chore: import upstream snapshot with attribution

examples/python/langfuse/seed.py

@@ -0,0 +1,318 @@
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2026 @ Strukto.AI All Rights Reserved. =========
+
+import os
+import time
+
+from dotenv import load_dotenv
+from langfuse import Langfuse, propagate_attributes
+
+load_dotenv("/Users/zecheng/strukto/mirage/.env.development")
+
+langfuse = Langfuse(
+    public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
+    secret_key=os.environ["LANGFUSE_SECRET_KEY"],
+    host=os.environ["LANGFUSE_HOST"],
+)
+
+print("Auth check:", langfuse.auth_check())
+
+print("\n=== Creating prompts ===")
+
+for name, prompt_text, cfg, label in [
+    ("summarize",
+     "Summarize the following text in {{style}} style:\n\n{{text}}", {
+         "model": "gpt-4o",
+         "temperature": 0.3
+     }, "production"),
+    ("summarize",
+     "You are an expert summarizer. Summarize in {{style}} style:\n\n{{text}}",
+     {
+         "model": "gpt-4o",
+         "temperature": 0.2
+     }, "staging"),
+    ("classify",
+     "Classify the sentiment as positive, negative, or neutral:\n\n{{text}}", {
+         "model": "gpt-4o-mini",
+         "temperature": 0
+     }, "production"),
+    ("extract-entities",
+     "Extract all named entities. Return as JSON.\n\n{{text}}", {
+         "model": "gpt-4o",
+         "temperature": 0
+     }, "production"),
+]:
+    langfuse.create_prompt(
+        name=name,
+        type="text",
+        prompt=prompt_text,
+        config=cfg,
+        labels=[label],
+    )
+    print(f"  created: {name} [{label}]")
+
+print("\n=== Creating traces ===")
+
+traces_data = [
+    {
+        "trace_name": "chat-completion",
+        "session_id": "chat-session-001",
+        "user_id": "user-alice",
+        "tags": ["chat", "geography"],
+        "input": {
+            "messages": [{
+                "role": "user",
+                "content": "What is the capital of France?"
+            }]
+        },
+        "output": {
+            "response": "The capital of France is Paris."
+        },
+        "model": "gpt-4o",
+    },
+    {
+        "trace_name": "chat-completion",
+        "session_id": "chat-session-001",
+        "user_id": "user-alice",
+        "tags": ["chat", "geography"],
+        "input": {
+            "messages": [{
+                "role": "user",
+                "content": "Tell me more about Paris"
+            }]
+        },
+        "output": {
+            "response":
+            "Paris is known for the Eiffel Tower and Louvre Museum."
+        },
+        "model": "gpt-4o",
+    },
+    {
+        "trace_name": "chat-completion",
+        "session_id": "chat-session-002",
+        "user_id": "user-bob",
+        "tags": ["chat", "science"],
+        "input": {
+            "messages": [{
+                "role": "user",
+                "content": "Explain quantum computing"
+            }]
+        },
+        "output": {
+            "response":
+            "Quantum computing uses qubits that can be in superposition..."
+        },
+        "model": "gpt-4o",
+    },
+    {
+        "trace_name": "summarize-document",
+        "session_id": "chat-session-002",
+        "user_id": "user-bob",
+        "tags": ["summarization", "science"],
+        "input": {
+            "text": "A long research paper about quantum computing..."
+        },
+        "output": {
+            "summary":
+            "This paper introduces a novel approach to error correction..."
+        },
+        "model": "gpt-4o",
+    },
+    {
+        "trace_name": "support-classify",
+        "session_id": "support-ticket-101",
+        "user_id": "user-charlie",
+        "tags": ["support", "classification"],
+        "input": {
+            "text": "I can't log in, keeps showing error 403"
+        },
+        "output": {
+            "category": "authentication",
+            "priority": "high"
+        },
+        "model": "gpt-4o-mini",
+    },
+    {
+        "trace_name": "support-respond",
+        "session_id": "support-ticket-101",
+        "user_id": "user-charlie",
+        "tags": ["support", "response"],
+        "input": {
+            "ticket": "Can't log in, error 403"
+        },
+        "output": {
+            "response": "Please try clearing your browser cookies..."
