From 1843b39289acc70a101d043210010e46be49e28c Mon Sep 17 00:00:00 2001 From: pkosanam Date: Wed, 6 Sep 2023 16:33:22 +0530 Subject: [PATCH 1/8] NR-157853 LangChain Vector Database --- data-sources/langchain-vectordb/config.yml | 20 ++++++++++++++++++++ data-sources/langchain-vectordb/logo.png | Bin 0 -> 42532 bytes 2 files changed, 20 insertions(+) create mode 100644 data-sources/langchain-vectordb/config.yml create mode 100644 data-sources/langchain-vectordb/logo.png diff --git a/data-sources/langchain-vectordb/config.yml b/data-sources/langchain-vectordb/config.yml new file mode 100644 index 0000000000..0ab63a9959 --- /dev/null +++ b/data-sources/langchain-vectordb/config.yml @@ -0,0 +1,20 @@ +id: langchain-vectordb +displayName: LangChain Vector Database +description: | + Implement monitoring and instrumentation for your LangChain Vector Database and ensure that your vector database is integrated into New Relic for effective performance analysis and insights. +install: + primary: + link: + url: https://github.com/newrelic/nr-openai-observability +icon: logo.png +keywords: + - langchain vector database + - langchain ai + - lang + - chain + - ai + - lang chain + - large language model + - natural language processing + - machine learning + - artificial intelligence \ No newline at end of file diff --git a/data-sources/langchain-vectordb/logo.png b/data-sources/langchain-vectordb/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..240e64b569d94a9a5107c65de9f59eaa25c60701 GIT binary patch literal 42532 zcmV*6Ky$xRNk&E-rT_p}MM6+kP&iBvrT_ph^TB)ojW}}KMv`DVv&!}V2lslLIY#t< z0`wCi%d#xXvMkH;k-7aqfUZbay4o{|G#6G+C4uod+UFd70<_mOJ5f92b7{S*0!cjY zBwU$!tyTDhnKr$`yk5^|p+G-ndycl!@E($IgQRNIc`D&tca9~>4Fa@@1Om}BLPn;* z#zwYl?-^{_#+J9>-3>y37~v6s#s20bimkun1db$0iX`773IG4Pl#dzO+G@Fbw6oLP z#i+B^Ucse{qQIF$~Vn4!}znK>v*edP(VE#k!#!+|HREkY# z-FbWQ6hfBDTdWHdBfT#D55;C;t|=kJQ+bLN{Owo8|4KdZ z|7YBd|9`JZPHZ(>rnc8LZtr4IEX%b~t!>-3ZPi}YOc`w%HEev{ZtXQQuW`>?M{aG~ 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.../langchain-vectordb-03.png | Bin 0 -> 298223 bytes .../langchain-vectordb-04.png | Bin 0 -> 630766 bytes .../langchain-vectordb-05.png | Bin 0 -> 415462 bytes .../langchain-vectordb.json | 792 ++++++++++++++++++ quickstarts/langchain-vectordb/config.yml | 61 ++ quickstarts/langchain-vectordb/logo.png | Bin 0 -> 42532 bytes 10 files changed, 926 insertions(+) create mode 100644 alert-policies/langchain-vectordb/APIResponseTime.yml create mode 100644 alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml create mode 100644 dashboards/langchain-vectordb/langchain-vectordb-01.png create mode 100644 dashboards/langchain-vectordb/langchain-vectordb-02.png create mode 100644 dashboards/langchain-vectordb/langchain-vectordb-03.png create mode 100644 dashboards/langchain-vectordb/langchain-vectordb-04.png create mode 100644 dashboards/langchain-vectordb/langchain-vectordb-05.png create mode 100644 dashboards/langchain-vectordb/langchain-vectordb.json create mode 100644 quickstarts/langchain-vectordb/config.yml create mode 100644 quickstarts/langchain-vectordb/logo.png diff --git a/alert-policies/langchain-vectordb/APIResponseTime.yml b/alert-policies/langchain-vectordb/APIResponseTime.yml new file mode 100644 index 0000000000..c279327bf2 --- /dev/null +++ b/alert-policies/langchain-vectordb/APIResponseTime.yml @@ -0,0 +1,32 @@ +# Name of the alert +name: API Response Time(seconds) + +# Description and details +description: |+ + This alert is triggered when the response time exceeds 2 seconds for 1 minute. + +# Type of alert +type: STATIC + +# NRQL query +nrql: + query: "FROM LlmVectorSearch SELECT latest(response_time) WHERE applicationName IN ('LangChain observability trace') TIMESERIES" + +# Function used to aggregate the NRQL query value(s) for comparison to the terms.threshold (Default: SINGLE_VALUE) +valueFunction: SINGLE_VALUE + +# List of Critical and Warning thresholds for the condition +terms: + - priority: CRITICAL + # Operator used to compare against the threshold. + operator: ABOVE + # Value that triggers a violation + threshold: 2 + # Time in seconds; 120 - 3600 + thresholdDuration: 120 + # How many data points must be in violation for the duration + thresholdOccurrences: ALL + +# Duration after which a violation automatically closes +# Time in seconds; 300 - 2592000 (Default: 86400 [1 day]) +violationTimeLimitSeconds: 86400 diff --git a/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml b/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml new file mode 100644 index 0000000000..2125a4c348 --- /dev/null +++ b/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml @@ -0,0 +1,41 @@ +# Name of the alert +name: Zero Documents Retrieved + +# Description and details +description: |+ + This alert is triggered when more than 20% of the searches return zero documents for 1 minute. + +# Type of alert +type: STATIC + +# NRQL query +nrql: + query: "SELECT filter(count(document_count), WHERE document_count=4) / count(document_count) * 100 from LlmVectorSearch WHERE applicationName IN ('LangChain observability trace')" + +# Function used to aggregate the NRQL query value(s) for comparison to the terms.threshold (Default: SINGLE_VALUE) +valueFunction: SINGLE_VALUE + +# List of Critical and Warning thresholds for the condition +terms: + - priority: CRITICAL + # Operator used to compare against the threshold. + operator: ABOVE + # Value that triggers a violation + threshold: + # Time in seconds; 120 - 3600 + thresholdDuration: 50 + # How many data points must be in violation for the duration + thresholdOccurrences: ALL + - priority: WARNING + # Operator used to compare against the threshold. + operator: ABOVE + # Value that triggers a violation + threshold: 20 + # Time in seconds; 120 - 3600 + thresholdDuration: 120 + # How many data points must be in violation for the duration + thresholdOccurrences: ALL + +# Duration after which a violation automatically closes +# Time in 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Vector Database", + "description": null, + "pages": [ + { + "name": "LangChain vector stores", + "description": null, + "widgets": [ + { + "title": "Response Time (seconds)", + "layout": { + "column": 1, + "row": 1, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.area" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "FROM LlmVectorSearch SELECT latest(response_time) WHERE applicationName IN ({{applicationName}}) TIMESERIES" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Searches by provider", + "layout": { + "column": 5, + "row": 1, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "FROM LlmVectorSearch SELECT count(*) WHERE applicationName IN ({{applicationName}}) FACET provider" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Average document retrieved", + "layout": { + "column": 9, + "row": 1, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT average(document_count) from LlmVectorSearch WHERE applicationName IN ({{applicationName}})" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "List of queries ", + "layout": { + "column": 1, + "row": 4, + "width": 3, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(query) FROM LlmVectorSearch WHERE applicationName IN ({{applicationName}}) FACET query " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "List of search id's", + "layout": { + "column": 4, + "row": 4, + "width": 3, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmVectorSearch WHERE applicationName IN ({{applicationName}}) FACET search_id " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Page source and content", + "layout": { + "column": 7, + "row": 4, + "width": 6, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT result_rank, document_metadata_source, document_page_content FROM LlmVectorSearchResult" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "", + "layout": { + "column": 1, + "row": 7, + "width": 12, + "height": 1 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" + }, + "rawConfiguration": { + "text": "# Popular content\nHere are the metrics of document meta details and the page content." + } + }, + { + "title": "Top metadata source", + "layout": { + "column": 1, + "row": 8, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) FROM LlmVectorSearchResult FACET document_metadata_source " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Top page content", + "layout": { + "column": 5, + "row": 8, + "width": 8, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) FROM LlmVectorSearchResult FACET document_page_content" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + } + ] + }, + { + "name": "LangChain", + "description": null, + "widgets": [ + { + "title": "", + "layout": { + "column": 1, + "row": 1, + "width": 12, + "height": 1 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" + }, + "rawConfiguration": { + "text": "# LLM Completion\nHere are the metrics related to prompt tokens, model names, duration and the completion details of your Large Language Models." + } + }, + { + "title": "Average tokens", + "layout": { + "column": 1, + "row": 2, + "width": 2, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT average(prompt_tokens) as 'Prompt tokens',average(completion_tokens) as 'Completion tokens',average(total_tokens) as 'Total tokens' from LlmCompletion " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 3, + "row": 2, + "width": 2, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmCompletion " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 5, + "row": 2, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmCompletion TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true + } + } + }, + { + "title": "Models", + "layout": { + "column": 9, + "row": 2, + "width": 4, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.pie" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": true + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmCompletion facet model_name" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds) by model name", + "layout": { + "column": 5, + "row": 4, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT median(duration.ms/1000) from LlmCompletion facet model_name " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "LLM completion's details", + "layout": { + "column": 1, + "row": 6, + "width": 12, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT model_name,messages,response from LlmCompletion " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "", + "layout": { + "column": 1, + "row": 10, + "width": 12, + "height": 1 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" + }, + "rawConfiguration": { + "text": "# LLM Chains\nWe get the chain details, duration and the number of chains triggered of your Large Language Models." + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 1, + "row": 11, + "width": 2, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmChain" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 3, + "row": 11, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmChain TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true + } + } + }, + { + "title": "Chain's details", + "layout": { + "column": 7, + "row": 11, + "width": 6, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT chat_history,input,outputs,guid,trace.id ,parent.id from LlmChain since 3 days ago" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Chains triggered", + "layout": { + "column": 3, + "row": 13, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmChain TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true + } + } + }, + { + "title": "", + "layout": { + "column": 1, + "row": 15, + "width": 12, + "height": 1 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" + }, + "rawConfiguration": { + "text": "# LLM Tools\nHere, we get the duration, tool details and it’s name of your Large Language Models." + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 1, + "row": 16, + "width": 2, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmTool " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 3, + "row": 16, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmTool TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true + } + } + }, + { + "title": "Tool details", + "layout": { + "column": 7, + "row": 16, + "width": 6, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT tool_name,tool_invocation_counter, tool_input,tool_output from LlmTool " + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds) by tool name ", + "layout": { + "column": 1, + "row": 19, + "width": 2, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT average(duration.ms/1000) as 'seconds' from LlmTool facet tool_name" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + }, + { + "title": "Duration (seconds) by tool name ", + "layout": { + "column": 3, + "row": 19, + "width": 6, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms / 1000),50) as 'seconds' from LlmTool facet tool_name TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true + } + } + }, + { + "title": "Top tool names", + "layout": { + "column": 9, + "row": 19, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmTool facet tool_name" + } + ], + "platformOptions": { + "ignoreTimeRange": false + } + } + } + ] + } + ], + "variables": [ + { + "name": "applicationName", + "items": null, + "defaultValues": [ + { + "value": { + "string": "*" + } + }, + { + "value": { + "string": "LangChain observability trace" + } + } + ], + "nrqlQuery": { + "accountIds": [], + "query": "FROM LlmVectorSearch SELECT uniques(applicationName) WHERE applicationName IS NOT NULL since 1 month ago" + }, + "title": "Select Your Application Name", + "type": "NRQL", + "isMultiSelection": true, + "replacementStrategy": "STRING" + } + ] + } \ No newline at end of file diff --git a/quickstarts/langchain-vectordb/config.yml b/quickstarts/langchain-vectordb/config.yml new file mode 100644 index 0000000000..528bed9b1b --- /dev/null +++ b/quickstarts/langchain-vectordb/config.yml @@ -0,0 +1,61 @@ +# Name of the quickstart +slug: langchain-vectordb + +# Title of the quickstart +title: LangChain Vector Database + +summary: | + A vector store takes care of storing embedded data and performing vector search on the vector database +# Description of the quickstart +description: | + ## Why monitor LangChain Vector Database? + LangChain is a framework for developing applications powered by language models. + By monitoring your LangChain app, we are providing you visibility on the chains, tools as well as inputs and outputs. + Monitoring can help optimize the performance of systems or applications that rely on vector data. For databases associated with hardware or equipment, monitoring can enable predictive maintenance by tracking usage and performance metrics to anticipate and prevent failures + + ## Comprehensive monitoring quickstart for LangChain + Our LangChain Vector Database quickstart provides metrics, including response time, zero documents retreived and many more. These metrics serve as valuable tools to empower you in maximizing the performance of LangChain Vector Database. + + ## What’s included in this quickstart? + New Relic LangChain Vector Database monitoring quickstart provides a variety of pre-built reports, which will help you gain insights into the health and performance of your LangChain. These reports include: + - Code-related insights to identify potential problems with your code and improve its performance + - Latency details and number of tokens + - Entity details such as the average number of tokens used, the cost per entity, and the total number of requests made + - Alerts for errors,requests per model and response time + +# The logo of the quickstart +icon: logo.png + +# Support level +level: New Relic + +# Authors of the quickstart +authors: + - New Relic + +keywords: + - langchain vector database + - langchain ai + - vector database + - ai + - lang chain + - large language model + - natural language processing + - machine learning + - artificial intelligence + - mlops + - NR1_addData + +dashboards: + - langchain-vectordb +alertPolicies: + - langchain-vectordb + +documentation: + - name: LangChain Vector Database integration documentation + description: | + Implement monitoring and instrumentation for your LangChain Vector Database, and ensure that your LangChain Vector Database data is integrated into New Relic for effective performance analysis and insights. + url: https://github.com/newrelic/nr-openai-observability + +dataSourceIds: + - langchain-vectordb diff --git a/quickstarts/langchain-vectordb/logo.png b/quickstarts/langchain-vectordb/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..240e64b569d94a9a5107c65de9f59eaa25c60701 GIT binary patch literal 42532 zcmV*6Ky$xRNk&E-rT_p}MM6+kP&iBvrT_ph^TB)ojW}}KMv`DVv&!