Fix GenAI observability Ollama autoconfiguration

genai-observability/README.adoc

@@ -14,8 +14,10 @@ The example uses `camel-ai-observability-starter` for Spring Boot configuration
 GenAI observability toggle (`camel.aiobservability.enabled`), together with `camel-ai-observability`
 for span and metric emission when a tracing or metrics backend is present.
 
-Two routes call two different (small) Ollama models, so every GenAI metric and span carries a
-distinct `gen_ai.request.model` and dashboards show per-model series side by side.
+Two routes share the Spring Boot starter's auto-configured `ollamaChatModel` bean. Every GenAI
+metric and span is tagged with the configured `gen_ai.request.model`; the dashboards therefore
+show one model series for this example. Change the model name and restart the application to
+compare another model.
 
 The `camel-observability-services-starter` dependency configures an opinionated observability
 setup: all Actuator endpoints move to a dedicated management port `9876` under the `/observe`
@@ -36,7 +38,7 @@ on the Actuator `ObservationRegistry`. Camel route spans (via `camel-opentelemet
 * Maven 3.9+
 * https://camel.apache.org/manual/camel-jbang.html[Camel CLI (JBang)] 4.22+
 * Docker (used by the Camel CLI to start the observability stack)
-* https://ollama.com/[Ollama] with `llama3.2:1b` and `qwen3:0.6b` pulled
+* https://ollama.com/[Ollama] with `llama3.2:1b` pulled
 
 == Build
 
@@ -52,7 +54,6 @@ Terminal 1 — Ollama:
 [source,shell]
 ----
 ollama pull llama3.2:1b
-ollama pull qwen3:0.6b
 ollama serve
 ----
 
@@ -77,7 +78,7 @@ To use different models than the defaults:
 
 [source,shell]
 ----
-mvn spring-boot:run -Dspring-boot.run.arguments="--langchain4j.ollama.chat-model-1.model-name=llama3.2 --langchain4j.ollama.chat-model-2.model-name=qwen3:1.7b"
+mvn spring-boot:run -Dspring-boot.run.arguments="--langchain4j.ollama.chat-model.model-name=qwen3:1.7b"
 ----
 
 == Verify GenAI observability
@@ -100,7 +101,7 @@ routes, JVM):
 
 * Camel overview: `http://localhost:3000/projects/camel/dashboards/overview`
 
-A GenAI dashboard (LLM calls, per-model latency, error ratio, token usage) can be created
+A GenAI dashboard (LLM calls, model-tagged latency, error ratio, token usage) can be created
 through the Perses REST API using the dashboard definition in `perses-genai-dashboard.json`:
 
 [source,shell]
@@ -128,28 +129,24 @@ image::docs/genai-dashboard-calls.png[GenAI Summary and LLM Calls sections]
   and input/output token counters. With calls that take longer than the timer period,
   "In-flight Calls" sits permanently at 1: the route serializes requests, and a new one
   starts as soon as the previous completes.
-* *Call rate* — calls per second, one series per `gen_ai.request.model`. A fast model
-  settles at the timer frequency; a slow model's rate is capped by its own latency.
-* *Error ratio* — failed calls (tagged `error!="none"`) over total, per model. Flat 0%
+* *Call rate* — calls per second, grouped by `gen_ai.request.model`. This example produces one
+  series; a fast model settles at the timer frequency, while a slow model's rate is capped by
+  its latency.
+* *Error ratio* — failed calls (tagged `error!="none"`) over total, grouped by model. Flat 0%
   lines mean every call succeeded.
-* *Mean / Max LLM latency* — per-model duration of the LLM call itself (the
+* *Mean / Max LLM latency* — model-tagged duration of the LLM call itself (the
   `gen_ai.client.operation` timer, not the whole route).
 
 image::docs/genai-dashboard-tokens.png[Token Usage section]
 
 * *Token throughput* and *Avg tokens per call* — from the `gen_ai.client.token.usage`
   counter, split by model and `gen_ai.token.type` (input/output).
 
-The screenshots were taken with `llama3.2` and `qwen3.5:0.8b` (via the model-name
-overrides shown above); the default models behave similarly, since the `qwen3` family
-also reasons before answering. The two models make the point of GenAI observability
-visible on identical prompts:
-`llama3.2` answers in about a second with a few dozen output tokens, while
-`qwen3.5:0.8b` - a *thinking* model - emits roughly 2K output tokens per call (mostly
-reasoning tokens before the one-sentence answer), which drives both its ~30-45s latency
-and its dominant share of token throughput. Same workload, an order of magnitude more
-token spend - exactly the kind of cost/latency trade-off these metrics are meant to
-surface.
+The screenshots were taken with `llama3.2` and `qwen3.5:0.8b` in separate application runs.
+Switching the configured model makes the cost/latency trade-off visible: `llama3.2` answers in
+about a second with a few dozen output tokens, while the *thinking* model `qwen3.5:0.8b` can
+emit roughly 2K output tokens per call (mostly reasoning tokens before the one-sentence answer),
+driving both its ~30-45s latency and its token throughput.
 
 == Help and contributions
 

genai-observability/pom.xml

@@ -34,7 +34,7 @@
 
     <properties>
         <category>AI</category>
-        <langchain4j-version>1.19.0</langchain4j-version>
+        <langchain4j-version>1.20.0</langchain4j-version>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
     </properties>
@@ -90,8 +90,8 @@
         </dependency>
         <dependency>
             <groupId>dev.langchain4j</groupId>
-            <artifactId>langchain4j-ollama</artifactId>
-            <version>${langchain4j-version}</version>
+            <artifactId>langchain4j-ollama-spring-boot4-starter</artifactId>
+            <version>${langchain4j-version}-beta30</version>
         </dependency>
         <!-- Micrometer Tracing with the OpenTelemetry bridge: Spring Boot auto-configures the
              OpenTelemetry SDK, an OTLP span exporter and a tracing handler on the

genai-observability/src/main/resources/application.properties

@@ -19,13 +19,11 @@
 spring.application.name=genai-observability
 server.port=8080
 
-# LangChain4j Ollama (used by ChatModelConfiguration to build the chatModel1/chatModel2 beans).
-# Two small models so the GenAI dashboards show per-model series.
+# LangChain4j Ollama. The Spring Boot starter auto-configures one ChatModel bean named ollamaChatModel.
 langchain4j.ollama.chat-model.base-url=http://localhost:11434
+langchain4j.ollama.chat-model.model-name=llama3.2:1b
 langchain4j.ollama.chat-model.temperature=0.2
 langchain4j.ollama.chat-model.timeout=PT120S
-langchain4j.ollama.chat-model-1.model-name=llama3.2:1b
-langchain4j.ollama.chat-model-2.model-name=qwen3:0.6b
 
 # Camel YAML routes
 camel.main.routes-include-pattern=camel/*

genai-observability/src/main/resources/camel/genai-route.camel.yaml

@@ -17,7 +17,7 @@
         - to:
             uri: langchain4j-chat:model1
             parameters:
-              chatModel: "#chatModel1"
+              chatModel: "#ollamaChatModel"
         - log:
             message: "[${header.CamelLangChain4jChatResponseModel}] LLM reply: ${body}"
 
@@ -33,6 +33,6 @@
         - to:
             uri: langchain4j-chat:model2
             parameters:
-              chatModel: "#chatModel2"
+              chatModel: "#ollamaChatModel"
         - log:
             message: "[${header.CamelLangChain4jChatResponseModel}] LLM reply: ${body}"