# DBNL Semantic Convention ## Mapping Fields to Semantically Understood Columns DBNL ingests data using traces produced by telemetry frameworks with different semantic conventions as well as tabular logs with a user defined format. To compute metrics and derive insights consistently across different data ingestion formats, we define a semantic convention for the data as stored within DBNL. If you are using [OTEL Trace Ingestion](https://docs.dbnl.com/v0.29.x/configuration/data-connections/otel-trace-ingestion) ensure that your spans adhere to this semantic convention, which adheres closely to the [OpenInference](https://github.com/Arize-ai/openinference) semantic convention. See the [Direct OTEL Ingestion Example](https://github.com/dbnlAI/docs/blob/main/examples/data-input/README.md#direct-otel-ingestion). If you are using [SDK Log Ingestion](https://docs.dbnl.com/v0.29.x/configuration/data-connections/sdk-log-ingestion) or [SQL Integration Ingestion](https://docs.dbnl.com/v0.29.x/configuration/data-connections/sql-integration-ingestion) you need provide a `spans` or `traces_data` column and ensure that your column names adhere to our semantic convention for best results. ## Required Fields The following fields are required regardless of which ingestion method you are using: * `input`: The text input to the LLM as a `string`. * `output`: The text response from the LLM as a `string`. * `timestamp`: The UTC timecode associated with the LLM call as a `timestamptz`. {% hint style="info" %} If you are uploading a dataframe with a `traces_data` column (see below) these fields are automatically created from the `resourceSpans` proviced. {% endhint %} One of the following fields are required for [Insights](https://docs.dbnl.com/v0.29.x/workflow/insights) to be produced: * `spans`: The spans representing operations within the AI app/agent invocation as a `list` ([see below](#spans-example)). For an example see the [SDK from JSON Ingestion Example](https://github.com/dbnlAI/docs/blob/main/examples/data-input/README.md#sdk-from-json). **or** * `traces_data`: Raw `resourceSpans` outputted by an OTEL collector as part of the OpenInference Semantic Convention. These will be automatically flattened and mapped to the appropriate fields of the semantic convention including `input`, `output`, `timestamp`, and `spans` when this column is present in the dataframe uploaded to [`dbnl.log()`](https://docs.dbnl.com/v0.29.x/reference/python-sdk/sdk-functions#log). For an example see the [SDK from OTEL Ingestion Example](https://github.com/dbnlAI/docs/blob/main/examples/data-input/README.md#sdk-from-otel). ## DBNL Semantic Convention The DBNL Semantic Convention is a mapping from well known formats into types and names that DBNL can recognize. If `traces_data` is uploaded, as many of the below fields as possible will be automatically created and mapped. | DBNL SemConv | DBNL Type | Description | | ---------------------------------------------------------------------------------- | ---------------------------------------------- | ----------------------------------------------------------------------------------- | | `input` (**Required**) | `string` (JSON escaped) | The input to the AI app invocation. | | `output` (**Required**) | `string` (JSON escaped) | The output from the AI app invocation. | | `timestamp` (**Required**) | `timestamptz` | The timestamp of the AI app invocation. | | `spans` (Required for [Insights](https://docs.dbnl.com/v0.29.x/workflow/insights)) | `list` ([see below](#spans-example)) | The spans representing operations within the AI app/agent invocation. | | `status` | `category` | The status of the AI app invocation (one of `OK`, `ERROR`, or `UNSET`). | | `duration_ms` | `int` | The duration of the AI app invocation in milliseconds. | | `session_id` | `string` | The session ID associated with the AI app invocation. | | `trace_id` | `string` | The trace ID associated with the AI app invocation. | | `user_id` | `string` | The user ID associated with the AI app invocation. | | `total_token_count` | `int` | The total number of tokens used in the AI app invocation. | | `prompt_token_count` | `int` | The number of prompt tokens used in the AI app invocation. | | `completion_token_count` | `int` | The