}V2lslLIY#t< z0`wCi%d#xXvMkH;k-7aqfUZbay4o{|G#6G+C4uod+UFd70<_mOJ5f92b7{S*0!cjY zBwU$!tyTDhnKr$`yk5^|p+G-ndycl!@E($IgQRNIc`D&tca9~>4Fa@@1Om}BLPn;* z#zwYl?-^{_#+J9>-3>y37~v6s#s20bimkun1db$0iX`773IG4Pl#dzO+G@Fbw6oLP z#i+B^Ucse{qQIF$~Vn4!}znK>v*edP(VE#k!#!+|HREkY# z-FbWQ6hfBDTdWHdBfT#D55;C;t|=kJQ+bLN{Owo8|4KdZ z|7YBd|9`JZPHZ(>rnc8LZtr4IEX%b~t!>-3ZPi}YOc`w%HEev{ZtXQQuW`>?M{aG~ zsa#QF4F+qP=kwr$1SHcEULO*lpz4~{X%9A?y6Vm=y4 z%%Cy!6vAw^k3HtWF4C=STiLeEb$((evKnZD4yc84+_fQ6GL8M&5*kU8B*{Ey0W7HF 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0ab63a9959..8ad1c143ed 100644 --- a/data-sources/langchain-vectordb/config.yml +++ b/data-sources/langchain-vectordb/config.yml @@ -4,8 +4,11 @@ description: | Implement monitoring and instrumentation for your LangChain Vector Database and ensure that your vector database is integrated into New Relic for effective performance analysis and insights. install: primary: - link: - url: https://github.com/newrelic/nr-openai-observability + nerdlet: + nerdletId: marketplace.install-data-source + nerdletState: + dataSourceId: langchain-vectordb + requiresAccount: false icon: logo.png keywords: - langchain vector database From 3f42a930b54aa01ad51e89d5e41db4031ee1925c Mon Sep 17 00:00:00 2001 From: pkosanam Date: Thu, 7 Sep 2023 19:45:00 +0530 Subject: [PATCH 4/8] fixed alert error --- alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml b/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml index 2125a4c348..5f7975d5de 100644 --- a/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml +++ b/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml @@ -21,9 +21,9 @@ terms: # Operator used to compare against the threshold. operator: ABOVE # Value that triggers a violation - threshold: + threshold: 50 # Time in seconds; 120 - 3600 - thresholdDuration: 50 + thresholdDuration: 120 # How many data points must be in violation for the duration thresholdOccurrences: ALL - priority: WARNING From 551ae27d2aec54064fc12e266da887b46cdcf9f4 Mon Sep 17 00:00:00 2001 From: pkosanam Date: Thu, 7 Sep 2023 21:25:03 +0530 Subject: [PATCH 5/8] updated dashboard and images --- .../langchain-vectordb-02.png | Bin 185674 -> 241221 bytes .../langchain-vectordb-03.png | Bin 298223 -> 355497 bytes .../langchain-vectordb-04.png | Bin 630766 -> 632672 bytes .../langchain-vectordb-05.png | Bin 415462 -> 386948 bytes 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"visualization": { - "id": "viz.table" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT result_rank, document_metadata_source, document_page_content FROM LlmVectorSearchResult" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT average(document_count) from LlmVectorSearch WHERE applicationName IN ({{applicationName}})" } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "List of queries ", + "layout": { + "column": 1, + "row": 4, + "width": 3, + "height": 3 }, - { - "title": "", - "layout": { - "column": 1, - "row": 7, - "width": 12, - "height": 1 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.markdown" - }, - "rawConfiguration": { - "text": "# Popular content\nHere are the metrics of document meta details and the page content." - } + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" }, - { - "title": "Top metadata source", - "layout": { - "column": 1, - "row": 8, - "width": 4, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.bar" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT count(*) FROM LlmVectorSearchResult FACET document_metadata_source " - } - ], - "platformOptions": { - "ignoreTimeRange": false + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(query) FROM LlmVectorSearch WHERE applicationName IN ({{applicationName}}) FACET query " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "List of search id's", + "layout": { + "column": 4, + "row": 4, + "width": 3, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" }, - { - "title": "Top page content", - "layout": { - "column": 5, - "row": 8, - "width": 8, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.bar" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT count(*) FROM LlmVectorSearchResult FACET document_page_content" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmVectorSearch WHERE applicationName IN ({{applicationName}}) FACET search_id " } + ], + "platformOptions": { + "ignoreTimeRange": false } } - ] - }, - { - "name": "LangChain", - "description": null, - "widgets": [ - { - "title": "", - "layout": { - "column": 1, - "row": 1, - "width": 12, - "height": 1 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.markdown" - }, - "rawConfiguration": { - "text": "# LLM Completion\nHere are the metrics related to prompt tokens, model names, duration and the completion details of your Large Language Models." - } + }, + { + "title": "Page source and content", + "layout": { + "column": 7, + "row": 4, + "width": 6, + "height": 3 }, - { - "title": "Average tokens", - "layout": { - "column": 1, - "row": 2, - "width": 2, - "height": 4 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.billboard" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT average(prompt_tokens) as 'Prompt tokens',average(completion_tokens) as 