Merge pull request #3 from run-llama/clelia/add-observability

Adding observability dashboard

.env.example

@@ -1,3 +1,6 @@
 OPENAI_API_KEY="sk-***"
 LLAMACLOUD_API_KEY="llx-***"
 ELEVENLABS_API_KEY="sk_***"
+pgql_db="postgres"
+pgql_user="localhost"
+pgql_psw="admin"

.github/workflows/release.yaml

@@ -0,0 +1,21 @@
+name: GitHub Release
+
+on:
+  push:
+    tags:
+      - "v[0-9].[0-9]+.[0-9]+*"
+
+jobs:
+  release:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Create GitHub Release
+        uses: ncipollo/release-action@v1
+        with:
+          generateReleaseNotes: true

README.md

@@ -40,6 +40,12 @@ uv run tools/create_llama_cloud_index.py
 
 And you're ready to set up the app!
 
+Launch Postgres and Jaeger:
+
+```bash
+docker compose up -d
+```
+
 Run the **MCP** server:
 
 ```bash

compose.yaml

@@ -0,0 +1,31 @@
+name: instrumentation
+
+services:
+  jaeger:
+    image: jaegertracing/all-in-one:latest
+    ports:
+      - 16686:16686
+      - 4317:4317
+      - 4318:4318
+      - 9411:9411
+    environment:
+      - COLLECTOR_ZIPKIN_HOST_PORT=:9411
+
+  postgres:
+    image: postgres
+    ports:
+      - 5432:5432
+    environment:
+      POSTGRES_DB: $pgql_db
+      POSTGRES_USER: $pgql_user
+      POSTGRES_PASSWORD: $pgql_psw
+    volumes:
+      - pgdata:/var/lib/postgresql/data
+
+  adminer:
+    image: adminer
+    ports:
+      - "8080:8080"
+
+volumes:
+  pgdata:

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "notebooklm-clone"
-version = "0.1.0"
-description = "Add your description here"
+version = "0.2.0"
+description = "An OSS and LlamaCloud-backed alternative to NotebookLM"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
@@ -15,10 +15,14 @@ dependencies = [
   "llama-index-embeddings-openai>=0.3.1",
   "llama-index-indices-managed-llama-cloud>=0.6.11",
   "llama-index-llms-openai>=0.4.7",
+  "llama-index-observability-otel>=0.1.1",
   "llama-index-tools-mcp>=0.2.5",
   "llama-index-workflows>=1.0.1",
   "mypy>=1.16.1",
+  "opentelemetry-exporter-otlp-proto-http>=1.34.1",
+  "plotly>=6.2.0",
   "pre-commit>=4.2.0",
+  "psycopg2-binary>=2.9.10",
   "pydub>=0.25.1",
   "pytest>=8.4.1",
   "pytest-asyncio>=1.0.0",

src/notebooklm_clone/Home.py

@@ -3,12 +3,36 @@ import io
 import os
 import asyncio
 import tempfile as temp
+from dotenv import load_dotenv
+import time
 import streamlit.components.v1 as components
 
 from pathlib import Path
 from audio import PODCAST_GEN
 from typing import Tuple
 from workflow import NotebookLMWorkflow, FileInputEvent, NotebookOutputEvent
+from instrumentation import OtelTracesSqlEngine
+from llama_index.observability.otel import LlamaIndexOpenTelemetry
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,
+)
+
+load_dotenv()
+
+# define a custom span exporter
+span_exporter = OTLPSpanExporter("http://0.0.0.0:4318/v1/traces")
+
+# initialize the instrumentation object
+instrumentor = LlamaIndexOpenTelemetry(
+    service_name_or_resource="agent.traces",
+    span_exporter=span_exporter,
+    debug=True,
+)
+sql_engine = OtelTracesSqlEngine(
+    engine_url=f"postgresql+psycopg2://{os.getenv('pgql_user')}:{os.getenv('pgql_psw')}@localhost:5432/{os.getenv('pgql_db')}",
+    table_name="agent_traces",
+    service_name="agent.traces",
+)
 
 WF = NotebookLMWorkflow(timeout=600)
 
@@ -24,6 +48,7 @@ async def run_workflow(file: io.BytesIO) -> Tuple[str, str, str, str, str]:
     content = file.getvalue()
     with open(fl.name, "wb") as f:
         f.write(content)
+    st_time = int(time.time() * 1000000)
     ev = FileInputEvent(file=fl.name)
     result: NotebookOutputEvent = await WF.run(start_event=ev)
     q_and_a = ""
@@ -34,7 +59,9 @@ async def run_workflow(file: io.BytesIO) -> Tuple[str, str, str, str, str]:
     mind_map = result.mind_map
     if Path(mind_map).is_file():
         mind_map = read_html_file(mind_map)
-        os.remove(mind_map)
+        os.remove(result.mind_map)
