first version

.env.example

@@ -1,3 +1,5 @@
 pgql_db="postgres"
 pgql_user="localhost"
-pgql_psw="admin"
+pgql_psw="admin"
+OPENAI_API_KEY="sk-***"
+LLAMACLOUD_API_KEY="llx-***"

CONTRIBUTING.md

@@ -0,0 +1,48 @@
+# Contributing to `agents-observability-demo`
+
+Do you want to contribute to this project? Make sure to read this guidelines first :)
+
+## Issue
+
+**When to do it**:
+
+- You found bugs but you don't know how to solve them or don't have time/will to do the solve
+- You want new features but you don't know how to implement them or don't have time/will to do the implementation
+
+> ⚠️ _Always check open and closed issues before you submit yours to avoid duplicates_
+
+**How to do it**:
+
+- Open an issue
+- Give the issue a meaningful title (short but effective problem/feature request description)
+- Describe the problem/feature request
+
+## Traditional contribution
+
+**When to do it**:
+
+- You found bugs and corrected them
+- You optimized/improved the code
+- You added new features that you think could be useful to others
+
+**How to do it**:
+
+1. Fork this repository
+2. Install `pre-commit` and make sure to have it within the Git Hooks for your fork:
+
+```bash
+pip install pre-commit
+pre-commit install
+```
+
+3. Change the things you want, and make sure tests still pass or add new ones:
+
+```bash
+pytest tests/test_*.py
+```
+
+3. Commit your changes
+4. Make sure your changes pass the pre-commit linting/type checking, if not modify them so that they pass
+5. Submit pull request (make sure to provide a thorough description of the changes)
+
+### Thanks for contributing!

LICENSE

@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) Clelia Astra Bertelli
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

README.md

@@ -0,0 +1,71 @@
+# Agentic Observability Demo
+
+## Your Accurate Flight Assistant
+
+This demo showcases a solution for agent observability and tracing by building on the LlamaIndex x OpenTelemetry integration.
+
+It traces the activity of an agent capable of extracting information from a University course syllabus (with LlamaExtract) and answer questions about it (with a LlamaCloud Index in the backend).
+
+> _The agents tool are served via MCP!_
+
+### Get it up and running!
+
+Get the GitHub repository:
+
+```bash
+git clone https://github.com/AstraBert/agents-observability-demo
+```
+
+Install dependencies:
+
+```bash
+cd agents-observability-demo/
+uv sync
+```
+
+And then modify the `.env.example` file with your API keys and move it to `.env`.
+
+```bash
+mv .env.example .env
+```
+
+Now, you will have to execute the following scripts:
+
+```bash
+uv run tools/create_llama_extract_agent.py
+uv run tools/create_llama_cloud_index.py
+```
+
+You're ready to set up the app!
+
+Launch a local Postgres database + a local Jaeger instance to record OpenTelemetry traces:
+
+```bash
+docker compose up -d
+```
+
+Run the MCP server:
+
+```bash
+uv run src/agents_observability_demo/server.py
+```
+
+In a separate window, run the websocket:
+
+```bash
+uv run src/agents_observability_demo/websocket.py
+```
+
+Last, run the Gradio frontend, and start exploring at http://localhost:7860:
+
+```bash
+uv run src/agents_observability_demo/main.py
+```
+
+### Contributing
+
+Contribute to this project following the [guidelines](./CONTRIBUTING.md).
+
+### License
+
+This project is provided under an [MIT License](LICENSE).

pyproject.toml

@@ -5,8 +5,14 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
-  "llama-index-llms-ollama>=0.6.2",
+  "fastmcp>=2.9.0",
+  "gradio>=5.34.2",
+  "llama-cloud-services>=0.6.36",
+  "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",
   "mypy>=1.16.1",
   "opentelemetry-exporter-otlp-proto-http>=1.34.1",
   "pandas>=2.3.0",
@@ -15,7 +21,8 @@ dependencies = [
   "psycopg2-binary>=2.9.10",
   "pytest>=8.4.1",
   "python-dotenv>=1.1.0",
-  "sqlalchemy>=2.0.41"
+  "sqlalchemy>=2.0.41",
+  "websockets>=15.0.1"
 ]
 
