Feature / Vector Store: Implement page

docs/_include/links.md

@@ -5,8 +5,17 @@
 [HoloViews]: https://www.holoviews.org/
 [Indexing, Columnar Storage, and Aggregations]: https://cratedb.com/product/features/indexing-columnar-storage-aggregations
 [JSON Database]: https://cratedb.com/solutions/json-database
+[LangChain and CrateDB: Code Examples]: https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning/llm-langchain
+[langchain-similarity-binder]: https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fvector_search.ipynb
+[langchain-similarity-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
+[langchain-similarity-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
+[langchain-rag-sql-binder]: https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fcratedb-vectorstore-rag-openai-sql.ipynb
+[langchain-rag-sql-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/cratedb-vectorstore-rag-openai-sql.ipynb
+[langchain-rag-sql-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/cratedb-vectorstore-rag-openai-sql.ipynb
 [Multi-model Database]: https://cratedb.com/solutions/multi-model-database
 [Nested Data Structure]: https://cratedb.com/product/features/nested-data-structure
 [Relational Database]: https://cratedb.com/solutions/relational-database
 [timeseries-queries-and-visualization-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/timeseries/timeseries-queries-and-visualization.ipynb
 [timeseries-queries-and-visualization-github]: https://github.com/crate/cratedb-examples/blob/main/topic/timeseries/timeseries-queries-and-visualization.ipynb
+[Vector Database (Product)]: https://cratedb.com/solutions/vector-database
+[Vector Database]: https://en.wikipedia.org/wiki/Vector_database

docs/conf.py

@@ -51,6 +51,7 @@
 
 myst_substitutions.update({
     "nb_colab": "[![Notebook on Colab](https://img.shields.io/badge/Open-Notebook%20on%20Colab-blue?logo=Google%20Colab)]",
+    "nb_binder": "[![Notebook on Binder](https://img.shields.io/badge/Open-Notebook%20on%20Binder-lightblue?logo=binder)]",
     "nb_github": "[![Notebook on GitHub](https://img.shields.io/badge/Open-Notebook%20on%20GitHub-darkgreen?logo=GitHub)]",
     "readme_github": "[![README](https://img.shields.io/badge/Open-README-darkblue?logo=GitHub)]",
     "blog": "[![Blog](https://img.shields.io/badge/Open-Blog-darkblue?logo=Markdown)]",

docs/domain/ml/index.md

@@ -3,6 +3,11 @@
 
 # Machine Learning
 
+:::{include} /_include/links.md
+:::
+:::{include} /_include/styles.html
+:::
+
 Integrate CrateDB with machine learning frameworks and
 tools, for [MLOps] and [Vector database] operations.
 
@@ -321,16 +326,12 @@ tensorflow
 [LangChain: Analyzing structured data]: https://python.langchain.com/docs/use_cases/qa_structured/sql
 [LangChain: Chatbots]: https://python.langchain.com/docs/use_cases/chatbots
 [LangChain: Retrieval augmented generation]: https://python.langchain.com/docs/use_cases/question_answering/
-[LangChain and CrateDB: Code Examples]: https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning/llm-langchain
 [langchain-conversational-history-binder]: https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fconversational_memory.ipynb
 [langchain-conversational-history-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/conversational_memory.ipynb
 [langchain-conversational-history-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/conversational_memory.ipynb
 [langchain-document-loader-binder]: https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fdocument_loader.ipynb
 [langchain-document-loader-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/document_loader.ipynb
 [langchain-document-loader-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/document_loader.ipynb
-[langchain-similarity-binder]: https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fvector_search.ipynb
-[langchain-similarity-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
-[langchain-similarity-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
 [Machine Learning and CrateDB: An introduction]: https://cratedb.com/blog/machine-learning-and-cratedb-part-one
 [Machine Learning and CrateDB: Getting Started With Jupyter]: https://cratedb.com/blog/machine-learning-cratedb-jupyter
 [Machine Learning and CrateDB: Experiment Design & Linear Regression]: https://cratedb.com/blog/machine-learning-and-cratedb-part-three-experiment-design-and-linear-regression
@@ -344,4 +345,3 @@ tensorflow
 [Time Series Modeling using Machine Learning]: https://cratedb.com/blog/introduction-to-time-series-modeling-with-cratedb-machine-learning-time-series-data
 [tracking-merlion-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/mlops-mlflow/tracking_merlion.ipynb
 [tracking-merlion-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/mlops-mlflow/tracking_merlion.ipynb
-[Vector database]: https://en.wikipedia.org/wiki/Vector_database

docs/feature/index.md

@@ -19,6 +19,7 @@ relational/index
 document/index
 search/index
 geospatial/index
+vector/index
 :::
 +++
 CrateDB combines the power of Lucene with the advantages of

docs/feature/relational/index.md

@@ -5,6 +5,8 @@
 
 :::{include} /_include/links.md
 :::
+:::{include} /_include/styles.html
+:::
 