'Completion tokens',average(total_tokens) as 'Total tokens' from LlmCompletion " - } - ], - "platformOptions": { - "ignoreTimeRange": false - } - } + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" }, - { - "title": "Duration (seconds)", - "layout": { - "column": 3, - "row": 2, - "width": 2, - "height": 4 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.billboard" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmCompletion " - } - ], - "platformOptions": { - "ignoreTimeRange": false + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT result_rank, document_metadata_source, document_page_content FROM LlmVectorSearchResult" } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "", + "layout": { + "column": 1, + "row": 7, + "width": 12, + "height": 1 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" }, - { - "title": "Duration (seconds)", - "layout": { - "column": 5, - "row": 2, - "width": 4, - "height": 2 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.line" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "legend": { - "enabled": true - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmCompletion TIMESERIES " - } - ], - "platformOptions": { - "ignoreTimeRange": false - }, - "yAxisLeft": { - "zero": true + "rawConfiguration": { + "text": "# Popular content\nThe following metrics will help you understand what content is popular in your system, so that you can improve your application, its content, and the indexing of that content." + } + }, + { + "title": "Top metadata source", + "layout": { + "column": 1, + "row": 8, + "width": 4, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) FROM LlmVectorSearchResult FACET document_metadata_source " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Top page content", + "layout": { + "column": 5, + "row": 8, + "width": 8, + "height": 3 }, - { - "title": "Models", - "layout": { - "column": 9, - "row": 2, - "width": 4, - "height": 4 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.pie" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": true - }, - "legend": { - "enabled": true - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT count(*) from LlmCompletion facet model_name" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) FROM LlmVectorSearchResult FACET document_page_content" } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + } + ] + }, + { + "name": "LangChain", + "description": null, + "widgets": [ + { + "title": "", + "layout": { + "column": 1, + "row": 1, + "width": 12, + "height": 1 }, - { - "title": "Duration (seconds) by model name", - "layout": { - "column": 5, - "row": 4, - "width": 4, - "height": 2 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.bar" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT median(duration.ms/1000) from LlmCompletion facet model_name " - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" + }, + "rawConfiguration": { + "text": "# LLM Completion\nThe following metrics will help you identify trends, compare models and optimize your LLM." + } + }, + { + "title": "Average tokens", + "layout": { + "column": 1, + "row": 2, + "width": 2, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT average(prompt_tokens) as 'Prompt tokens',average(completion_tokens) as 'Completion tokens',average(total_tokens) as 'Total tokens' from LlmCompletion " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 3, + "row": 2, + "width": 2, + "height": 4 }, - { - "title": "LLM completion's details", - "layout": { - "column": 1, - "row": 6, - "width": 12, - "height": 4 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.table" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT model_name,messages,response from LlmCompletion " - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmCompletion " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 5, + "row": 2, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" }, - { - "title": "", - "layout": { - "column": 1, - "row": 10, - "width": 12, - "height": 1 + "rawConfiguration": { + "facet": { + "showOtherSeries": false }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.markdown" + "legend": { + "enabled": true }, - "rawConfiguration": { - "text": "# LLM Chains\nWe get the chain details, duration and the number of chains triggered of your Large Language Models." + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmCompletion TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true } + } + }, + { + "title": "Models", + "layout": { + "column": 9, + "row": 2, + "width": 4, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.pie" }, - { - "title": "Duration (seconds)", - "layout": { - "column": 1, - "row": 11, - "width": 2, - "height": 4 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.billboard" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmChain" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "rawConfiguration": { + "facet": { + "showOtherSeries": true + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmCompletion facet model_name" } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Duration (seconds) by model name", + "layout": { + "column": 5, + "row": 4, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" }, - { - "title": "Duration (seconds)", - "layout": { - "column": 3, - "row": 11, - "width": 4, - "height": 2 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.line" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "legend": { - "enabled": true - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmChain TIMESERIES " - } - ], - "platformOptions": { - "ignoreTimeRange": false - }, - "yAxisLeft": { - "zero": true + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT median(duration.ms/1000) from LlmCompletion facet model_name " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "LLM completion's details", + "layout": { + "column": 1, + "row": 6, + "width": 12, + "height": 4 }, - { - "title": "Chain's details", - "layout": { - "column": 7, - "row": 11, - "width": 6, - "height": 4 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.table" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT chat_history,input,outputs,guid,trace.id ,parent.id from LlmChain since 3 days ago" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT model_name,messages,response from LlmCompletion " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "", + "layout": { + "column": 1, + "row": 10, + "width": 12, + "height": 1 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" }, - { - "title": "Chains triggered", - "layout": { - "column": 3, - "row": 13, - "width": 4, - "height": 2 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.line" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "legend": { - "enabled": true - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT count(*) from LlmChain TIMESERIES " - } - ], - "platformOptions": { - "ignoreTimeRange": false - }, - "yAxisLeft": { - "zero": true + "rawConfiguration": { + "text": "# LLM Chains\nThe following metrics will help you to get a better understanding of how your LLM chains are performing and to identify any potential problems." + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 1, + "row": 11, + "width": 2, + "height": 4 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmChain" } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 3, + "row": 11, + "width": 4, + "height": 2 }, - { - "title": "", - "layout": { - "column": 1, - "row": 15, - "width": 12, - "height": 1 + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.markdown" + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmChain TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false }, - "rawConfiguration": { - "text": "# LLM Tools\nHere, we get the duration, tool details and it’s name of your Large Language Models." + "yAxisLeft": { + "zero": true } + } + }, + { + "title": "Chain's details", + "layout": { + "column": 7, + "row": 11, + "width": 6, + "height": 4 }, - { - "title": "Duration (seconds)", - "layout": { - "column": 1, - "row": 16, - "width": 2, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.billboard" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmTool " - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT chat_history,input,outputs,guid,trace.id ,parent.id from LlmChain since 3 days ago" } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Chains triggered", + "layout": { + "column": 3, + "row": 13, + "width": 4, + "height": 2 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" }, - { - "title": "Duration (seconds)", - "layout": { - "column": 3, - "row": 16, - "width": 4, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.line" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "legend": { - "enabled": true - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmTool TIMESERIES " - } - ], - "platformOptions": { - "ignoreTimeRange": false - }, - "yAxisLeft": { - "zero": true + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmChain TIMESERIES " } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true } + } + }, + { + "title": "", + "layout": { + "column": 1, + "row": 15, + "width": 12, + "height": 1 }, - { - "title": "Tool details", - "layout": { - "column": 7, - "row": 16, - "width": 6, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.table" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT tool_name,tool_invocation_counter, tool_input,tool_output from LlmTool " - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.markdown" + }, + "rawConfiguration": { + "text": "# LLM Tools\nThe following metrics helps you to understand how your LLM tools are performing and to identify any potential problems.\n " + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 1, + "row": 16, + "width": 2, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmTool " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Duration (seconds)", + "layout": { + "column": 3, + "row": 16, + "width": 4, + "height": 3 }, - { - "title": "Duration (seconds) by tool name ", - "layout": { - "column": 1, - "row": 19, - "width": 2, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.billboard" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT average(duration.ms/1000) as 'seconds' from LlmTool facet tool_name" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms/1000),95) as 'seconds',percentile((duration.ms/1000),99) as 'seconds',percentile((duration.ms/1000),50) as 'seconds' from LlmTool TIMESERIES " } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true } + } + }, + { + "title": "Tool details", + "layout": { + "column": 7, + "row": 16, + "width": 6, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.table" }, - { - "title": "Duration (seconds) by tool name ", - "layout": { - "column": 3, - "row": 19, - "width": 6, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.line" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "legend": { - "enabled": true - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT percentile((duration.ms / 1000),50) as 'seconds' from LlmTool facet tool_name TIMESERIES " - } - ], - "platformOptions": { - "ignoreTimeRange": false - }, - "yAxisLeft": { - "zero": true + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT tool_name,tool_invocation_counter, tool_input,tool_output from LlmTool " } + ], + "platformOptions": { + "ignoreTimeRange": false } + } + }, + { + "title": "Duration (seconds) by tool name ", + "layout": { + "column": 1, + "row": 19, + "width": 2, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.billboard" }, - { - "title": "Top tool names", - "layout": { - "column": 9, - "row": 19, - "width": 4, - "height": 3 - }, - "linkedEntityGuids": null, - "visualization": { - "id": "viz.bar" - }, - "rawConfiguration": { - "facet": { - "showOtherSeries": false - }, - "nrqlQueries": [ - { - "accountIds": [], - "query": "SELECT count(*) from LlmTool facet tool_name" - } - ], - "platformOptions": { - "ignoreTimeRange": false + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT average(duration.ms/1000) as 'seconds' from LlmTool facet tool_name" } + ], + "platformOptions": { + "ignoreTimeRange": false } } - ] - } - ], - "variables": [ - { - "name": "applicationName", - "items": null, - "defaultValues": [ - { - "value": { - "string": "*" + }, + { + "title": "Duration (seconds) by tool name ", + "layout": { + "column": 3, + "row": 19, + "width": 6, + "height": 3 + }, + "linkedEntityGuids": null, + "visualization": { + "id": "viz.line" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "legend": { + "enabled": true + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT percentile((duration.ms / 1000),50) as 'seconds' from LlmTool facet tool_name TIMESERIES " + } + ], + "platformOptions": { + "ignoreTimeRange": false + }, + "yAxisLeft": { + "zero": true } + } + }, + { + "title": "Top tool names", + "layout": { + "column": 9, + "row": 19, + "width": 4, + "height": 3 }, - { - "value": { - "string": "LangChain observability trace" + "linkedEntityGuids": null, + "visualization": { + "id": "viz.bar" + }, + "rawConfiguration": { + "facet": { + "showOtherSeries": false + }, + "nrqlQueries": [ + { + "accountIds": [], + "query": "SELECT count(*) from LlmTool facet tool_name" + } + ], + "platformOptions": { + "ignoreTimeRange": false } } - ], - "nrqlQuery": { - "accountIds": [], - "query": "FROM LlmVectorSearch SELECT uniques(applicationName) WHERE applicationName IS NOT NULL since 1 month ago" + } + ] + } + ], + "variables": [ + { + "name": "applicationName", + "items": null, + "defaultValues": [ + { + "value": { + "string": "*" + } }, - "title": "Select Your Application Name", - "type": "NRQL", - "isMultiSelection": true, - "replacementStrategy": "STRING" - } - ] - } \ No newline at end of file + { + "value": { + "string": "LangChain observability trace" + } + } + ], + "nrqlQuery": { + "accountIds": [], + "query": "FROM LlmVectorSearch SELECT uniques(applicationName) WHERE applicationName IS NOT NULL since 1 month ago" + }, + "title": "Select Your Application Name", + "type": "NRQL", + "isMultiSelection": true, + "replacementStrategy": "STRING" + } + ] +} \ No newline at end of file From 4599828bdd7be1baf4e7f7f37f8fa51f93bc8d5a Mon Sep 17 00:00:00 2001 From: pkosanam Date: Thu, 7 Sep 2023 21:29:01 +0530 Subject: [PATCH 6/8] updated content --- quickstarts/langchain-vectordb/config.yml | 78 ++++++++++------------- 1 file changed, 32 insertions(+), 46 deletions(-) diff --git a/quickstarts/langchain-vectordb/config.yml b/quickstarts/langchain-vectordb/config.yml index 528bed9b1b..90a5e7bc46 100644 --- a/quickstarts/langchain-vectordb/config.yml +++ b/quickstarts/langchain-vectordb/config.yml @@ -1,61 +1,47 @@ -# Name of the quickstart slug: langchain-vectordb +description: | + ## Why should you monitor your usage of Vector Stores? -# Title of the quickstart -title: LangChain Vector Database + Monitor your vector searches to get visibility on what you send to your vector store, responses retrieved from them, latency, usage and errors. -summary: | - A vector store takes care of storing embedded data and performing vector search on the vector database -# Description of the quickstart -description: | - ## Why monitor LangChain Vector Database? - LangChain is a framework for developing applications powered by language models. - By monitoring your LangChain app, we are providing you visibility on the chains, tools as well as inputs and outputs. - Monitoring can help optimize the performance of systems or applications that rely on vector data. For databases associated with hardware or equipment, monitoring can enable predictive maintenance by tracking usage and performance metrics to anticipate and prevent failures - - ## Comprehensive monitoring quickstart for LangChain - Our LangChain Vector Database quickstart provides metrics, including response time, zero documents retreived and many more. These metrics serve as valuable tools to empower you in maximizing the performance of LangChain Vector Database. + ### Track the query performance of your Vector DB + Track the behavior of your vector stores. Monitor the latency, queries, the number of documents retrieved, and the content of the documents so that you can evaluate their relevance. - ## What’s included in this quickstart? - New Relic LangChain Vector Database monitoring quickstart provides a variety of pre-built reports, which will help you gain insights into the health and performance of your LangChain. These reports include: - - Code-related