 [tool.mypy]

src/agents_observability/main.py

@@ -1 +0,0 @@
-"""Main script"""

src/agents_observability_demo/agent.py

@@ -0,0 +1,28 @@
+import os
+from dotenv import load_dotenv
+
+from llama_index.tools.mcp import McpToolSpec, BasicMCPClient
+from llama_index.core.agent.workflow import FunctionAgent
+from llama_index.llms.openai import OpenAI
+
+load_dotenv()
+
+llm = OpenAI(model="gpt-4.1", api_key=os.getenv("OPENAI_API_KEY"))
+mcp_client = BasicMCPClient(command_or_url="http://localhost:8000/mcp")
+tool_spec = McpToolSpec(client=mcp_client)
+tools = tool_spec.to_tool_list()
+
+agent = FunctionAgent(
+    name="SyllabusAgent",
+    description="Agent to extract information about course syllabus and to answer questions about it.",
+    tools=tools,
+    system_prompt="""
+    You are SyllabusAgent. You have two main tasks:
+    1. Extract information from a syllabus file (in PDF format) and return a summary of that information to the user. Use the 'syllabus_extractor_tool' for this task. Always report to the user the information you extracted in a human-readable format.
+    2. Answer questions about courses syllabi. Use the 'answer_questions_tool' for this task. Always report the answer to the user.
+
+    Choose the tools based on the task you are asked to perfom.
+    """,
+    llm=llm,
+    timeout=600,
+)

src/agents_observability_demo/main.py

@@ -0,0 +1,111 @@
+"""Main script"""
+
+import websockets
+import gradio as gr
+import os
+import pandas as pd
+
+from dotenv import load_dotenv
+from typing import Optional
+from utils 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",
+)
+
+
+async def websocket_chat(question: str, file: Optional[str]):
+    uri = "ws://localhost:8765"
+    try:
+        async with websockets.connect(uri) as websocket:
+            if file:
+                prompt = file
+            else:
+                prompt = question
+            await websocket.send(prompt)
+            full_response = ""
+
+            while True:
+                message = await websocket.recv()
+                if message == "[END]":
+                    break
+                full_response += message
+                yield full_response
+            yield full_response
+
+    except Exception as e:
+        yield f"Error: {e}"
+
+
+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)
+    print(df)
+    return df
+
+
+def launch_interface():
+    with gr.Blocks(
+        theme=gr.themes.Citrus(primary_hue="indigo", secondary_hue="teal")
+    ) as frontend:
+        gr.HTML("<h1 align='center'>Syllabus Extraction Agent</h1>")
+        gr.HTML(
+            "<h2 align='center'>Extract information and ask questions about your Uni courses!</h2>"
+        )
+        with gr.Row():
+            with gr.Column():
+                file_upload = gr.File(
+                    value=None, label="Uplod Syllabus File", file_types=[".pdf", ".PDF"]
+                )
+                usr_txt = gr.Textbox(label="Prompt", placeholder="Ask a question...")
+        with gr.Row():
+            resp = gr.Markdown(
+                label="Agent Output",
+                container=True,
+                show_label=True,
+                show_copy_button=True,
+            )
+        with gr.Row():
+            gr.Button("Submit!").click(
+                fn=websocket_chat, inputs=[usr_txt, file_upload], outputs=[resp]
+            )
+
+    with gr.Blocks(
+        theme=gr.themes.Citrus(primary_hue="indigo", secondary_hue="teal")
+    ) as traces:
+        gr.HTML("<h1 align='center'>Agent Traces</h1>")
+        gr.HTML("<h2 align='center'>Monitor information about your agent</h2>")
+        with gr.Row():
+            with gr.Column():
+                sql_query = gr.Textbox(label="Query SQL database")
+                btn = gr.Button("Query")
+                df_display = gr.DataFrame(value=display_sql(), label="Traces")
+            btn.click(fn=filter_traces, inputs=[sql_query], outputs=[df_display])
+
+    iface = gr.TabbedInterface(
+        interface_list=[frontend, traces], tab_names=["Agent", "Traces"]
+    )
+    iface.launch()
+
+
+if __name__ == "__main__":
+    launch_interface()