+    end_time = int(time.time() * 1000000)
+    sql_engine.to_sql_database(start_time=st_time, end_time=end_time)
     return result.md_content, result.summary, q_and_a, bullet_points, mind_map
 
 
@@ -138,3 +165,6 @@ if file_input is not None:
 
 else:
     st.info("Please upload a PDF file to get started.")
+
+if __name__ == "__main__":
+    instrumentor.start_registering()

src/notebooklm_clone/instrumentation.py

@@ -0,0 +1,168 @@
+import requests
+import time
+import csv
+import pandas as pd
+import tempfile as temp
+import os
+
+from sqlalchemy import Engine, create_engine, Connection, Result
+from typing import Optional, Dict, Any, List, Literal, Union, cast
+
+
+class OtelTracesSqlEngine:
+    def __init__(
+        self,
+        engine: Optional[Engine] = None,
+        engine_url: Optional[str] = None,
+        table_name: Optional[str] = None,
+        service_name: Optional[str] = None,
+    ):
+        self.service_name: str = service_name or "service"
+        self.table_name: str = table_name or "otel_traces"
+        self._connection: Optional[Connection] = None
+        if engine:
+            self._engine: Engine = engine
+        elif engine_url:
+            self._engine = create_engine(url=engine_url)
+        else:
+            raise ValueError("One of engine or engine_setup_kwargs must be set")
+
+    def _connect(self) -> None:
+        self._connection = self._engine.connect()
+
+    def _export(
+        self,
+        start_time: Optional[int] = None,
+        end_time: Optional[int] = None,
+        limit: Optional[int] = None,
+    ) -> Dict[str, Any]:
+        url = "http://localhost:16686/api/traces"
+        params = {
+            "service": self.service_name,
+            "start": start_time
+            or int(time.time() * 1000000) - (24 * 60 * 60 * 1000000),
+            "end": end_time or int(time.time() * 1000000),
+            "limit": limit or 1000,
+        }
+        response = requests.get(url, params=params)
+        print(response.json())
+        return response.json()
+
+    def _to_pandas(self, data: Dict[str, Any]) -> pd.DataFrame:
+        rows: List[Dict[str, Any]] = []
+        # Loop over each trace
+        for trace in data.get("data", []):
+            trace_id = trace.get("traceID")
+            service_map = {
+                pid: proc.get("serviceName")
+                for pid, proc in trace.get("processes", {}).items()
+            }
+
+            for span in trace.get("spans", []):
+                span_id = span.get("spanID")
+                operation = span.get("operationName")
+                start = span.get("startTime")
+                duration = span.get("duration")
+                process_id = span.get("processID")
+                service = service_map.get(process_id, "")
+                status = next(
+                    (
+                        tag.get("value")
+                        for tag in span.get("tags", [])
+                        if tag.get("key") == "otel.status_code"
+                    ),
+                    "",
+                )
+                parent_span_id = None
+                if span.get("references"):
+                    parent_span_id = span["references"][0].get("spanID")
+
+                rows.append(
+                    {
+                        "trace_id": trace_id,
+                        "span_id": span_id,
+                        "parent_span_id": parent_span_id,
+                        "operation_name": operation,
+                        "start_time": start,
+                        "duration": duration,
+                        "status_code": status,
+                        "service_name": service,
+                    }
+                )
+
+        # Define the CSV header
+        fieldnames = [
+            "trace_id",
+            "span_id",
+            "parent_span_id",
+            "operation_name",
+            "start_time",
