ML: Refactor locations and layout

.github/dependabot.yml

@@ -211,12 +211,12 @@ updates:
 
   # Topics.
 
-  - directory: "/topic/machine-learning/automl"
+  - directory: "/topic/machine-learning/pycaret"
     package-ecosystem: "pip"
     schedule:
       interval: "daily"
 
-  - directory: "/topic/machine-learning/llm-langchain"
+  - directory: "/topic/machine-learning/langchain"
     package-ecosystem: "pip"
     schedule:
       interval: "daily"
@@ -226,7 +226,7 @@ updates:
     schedule:
       interval: "daily"
 
-  - directory: "/topic/machine-learning/mlops-mlflow"
+  - directory: "/topic/machine-learning/mlflow"
     package-ecosystem: "pip"
     schedule:
       interval: "daily"

.github/workflows/ml-langchain.yml

@@ -4,13 +4,13 @@ on:
   pull_request:
     paths:
     - '.github/workflows/ml-langchain.yml'
-    - 'topic/machine-learning/llm-langchain/**'
+    - 'topic/machine-learning/langchain/**'
     - '/requirements.txt'
   push:
     branches: [ main ]
     paths:
     - '.github/workflows/ml-langchain.yml'
-    - 'topic/machine-learning/llm-langchain/**'
+    - 'topic/machine-learning/langchain/**'
     - '/requirements.txt'
 
   # Allow job to be triggered manually.
@@ -78,13 +78,13 @@ jobs:
           cache: 'pip'
           cache-dependency-path: |
             requirements.txt
-            topic/machine-learning/llm-langchain/requirements.txt
-            topic/machine-learning/llm-langchain/requirements-dev.txt
+            topic/machine-learning/langchain/requirements.txt
+            topic/machine-learning/langchain/requirements-dev.txt
 
       - name: Install utilities
         run: |
           pip install -r requirements.txt
 
-      - name: Validate topic/machine-learning/llm-langchain
+      - name: Validate topic/machine-learning/langchain
         run: |
-          ngr test --accept-no-venv topic/machine-learning/llm-langchain
+          ngr test --accept-no-venv topic/machine-learning/langchain

.github/workflows/application-llm.yml → .github/workflows/ml-llm.yml

@@ -1,15 +1,15 @@
-name: "LLM"
+name: "llm"
 
 on:
   pull_request:
     paths:
-    - '.github/workflows/application-llm.yml'
-    - 'application/llm/**'
+    - '.github/workflows/ml-llm.yml'
+    - 'topic/machine-learning/llm/**'
   push:
     branches: [ main ]
     paths:
-    - '.github/workflows/application-llm.yml'
-    - 'application/llm/**'
+    - '.github/workflows/ml-llm.yml'
+    - 'topic/machine-learning/llm/**'
 
   # Allow job to be triggered manually.
   workflow_dispatch:
@@ -53,9 +53,9 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
 
-      - name: Validate application/llm
+      - name: Validate topic/machine-learning/llm
         run: |
           # TODO: Generalize invocation into `ngr` test runner.
-          cd application/llm
+          cd topic/machine-learning/llm
           uv pip install -r requirements.txt
           uvx xonsh test.xsh

.github/workflows/ml-mlflow.yml

@@ -5,13 +5,13 @@ on:
     branches: ~
     paths:
     - '.github/workflows/ml-mlflow.yml'
-    - 'topic/machine-learning/mlops-mlflow/**'
+    - 'topic/machine-learning/mlflow/**'
     - '/requirements.txt'
   push:
     branches: [ main ]
     paths:
     - '.github/workflows/ml-mlflow.yml'
-    - 'topic/machine-learning/mlops-mlflow/**'
+    - 'topic/machine-learning/mlflow/**'
     - '/requirements.txt'
 
   # Allow job to be triggered manually.
@@ -62,13 +62,13 @@ jobs:
           cache: 'pip'
           cache-dependency-path: |
             requirements.txt
-            topic/machine-learning/mlops-mlflow/requirements.txt
-            topic/machine-learning/mlops-mlflow/requirements-dev.txt
+            topic/machine-learning/mlflow/requirements.txt
+            topic/machine-learning/mlflow/requirements-dev.txt
 
