Document LLM Observability LLM provider integration endpoints

examples/v2_llm-observability_CreateLLMObsIntegrationInference.rs

@@ -0,0 +1,129 @@
+// Run an LLM inference returns "OK" response
+use datadog_api_client::datadog;
+use datadog_api_client::datadogV2::api_llm_observability::LLMObservabilityAPI;
+use datadog_api_client::datadogV2::model::LLMObsAnthropicEffort;
+use datadog_api_client::datadogV2::model::LLMObsAnthropicMetadata;
+use datadog_api_client::datadogV2::model::LLMObsAnthropicThinkingConfig;
+use datadog_api_client::datadogV2::model::LLMObsAnthropicThinkingType;
+use datadog_api_client::datadogV2::model::LLMObsAzureOpenAIMetadata;
+use datadog_api_client::datadogV2::model::LLMObsBedrockMetadata;
+use datadog_api_client::datadogV2::model::LLMObsInferenceContent;
+use datadog_api_client::datadogV2::model::LLMObsInferenceContentValue;
+use datadog_api_client::datadogV2::model::LLMObsInferenceFunction;
+use datadog_api_client::datadogV2::model::LLMObsInferenceMessage;
+use datadog_api_client::datadogV2::model::LLMObsInferenceTool;
+use datadog_api_client::datadogV2::model::LLMObsInferenceToolCall;
+use datadog_api_client::datadogV2::model::LLMObsInferenceToolResult;
+use datadog_api_client::datadogV2::model::LLMObsIntegrationInferenceRequest;
+use datadog_api_client::datadogV2::model::LLMObsIntegrationName;
+use datadog_api_client::datadogV2::model::LLMObsOpenAIMetadata;
+use datadog_api_client::datadogV2::model::LLMObsOpenAIReasoningEffort;
+use datadog_api_client::datadogV2::model::LLMObsOpenAIReasoningSummary;
+use datadog_api_client::datadogV2::model::LLMObsVertexAIMetadata;
+use serde_json::Value;
+use std::collections::BTreeMap;
+
+#[tokio::main]
+async fn main() {
+    let body = LLMObsIntegrationInferenceRequest::new(
+        vec![LLMObsInferenceMessage::new()
+            .content("What is the capital of France?".to_string())
+            .contents(vec![LLMObsInferenceContent::new(
+                "text".to_string(),
+                LLMObsInferenceContentValue::new()
+                    .text("Hello, how can I help you?".to_string())
+                    .tool_call(
+                        LLMObsInferenceToolCall::new()
+                            .arguments(BTreeMap::from([(
+                                "location".to_string(),
+                                Value::from("San Francisco"),
+                            )]))
+                            .name("get_weather".to_string())
+                            .tool_id("call_abc123".to_string())
+                            .type_("function".to_string()),
+                    )
+                    .tool_call_result(
+                        LLMObsInferenceToolResult::new()
+                            .name("get_weather".to_string())
+                            .result("The weather in San Francisco is 68°F and sunny.".to_string())
+                            .tool_id("call_abc123".to_string())
+                            .type_("function".to_string()),
+                    ),
+            )])
+            .id("msg_001".to_string())
+            .role("user".to_string())
+            .tool_calls(vec![LLMObsInferenceToolCall::new()
+                .arguments(BTreeMap::from([(
+                    "location".to_string(),
+                    Value::from("San Francisco"),
+                )]))
+                .name("get_weather".to_string())
+                .tool_id("call_abc123".to_string())
+                .type_("function".to_string())])
+            .tool_results(vec![LLMObsInferenceToolResult::new()
+                .name("get_weather".to_string())
+                .result("The weather in San Francisco is 68°F and sunny.".to_string())
+                .tool_id("call_abc123".to_string())
+                .type_("function".to_string())])],
+        "gpt-4o".to_string(),
+    )
+    .anthropic_metadata(
+        LLMObsAnthropicMetadata::new()
+            .effort(Some(LLMObsAnthropicEffort::MEDIUM))
+            .thinking(
+                LLMObsAnthropicThinkingConfig::new(LLMObsAnthropicThinkingType::ENABLED)
+                    .budget_tokens(Some(1024)),
+            ),
+    )
+    .azure_openai_metadata(
+        LLMObsAzureOpenAIMetadata::new()
+            .deployment_id("my-gpt4-deployment".to_string())
+            .model_version("0613".to_string())
+            .resource_name("my-azure-resource".to_string()),
+    )
+    .bedrock_metadata(LLMObsBedrockMetadata::new().region("us-east-1".to_string()))
+    .frequency_penalty(Some(0.0 as f64))
+    .json_schema(Some(
+        r#"{"type":"object","properties":{"answer":{"type":"string"}}}"#.to_string(),
+    ))
+    .max_completion_tokens(Some(1024))
+    .max_tokens(Some(1024))
+    .openai_metadata(
+        LLMObsOpenAIMetadata::new()
+            .reasoning_effort(Some(LLMObsOpenAIReasoningEffort::MEDIUM))
+            .reasoning_summary(Some(LLMObsOpenAIReasoningSummary::AUTO)),
+    )
+    .presence_penalty(Some(0.0 as f64))
+    .temperature(Some(0.7 as f64))
+    .tools(vec![LLMObsInferenceTool::new(
+        LLMObsInferenceFunction::new(
+            "get_weather".to_string(),
+            BTreeMap::from([("type".to_string(), Value::from("object"))]),
+        )
+        .description("Get the current weather for a location.".to_string()),
+        "function".to_string(),
+    )])
+    .top_k(Some(50))
+    .top_p(Some(1.0 as f64))
+    .vertex_ai_metadata(
+        LLMObsVertexAIMetadata::new()
+            .location("us-central1".to_string())
+            .project("my-gcp-project".to_string())
+            .project_ids(vec!["my-gcp-project".to_string()]),
+    );
+    let mut configuration = datadog::Configuration::new();
+    configuration.set_unstable_operation_enabled("v2.CreateLLMObsIntegrationInference", true);
+    let api = LLMObservabilityAPI::with_config(configuration);
+    let resp = api
+        .create_llm_obs_integration_inference(
+            LLMObsIntegrationName::OPENAI,
+            "account_id".to_string(),
+            body,
+        )
+        .await;
+    if let Ok(value) = resp {
+        println!("{:#?}", value);
+    } else {
+        println!("{:#?}", resp.unwrap_err());
+    }
+}

examples/v2_llm-observability_ListLLMObsIntegrationAccounts.rs

@@ -0,0 +1,19 @@
+// List LLM integration accounts returns "OK" response
+use datadog_api_client::datadog;
+use datadog_api_client::datadogV2::api_llm_observability::LLMObservabilityAPI;
+use datadog_api_client::datadogV2::model::LLMObsIntegrationName;
+
+#[tokio::main]
+async fn main() {
+    let mut configuration = datadog::Configuration::new();
+    configuration.set_unstable_operation_enabled("v2.ListLLMObsIntegrationAccounts", true);
+    let api = LLMObservabilityAPI::with_config(configuration);
+    let resp = api
+        .list_llm_obs_integration_accounts(LLMObsIntegrationName::OPENAI)
+        .await;
+    if let Ok(value) = resp {
+        println!("{:#?}", value);
+    } else {
+        println!("{:#?}", resp.unwrap_err());
+    }
+}

examples/v2_llm-observability_ListLLMObsIntegrationModels.rs

@@ -0,0 +1,19 @@
+// List LLM integration models returns "OK" response
+use datadog_api_client::datadog;
+use datadog_api_client::datadogV2::api_llm_observability::LLMObservabilityAPI;
+use datadog_api_client::datadogV2::model::LLMObsIntegrationName;
+
+#[tokio::main]
+async fn main() {
+    let mut configuration = datadog::Configuration::new();
+    configuration.set_unstable_operation_enabled("v2.ListLLMObsIntegrationModels", true);
+    let api = LLMObservabilityAPI::with_config(configuration);
+    let resp = api
+        .list_llm_obs_integration_models(LLMObsIntegrationName::OPENAI, "account_id".to_string())
+        .await;
+    if let Ok(value) = resp {
+        println!("{:#?}", value);
+    } else {
+        println!("{:#?}", resp.unwrap_err());
+    }
+}

src/datadog/configuration.rs

@@ -150,6 +150,7 @@ impl Default for Configuration {
             ("v2.create_llm_obs_dataset_records".to_owned(), false),
             ("v2.create_llm_obs_experiment".to_owned(), false),
             ("v2.create_llm_obs_experiment_events".to_owned(), false),
+            ("v2.create_llm_obs_integration_inference".to_owned(), false),
             ("v2.create_llm_obs_project".to_owned(), false),
             ("v2.delete_llm_obs_annotation_queue".to_owned(), false),
             (
@@ -173,6 +174,8 @@ impl Default for Configuration {
             ("v2.list_llm_obs_datasets".to_owned(), false),
             ("v2.list_llm_obs_experiment_events".to_owned(), false),
             ("v2.list_llm_obs_experiments".to_owned(), false),
+            ("v2.list_llm_obs_integration_accounts".to_owned(), false),
+            ("v2.list_llm_obs_integration_models".to_owned(), false),
             ("v2.list_llm_obs_projects".to_owned(), false),
             ("v2.list_llm_obs_spans".to_owned(), false),
             ("v2.search_llm_obs_experimentation".to_owned(), false),