number of completion tokens used in the AI app invocation. | | `total_cost` | `float` | The total cost of the AI app invocation. | | `prompt_cost` | `float` | The cost of the prompt tokens in the AI app invocation. | | `completion_cost` | `float` | The cost of the completion tokens in the AI app invocation. | | `tool_call_count` | `int` | The number of tool calls made during the AI app invocation. | | `tool_call_error_count` | `int` | The number of tool call errors during the AI app invocation. | | `tool_call_name_counts` | `map` | A map of tool call names to their respective counts during the AI app invocation. | | `tool_call_success_count_by_name` | `map` | A map of tool call names to their success counts during the AI app invocation. | | `tool_call_error_count_by_name` | `map` | A map of tool call names to their error status counts during the AI app invocation. | | `llm_call_count` | `int` | The number of LLM calls made during the AI app invocation. | | `llm_call_error_count` | `int` | The number of LLM call errors during the AI app invocation. | | `llm_call_model_counts` | `map` | A map of LLM models to their respective call counts during the AI app invocation. | | `llm_call_success_count_by_name` | `map` | A map of LLM models to their success counts during the AI app invocation. | | `llm_call_error_count_by_name` | `map` | A map of LLM models to their error status counts during the AI app invocation. | | `feedback_score` | `float` | The feedback score for the AI app invocation from 1 (bad) to 5 (great). | | `feedback_text` | `string` (JSON escaped) | The feedback text for the AI app invocation. | | `call_sequence` | `list` | The sequence of calls (e.g. tools, llms) made during the AI app invocation. | {% hint style="warning" %} Note: `ROOT`, `FIRST`, `LAST` and `ANY` are used as aliases for certain spans in a trace. {% endhint %} ### `traces_data` Example Example `resourceSpans` output from an OTEL collector that will be automatically flattened into `input`, `output`, `timestamp`, `spans`, and other columns when passed in a `traces_data` column to [`dbnl.log()`](https://docs.dbnl.com/v0.29.x/reference/python-sdk/sdk-functions#log) from the [SDK from OTEL Example](https://github.com/dbnlAI/examples/tree/main/adk_calculator_sdk_from_otel).
Raw OTEL `resourceSpans` example ``` { "resourceSpans": [ { "resource": { "attributes": [ { "key": "telemetry.sdk.language", "value": { "stringValue": "python" } }, { "key": "telemetry.sdk.name", "value": { "stringValue": "opentelemetry" } }, { "key": "telemetry.sdk.version", "value": { "stringValue": "1.37.0" } }, { "key": "service.name", "value": { "stringValue": "unknown_service" } } ] }, "scopeSpans": [ { "scope": { "name": "openinference.instrumentation.google_adk", "version": "0.1.6" }, "spans": [ { "traceId": "dc4e1b0aa335abbcb853b9e14ab3d310", "spanId": "2b45c26b8bf17c85", "parentSpanId": "0c243259fcccfbd6", "flags": 256, "name": "execute_tool add_two_numbers", "kind": 1, "startTimeUnixNano": "1763583600368122000", "endTimeUnixNano": "1763583600369032000", "attributes": [ { "key": "session.id", "value": { "stringValue": "c116e25e-5226-4461-85af-a26bb4177680" } }, { "key": "user.id", "value": { "stringValue": "test-user" } }, { "key": "gen_ai.operation.name", "value": { "stringValue": "execute_tool" } }, { "key": "gen_ai.tool.description", "value": { "stringValue": "Returns the sum of two numbers by adding them together" } }, { "key": "gen_ai.tool.name", "value": { "stringValue": "add_two_numbers" } }, { "key": "gen_ai.tool.type", "value": { "stringValue": "FunctionTool" } }, { "key": "gcp.vertex.agent.llm_request", "value": { "stringValue": "{}" } }, { "key": "gcp.vertex.agent.llm_response", "value": { "stringValue": "{}" } }, { "key": "gcp.vertex.agent.tool_call_args", "value": { "stringValue": "{"a": 5, "b": 92}" } }, { "key": "gen_ai.tool.call.id", "value": { "stringValue": "adk-9c9908e2-a2a5-4994-be58-458cb25bc718" } }, { "key": "gcp.vertex.agent.event_id", "value": { "stringValue": "15263715-53d5-4b2c-a515-6e586596804f" } }, { "key": "gcp.vertex.agent.tool_response", "value": { "stringValue": "{"status": "ok", "result": 97}" } }, { "key": "tool.name", "value": { "stringValue": "add_two_numbers" } }, { "key": "tool.description", "value": { "stringValue": "Returns