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Kacper Łukawski
@@ -12,21 +12,21 @@ aliases:
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These tutorials demonstrate different ways you can build vector search into your applications.
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| Tutorial | Description | Stack |
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|---------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|--------------------------------------------------|
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| [Configure Optimal Use](../tutorials/optimize/) | Configure Qdrant collections for best resource use. | Qdrant |
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| [Separate Partitions](../tutorials/multiple-partitions/) | Serve vectors for many independent users. | Qdrant |
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| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
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| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
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| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
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| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
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| [Aleph Alpha Search](../tutorials/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
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| [Mighty Semantic Search](../tutorials/mighty/) | Build a simple semantic search with an on-demand NLP service. | Qdrant, Mighty |
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| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
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| [Multitenancy with LlamaIndex](../tutorials/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
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| [HuggingFace datasets](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
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| [Measure retrieval quality](../tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
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| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
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| [Implement custom connector for Cohere RAG](../tutorials/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
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| [Automate customer support](../tutorials/customer-support-oci-cohere-airbyte/) | Unleash your customer support team from answering the same questions over and over again | Qdrant, Cohere, Command-R, Oracle Cloud, Airbyte |
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| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
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| Tutorial | Description | Stack |
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|---------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|---------------------------------------------|
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| [Configure Optimal Use](../tutorials/optimize/) | Configure Qdrant collections for best resource use. | Qdrant |
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| [Separate Partitions](../tutorials/multiple-partitions/) | Serve vectors for many independent users. | Qdrant |
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| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
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| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
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| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
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| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
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| [Aleph Alpha Search](../tutorials/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
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| [Mighty Semantic Search](../tutorials/mighty/) | Build a simple semantic search with an on-demand NLP service. | Qdrant, Mighty |
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| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
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| [Multitenancy with LlamaIndex](../tutorials/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
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| [HuggingFace datasets](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
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| [Measure retrieval quality](../tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
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| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
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| [Implement custom connector for Cohere RAG](../tutorials/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
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| [Automate customer support](../tutorials/customer-support-cohere-airbyte/) | Unleash your customer support team from answering the same questions over and over again | Qdrant, Cohere, Command-R, Airbyte |
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| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
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+3
-5
@@ -43,8 +43,6 @@ an ingestion pipeline and then a Retrieval Augmented Generation application that
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- **RAG:** Cohere [RAG](https://docs.cohere.com/docs/retrieval-augmented-generation-rag) using our knowledge base
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through a custom connector
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[//]: # (TODO: the dataset has to be published somewhere)
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All the selected components might be running on [Oracle Cloud](https://www.oracle.com/cloud/) infrastructure only. Thanks to the availability of
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the Cohere models on OCI, you can build a fully private customer support system that does not require any data to leave
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your infrastructure.
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@@ -118,18 +116,18 @@ use the following connectors:
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Airbyte UI will guide you through the process of setting up the source and destination and connecting them. Here is how
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the configuration of the source might look like:
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Qdrant is our target destination, so we need to set up the connection to it. We need to specify which fields should be
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included to generate the embeddings. In our case it makes complete sense to embed just the questions, as we are going
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to look for similar questions asked in the past and provide the answers.
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Once we have the destination set up, we can finally configure a connection. The connection will define the schedule
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of the data synchronization.
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Airbyte should now be ready to accept any data updates from the source and load them into Qdrant. You can monitor the
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progress of the synchronization in the UI.
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