llm: Add Text-to-SQL example with Gemma3 via Ollama

Gemma3 works well for basic Text-to-SQL at least.

Author: amotl

topic/machine-learning/llm/requirements.txt

@@ -1,2 +1,3 @@
 llm<0.27
 llm-anthropic<0.18
+llm-ollama<0.14

topic/machine-learning/llm/sql-request.txt

@@ -0,0 +1,15 @@
+Given an input question, first create a syntactically correct CrateDB query to run,
+then look at the results of the query and return the answer. You can order the
+results by a relevant column to return the most interesting examples in the database.
+
+Never query for all the columns from a specific table, only ask for a few relevant
+columns given the question.
+
+Pay attention to use only the column names that you can see in the schema description.
+Be careful to not query for columns that do not exist. Pay attention to which column
+is in which table. Also, qualify column names with the table name when needed. You
+are required to use the following format, each taking one line:
+
+Question: Question here
+SQLQuery: SQL Query to run
+Only use tables listed below.

topic/machine-learning/llm/sql-response.txt

@@ -0,0 +1,2 @@
+Given an input question, synthesize a response from the query results.
+Query:

topic/machine-learning/llm/test.xsh

@@ -10,7 +10,6 @@ $XONSH_SHOW_TRACEBACK = True
 # OpenAI GPT
 # ----------
 response=$(
-
   llm prompt --model gpt-4.1 --option temperature 0.0 --no-stream \
     --fragment https://cdn.crate.io/about/v1/llms-full.txt \
     --system-fragment https://cdn.crate.io/about/v1/instructions-general.md \
@@ -24,11 +23,26 @@ assert "UUIDv7" in response, "GPT: UUIDv7 was not in response"
 # Anthropic Claude Sonnet
 # -----------------------
 response=$(
-
   llm prompt --model claude-4-sonnet --option temperature 0.0 --no-stream \
     --fragment https://cdn.crate.io/about/v1/llms-full.txt \
     --system-fragment https://cdn.crate.io/about/v1/instructions-general.md \
     "CrateDB does not seem to provide an AUTOINCREMENT feature?"
 )
 print(response)
 assert "UUIDv7" in response, "Sonnet: UUIDv7 was not in response"
+
+
+# ------------
+# Google Gemma
+# ------------
+# - Gemma3 works well for basic Text-to-SQL.
+if False:
+    $OLLAMA_HOST="http://100.127.86.113:11434/"
+    response=$(
+      llm prompt --model gemma3:1b --option temperature 0.0 --no-stream \
+        --fragment sql-request.txt \
+        --system "Table 'time_series_data' has columns: timestamp (TIMESTAMP), value (DOUBLE PRECISION), location (VARCHAR), sensor_id (INTEGER)" \
+        "Question: What is the average value for sensor 1?"
+    )
+    print(response)
+    assert "SQLQuery: SELECT AVG(value) FROM time_series_data WHERE sensor_id = 1" in response