the sum of two numbers by adding them together" } }, { "key": "tool.parameters", "value": { "stringValue": "{"a": 5, "b": 92}" } }, { "key": "input.value", "value": { "stringValue": "{"a": 5, "b": 92}" } }, { "key": "input.mime_type", "value": { "stringValue": "application/json" } }, { "key": "output.value", "value": { "stringValue": "{"id":"adk-9c9908e2-a2a5-4994-be58-458cb25bc718","name":"add_two_numbers","response":{"status":"ok","result":97}}" } }, { "key": "output.mime_type", "value": { "stringValue": "application/json" } }, { "key": "openinference.span.kind", "value": { "stringValue": "TOOL" } } ], "status": { "code": 1 } }, { "traceId": "dc4e1b0aa335abbcb853b9e14ab3d310", "spanId": "0c243259fcccfbd6", "parentSpanId": "c6b82dda06712053", "flags": 256, "name": "call_llm", "kind": 1, "startTimeUnixNano": "1763583599472623000", "endTimeUnixNano": "1763583600369290000", "attributes": [ { "key": "session.id", "value": { "stringValue": "c116e25e-5226-4461-85af-a26bb4177680" } }, { "key": "user.id", "value": { "stringValue": "test-user" } }, { "key": "gen_ai.system", "value": { "stringValue": "gcp.vertex.agent" } }, { "key": "gen_ai.request.model", "value": { "stringValue": "gemini-2.5-flash" } }, { "key": "gcp.vertex.agent.invocation_id", "value": { "stringValue": "e-f1db027b-3e41-4912-a493-68b8de744e87" } }, { "key": "gcp.vertex.agent.session_id", "value": { "stringValue": "c116e25e-5226-4461-85af-a26bb4177680" } }, { "key": "gcp.vertex.agent.event_id", "value": { "stringValue": "2522b0f5-364e-4407-b8c0-8c33e0dbf915" } }, { "key": "gcp.vertex.agent.llm_request", "value": { "stringValue": "{"model": "gemini-2.5-flash", "config": {"http_options": {"headers": {"x-goog-api-client": "google-adk/1.18.0 gl-python/3.12.7", "user-agent": "google-adk/1.18.0 gl-python/3.12.7"}}, "system_instruction": "Answer user math questions using the tools available to you, even if there are errors or inaccurate responses from the tools. Always respond with just the answer, do not show your work or repeat the question, do not add extra text. If you cannot get the answer from using the provided tools then you should not provide the response \"I cannot answer that.\", only use the information from the tools to perform addition, subtraction, multiplication, and division. Do not evaluate the input without using the tools. Do not try to correct mistakes made by the tools.\n\nYou are an agent. Your internal name is \"agents\". The description about you is \"A calculator tool that can perform basic arithmetic using agentic tools.\".", "tools": [{"function_declarations": [{"description": "Returns the sum of two numbers by adding them together", "name": "add_two_numbers", "parameters": {"properties": {"a": {"type": "NUMBER"}, "b": {"type": "NUMBER"}}, "required": ["a", "b"], "type": "OBJECT"}}, {"description": "Returns the result of subtracting the second number from the first number", "name": "subtract_two_numbers", "parameters": {"properties": {"a": {"type": "NUMBER"}, "b": {"type": "NUMBER"}}, "required": ["a", "b"], "type": "OBJECT"}}, {"description": "Returns the product of multiplying two numbers together", "name": "multiply_two_numbers", "parameters": {"properties": {"a": {"type": "NUMBER"}, "b": {"type": "NUMBER"}}, "required": ["a", "b"], "type": "OBJECT"}}, {"description": "Returns the result of dividing the first number by the second number", "name": "divide_two_numbers", "parameters": {"properties": {"a": {"type": "NUMBER"}, "b": {"type": "NUMBER"}}, "required": ["a", "b"], "type": "OBJECT"}}]}]}, "contents": [{"parts": [{"text": "5+92"}], "role": "user"}]}" } }, { "key": "gcp.vertex.agent.llm_response", "value": { "stringValue": "{"model_version":"gemini-2.5-flash","content":{"parts":[{"function_call":{"args":{"a":5,"b":92},"name":"add_two_numbers"},"thought_signature":"CoUCAdHtim-g6dbRBQsdO54p3S2JtxKS7lvk5qqQ7ut0JaFliJhDU8Ktf_zxqGL3wvGvFX3gDudciGCWYWk5WSL08MTBLwMffoiOTkjr37bFZAGCyBMoaVZHv7P2C8TRHmoQg3foaAb-755l2YPq93qeE-mEU4boygh9F_KN96AWSEdcF55qXeLTCEGSBue-yg1h1sQCcYZ7bT0KHRsQbQ-LdMra2YXXToDBFIqX4wwsmene36tBzfZVUH849h_3cp43tSC8EriOwnPeMlWlS137_i6kDsjqrwmNy-yx4F9Lco0geFreekfcJcPJKDWOK66VCaMPcSrHceuGZkvmZjvIvLxPcwaN"}],"role":"model"},"finish_reason":"STOP","usage_metadata":{"candidates_token_count":23,"prompt_token_count":369,"prompt_tokens_details":[{"modality":"TEXT","token_count":369}],"thoughts_token_count":68,"total_token_count":460}}" } }, { "key": "gen_ai.usage.input_tokens", "value": { "intValue": "369" } }, { "key": "gen_ai.usage.output_tokens", "value": { "intValue": "23" } }, { "key": "gen_ai.response.finish_reasons", "value": { "arrayValue": { "values": [{ "stringValue": "stop" }] } } }, { "key": "llm.provider", "value": { "stringValue": "google" } }, { "key": "input.value", "value": { "stringValue": "{"model":"gemini-2.5-flash","contents":[{"parts":[{"text":"5+92"}],"role":"user"}],"config":{"http_options":{"headers":{"x-goog-api-client":"google-adk/1.18.0 gl-python/3.12.7","user-agent":"google-adk/1.18.0 gl-python/3.12.7"}},"system_instruction":"Answer user math questions using the tools available to you, even if there are errors or inaccurate responses from the tools. Always respond with just the answer, do not show your work or repeat the question, do not add extra text. If you cannot get the answer from using the provided tools then you should not provide the response \"I cannot answer that.\", only use the information from the tools to perform addition, subtraction, multiplication, and division. Do not evaluate the input without using the tools. Do not try to correct mistakes made by the tools.\n\nYou are an agent. Your internal name is \"agents\". The description about you is \"A calculator tool that can perform basic arithmetic using agentic tools.\".","tools":[{"function_declarations":[{"description":"Returns the sum of two numbers by adding them together","name":"add_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}},{"description":"Returns the result of subtracting the second number from the first number","name":"subtract_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}},{"description":"Returns the product of multiplying two numbers together","name":"multiply_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}},{"description":"Returns the result of dividing the first number by the second number","name":"divide_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}}]}]},"live_connect_config":{"input_audio_transcription":{},"output_audio_transcription":{}}}" } }, { "key": "input.mime_type", "value": { "stringValue": "application/json" } }, { "key": "llm.tools.0.tool.json_schema", "value": { "stringValue": "{"description":"Returns the sum of two numbers by adding them together","name":"add_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}}" } }, { "key": "llm.tools.1.tool.json_schema", "value": { "stringValue": "{"description":"Returns the result of subtracting the second number from the first number","name":"subtract_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}}" } }, { "key": "llm.tools.2.tool.json_schema", "value": { "stringValue": "{"description":"Returns the product of multiplying two numbers together","name":"multiply_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}}" } }, { "key": "llm.tools.3.tool.json_schema", "value": { "stringValue": "{"description":"Returns the result of dividing the first number by the second number","name":"divide_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}}" } }, { "key": "llm.model_name", "value": { "stringValue": "gemini-2.5-flash" } }, { "key": "llm.invocation_parameters", "value": { "stringValue": "{"http_options":{"headers":{"x-goog-api-client":"google-adk/1.18.0 gl-python/3.12.7","user-agent":"google-adk/1.18.0 gl-python/3.12.7"}},"system_instruction":"Answer user math questions using the tools available to you, even if there are errors or inaccurate responses from the tools. Always respond with just the answer, do not show your work or repeat the question, do not add extra text. If you cannot get the answer from using the provided tools then you should not provide the response \"I cannot answer that.\", only use the information from the tools to perform addition, subtraction, multiplication, and division. Do not evaluate the input without using the tools. Do not try to correct mistakes made by the tools.\n\nYou are an agent. Your internal name is \"agents\". The description about you is \"A calculator tool that can perform basic arithmetic using agentic tools.