 :::::{grid}
 :padding: 0
@@ -209,7 +211,3 @@ SET optimizer_reorder_nested_loop_join = false;
 [manual-join-concept]: inv:crate-reference#concept-joins
 [manual-join-types]: inv:crate-reference#sql_joins
 [manual-joined-relation]: inv:crate-reference#sql-select-joined-relation
-
-
-```{include} /_include/styles.html
-```

docs/feature/vector/index.md

@@ -1,20 +1,155 @@
----
-orphan: true
----
-
 (hnsw)=
 (vector)=
 
 # Vector Store
 
-:::{todo} Implement.
+:::{include} /_include/links.md
+:::
+:::{include} /_include/styles.html
+:::
+
+:::::{grid}
+:padding: 0
+
+::::{grid-item}
+:class: rubric-slim
+:columns: 9
+
+
+:::{rubric} Overview
+:::
+CrateDB can be used as a [Vector Database] feature for storing and retrieving
+vector embeddings.
+
+:::{rubric} About
+:::
+CrateDB's `FLOAT_VECTOR` type and its `KNN_MATCH` function can be used
+for storing and retrieving embeddings, and for conducting HNSW similarity
+searches.
+::::
+
+
+
+::::{grid-item}
+:class: rubric-slim
+:columns: 3
+
+```{rubric} Reference Manual
+```
+- [](inv:crate-reference#type-float_vector)
+- [](inv:crate-reference#scalar_knn_match)
+
+```{rubric} Related
+```
+- {ref}`sql`
+- {ref}`machine-learning`
+- {ref}`query`
+
+{tags-primary}`SQL`
+{tags-primary}`Vector Store`
+{tags-primary}`Machine Learning`
+::::
+
+:::::
+
+
+:::{rubric} Tutorials
+:::
+
+::::{info-card}
+:::{grid-item} **Vector Support and KNN Search through SQL**
+:columns: 9
+
+The addition of vector support and KNN search makes CrateDB the optimal
+multi-model database for all types of data. Whether it is structured,
+semi-structured, or unstructured data, CrateDB stands as the all-in-one
+solution, capable of handling diverse data types with ease.
+
+In this feature-focused blog post, we will introduce how CrateDB can be
+used as a vector database and how the vector store is implemented.
+We will also explore the possibilities of the K-Nearest Neighbors (KNN)
+search, and demonstrate vector capabilities with easy-to-follow examples.
+
+{{ '{}[Vector support and KNN search in CrateDB]'.format(blog) }}
 :::
+:::{grid-item}
+:columns: 3
+{tags-primary}`Introduction` \
+{tags-secondary}`Vector Store` \
+{tags-secondary}`SQL`
+:::
+::::
+
+
+::::{info-card}
+:::{grid-item} **Retrieval Augmented Generation (RAG) with CrateDB and SQL**
+:columns: 9
+
+This notebook illustrates CrateDB's vector store using pure SQL on behalf
+of an example exercising a RAG workflow.
+
+It uses the white-paper [Time series data in manufacturing] as input data,
+generates embeddings using OpenAI's ChatGPT, stores them into a table
+using `FLOAT_VECTOR(1536)`, and queries it using the `KNN_MATCH` function.
+
+{{ '{}[langchain-rag-sql-github]'.format(nb_github) }} {{ '{}[langchain-rag-sql-colab]'.format(nb_colab) }} {{ '{}[langchain-rag-sql-binder]'.format(nb_binder) }}
+:::
+:::{grid-item}
+:columns: 3
+{tags-primary}`Fundamentals` \
+{tags-secondary}`Vector Store` \
+{tags-secondary}`LangChain` \
+{tags-secondary}`pandas` \
+{tags-secondary}`SQL`
+:::
+::::
+
+
+:::{rubric} Videos
+:::
+
+::::{info-card}
+
+:::{grid-item} **How to Use Private Data in Generative AI?**
+:columns: auto auto 8 8
+
+In this video recorded at FOSDEM 2024, we explain how to leverage private data
+in generative AI and which end to end solution is needed to leverage Retrieval
+Augmented Generation (RAG).
+
+- [How to Use Private Data in Generative AI?]
+:::
+
+:::{grid-item}
+:columns: auto auto 4 4
+
+<iframe width="240" src="https://www.youtube-nocookie.com/embed/icquKckM4o0?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+&nbsp;
+
+{tags-primary}`Fundamentals` \
+{tags-secondary}`RAG`
+:::
+
+::::
+
 
 
 :::{seealso}
+**Features:**
+[](#querying) •
+[](#fulltext)
+
+**Domains:**
+[](#industrial) •
+[](#machine-learning) •
+[](#timeseries)
+
 **Product:**
-[Relational Database]
+[Relational Database] •
+[Vector Database][Vector Database (Product)]
 :::
 
 
-[Relational Database]: https://cratedb.com/solutions/relational-database
+[How to Use Private Data in Generative AI?]: https://youtu.be/icquKckM4o0?feature=shared
+[Time series data in manufacturing]: https://github.com/crate/cratedb-datasets/raw/main/machine-learning/fulltext/White%20paper%20-%20Time-series%20data%20in%20manufacturing.pdf
+[Vector support and KNN search in CrateDB]: https://cratedb.com/blog/unlocking-the-power-of-vector-support-and-knn-search-in-cratedb