From 194337724a8040b0bb5df7b985e4fd9de31f95a4 Mon Sep 17 00:00:00 2001 From: Mikyo King Date: Sat, 19 Oct 2024 17:03:40 -0600 Subject: [PATCH 01/46] docs: add deployment options link (#5124) --- docs/deployment/README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/docs/deployment/README.md b/docs/deployment/README.md index 1bbd1c1c0c..fcbe9bc9cb 100644 --- a/docs/deployment/README.md +++ b/docs/deployment/README.md @@ -10,8 +10,17 @@ Phoenix can natively be run as a container that collects traces and evaluations
QuickstartHow to deploy an LLM application with Phoenix observabilityquickstart.pngdeploying-phoenix.md
Hosted PhoenixTry a free cloud instance of Phoenix through our websiteScreenshot 2024-10-09 at 6.34.18 PM.pnghosted-phoenix.md
PersistenceHow to setup a persistent disc or databasepersistence.pngpersistence.md
KubernetesHow to deploy Phoenix on K8sk8s.pngkubernetes.md
DockerHow to deploy Phoenix using Dockerdocker.pngdocker.md
-## Images +# Images This table lists the images we publish that can be used to run Phoenix.
Image TagDescription
arizephoenix/phoenix:latestLatest released version of Phoenix using root permissions.
arizephoenix/phoenix:latest-nonrootLatest released version of Phoenix using nonroot permissions. Ensure the image has the required filesystem permissions before using.
arizephoenix/phoenix:latest-debugLatest released version of Phoenix using a debug base image.
arizephoenix/phoenix:version-X.X.XBuild for a specific release version using root permissions.
arizephoenix/phoenix:version-X.X.X-nonrootBuild for a specific release version using nonroot permissions.
arizephoenix/phoenix:version-X.X.X-debugBuild for a specific release version using a debug image.
+ + +# Services + +You can deploy Phoenix on any cloud provider or on-premise. Here are some services that Phoenix can be deployed on: + +## Railway + +[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/PTHRoq?referralCode=Xe2txW) From e22643ae2012e55447ca309b28cf793da6008934 Mon Sep 17 00:00:00 2001 From: Mikyo King Date: Sun, 20 Oct 2024 16:43:30 +0000 Subject: [PATCH 02/46] docs: add embeddings extra to quickstart (GITBOOK-906) --- docs/inferences/phoenix-inferences.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/inferences/phoenix-inferences.md b/docs/inferences/phoenix-inferences.md index 153b9b057a..8416940032 100644 --- a/docs/inferences/phoenix-inferences.md +++ b/docs/inferences/phoenix-inferences.md @@ -18,10 +18,10 @@ We will begin by logging just a training set. Then proceed to add a production s ### Step 1: Install and load dependencies -Use `pip` or `conda`to install `arize-phoenix`. +Use `pip` or `conda`to install `arize-phoenix`. Note that since we are going to do embedding analysis we must also add the embeddings extra. ```python -!pip install arize-phoenix +!pip install 'arize-phoenix[embeddings]' import phoenix as px ``` From 11534f275645c3ef890e2b133365bd8dfa959d1e Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Tue, 22 Oct 2024 17:15:45 +0000 Subject: [PATCH 03/46] docs: Autogen docs updates (GITBOOK-907) --- docs/tracing/integrations-tracing/autogen-support.md | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/docs/tracing/integrations-tracing/autogen-support.md b/docs/tracing/integrations-tracing/autogen-support.md index fd039c395f..e90d1f410b 100644 --- a/docs/tracing/integrations-tracing/autogen-support.md +++ b/docs/tracing/integrations-tracing/autogen-support.md @@ -11,11 +11,10 @@ The AutoGen Agent framework allows creation of multiple agents and connection of First install dependencies ```shell -pip install openinference-instrumentation-openai arize-phoenix-otel +pip install openinference-instrumentation-openai arize-phoenix-otel arize-phoenix ``` Then instrument the application -``` ```python from openinference.instrumentation.openai import OpenAIInstrumentor @@ -23,7 +22,6 @@ from phoenix.otel import register import phoenix as px px.launch_app() - tracer_provider = register() OpenAIInstrumentor().instrument(tracer_provider=tracer_provider) ``` @@ -32,8 +30,4 @@ The Phoenix support is simple in its first incarnation but allows for capturing