+            "duration",
+            "status_code",
+            "service_name",
+        ]
+
+        fl = temp.NamedTemporaryFile(suffix=".csv", delete=False, delete_on_close=False)
+        # Write to CSV
+        with open(fl.name, "w", newline="") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(rows)
+
+        df = pd.read_csv(fl)
+        os.remove(fl.name)
+        return df
+
+    def _to_sql(
+        self,
+        dataframe: pd.DataFrame,
+        if_exists_policy: Optional[Literal["fail", "replace", "append"]] = None,
+    ) -> None:
+        if not self._connection:
+            self._connect()
+        dataframe.to_sql(
+            name=self.table_name,
+            con=self._connection,
+            if_exists=if_exists_policy or "append",
+        )
+
+    def to_sql_database(
+        self,
+        start_time: Optional[int] = None,
+        end_time: Optional[int] = None,
+        limit: Optional[int] = None,
+        if_exists_policy: Optional[Literal["fail", "replace", "append"]] = None,
+    ) -> None:
+        data = self._export(start_time=start_time, end_time=end_time, limit=limit)
+        df = self._to_pandas(data=data)
+        self._to_sql(dataframe=df, if_exists_policy=if_exists_policy)
+
+    def execute(
+        self,
+        statement: Any,
+        parameters: Optional[Any] = None,
+        execution_options: Optional[Any] = None,
+        return_pandas: bool = False,
+    ) -> Union[Result, pd.DataFrame]:
+        if not self._connection:
+            self._connect()
+        if not return_pandas:
+            self._connection = cast(Connection, self._connection)
+            return self._connection.execute(
+                statement=statement,
+                parameters=parameters,
+                execution_options=execution_options,
+            )
+        return pd.read_sql(sql=statement, con=self._connection)
+
+    def to_pandas(
+        self,
+    ) -> pd.DataFrame:
+        if not self._connection:
+            self._connect()
+        return pd.read_sql_table(table_name=self.table_name, con=self._connection)
+
+    def disconnect(self) -> None:
+        if not self._connection:
+            raise ValueError("Engine was never connected!")
+        self._engine.dispose(close=True)

src/notebooklm_clone/pages/2_Observability_Dashboard.py

@@ -0,0 +1,169 @@
+import sys
+import os
+import pandas as pd
+import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from dotenv import load_dotenv
+from instrumentation import OtelTracesSqlEngine
+from sqlalchemy import text
+
+load_dotenv()
+
+sql_engine = OtelTracesSqlEngine(
+    engine_url=f"postgresql+psycopg2://{os.getenv('pgql_user')}:{os.getenv('pgql_psw')}@localhost:5432/{os.getenv('pgql_db')}",
+    table_name="agent_traces",
+    service_name="agent.traces",
+)
+
+
+def display_sql() -> pd.DataFrame:
+    query = """CREATE TABLE IF NOT EXISTS agent_traces (
+    trace_id TEXT NOT NULL,
+    span_id TEXT NOT NULL,
+    parent_span_id TEXT NULL,
+    operation_name TEXT NOT NULL,
+    start_time BIGINT NOT NULL,
+    duration INTEGER NOT NULL,
+    status_code TEXT NOT NULL,
+    service_name TEXT NOT NULL
+    );"""
+    sql_engine.execute(text(query))
+    return sql_engine.to_pandas()
+
+
+def filter_traces(sql_query: str):
+    df = sql_engine.execute(text(sql_query), return_pandas=True)
+    return df
+
+
+def create_latency_chart(df: pd.DataFrame):
+    """Create a line chart showing latency (duration) over time"""
+    if df.empty:
+        st.warning("No data available for latency chart")
+        return
+
+    # Convert start_time from nanoseconds to datetime
+    df_chart = df.copy()
+    progressive_count = list(range(0, len(df_chart["start_time"])))
+    df_chart["progressive_count"] = progressive_count
+
+    fig = px.line(
+        df_chart,
+        x="progressive_count",
+        y="duration",
+        title="Latency Overview",
