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Fix broken links
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Abdon Pijpelink
@@ -202,7 +202,7 @@ In addition to that, we continuously look for improvements in:
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|----------------------------------|-------------|
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| **Memory Efficiency & Compression** | Techniques such as [**quantization**](documentation/guides/quantization/) and [**HNSW compression**](/blog/qdrant-1.13.x/#hnsw-graph-compression) to reduce storage requirements |
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| **Memory Efficiency & Compression** | Techniques such as [**quantization**](/documentation/guides/quantization/) and [**HNSW compression**](/blog/qdrant-1.13.x/#hnsw-graph-compression) to reduce storage requirements |
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| **Retrieval Algorithms** | Support for the latest retrieval algorithms, including [**sparse neural retrieval**](/articles/modern-sparse-neural-retrieval/), [**hybrid search**](/documentation/concepts/hybrid-queries/) methods, and [**re-rankers**](/documentation/fastembed/fastembed-rerankers/). |
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| **Vector Data Analysis & Visualization** | Tools like the [**distance matrix API**](/blog/qdrant-1.12.x/#distance-matrix-api-for-data-insights) provide insights into vectorized data, and a [**Web UI**](/blog/qdrant-1.11.x/#web-ui-search-quality-tool) allows for intuitive exploration of data. |
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| **Search Speed & Scalability** | Includes optimizations for [**multi-tenant environments**](/articles/multitenancy/) to ensure efficient and scalable search. |
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@@ -73,7 +73,7 @@ Quotient AI is another platform designed to streamline the evaluation of RAG sys
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### Arize Phoenix: Visually Deconstructing Response Generation
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[Arize Phoenix](https://docs.arize.com/phoenix) is an open-source tool that helps improve the performance of RAG systems by tracking how a response is built step-by-step. You can see these steps visually in Phoenix, which helps identify slowdowns and errors. You can define "[evaluators](https://docs.arize.com/phoenix/evaluation/concepts-evals/evaluation)" that use LLMs to assess the quality of outputs, detect hallucinations, and check answer accuracy. Phoenix also calculates key metrics like latency, token usage, and errors, giving you an idea of how efficiently your RAG system is working.
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[Arize Phoenix](https://docs.arize.com/phoenix) is an open-source tool that helps improve the performance of RAG systems by tracking how a response is built step-by-step. You can see these steps visually in Phoenix, which helps identify slowdowns and errors. You can define "[evaluators](https://arize.com/docs/phoenix/evaluation/concepts-evals/evaluators)" that use LLMs to assess the quality of outputs, detect hallucinations, and check answer accuracy. Phoenix also calculates key metrics like latency, token usage, and errors, giving you an idea of how efficiently your RAG system is working.
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**Figure 3:** *The Arize Phoenix tool is intuitive to use and shows the entire process architecture as well as the steps that take place inside of retrieval, context and generation.*
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@@ -8,7 +8,7 @@ weight: 3
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# Combining Vector Search and Filtering
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We've talked about how Qdrant uses the [HNSW](documentation/concepts/indexing/#filtrable-index) graph to efficiently search dense vectors. But in real-world applications, you'll often want to constrain your search using filters. This creates unique challenges for graph traversal that Qdrant solves elegantly.
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We've talked about how Qdrant uses the [HNSW](/documentation/concepts/indexing/#filtrable-index) graph to efficiently search dense vectors. But in real-world applications, you'll often want to constrain your search using filters. This creates unique challenges for graph traversal that Qdrant solves elegantly.
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<div class="video">
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<iframe
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@@ -96,6 +96,6 @@ docs = (Pipeline()
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- Chonkie [GitHub Repository](https://github.com/chonkie-inc/chonkie)
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- Chonkie [Documentation](https://chonkie.ai)
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- QdrantHandshake [API Reference](https://chonkie.ai/oss/handshakes/qdrant-handshake)
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- Chonkie [Chunking Strategies](https://chonkie.ai/oss/chunkers/overview)
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- QdrantHandshake [API Reference](https://docs.chonkie.ai/oss/handshakes/qdrant-handshake)
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- Chonkie [Chunking Strategies](https://docs.chonkie.ai/oss/chunkers/overview)
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- Qdrant Python Client [Documentation](https://python-client.qdrant.tech/)
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@@ -38,7 +38,7 @@ as the documents may contain sensitive information.
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### Creating vector store
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[QdrantVectorStore](https://docs.llamaindex.ai/en/stable/examples/vector_stores/QdrantIndexDemo.html) is a
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[QdrantVectorStore](https://developers.llamaindex.ai/python/examples/vector_stores/qdrantindexdemo/) is a
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wrapper around Qdrant that provides all the necessary methods to work with your vector database in LlamaIndex.
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Let's create a vector store for our collection. It requires setting a collection name and passing an instance
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of `QdrantClient`.
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+2
-2
@@ -27,10 +27,10 @@ Directory.
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> **Note:** In this tutorial, we are going to build a solid foundation for such a system. However, it is up to your organization's setup to implement the entire solution.
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- **Dataset** - a collection of documents, using different formats, such as PDF or DOCx, scraped from internet
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- **Asymmetric semantic embeddings** - [Aleph Alpha embedding](https://docs.aleph-alpha.com/api/pharia-inference/semantic-embed/) to
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- **Asymmetric semantic embeddings** - [Aleph Alpha embedding](https://docs.aleph-alpha.com/products/apis/pharia-inference/semantic-embed/) to
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convert the queries and the documents into vectors
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- **Large Language Model** - the [Luminous-extended-control
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model](https://docs.aleph-alpha.com/api/pharia-inference/available-models/), but you can play with a different one from the
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model](https://docs.aleph-alpha.com/products/apis/pharia-inference/available-models/), but you can play with a different one from the
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Luminous family
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- **Qdrant Hybrid Cloud** - a knowledge base to store the vectors and search over the documents
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- **STACKIT** - a [German business cloud](https://www.stackit.de) to run the Qdrant Hybrid Cloud and the application
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+2
-2
@@ -131,14 +131,14 @@ progress of the synchronization in the UI.