Agent Reply

-The individual prompt and responses are captured directly through OpenAI calls. - -{% hint style="info" %} -As callbacks are supported in AutoGen Phoenix will add more agent level information. -{% endhint %} +The individual prompt and responses are captured directly through OpenAI calls. If you're using a different underlying model provider than OpenAI, instrument your application using the respective instrumentor instead. From f84601c4d5fe954aeed4f79f991e6c3c22313a0e Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Thu, 24 Oct 2024 17:41:12 +0000 Subject: [PATCH 04/46] docs: Add explicit docs for LlamaIndex Workflows and LangGraph (GITBOOK-910) --- docs/SUMMARY.md | 2 + .../tracing/integrations-tracing/langchain.md | 2 +- .../tracing/integrations-tracing/langgraph.md | 162 +++++++++++++++++ .../integrations-tracing/llamaindex-1.md | 165 ++++++++++++++++++ .../integrations-tracing/llamaindex.md | 5 +- 5 files changed, 332 insertions(+), 4 deletions(-) create mode 100644 docs/tracing/integrations-tracing/langgraph.md create mode 100644 docs/tracing/integrations-tracing/llamaindex-1.md diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md index a2c8ced95b..6939ea21c7 100644 --- a/docs/SUMMARY.md +++ b/docs/SUMMARY.md @@ -23,7 +23,9 @@ * [Integrations: Tracing](tracing/integrations-tracing/README.md) * [OpenAI](tracing/integrations-tracing/openai.md) * [LlamaIndex](tracing/integrations-tracing/llamaindex.md) + * [LlamaIndex Workflows](tracing/integrations-tracing/llamaindex-1.md) * [LangChain](tracing/integrations-tracing/langchain.md) + * [LangGraph](tracing/integrations-tracing/langgraph.md) * [Haystack](tracing/integrations-tracing/haystack.md) * [Anthropic](tracing/integrations-tracing/anthropic.md) * [Groq](tracing/integrations-tracing/groq.md) diff --git a/docs/tracing/integrations-tracing/langchain.md b/docs/tracing/integrations-tracing/langchain.md index d18cf0881b..be732386c4 100644 --- a/docs/tracing/integrations-tracing/langchain.md +++ b/docs/tracing/integrations-tracing/langchain.md @@ -149,7 +149,7 @@ LangChainInstrumentor().instrument(tracer_provider=tracer_provider) ## Run LangChain -By instrumenting LangChain, spans will be created whenever a a chain is run and will be sent to the Phoenix server for collection. +By instrumenting LangChain, spans will be created whenever a chain is run and will be sent to the Phoenix server for collection. ```python from langchain_core.prompts import ChatPromptTemplate diff --git a/docs/tracing/integrations-tracing/langgraph.md b/docs/tracing/integrations-tracing/langgraph.md new file mode 100644 index 0000000000..801301354d --- /dev/null +++ b/docs/tracing/integrations-tracing/langgraph.md @@ -0,0 +1,162 @@ +# LangGraph + +Phoenix has first-class support for [LangGraph](https://www.langchain.com/langgraph) applications. + +{% hint style="info" %} +LangGraph is supported by our [LangChain instrumentor](langchain.md). If you've already set up instrumentation with LangChain, you don't need to complete the set up below +{% endhint %} + +## Launch Phoenix + +{% tabs %} +{% tab title="Notebook" %} +**Install packages:** + +```bash +pip install arize-phoenix +``` + +**Launch Phoenix:** + +```python +import phoenix as px +px.launch_app() +``` + +**Connect your notebook to Phoenix:** + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-llm-app", # Default is 'default' +) +``` + +{% hint style="info" %} +By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See [persistence.md](../../deployment/persistence.md "mention") or use one of the other deployment options to retain traces. +{% endhint %} +{% endtab %} + +{% tab title="Command Line" %} +**Launch your local Phoenix instance:** + +```bash +python3 -m phoenix.server.main serve +``` + +For details on customizing a local terminal deployment, see [Terminal Setup](https://docs.arize.com/phoenix/setup/environments#terminal). + +**Install packages:** + +```bash +pip install arize-phoenix-otel +``` + +**Connect your application to your instance using:** + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-llm-app", # Default is 'default' + endpoint="http://localhost:6006/v1/traces", +) +``` + +See [deploying-phoenix.md](../../deployment/deploying-phoenix.md "mention") for more details +{% endtab %} + +{% tab title="Docker" %} +**Pull latest Phoenix image from** [**Docker Hub**](https://hub.docker.com/r/arizephoenix/phoenix)**:** + +```bash +docker pull arizephoenix/phoenix:latest +``` + +**Run your containerized instance:** + +```bash +docker run -p 6006:6006 arizephoenix/phoenix:latest +``` + +This will expose the Phoenix on `localhost:6006` + +**Install packages:** + +```bash +pip install arize-phoenix-otel +``` + +**Connect your application to your instance using:** + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-llm-app", # Default is 'default' + endpoint="http://localhost:6006/v1/traces", +) +``` + +For more info on using Phoenix with Docker, see [#docker](langgraph.md#docker "mention") +{% endtab %} + +{% tab title="app.phoenix.arize.com" %} +If you don't want to host an instance of Phoenix yourself or use a notebook instance, you can use a persistent instance provided on our site. Sign up for an Arize Phoenix account at[https://app.phoenix.arize.com/login](https://app.phoenix.arize.com/login) + +**Install packages:** + +```bash +pip install arize-phoenix-otel +``` + +**Connect your application to your cloud instance:** + +```python +import os +from phoenix.otel import register + +# Add Phoenix API Key for tracing +os.environ["PHOENIX_CLIENT_HEADERS"] = "api_key=...:..." + +# configure the Phoenix tracer +register( + project_name="my-llm-app", # Default is 'default' + endpoint="https://app.phoenix.arize.com/v1/traces", +) +``` + +Your **Phoenix API key** can be found on the Keys section of your [dashboard](https://app.phoenix.arize.com). +{% endtab %} +{% endtabs %} + +## Install + +```bash +pip install openinference-instrumentation-langchain +``` + +## Setup + +Initialize the LangChainInstrumentor before your application code. Our LangChainInstrumentor works for both standard LangChain applications and for LangGraph agents. + +```python +from openinference.instrumentation.langchain import LangChainInstrumentor + +LangChainInstrumentor().instrument(tracer_provider=tracer_provider) +``` + +## Run LangGraph + +By instrumenting LangGraph, spans will be created whenever an agent is invoked and will be sent to the Phoenix server for collection. + +## Observe + +Now that you have tracing setup, all invocations of chains will be streamed to your running Phoenix for observability and evaluation. + +## Resources + +* [Example notebook](https://github.com/Arize-ai/phoenix/blob/main/tutorials/tracing/langgraph\_agent\_tracing\_tutorial.ipynb) +* [OpenInference package](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-langchain) +* [Blog walkthrough](https://arize.com/blog/langgraph/) diff --git a/docs/tracing/integrations-tracing/llamaindex-1.md b/docs/tracing/integrations-tracing/llamaindex-1.md new file mode 100644 index 0000000000..7226f7d1d0 --- /dev/null +++ b/docs/tracing/integrations-tracing/llamaindex-1.md @@ -0,0 +1,165 @@ +--- +description: How to use the python LlamaIndexInstrumentor to trace LlamaIndex Workflows +--- + +# LlamaIndex Workflows + +[LlamaIndex Workflows](https://www.llamaindex.ai/blog/introducing-workflows-beta-a-new-way-to-create-complex-ai-applications-with-llamaindex) are a subset of the LlamaIndex package specifically designed to support agent development. + +{% hint style="info" %} +Our [LlamaIndexInstrumentor](llamaindex.md) automatically captures traces for LlamaIndex Workflows agents. If you've already enabled that instrumentor, you do not need to complete the steps below. +{% endhint %} + +## Launch Phoenix + +{% tabs %} +{% tab title="Notebook" %} +**Install packages:** + +```bash +pip install arize-phoenix +``` + +**Launch Phoenix:** + +```python +import phoenix as px +px.launch_app() +``` + +**Connect your notebook to