. - **Embedding Model:** `BAAI/bge-m3`, lightweight embedding model deployed from within the Haystack pipeline.
- **Vector DB:** [Qdrant Hybrid Cloud](https://qdrant.tech) running on OpenShift. - **Vector DB:** [Qdrant Hybrid Cloud](https://qdrant.tech) running on OpenShift.
- **Framework:** [Haystack 2.x](https://haystack.deepset.ai/) to connect all and [Hayhooks](https://docs.haystack.deepset.ai/docs/hayhooks) to serve the app through HTTP endpoints. - **Framework:** [Haystack 2.x](https://haystack.deepset.ai/) to connect all and [Hayhooks](https://docs.haystack.deepset.ai/docs/hayhooks) to serve the app through HTTP endpoints.