src/agents_observability_demo/models.py

@@ -0,0 +1,30 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+
+class Syllabus(BaseModel):
+    course_name: str = Field(description="Name of the course.")
+    teaching_languages: List[str] = Field(
+        description="Languages in which the course is taught. Assume the language to be the one in which the document is written, if teaching languages are not provided."
+    )
+    course_year: int = Field(
+        description="Year in which the course is offered (use 1 for 1st year, 2 for 2nd year, etc.)",
+        examples=[1, 2, 6, 3, 4, 5],
+        ge=1,
+        le=6,
+    )
+    keywords: List[str] = Field(
+        description="Keywords to describe the course, if any.",
+        default_factory=list,
+    )
+    summary: str = Field(
+        description="Summary of the course.",
+    )
+    learning_objectives: List[str] = Field(
+        description="List of learning objectives, if any.",
+        default_factory=list,
+    )
+    evaluation_method: Optional[str] = Field(
+        description="How is the evaluation of the course performed, if there is an evaluation. Leave empty if there is no evaluation.",
+        default=None,
+    )

src/agents_observability_demo/server.py

@@ -0,0 +1,49 @@
+from utils import answer_question_about_course, extract_syllabus
+from fastmcp import FastMCP
+from typing import Union, Literal
+
+mcp: FastMCP = FastMCP("Syllabus Extraction MCP")
+
+SYLLABUS_EXTRACTOR_TOOL_DESCRIPTION = """
+Tool to extract information from a syllabus PDF file.
+
+Args:
+    file_path (str): The path to the file containing the syllabus.
+
+Returns:
+    str: the extracted information from the syllabus in JSON-like format, if any.
+"""
+
+ANSWER_QUESTIONS_TOOL_DESCRIPTION = """
+Tool to answer question about a course syllabus.
+
+Args:
+    question (str): The question about the syllabus.
+
+Returns:
+    str: The answer to the question, if any.
+"""
+
+
+@mcp.tool(
+    name="syllabus_extractor_tool", description=SYLLABUS_EXTRACTOR_TOOL_DESCRIPTION
+)
+async def syllabus_extractor_tool(
+    file_path: str,
+) -> Union[str, Literal["Sorry, no information could be extracted from the syllabus."]]:
+    extraction_info = await extract_syllabus(filename=file_path)
+    if not extraction_info:
+        return "Sorry, no information could be extracted from the syllabus."
+    return extraction_info
+
+
+@mcp.tool(name="answer_questions_tool", description=ANSWER_QUESTIONS_TOOL_DESCRIPTION)
+async def answer_questions_tool(question: str):
+    response = await answer_question_about_course(question=question)
+    if not response:
+        return "Sorry, no answer could be found for this question."
+    return response
+
+
+if __name__ == "__main__":
+    mcp.run("streamable-http")

src/agents_observability_demo/utils.py

@@ -4,9 +4,32 @@ import csv
 import pandas as pd
 import tempfile as temp
 import os
+import json
 
+from dotenv import load_dotenv
 from sqlalchemy import Engine, create_engine, Connection, Result
-from typing import Optional, Dict, Any, List, Literal, Union
+from typing import Optional, Dict, Any, List, Literal, Union, cast
+from llama_cloud_services import LlamaExtract
+from llama_cloud.client import AsyncLlamaCloud
+from llama_index.indices.managed.llama_cloud import LlamaCloudIndex
+from llama_index.llms.openai import OpenAI
+
+load_dotenv()
+if (
+    os.getenv("LLAMACLOUD_API_KEY", None)
+    and os.getenv("EXTRACT_AGENT_ID", None)
+    and os.getenv("LLAMACLOUD_PIPELINE_ID", None)
+    and os.getenv("OPENAI_API_KEY", None)
+):
+    LLM = OpenAI(model="gpt-4.1", api_key=os.getenv("OPENAI_API_KEY"))
+    CLIENT = AsyncLlamaCloud(token=os.getenv("LLAMACLOUD_API_KEY"))
+    EXTRACT_AGENT = LlamaExtract(api_key=os.getenv("LLAMACLOUD_API_KEY")).get_agent(
+        id=os.getenv("EXTRACT_AGENT_ID")
+    )
+    PIPELINE_ID = os.getenv("LLAMACLOUD_PIPELINE_ID")
+    QE = LlamaCloudIndex(
+        api_key=os.getenv("LLAMACLOUD_API_KEY"), pipeline_id=PIPELINE_ID
+    ).as_query_engine(llm=LLM)
 