+        labels={"duration": "Duration (ns)", "timestamp": "Time"},
+        hover_data=["operation_name", "status_code"],
+    )
+
+    fig.update_layout(
+        xaxis_title="Time", yaxis_title="Duration (nanoseconds)", hovermode="x unified"
+    )
+
+    st.plotly_chart(fig)
+
+
+def create_status_pie_chart(df: pd.DataFrame):
+    """Create a pie chart showing status code distribution"""
+    if df.empty:
+        st.warning("No data available for status code chart")
+        return
+
+    # Count status codes
+    status_counts = df["status_code"].value_counts()
+
+    # Map common status codes to more readable labels
+    status_labels = {
+        "OK": "OK",
+        "ERROR": "ERROR",
+        "UNSET": "UNSET",
+        "200": "OK (200)",
+        "500": "ERROR (500)",
+        "404": "ERROR (404)",
+    }
+
+    # Create labels and values for the pie chart
+    labels = [status_labels.get(status, status) for status in status_counts.index]
+    values = status_counts.values
+
+    # Define colors
+    colors = []
+    for status in status_counts.index:
+        if status in ["OK", "200"]:
+            colors.append("#28a745")  # Green for OK
+        elif status in ["ERROR", "500", "404"]:
+            colors.append("#dc3545")  # Red for ERROR
+        else:
+            colors.append("#6c757d")  # Gray for others
+
+    fig = go.Figure(
+        data=[go.Pie(labels=labels, values=values, hole=0.3, marker_colors=colors)]
+    )
+
+    fig.update_layout(
+        title="Status Code Distribution",
+        annotations=[dict(text="Status", x=0.5, y=0.5, font_size=20, showarrow=False)],
+    )
+
+    st.plotly_chart(fig)
+
+
+# Streamlit UI
+st.set_page_config(page_title="NotebookLlaMa - Observability Dashboard", page_icon="🔍")
+
+st.sidebar.header("Observability Dashboard🔍")
+st.sidebar.info("To switch to the other pages, select them from above!🔺")
+st.markdown("---")
+st.markdown("## NotebookLlaMa - Observability Dashboard🔍")
+
+# Get the data
+df_data = display_sql()
+
+# Charts section
+st.markdown("## 📊 Analytics Overview")
+
+if not df_data.empty:
+    col1, col2 = st.columns(2)
+
+    with col1:
+        create_latency_chart(df_data)
+
+    with col2:
+        create_status_pie_chart(df_data)
+else:
+    st.info("No trace data available yet. Charts will appear once data is collected.")
+
+st.markdown("---")
+
+# SQL Query section
+st.markdown("### SQL Query")
+sql_query = st.text_input(label="")
+
+st.markdown("## Traces Table")
+dataframe = st.dataframe(data=df_data)
+
+if st.button("Run SQL query", type="primary"):
+    if sql_query.strip():
+        try:
+            filtered_df = filter_traces(sql_query=sql_query)
+            st.markdown("### Query Results")
+            dataframe = st.dataframe(data=filtered_df)
+
+            # Update charts with filtered data
+            if not filtered_df.empty:
+                st.markdown("### Updated Charts")
+                col1, col2 = st.columns(2)
+
+                with col1:
+                    create_latency_chart(filtered_df)
+
+                with col2:
+                    create_status_pie_chart(filtered_df)
+        except Exception as e:
+            st.error(f"Error executing query: {str(e)}")
+    else:
+        st.warning("Please enter a SQL query")

tests/test_sql_engine.py

@@ -0,0 +1,84 @@
+import socket
+import pytest
+import pandas as pd
+import os
+from dotenv import load_dotenv
+
+from src.notebooklm_clone.instrumentation import OtelTracesSqlEngine
+from sqlalchemy import text
+
+ENV = load_dotenv()
+
+
+def is_port_open(host: str, port: int, timeout: float = 2.0) -> bool:
+    """Check if a TCP port is open on a given host."""
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+        sock.settimeout(timeout)
+        result = sock.connect_ex((host, port))
+        return result == 0
+
+
+@pytest.fixture()
+def otel_data() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "trace_id": ["abc123", "abc123", "def456"],