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## RAG connector
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One of our previous tutorials, guides you step-by-step on [implementing custom connector for Cohere
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RAG](documentation/examples/cohere-rag-connector/) with Cohere Embed v3 and Qdrant. You can just point it to use your Hybrid Cloud
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RAG](/documentation/examples/cohere-rag-connector/) with Cohere Embed v3 and Qdrant. You can just point it to use your Hybrid Cloud
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Qdrant instance running on AWS. Created connector might be deployed to Amazon Web Services in various ways, even in a
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[Serverless](https://aws.amazon.com/serverless/) manner using [AWS
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Lambda](https://aws.amazon.com/lambda/?c=ser&sec=srv).
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In general, RAG connector has to expose a single endpoint that will accept POST requests with `query` parameter and
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return the matching documents as JSON document with a specific structure. Our FastAPI implementation created [in the
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related tutorial](documentation/examples/cohere-rag-connector/) is a perfect fit for this task. The only difference is that you
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related tutorial](/documentation/examples/cohere-rag-connector/) is a perfect fit for this task. The only difference is that you
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should point it to the Cohere models and Qdrant running on AWS infrastructure.
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> Our connector is a lightweight web service that exposes a single endpoint and glues the Cohere embedding model with
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@@ -67,7 +67,7 @@ addition, there are a few optional parameters:
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dataTypePayloadKey: '_datatype';
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```
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- `collectionCreateOptions`: [Additional options](<(https://qdrant.tech/documentation/concepts/collections/#create-a-collection)>) when creating the Qdrant collection.
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- `collectionCreateOptions`: [Additional options](/documentation/concepts/collections/#create-a-collection) when creating the Qdrant collection.
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## Usage
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@@ -37,6 +37,6 @@ index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
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## Further Reading
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- [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/examples/vector_stores/QdrantIndexDemo/)
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- [LlamaIndex Documentation](https://developers.llamaindex.ai/python/examples/vector_stores/qdrantindexdemo/)
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- [Example Notebook](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/QdrantIndexDemo.ipynb)
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- [Source Code](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant)
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@@ -181,5 +181,5 @@ for point in results.points:
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## Further Reading
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- [Mirror Security Docs](https://docs.mirrorsecurity.io/introduction)
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- [Mirror Security Docs](https://docs.mirrorsecurity.io/docs/getting-started/introduction)
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- [Mirror Security Blog](https://mirrorsecurity.io/blog)
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@@ -4,7 +4,7 @@ title: Microsoft NLWeb
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# NLWeb
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Microsoft's [NLWeb](https://github.com/microsoft/NLWeb) is a proposed framework that enables natural language interfaces for websites, using Schema.org, formats like RSS and the emerging [MCP protocol](https://github.com/microsoft/NLWeb/blob/main/docs/RestAPI.md).
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Microsoft's [NLWeb](https://github.com/nlweb-ai/NLWeb) is a proposed framework that enables natural language interfaces for websites, using Schema.org, formats like RSS and the emerging [MCP protocol](https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-rest-api.md).
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Qdrant is supported as a vector store backend within NLWeb for embedding storage and context retrieval.
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@@ -85,12 +85,12 @@ To start NLWeb, from the `code` directory, run:
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python app-file.py
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```
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You can now query your content via natural language using either the web UI at <http://localhost:8000/> or directly through the MCP-compatible [REST API](https://github.com/microsoft/NLWeb/blob/main/docs/RestAPI.md).
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You can now query your content via natural language using either the web UI at <http://localhost:8000/> or directly through the MCP-compatible [REST API](https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-rest-api.md).
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## Further Reading
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* [Source](https://github.com/microsoft/NLWeb)
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* [Life of a Chat Query](https://github.com/microsoft/NLWeb/tree/main/docs/LifeOfAChatQuery.md)
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* [Modifying behavior by changing prompts](https://github.com/microsoft/NLWeb/tree/main/docs/Prompts.md)
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* [Modifying control flow](https://github.com/microsoft/NLWeb/tree/main/docs/ControlFlow.md)
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* [Modifying the user interface](https://github.com/microsoft/NLWeb/tree/main/docs/UserInterface.md)
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* [Life of a Chat Query](https://github.com/nlweb-ai/NLWeb/blob/main/docs/life-of-a-chat-query.md)
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* [Modifying behavior by changing prompts](https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-prompts.md)
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* [Modifying control flow](https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-control-flow.md)
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* [Modifying the user interface](https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-user-interface.md)
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@@ -30,7 +30,7 @@ All interaction with Qdrant takes place via the REST API. We recommend using RES
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| API | Documentation |
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| -------- | ------------------------------------------------------------------------------------ |
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| REST API | [OpenAPI Specification](https://api.qdrant.tech/api-reference) |
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| gRPC API | [gRPC Documentation](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md) |
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| gRPC API | [gRPC protobuf definitions](https://github.com/qdrant/qdrant/tree/master/lib/api/src/grpc/proto) |
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### gRPC Interface
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@@ -130,7 +130,7 @@ helm upgrade --install qdrant-private-cloud oci://registry.cloud.qdrant.io/qdran
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Note, that the image tag values are automatically derived from the chart's appVersions and should not be overridden in the `values.yaml`.
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For more information also see the [Helm Upgrade Documentation](https://helm.sh/docs/helm/upgrade/).
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For more information also see the [Helm Upgrade Documentation](https://helm.sh/docs/helm/helm_upgrade/).
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### Mirroring images and charts
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