 
 class OtelTracesSqlEngine:
@@ -45,6 +68,7 @@ class OtelTracesSqlEngine:
             "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:
@@ -141,16 +165,44 @@ class OtelTracesSqlEngine:
         statement: str,
         parameters: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
         execution_options: Optional[Dict[str, Any]] = None,
-    ) -> Result:
+        return_pandas: bool = False,
+    ) -> Union[Result, pd.DataFrame]:
         if not self._connection:
             self._connect()
-        return self._connection.execute(
-            statement=statement,
-            parameters=parameters,
-            execution_options=execution_options,
-        )
+        if not return_pandas:
+            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)
+
+
+async def extract_syllabus(filename: str) -> Union[str, None]:
+    with open(filename, "rb") as f:
+        file = await CLIENT.files.upload_file(upload_file=f)
+    files = [{"file_id": file.id}]
+    await CLIENT.pipelines.add_files_to_pipeline_api(
+        pipeline_id=PIPELINE_ID, request=files
+    )
+    extraction_output = await EXTRACT_AGENT.aextract(files=filename)
+    if extraction_output:
+        return json.dumps(extraction_output.data, indent=4)
+    return None
+
+
+async def answer_question_about_course(question: str) -> Union[None, str]:
+    answer = await QE.aquery(question)
+    return cast(Union[None, str], answer.response)

src/agents_observability_demo/websocket.py

@@ -0,0 +1,63 @@
+import json
+import asyncio
+import websockets
+import time
+import os
+
+from dotenv import load_dotenv
+from agent import agent
+from utils import OtelTracesSqlEngine
+from llama_index.observability.otel import LlamaIndexOpenTelemetry
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,
+)
+from llama_index.core.agent.workflow.workflow_events import ToolCall, ToolCallResult
+
+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",
+)
+
+
+async def run_agent(websocket):
+    async for prompt in websocket:
+        handler = agent.run(user_msg=prompt)
+        start_time = int(time.time() * 1000000)
+        async for event in handler.stream_events():
+            if isinstance(event, ToolCallResult):
+                await websocket.send(
+                    f"**Result from `{event.tool_name}`**:\n\n{event.tool_output.content}\n\n"
+                )
+            elif isinstance(event, ToolCall):
+                await websocket.send(
+                    f"### Calling tool: `{event.tool_name}`\n\n```json\n{json.dumps(event.tool_kwargs, indent=4)}\n```\n\n"
+                )
+        response = await handler
+        response = str(response)
+        end_time = int(time.time() * 1000000)
+        await websocket.send("### Final output\n\n" + response)
+        await websocket.send("[END]")
+        sql_engine.to_sql_database(start_time=start_time, end_time=end_time)
+
+
+async def main():
+    instrumentor.start_registering()
+    print("Starting server on ws://localhost:8765")
+    async with websockets.serve(run_agent, "localhost", 8765):
+        await asyncio.Future()  # Run forever
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

tests/test_models.py

@@ -0,0 +1,23 @@
+import pytest
+
+from src.agents_observability_demo.models import Syllabus
+from pydantic_core import ValidationError
+
+
+def test_syllabus_model() -> None:
+    s1 = Syllabus(
+        course_name="English Literature",
+        teaching_languages=["English"],
+        summary="Some summary.",
+        course_year=1,
+    )
+    assert s1.keywords == []
+    assert s1.learning_objectives == []
+    assert s1.evaluation_method is None
+    with pytest.raises(ValidationError):
+        Syllabus(
+            course_name="Some name",
+            teaching_languages=["Italian"],
+            summary="Hello",
+            course_year=7,
+        )

tests/test_sql_engine.py

@@ -3,7 +3,7 @@ import pytest
 import pandas as pd
 import os
 
-from src.agents_observability.utils import OtelTracesSqlEngine
+from src.agents_observability_demo.utils import OtelTracesSqlEngine
 from sqlalchemy import text
 from dotenv import load_dotenv
 