+        },
+        "model": "gpt-4o",
+    },
+    {
+        "trace_name": "entity-extraction",
+        "user_id": "user-alice",
+        "tags": ["extraction", "ner"],
+        "input": {
+            "text": "Apple CEO Tim Cook announced new products in Cupertino."
+        },
+        "output": {
+            "entities": [{
+                "name": "Apple",
+                "type": "ORG"
+            }, {
+                "name": "Tim Cook",
+                "type": "PERSON"
+            }]
+        },
+        "model": "gpt-4o",
+    },
+    {
+        "trace_name": "chat-completion",
+        "user_id": "user-dave",
+        "tags": ["chat", "creative"],
+        "input": {
+            "messages": [{
+                "role": "user",
+                "content": "Write a haiku about programming"
+            }]
+        },
+        "output": {
+            "response":
+            "Code flows like water\n"
+            "Bugs swim in the logic stream\n"
+            "Debug, compile, run",
+        },
+        "model": "gpt-4o",
+    },
+]
+
+for td in traces_data:
+    with propagate_attributes(
+            trace_name=td["trace_name"],
+            session_id=td.get("session_id"),
+            user_id=td.get("user_id"),
+            tags=td.get("tags"),
+            metadata={"env": "production"},
+    ):
+        with langfuse.start_as_current_observation(
+                name=td["trace_name"],
+                as_type="span",
+                input=td["input"],
+                output=td["output"],
+        ):
+            with langfuse.start_as_current_observation(
+                    name=f"{td['trace_name']}-llm",
+                    as_type="generation",
+                    model=td["model"],
+                    input=td["input"],
+                    output=td["output"],
+                    usage_details={
+                        "input_tokens": 50 + len(str(td["input"])),
+                        "output_tokens": 30 + len(str(td["output"])),
+                    },
+            ):
+                pass
+    print(f"  created: {td['trace_name']} "
+          f"(session={td.get('session_id', '-')})")
+
+print(f"\n  total: {len(traces_data)} traces")
+
+print("\n=== Creating datasets ===")
+
+langfuse.create_dataset(name="qa-eval", description="QA evaluation")
+
+qa_items = [
+    ({
+        "question": "What is the capital of France?"
+    }, {
+        "answer": "Paris"
+    }),
+    ({
+        "question": "Who wrote Romeo and Juliet?"
+    }, {
+        "answer": "Shakespeare"
+    }),
+    ({
+        "question": "What is the speed of light?"
+    }, {
+        "answer": "299,792,458 m/s"
+    }),
+    ({
+        "question": "What is the largest planet?"
+    }, {
+        "answer": "Jupiter"
+    }),
+    ({
+        "question": "Who painted the Mona Lisa?"
+    }, {
+        "answer": "da Vinci"
+    }),
+]
+for inp, exp in qa_items:
+    langfuse.create_dataset_item(
+        dataset_name="qa-eval",
+        input=inp,
+        expected_output=exp,
+    )
+print(f"  qa-eval: {len(qa_items)} items")
+
+langfuse.create_dataset(name="sentiment-eval",
+                        description="Sentiment classification")
+
+sent_items = [
+    ({
+        "text": "I love this product!"
+    }, {
+        "sentiment": "positive"
+    }),
+    ({
+        "text": "Terrible experience"
+    }, {
+        "sentiment": "negative"
+    }),
+    ({
+        "text": "It was okay"
+    }, {
+        "sentiment": "neutral"
+    }),
+    ({
+        "text": "Best purchase ever"
+    }, {
+        "sentiment": "positive"
+    }),
+    ({
+        "text": "Completely broken"
+    }, {
+        "sentiment": "negative"
+    }),
+]
+for inp, exp in sent_items:
+    langfuse.create_dataset_item(
+        dataset_name="sentiment-eval",
+        input=inp,
+        expected_output=exp,
+    )
+print(f"  sentiment-eval: {len(sent_items)} items")
+
+print("\n=== Flushing ===")
+langfuse.flush()
+time.sleep(3)
+
+print("\n=== Verifying ===")
+traces = langfuse.fetch_traces(limit=5)
+print(f"  traces: {len(traces.data)} found")
+for t in traces.data[:3]:
+    print(f"    {t.name} (session={t.session_id})")
+
+prompt = langfuse.get_prompt("summarize")
+print(f"  summarize prompt: v{prompt.version}")
+
+print("\nDone!")