\".","tools":[{"function_declarations":[{"description":"Returns the sum of two numbers by adding them together","name":"add_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}},{"description":"Returns the result of subtracting the second number from the first number","name":"subtract_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}},{"description":"Returns the product of multiplying two numbers together","name":"multiply_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}},{"description":"Returns the result of dividing the first number by the second number","name":"divide_two_numbers","parameters":{"properties":{"a":{"type":"NUMBER"},"b":{"type":"NUMBER"}},"required":["a","b"],"type":"OBJECT"}}]}]}" } }, { "key": "llm.input_messages.0.message.role", "value": { "stringValue": "system" } }, { "key": "llm.input_messages.0.message.content", "value": { "stringValue": "Answer user math questions using the tools available to you, even if there are errors or inaccurate responses from the tools. Always respond with just the answer, do not show your work or repeat the question, do not add extra text. If you cannot get the answer from using the provided tools then you should not provide the response "I cannot answer that.", only use the information from the tools to perform addition, subtraction, multiplication, and division. Do not evaluate the input without using the tools. Do not try to correct mistakes made by the tools. You are an agent. Your internal name is "agents". 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### Spans Example ``` struct< trace_id: string, span_id: string, trace_state: string, parent_span_id: string, name: string, kind: string, start_time: timestamptz, end_time: timestamptz, attributes: map, events: list< struct< timestamp: timestamptz, name: string, attributes: map > >, links: list< struct< trace_id: string, span_id: string, trace_state: string, attributes: map > >, status: struct< code: string, message: string > > ``` Example of the entire Semantic Convention with spans in raw JSON from the [SDK from JSON Ingestion Example](https://github.com/dbnlAI/docs/blob/main/examples/data-input/README.md#sdk-from-json):
Raw JSON of Semantic Convention (with `spans`) ``` { "trace_id": "190e51c28c9fba62e5b4592a76337a9e", "session_id": "714fc40d-24ee-4d4a-ab69-2bc3bfc0540a", "input": ""{\"input\": \"79-81+53\"}"", "output": ""{\"output\": \"51\"}"", "timestamp": "2025-11-20T10:29:20.446953Z", "duration_ms": 2359, "status": "OK", "status_message": "", "total_token_count": 1312, "prompt_token_count": 1263, "completion_token_count": 49, "total_cost": 0.00010942499999999999, "prompt_cost": 9.472499999999998e-5, "completion_cost": 1.47e-5, "tool_call_count": 0, "tool_call_error_count": 0, "tool_call_name_counts": {}, "llm_call_count": 5, "llm_call_error_count": 0, "llm_call_model_counts": { ""gcp.vertex.agent"": 2, ""gemini-2.5-flash"": 3 }, "call_sequence": [ "llm:"gemini-2.5-flash"", "llm:"gcp.vertex.agent"", "llm:"gemini-2.5-flash"", "llm:"gcp.vertex.agent"", "llm:"gemini-2.5-flash"" ], "spans": [ { "trace_id": "190e51c28c9fba62e5b4592a76337a9e", "span_id": "2020c7f661c51448", "trace_state": "", 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"value": ""{\"output\": \"\"}"" }, { "key": "output.mime_type", "value": ""application/json"" }, { "key": "llm.model_name", "value": ""gemini-2.5-flash"" }, { "key": "llm.token_count.prompt", "value": "421" }, { "key": "llm.token_count.completion", "value": "23" }, { "key": "llm.token_count.total", "value": ""444"" }, { "key": "llm.input_messages.0.message.role", "value": ""user"" }, { "key": "llm.input_messages.0.message.content", "value": ""[{\"text\": \"79-81+53\"}]"" }, { "key": "llm.input_messages.1.message.role", "value": ""model"" }, { "key": "llm.input_messages.1.message.content", "value": ""[{\"function_call\": {\"args\": {\"a\": 79, \"b\": 81}, \"name\": \"subtract_two_numbers\"}, \"thought_signature\": \"\"}]"" }, { "key": "llm.input_messages.2.message.role", "value": ""user"" }, { "key": "llm.input_messages.2.message.content", "value": ""[{\"function_response\": {\"name\": \"subtract_two_numbers\", \"response\": {\"status\": \"ok\", \"result\": -2}}}]"" }, { "key": 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