       - name: Install utilities
         run: |
           pip install -r requirements.txt
 
-      - name: Validate topic/machine-learning/mlops-mlflow
+      - name: Validate topic/machine-learning/mlflow
         run: |
-          ngr test --accept-no-venv topic/machine-learning/mlops-mlflow
+          ngr test --accept-no-venv topic/machine-learning/mlflow

.github/workflows/application-open-webui.yml → .github/workflows/ml-open-webui.yml

@@ -3,13 +3,13 @@ name: "Open WebUI"
 on:
   pull_request:
     paths:
-    - '.github/workflows/application-open-webui.yml'
-    - 'application/open-webui/**'
+    - '.github/workflows/ml-open-webui.yml'
+    - 'topic/machine-learning/open-webui/**'
   push:
     branches: [ main ]
     paths:
-    - '.github/workflows/application-open-webui.yml'
-    - 'application/open-webui/**'
+    - '.github/workflows/ml-open-webui.yml'
+    - 'topic/machine-learning/open-webui/**'
 
   # Allow job to be triggered manually.
   workflow_dispatch:
@@ -43,12 +43,12 @@ jobs:
       - name: Acquire sources
         uses: actions/checkout@v4
 
-      - name: Validate application/open-webui
+      - name: Validate topic/machine-learning/open-webui
         run: |
           # TODO: Generalize invocation into `ngr` test runner.
 
           # Invoke software stack.
-          cd application/open-webui
+          cd topic/machine-learning/open-webui
           docker compose up --detach
 
           # Invoke validation payload.

.github/workflows/ml-automl.yml → .github/workflows/ml-pycaret.yml

@@ -1,17 +1,17 @@
-name: AutoML
+name: PyCaret
 
 on:
   pull_request:
     branches: ~
     paths:
-    - '.github/workflows/ml-automl.yml'
-    - 'topic/machine-learning/automl/**'
+    - '.github/workflows/ml-pycaret.yml'
+    - 'topic/machine-learning/pycaret/**'
     - '/requirements.txt'
   push:
     branches: [ main ]
     paths:
-    - '.github/workflows/ml-automl.yml'
-    - 'topic/machine-learning/automl/**'
+    - '.github/workflows/ml-pycaret.yml'
+    - 'topic/machine-learning/pycaret/**'
     - '/requirements.txt'
 
   # Allow job to be triggered manually.
@@ -72,6 +72,6 @@ jobs:
         run: |
           pip install -r requirements.txt
 
-      - name: Validate topic/machine-learning/automl
+      - name: Validate topic/machine-learning/pycaret
         run: |
-          ngr test --accept-no-venv topic/machine-learning/automl
+          ngr test --accept-no-venv topic/machine-learning/pycaret

topic/machine-learning/llm-langchain/README.md → topic/machine-learning/langchain/README.md

@@ -45,22 +45,22 @@ and [CrateDB].
 - [requirements.txt](requirements.txt): Pulls in a patched version of LangChain,
   as well as the CrateDB client driver and the `crash` command-line interface.
 
-- `vector_store.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](vector_search.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fvector_search.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb)
+- `vector_store.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](vector_search.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Flangchain%2Fvector_search.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/langchain/vector_search.ipynb)
 
   This notebook explores CrateDB's [`FLOAT_VECTOR`] and [`KNN_MATCH`] functionalities for storing and retrieving
   embeddings, and for conducting similarity searches.
 
-- `document_loader.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](document_loader.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fdocument_loader.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/document_loader.ipynb)
+- `document_loader.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](document_loader.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Flangchain%2Fdocument_loader.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/langchain/document_loader.ipynb)
 
   The notebook about the Document Loader demonstrates how to query a database table in CrateDB and use it as a
   source provider for LangChain documents.
 