@@ -74,3 +74,11 @@ def test_engine(otel_data: pd.DataFrame) -> None:
     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)

tools/create_llama_cloud_index.py

@@ -0,0 +1,59 @@
+import os
+from dotenv import load_dotenv
+
+from llama_cloud import (
+    PipelineCreateEmbeddingConfig_OpenaiEmbedding,
+    PipelineTransformConfig_Advanced,
+    AdvancedModeTransformConfigChunkingConfig_Sentence,
+    AdvancedModeTransformConfigSegmentationConfig_Page,
+    PipelineCreate,
+)
+from llama_cloud.client import LlamaCloud
+from llama_index.embeddings.openai import OpenAIEmbedding
+
+
+def main():
+    load_dotenv()
+
+    embed_model = OpenAIEmbedding(
+        model="text-embedding-3-small", api_key=os.getenv("OPENAI_API_KEY")
+    )
+
+    client = LlamaCloud(token=os.getenv("LLAMACLOUD_API_KEY"))
+
+    embedding_config = PipelineCreateEmbeddingConfig_OpenaiEmbedding(
+        type="OPENAI_EMBEDDING",
+        component=embed_model,
+    )
+
+    segm_config = AdvancedModeTransformConfigSegmentationConfig_Page(mode="page")
+    chunk_config = AdvancedModeTransformConfigChunkingConfig_Sentence(
+        chunk_size=1024,
+        chunk_overlap=200,
+        separator="<whitespace>",
+        paragraph_separator="\n\n\n",
+        mode="sentence",
+    )
+
+    transform_config = PipelineTransformConfig_Advanced(
+        segmentation_config=segm_config,
+        chunking_config=chunk_config,
+        mode="advanced",
+    )
+
+    pipeline_request = PipelineCreate(
+        name="syllabus_pipeline",
+        embedding_config=embedding_config,
+        transform_config=transform_config,
+    )
+
+    pipeline = client.pipelines.upsert_pipeline(request=pipeline_request)
+
+    with open(".env", "a") as f:
+        f.write(f'\nLLAMACLOUD_PIPELINE_ID="{pipeline.id}"')
+
+    return 0
+
+
+if __name__ == "__main__":
+    main()

tools/create_llama_extract_agent.py

@@ -0,0 +1,23 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from llama_cloud_services import LlamaExtract
+from src.agents_observability_demo.models import Syllabus
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def main() -> int:
+    conn = LlamaExtract(api_key=os.getenv("LLAMACLOUD_API_KEY"))
+    agent = conn.create_agent(name="syllabus_extract_agent", data_schema=Syllabus)
+    _id = agent.id
+    with open(".env", "a") as f:
+        f.write(f'\nEXTRACT_AGENT_ID="{_id}"')
+    return 0
+
+
+if __name__ == "__main__":
+    main()

uv.lock

@@ -7,8 +7,14 @@ name = "agentic-observability"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "llama-index-llms-ollama" },
+    { name = "fastmcp" },
+    { name = "gradio" },
+    { name = "llama-cloud-services" },
+    { name = "llama-index-embeddings-openai" },
+    { name = "llama-index-indices-managed-llama-cloud" },
+    { name = "llama-index-llms-openai" },
     { name = "llama-index-observability-otel" },
+    { name = "llama-index-tools-mcp" },
     { name = "mypy" },
     { name = "opentelemetry-exporter-otlp-proto-http" },
     { name = "pandas" },
@@ -18,12 +24,19 @@ dependencies = [
     { name = "pytest" },
     { name = "python-dotenv" },
     { name = "sqlalchemy" },
+    { name = "websockets" },
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+    { name = "llama-index-embeddings-openai", specifier = ">=0.3.1" },
+    { name = "llama-index-indices-managed-llama-cloud", specifier = ">=0.6.11" },
+    { name = "llama-index-llms-openai", specifier = ">=0.4.7" },
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     { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.34.1" },
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     { name = "python-dotenv", specifier = ">=1.1.0" },
     { name = "sqlalchemy", specifier = ">=2.0.41" },
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