insights to identify potential problems with your code and improve its performance - - Latency details and number of tokens - - Entity details such as the average number of tokens used, the cost per entity, and the total number of requests made - - Alerts for errors,requests per model and response time + ### Track your app: + By tracking key metrics like latency, throughput, error rates, and input & output, you can gain insights into your app's performance and identify areas of improvement. -# The logo of the quickstart -icon: logo.png -# Support level -level: New Relic + ### What’s included in this quickstart? + + New Relic LangChain monitoring quickstart provides a variety of pre-built dashboards, which will help you gain insights into the health and performance of your LangChain app. These reports include: + + - Vector searches + - Alerts for errors, search per vector store, and response time + - Identify popular queries, sources, and content -# Authors of the quickstart +summary: | +Monitor your Vector search's performance and quality with New Relic LangChain Vector Stores quickstart. +icon: logo.png +level: New Relic authors: - New Relic - -keywords: - - langchain vector database - - langchain ai - - vector database - - ai - - lang chain - - large language model - - natural language processing - - machine learning - - artificial intelligence - - mlops - - NR1_addData - -dashboards: - - langchain-vectordb -alertPolicies: - - langchain-vectordb - +title: LangChain Vector Stores documentation: - name: LangChain Vector Database integration documentation description: | - Implement monitoring and instrumentation for your LangChain Vector Database, and ensure that your LangChain Vector Database data is integrated into New Relic for effective performance analysis and insights. + Implement monitoring and instrumentation for your Vector store, and ensure that your observability data is integrated into New Relic for effective performance analysis and insights. url: https://github.com/newrelic/nr-openai-observability - dataSourceIds: - langchain-vectordb +keywords: + - langchain + - vector store + - vector search + - vectordb + - vector db + - mlops + - NR1_addData +dashboards: + - langchain-vectordb +alertPolicies: + - langchain-vectordb \ No newline at end of file From c4abfcfea7984b419e58f64512e17524a1a249eb Mon Sep 17 00:00:00 2001 From: pkosanam Date: Thu, 7 Sep 2023 22:05:32 +0530 Subject: [PATCH 7/8] updated alert condition --- alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml b/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml index 5f7975d5de..c7088edb3e 100644 --- a/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml +++ b/alert-policies/langchain-vectordb/ZeroDocumentsRetrieved.yml @@ -10,7 +10,7 @@ type: STATIC # NRQL query nrql: - query: "SELECT filter(count(document_count), WHERE document_count=4) / count(document_count) * 100 from LlmVectorSearch WHERE applicationName IN ('LangChain observability trace')" + query: "SELECT filter(count(document_count), WHERE document_count=0) / count(document_count) * 100 from LlmVectorSearch WHERE applicationName IN ('LangChain observability trace')" # Function used to aggregate the NRQL query value(s) for comparison to the terms.threshold (Default: SINGLE_VALUE) valueFunction: SINGLE_VALUE From 6ae4099b5cb1d94e5d7b51e9817d9bdcb264073b Mon Sep 17 00:00:00 2001 From: pkosanam Date: Thu, 7 Sep 2023 22:07:59 +0530 Subject: [PATCH 8/8] fixed linting issue --- quickstarts/langchain-vectordb/config.yml | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/quickstarts/langchain-vectordb/config.yml b/quickstarts/langchain-vectordb/config.yml index 90a5e7bc46..563b604760 100644 --- a/quickstarts/langchain-vectordb/config.yml +++ b/quickstarts/langchain-vectordb/config.yml @@ -4,23 +4,22 @@ description: | Monitor your vector searches to get visibility on what you send to your vector store, responses retrieved from them, latency, usage and errors. - ### Track the query performance of your Vector DB - Track the behavior of your vector stores. Monitor the latency, queries, the number of documents retrieved, and the content of the documents so that you can evaluate their relevance. + ### Track the query performance of your Vector DB + Track the behavior of your vector stores. Monitor the latency, queries, the number of documents retrieved, and the content of the documents so that you can evaluate their relevance. - ### Track your app: + ### Track your app: By tracking key metrics like latency, throughput, error rates, and input & output, you can gain insights into your app's performance and identify areas of improvement. - ### What’s included in this quickstart? - New Relic LangChain monitoring quickstart provides a variety of pre-built dashboards, which will help you gain insights into the health and performance of your LangChain app. These reports include: + New Relic LangChain monitoring quickstart provides a variety of pre-built dashboards, which will help you gain insights into the health and performance of your LangChain app. These reports include: - Vector searches - Alerts for errors, search per vector store, and response time - Identify popular queries, sources, and content summary: | -Monitor your Vector search's performance and quality with New Relic LangChain Vector Stores quickstart. + Monitor your Vector search's performance and quality with New Relic LangChain Vector Stores quickstart. icon: logo.png level: New Relic authors: