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Fix code formatting and add tutorial to the list

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Kacper Łukawski
2024-03-22 11:21:21 +01:00
parent 190edef81a
commit 1c1691c5f6
2 changed files with 18 additions and 17 deletions
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qdrant-landing/content/documentation/tutorials/_index.md
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@@ -12,19 +12,20 @@ aliases:
These tutorials demonstrate different ways you can build vector search into your applications.
| Tutorial | Description | Stack |
|----------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
| [Configure Optimal Use](../tutorials/optimize/) | Configure Qdrant collections for best resource use. | Qdrant |
| [Separate Partitions](../tutorials/multiple-partitions/) | Serve vectors for many independent users. | Qdrant |
| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
| [Aleph Alpha Search](../tutorials/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
| [Mighty Semantic Search](../tutorials/mighty/) | Build a simple semantic search with an on-demand NLP service. | Qdrant, Mighty |
| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
| [Multitenancy with LlamaIndex](../tutorials/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
| [HuggingFace datasets](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
| [Measure retrieval quality](../tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
| Tutorial | Description | Stack |
|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------|--------------------------------------------------------------------------|
| [Configure Optimal Use](../tutorials/optimize/) | Configure Qdrant collections for best resource use. | Qdrant |
| [Separate Partitions](../tutorials/multiple-partitions/) | Serve vectors for many independent users. | Qdrant |
| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
| [Aleph Alpha Search](../tutorials/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
| [Mighty Semantic Search](../tutorials/mighty/) | Build a simple semantic search with an on-demand NLP service. | Qdrant, Mighty |
| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
| [Multitenancy with LlamaIndex](../tutorials/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
| [HuggingFace datasets](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
| [Measure retrieval quality](../tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
| [Personalize learning experience with RAG](../tutorials/student-rag-haystack-red-hat-openshift-hc/) | Speak to the course materials as if you were speaking to the teacher | Qdrant, Red Hat OpenShift, Haystack, Hugging Face, Mistral, Hybrid Cloud |
| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
qdrant-landing/content/documentation/tutorials/student-rag-haystack-red-hat-openshift-hc.md
+1 -1
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@@ -380,7 +380,7 @@ search_pipeline.connect("prompt_builder.prompt", "llm.prompt")
The `PromptBuilder` is a Jinja2 template that will be filled with the documents and the query. The
`HuggingFaceTGIGenerator` connects to the LLM service and generates the answer. Let's run the pipeline again:
```python[search-pipeline.yaml](..%2F..%2F..%2F..%2F..%2F..%2F..%2F..%2F..%2Ftmp%2Fsearch-pipeline.yaml)
```python
query = "How to install an application using the OpenShift web console?"
response = search_pipeline.run(data={