+            "span_id": ["span1", "span2", "span3"],
+            "parent_span_id": [None, "span1", "span2"],
+            "operation_name": [
+                "ServiceA.handle_request",
+                "ServiceA.query_db",
+                "ServiceB.send_email",
+            ],
+            "start_time": [1750618321000000, 1750618321000100, 1750618321000200],
+            "duration": [150, 300, 500],
+            "status_code": ["OK", "OK", "ERROR"],
+            "service_name": ["service-a", "service-a", "service-b"],
+        }
+    )
+
+
+@pytest.mark.skipif(
+    condition=not is_port_open(host="localhost", port=5432) and not ENV,
+    reason="Either Postgres is currently unavailable or you did not set any env variables in a .env file",
+)
+def test_engine(otel_data: pd.DataFrame) -> None:
+    engine_url = f"postgresql+psycopg2://{os.getenv('pgql_user')}:{os.getenv('pgql_psw')}@localhost:5432/{os.getenv('pgql_db')}"
+    sql_engine = OtelTracesSqlEngine(engine_url=engine_url, table_name="test")
+    res = sql_engine.execute(text("DROP TABLE IF EXISTS test;"))
+    res.close()
+    sql_engine._to_sql(dataframe=otel_data)
+    res1 = sql_engine.execute(
+        text(
+            "SELECT span_id, operation_name, duration FROM test WHERE status_code = 'ERROR'"
+        )
+    )
+    res2 = sql_engine.execute(
+        text(
+            "SELECT service_name, AVG(duration) AS avg_duration FROM test GROUP BY service_name;"
+        )
+    )
+    res1_data = res1.fetchall()
+    res2_data = res2.fetchall()
+
+    # Compare just the values
+    assert len(res1_data) == 1
+    assert res1_data[0].span_id == "span3"
+    assert res1_data[0].operation_name == "ServiceB.send_email"
+    assert res1_data[0].duration == 500
+
+    assert len(res2_data) == 2
+    # Sort by service_name for consistent comparison
+    res2_sorted = sorted(res2_data, key=lambda x: x.service_name)
+    assert res2_sorted[0].service_name == "service-a"
+    assert res2_sorted[0].avg_duration == 225.0
+    assert res2_sorted[1].service_name == "service-b"
+    assert res2_sorted[1].avg_duration == 500.0
+    assert isinstance(sql_engine.to_pandas(), pd.DataFrame)
+    res3 = sql_engine.execute(
+        text(
+            "SELECT service_name, AVG(duration) AS avg_duration FROM test GROUP BY service_name;"
+        ),
+        return_pandas=True,
+    )
+    assert isinstance(res3, pd.DataFrame)

uv.lock

@@ -544,6 +544,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1d/9a/4114a9057db2f1462d5c8f8390ab7383925fe1ac012eaa42402ad65c2963/GitPython-3.1.44-py3-none-any.whl", hash = "sha256:9e0e10cda9bed1ee64bc9a6de50e7e38a9c9943241cd7f585f6df3ed28011110", size = 207599, upload-time = "2025-01-02T07:32:40.731Z" },
 ]
 
+[[package]]
+name = "googleapis-common-protos"
+version = "1.70.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "protobuf" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/39/24/33db22342cf4a2ea27c9955e6713140fedd51e8b141b5ce5260897020f1a/googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257", size = 145903, upload-time = "2025-04-14T10:17:02.924Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/86/f1/62a193f0227cf15a920390abe675f386dec35f7ae3ffe6da582d3ade42c7/googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8", size = 294530, upload-time = "2025-04-14T10:17:01.271Z" },
+]
+
 [[package]]
 name = "greenlet"
 version = "3.2.3"
@@ -644,6 +656,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442, upload-time = "2024-09-15T18:07:37.964Z" },
 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "8.7.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "zipp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641, upload-time = "2025-04-27T15:29:01.736Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656, upload-time = "2025-04-27T15:29:00.214Z" },
+]
+
 [[package]]
 name = "iniconfig"
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