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  <channel>
    <title>topic Vectorsearch ConnectionResetError Max retries exceeded in Machine Learning</title>
    <link>https://community.databricks.com/t5/machine-learning/vectorsearch-connectionreseterror-max-retries-exceeded/m-p/80353#M3510</link>
    <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;we are serving a unity catalog langchain model with databricks model serving. When I run the predict() function on the model in a notebook, I get the expected output. But when I query the served model, errors occur in the service logs:&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Error message:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;2024-07-24T12:05:38.+0000 WARNING urllib3.connectionpool Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/2.0/&amp;lt;workspace-id&amp;gt;/vector-search/endpoints/my_vs_endpoint/indexes/my_vs_index/query&lt;/P&gt;&lt;P&gt;2024-07-24T12:05:44.+0000 WARNING mlflowserving.scoring_server LangChain invocation failed with MlflowException: 1 tasks failed. Errors: {0: 'error: ConnectionError(MaxRetryError("HTTPSConnectionPool(host=\'6cb7f5a6-3135-4280-987f-9758c9fa3753.vector-search.cloud.databricks.com\', port=443): Max retries exceeded with url:/api/2.0/&amp;lt;workspace-id&amp;gt;/vector-search/endpoints/my_vs_endpoint/indexes/my_vs_index/query (Caused by ProtocolError(\'Connection aborted.\', ConnectionResetError(104, \'Connection reset by peer\')))")) urllib3.exceptions.ProtocolError: (\'Connection aborted.\', ConnectionResetError(104, \'Connection reset by peer\'))\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File "/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/requests/adapters.py", line 667, in send\n resp = conn.urlopen(\n File "/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 873, in urlopen\n&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Request:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;{&lt;BR /&gt;"dataframe_split":&lt;BR /&gt;{&lt;BR /&gt;"columns":["&lt;SPAN&gt;payload&lt;/SPAN&gt;"],&lt;BR /&gt;"data": ["my_data"]&lt;BR /&gt;}&lt;BR /&gt;}&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Response:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;{&lt;BR /&gt;"predictions": null&lt;BR /&gt;}&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Working code in my notebook:&lt;/STRONG&gt;&lt;/P&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;import&lt;/SPAN&gt;&lt;SPAN&gt; mlflow&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;import&lt;/SPAN&gt;&lt;SPAN&gt; pandas &lt;/SPAN&gt;&lt;SPAN&gt;as&lt;/SPAN&gt;&lt;SPAN&gt; pd&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;my_model &lt;/SPAN&gt;&lt;SPAN&gt;=&lt;/SPAN&gt;&lt;SPAN&gt; mlflow.pyfunc.&lt;/SPAN&gt;&lt;SPAN&gt;load_model&lt;/SPAN&gt;&lt;SPAN&gt;(model_info.model_uri)&lt;/SPAN&gt;&lt;/DIV&gt;&lt;BR /&gt;&lt;DIV&gt;&lt;SPAN&gt;my_model.&lt;/SPAN&gt;&lt;SPAN&gt;predict&lt;/SPAN&gt;&lt;SPAN&gt;(&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;pd.&lt;/SPAN&gt;&lt;SPAN&gt;DataFrame&lt;/SPAN&gt;&lt;SPAN&gt;(&lt;/SPAN&gt;&lt;SPAN&gt;columns&lt;/SPAN&gt;&lt;SPAN&gt;=&lt;/SPAN&gt;&lt;SPAN&gt;[&lt;/SPAN&gt;&lt;SPAN&gt;"payload"&lt;/SPAN&gt;&lt;SPAN&gt;], &lt;/SPAN&gt;&lt;SPAN&gt;data&lt;/SPAN&gt;&lt;SPAN&gt;=&lt;/SPAN&gt;&lt;SPAN&gt;[&lt;/SPAN&gt;&lt;SPAN&gt;"my_data"&lt;/SPAN&gt;&lt;SPAN&gt;]))&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;The served model consist of a query to a databricks vectorsearch index. I should be later extended by a llm.&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;STRONG&gt;req.txt:&lt;/STRONG&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;ipykernel&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;6.29.4&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;numpy&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;1.26.4&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;databricks-connect&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;14.3.2&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;databricks-sdk&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.28.0&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;databricks-vectorsearch&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.40&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;langchain&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.2.10&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;langchain-community&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.2.9&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;mlflow&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;2.14.3&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;openpyxl&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;3.1.5&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;setuptools&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;71.1.0&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;DBR 15.2&lt;/SPAN&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</description>
    <pubDate>Wed, 24 Jul 2024 12:21:38 GMT</pubDate>
    <dc:creator>RobinK</dc:creator>
    <dc:date>2024-07-24T12:21:38Z</dc:date>
    <item>
      <title>Vectorsearch ConnectionResetError Max retries exceeded</title>
      <link>https://community.databricks.com/t5/machine-learning/vectorsearch-connectionreseterror-max-retries-exceeded/m-p/80353#M3510</link>
      <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;we are serving a unity catalog langchain model with databricks model serving. When I run the predict() function on the model in a notebook, I get the expected output. But when I query the served model, errors occur in the service logs:&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Error message:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;2024-07-24T12:05:38.+0000 WARNING urllib3.connectionpool Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectionResetError(104, 'Connection reset by peer')': /api/2.0/&amp;lt;workspace-id&amp;gt;/vector-search/endpoints/my_vs_endpoint/indexes/my_vs_index/query&lt;/P&gt;&lt;P&gt;2024-07-24T12:05:44.+0000 WARNING mlflowserving.scoring_server LangChain invocation failed with MlflowException: 1 tasks failed. Errors: {0: 'error: ConnectionError(MaxRetryError("HTTPSConnectionPool(host=\'6cb7f5a6-3135-4280-987f-9758c9fa3753.vector-search.cloud.databricks.com\', port=443): Max retries exceeded with url:/api/2.0/&amp;lt;workspace-id&amp;gt;/vector-search/endpoints/my_vs_endpoint/indexes/my_vs_index/query (Caused by ProtocolError(\'Connection aborted.\', ConnectionResetError(104, \'Connection reset by peer\')))")) urllib3.exceptions.ProtocolError: (\'Connection aborted.\', ConnectionResetError(104, \'Connection reset by peer\'))\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File "/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/requests/adapters.py", line 667, in send\n resp = conn.urlopen(\n File "/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 873, in urlopen\n&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Request:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;{&lt;BR /&gt;"dataframe_split":&lt;BR /&gt;{&lt;BR /&gt;"columns":["&lt;SPAN&gt;payload&lt;/SPAN&gt;"],&lt;BR /&gt;"data": ["my_data"]&lt;BR /&gt;}&lt;BR /&gt;}&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Response:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;{&lt;BR /&gt;"predictions": null&lt;BR /&gt;}&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Working code in my notebook:&lt;/STRONG&gt;&lt;/P&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;import&lt;/SPAN&gt;&lt;SPAN&gt; mlflow&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;import&lt;/SPAN&gt;&lt;SPAN&gt; pandas &lt;/SPAN&gt;&lt;SPAN&gt;as&lt;/SPAN&gt;&lt;SPAN&gt; pd&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;my_model &lt;/SPAN&gt;&lt;SPAN&gt;=&lt;/SPAN&gt;&lt;SPAN&gt; mlflow.pyfunc.&lt;/SPAN&gt;&lt;SPAN&gt;load_model&lt;/SPAN&gt;&lt;SPAN&gt;(model_info.model_uri)&lt;/SPAN&gt;&lt;/DIV&gt;&lt;BR /&gt;&lt;DIV&gt;&lt;SPAN&gt;my_model.&lt;/SPAN&gt;&lt;SPAN&gt;predict&lt;/SPAN&gt;&lt;SPAN&gt;(&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;pd.&lt;/SPAN&gt;&lt;SPAN&gt;DataFrame&lt;/SPAN&gt;&lt;SPAN&gt;(&lt;/SPAN&gt;&lt;SPAN&gt;columns&lt;/SPAN&gt;&lt;SPAN&gt;=&lt;/SPAN&gt;&lt;SPAN&gt;[&lt;/SPAN&gt;&lt;SPAN&gt;"payload"&lt;/SPAN&gt;&lt;SPAN&gt;], &lt;/SPAN&gt;&lt;SPAN&gt;data&lt;/SPAN&gt;&lt;SPAN&gt;=&lt;/SPAN&gt;&lt;SPAN&gt;[&lt;/SPAN&gt;&lt;SPAN&gt;"my_data"&lt;/SPAN&gt;&lt;SPAN&gt;]))&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;The served model consist of a query to a databricks vectorsearch index. I should be later extended by a llm.&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;STRONG&gt;req.txt:&lt;/STRONG&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;ipykernel&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;6.29.4&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;numpy&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;1.26.4&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;databricks-connect&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;14.3.2&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;databricks-sdk&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.28.0&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;databricks-vectorsearch&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.40&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;langchain&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.2.10&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;langchain-community&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;0.2.9&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;mlflow&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;2.14.3&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;openpyxl&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;3.1.5&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;setuptools&lt;/SPAN&gt;&lt;SPAN&gt;==&lt;/SPAN&gt;&lt;SPAN&gt;71.1.0&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;DBR 15.2&lt;/SPAN&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</description>
      <pubDate>Wed, 24 Jul 2024 12:21:38 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/vectorsearch-connectionreseterror-max-retries-exceeded/m-p/80353#M3510</guid>
      <dc:creator>RobinK</dc:creator>
      <dc:date>2024-07-24T12:21:38Z</dc:date>
    </item>
    <item>
      <title>Re: Vectorsearch ConnectionResetError Max retries exceeded</title>
      <link>https://community.databricks.com/t5/machine-learning/vectorsearch-connectionreseterror-max-retries-exceeded/m-p/80640#M3525</link>
      <description>&lt;P&gt;downgrading&amp;nbsp;&lt;SPAN&gt;langchain-community to version&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN&gt;0.2.4 solved my problem.&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Fri, 26 Jul 2024 04:44:13 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/vectorsearch-connectionreseterror-max-retries-exceeded/m-p/80640#M3525</guid>
      <dc:creator>RobinK</dc:creator>
      <dc:date>2024-07-26T04:44:13Z</dc:date>
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