-- `conversational_memory.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](conversational_memory.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fconversational_memory.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/conversational_memory.ipynb)
+- `conversational_memory.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](conversational_memory.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Flangchain%2Fconversational_memory.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/langchain/conversational_memory.ipynb)
 
   LangChain also supports managing conversation history in SQL databases. This notebook exercises
   how that works with CrateDB.
 
-- `cratedb-vectorstore-rag-openai-sql.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](conversational_memory.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Fllm-langchain%2Fcratedb-vectorstore-rag-openai-sql.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/cratedb-vectorstore-rag-openai-sql.ipynb)
+- `cratedb-vectorstore-rag-openai-sql.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](conversational_memory.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crate/cratedb-examples/main?labpath=topic%2Fmachine-learning%2Flangchain%2Fcratedb-vectorstore-rag-openai-sql.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/langchain/cratedb-vectorstore-rag-openai-sql.ipynb)
 
   This example intentionally shows how to use the CrateDB Vector Store using SQL.
   There might be cases where the default parameters of the LangChain integration
@@ -74,7 +74,7 @@ and [CrateDB].
   [document_loader.py](document_loader.py), and
   [conversational_memory.py](conversational_memory.py).
 
-- `cratedb_rag_customer_support_langchain.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](cratedb_rag_customer_support_langchain.ipynb)[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/cratedb_rag_customer_support_langchain.ipynb)
+- `cratedb_rag_customer_support_langchain.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](cratedb_rag_customer_support_langchain.ipynb)[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/langchain/cratedb_rag_customer_support_langchain.ipynb)
 
   This example illustrates the RAG implementation of a customer support scenario.
   The dataset used in this example is based on a collection of customer support interactions 
@@ -84,7 +84,7 @@ and [CrateDB].
   augmented generation (RAG) pipeline. To implement RAG we use the Python client driver for 
   CrateDB and vector store support in LangChain.
 
-- `cratedb_rag_customer_support_vertexai.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](cratedb_rag_customer_support_vertexai.ipynb)[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/cratedb_rag_customer_support_vertexai.ipynb)
+- `cratedb_rag_customer_support_vertexai.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](cratedb_rag_customer_support_vertexai.ipynb)[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/langchain/cratedb_rag_customer_support_vertexai.ipynb)
 
   This example illustrates the RAG implementation of a customer support scenario. 
   It is based on the previous notebook, and it illustrates how to use Vertex AI platform

topic/machine-learning/automl/README.md → topic/machine-learning/pycaret/README.md

@@ -53,7 +53,7 @@ and [CrateDB].
 - [requirements.txt](requirements.txt): For pulling the required dependencies to
   run the example programs.
 
-- `automl_classification_with_pycaret.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](automl_classification_with_pycaret.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/automl/automl_classification_with_pycaret.ipynb)
+- `automl_classification_with_pycaret.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](automl_classification_with_pycaret.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/pycaret/automl_classification_with_pycaret.ipynb)
 
   This notebook explores the PyCaret framework and shows how to use it to
   train different classification models - using a user churn dataset as an
@@ -62,7 +62,7 @@ and [CrateDB].
   model. The notebook also shows how to use CrateDB as storage for both the raw
   data and the expirement tracking and model registry data.
 
-- `automl_timeseries_forecasting_with_pycaret.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](automl_timeseries_forecasting_with_pycaret.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/automl/automl_timeseries_forecasting_with_pycaret.ipynb)
+- `automl_timeseries_forecasting_with_pycaret.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](automl_timeseries_forecasting_with_pycaret.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/pycaret/automl_timeseries_forecasting_with_pycaret.ipynb)
 
   This notebook explores the PyCaret framework and shows how to use it to
   train various timeseries forecasting models - using a real-world sales dataset
@@ -92,7 +92,7 @@ test runner program on the top-level folder of this repository. This is the
 same code path the CI jobs are taking.
 ```shell
 pip install -U -r requirements.txt
-ngr test topic/machine-learning/automl
+ngr test topic/machine-learning/pycaret
 ```