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# Observability計装（後述）
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from observability import setUpOpenTelemetry, setUpDataDog, setUpTraceLoop
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# 計装方法を切り替え
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setUpOpenTelemetry()
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# setUpDataDog()
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# setUpTraceLoop()
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# LLMアプリケーション
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def main():
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    from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
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    from langchain_core.messages import SystemMessage
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    from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
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    from langchain_core.runnables import RunnablePassthrough
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    from langchain_community.document_loaders import WebBaseLoader
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    from langchain_text_splitters import RecursiveCharacterTextSplitter
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    from langchain_chroma import Chroma
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    # ベクトルDBにWikipediaのラーメン二郎の記事を埋め込み
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    loader = WebBaseLoader(
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        "https://ja.wikipedia.org/wiki/%E3%83%A9%E3%83%BC%E3%83%A1%E3%83%B3%E4%BA%8C%E9%83%8E")
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    docs = loader.load()
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    splitter = RecursiveCharacterTextSplitter(
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        chunk_size=1000,
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        chunk_overlap=200,
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    )
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    chunks = splitter.split_documents(docs)
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    embedding_model = GoogleGenerativeAIEmbeddings(
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        model="gemini-embedding-001")
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    vector_store = Chroma(collection_name="example-collection",
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                          embedding_function=embedding_model)
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    vector_store.add_documents(chunks)
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    # ベクトルDBをRAGのretrieverとして利用
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    retriever = vector_store.as_retriever(
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        search_type="similarity", search_kwargs={"k": 3})
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    # プロンプトテンプレートの定義
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    template = ChatPromptTemplate.from_messages([
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        SystemMessage(content="""Answer the question based on the context below. If the question cannot be answered using the information provided, answer with "I don't know"."""),
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        HumanMessagePromptTemplate.from_template("Context: {context}"),
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        HumanMessagePromptTemplate.from_template("Questtion: {question}"),
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    ])
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    # RAGチャットボットの定義
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    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash-lite")
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    ramen_chatbot = (
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        {
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            "question": RunnablePassthrough(),
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            "context": lambda q: retriever.invoke(q),
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        }
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        | template
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        | model
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    )
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    response = ramen_chatbot.invoke("ラーメン二郎とは何ですか？")
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    print(response.text)
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main()

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import os
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service = os.getenv("OTEL_SERVICE_NAME")
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otlp_endpoint = '{}/v1'.format(os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT"))
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# Datadog SDKを使った計装
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def setUpDataDog():
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    # cf. https://docs.datadoghq.com/ja/llm_observability/instrumentation/sdk/?tab=python
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    from ddtrace.llmobs import LLMObs
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    LLMObs.enable(
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        service=service,
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    )
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# Traceloop SDKを使った計装
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def setUpTraceLoop():
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    from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
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    from traceloop.sdk import Traceloop
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    Traceloop.init(exporter=OTLPSpanExporter(
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        endpoint=f'{otlp_endpoint}/traces', insecure=True))
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# OpenTelemetryネイティブな計装
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def setUpOpenTelemetry():
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    from opentelemetry import trace, _logs, metrics
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    # Trace
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    from opentelemetry.sdk.trace import TracerProvider
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    from opentelemetry.sdk.trace.export import BatchSpanProcessor
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    from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
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    # Metrics
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    from opentelemetry.sdk.metrics import MeterProvider
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    from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
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    from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
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    # Log
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    import logging
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    from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
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    from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
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    from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter
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    # LLM
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    from opentelemetry.instrumentation.langchain import LangchainInstrumentor
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    from opentelemetry.instrumentation.google_generativeai import GoogleGenerativeAiInstrumentor
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    from opentelemetry.instrumentation.chromadb import ChromaInstrumentor
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    # Trace
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    provider = TracerProvider()
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    processor = BatchSpanProcessor(OTLPSpanExporter(
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        endpoint=f'{otlp_endpoint}/traces', insecure=True))
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    provider.add_span_processor(processor)
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    trace.set_tracer_provider(provider)
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    # Metrics
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    metric_reader = PeriodicExportingMetricReader(
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        OTLPMetricExporter(endpoint=f'{otlp_endpoint}/metrics', insecure=True))
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    provider = MeterProvider(metric_readers=[metric_reader])
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    metrics.set_meter_provider(provider)
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    # Log
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    provider = LoggerProvider()
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    processor = BatchLogRecordProcessor(
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        OTLPLogExporter(endpoint=f'{otlp_endpoint}/logs', insecure=True))
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    provider.add_log_record_processor(processor)
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    _logs.set_logger_provider(provider)
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    handler = LoggingHandler(level=logging.INFO, logger_provider=provider)
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    logging.basicConfig(handlers=[handler], level=logging.INFO)
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    LangchainInstrumentor().instrument()
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    GoogleGenerativeAiInstrumentor().instrument()
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    ChromaInstrumentor().instrument()

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def dummy_llm():
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    import time
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    from opentelemetry import trace
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    from opentelemetry.trace import SpanKind
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    tracer = trace.get_tracer(__name__)
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    # 実験1: 仕様に準拠したダミーのスパンを生成（ベースライン）
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    # 実験2: スパン名を変更
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    with tracer.start_as_current_span("generate_content gemini-2.5-flash-lite", kind=SpanKind.CLIENT) as llm_span:
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        # 実験3: Requiredとなっているgen_ai.operation.nameを削除
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        llm_span.set_attribute("gen_ai.operation.name", "generate_content")
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        # 実験4: Deprecatedであるgen_ai.system及びRequiredとなっているgen_ai.provider.nameを削除
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        # 実験5: 実験3と4を両方実施
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        llm_span.set_attribute("gen_ai.provider.name", "google")
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        llm_span.set_attribute("gen_ai.system", "google")
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        llm_span.set_attribute(
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            "gen_ai.request.model", "gemini-2.5-flash-lite")
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        llm_span.set_attribute("gen_ai.request.temperature", 0.5)
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        llm_span.set_attribute("gen_ai.response.model",
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                               "gemini-2.5-flash-lite")
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        llm_span.set_attribute("gen_ai.usage.input_tokens", 50)
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        llm_span.set_attribute("gen_ai.usage.output_tokens", 100)
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        input_messages = [
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            {
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                "role": "user",
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                "parts": [
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                    {
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                        "type": "text",
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                        "content": "Weather in Paris?"
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                    }
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                ]
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            }
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        ]
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        output_messages = [
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            {
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                "role": "assistant",
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                "parts": [
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                    {
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                        "type": "text",
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                        "content": "The weather in Paris is currently rainy with a temperature of 57°F."
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                    }
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                ],
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                "finish_reason": "stop"
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            }
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        ]
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        # 実験6: Input/Outputの属性を削除
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        llm_span.set_attribute("gen_ai.input.messages", json.dumps(
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            input_messages, ensure_ascii=False))
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        llm_span.set_attribute("gen_ai.output.messages", json.dumps(
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            output_messages, ensure_ascii=False))
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        time.sleep(1)
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dummy_llm()