Phoenix:** + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-llm-app", # Default is 'default' +) +``` + +{% hint style="info" %} +By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See [persistence.md](../../deployment/persistence.md "mention") or use one of the other deployment options to retain traces. +{% endhint %} +{% endtab %} + +{% tab title="Command Line" %} +**Launch your local Phoenix instance:** + +```bash +python3 -m phoenix.server.main serve +``` + +For details on customizing a local terminal deployment, see [Terminal Setup](https://docs.arize.com/phoenix/setup/environments#terminal). + +**Install packages:** + +```bash +pip install arize-phoenix-otel +``` + +**Connect your application to your instance using:** + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-llm-app", # Default is 'default' + endpoint="http://localhost:6006/v1/traces", +) +``` + +See [deploying-phoenix.md](../../deployment/deploying-phoenix.md "mention") for more details +{% endtab %} + +{% tab title="Docker" %} +**Pull latest Phoenix image from** [**Docker Hub**](https://hub.docker.com/r/arizephoenix/phoenix)**:** + +```bash +docker pull arizephoenix/phoenix:latest +``` + +**Run your containerized instance:** + +```bash +docker run -p 6006:6006 arizephoenix/phoenix:latest +``` + +This will expose the Phoenix on `localhost:6006` + +**Install packages:** + +```bash +pip install arize-phoenix-otel +``` + +**Connect your application to your instance using:** + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-llm-app", # Default is 'default' + endpoint="http://localhost:6006/v1/traces", +) +``` + +For more info on using Phoenix with Docker, see [#docker](llamaindex-1.md#docker "mention") +{% endtab %} + +{% tab title="app.phoenix.arize.com" %} +If you don't want to host an instance of Phoenix yourself or use a notebook instance, you can use a persistent instance provided on our site. Sign up for an Arize Phoenix account at[https://app.phoenix.arize.com/login](https://app.phoenix.arize.com/login) + +**Install packages:** + +```bash +pip install arize-phoenix-otel +``` + +**Connect your application to your cloud instance:** + +```python +import os +from phoenix.otel import register + +# Add Phoenix API Key for tracing +os.environ["PHOENIX_CLIENT_HEADERS"] = "api_key=...:..." + +# configure the Phoenix tracer +register( + project_name="my-llm-app", # Default is 'default' + endpoint="https://app.phoenix.arize.com/v1/traces", +) +``` + +Your **Phoenix API key** can be found on the Keys section of your [dashboard](https://app.phoenix.arize.com). +{% endtab %} +{% endtabs %} + +## Install + +```bash +pip install openinference-instrumentation-llama_index +``` + +## Setup + +Initialize the LlamaIndexInstrumentor before your application code. This instrumentor will trace both LlamaIndex Workflows calls, as well as calls to the general LlamaIndex package. + +```python +from openinference.instrumentation.llama_index import LlamaIndexInstrumentor + +LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider) +``` + +## Run LlamaIndex Workflows + +By instrumenting LlamaIndex, spans will be created whenever an agent is invoked and will be sent to the Phoenix server for collection. + +## Observe + +Now that you have tracing setup, all invocations of chains will be streamed to your running Phoenix for observability and evaluation. + +## Resources + +* [Example project](https://github.com/Arize-ai/phoenix/tree/main/examples/llamaindex-workflows-research-agent) +* [OpenInference package](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-langchain) diff --git a/docs/tracing/integrations-tracing/llamaindex.md b/docs/tracing/integrations-tracing/llamaindex.md index f4004af90d..44c6b89813 100644 --- a/docs/tracing/integrations-tracing/llamaindex.md +++ b/docs/tracing/integrations-tracing/llamaindex.md @@ -55,7 +55,7 @@ LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider) Using phoenix as a callback requires an install of \`llama-index-callbacks-arize-phoenix>0.1.3' {% endhint %} -llama-index 0.10 introduced modular sub-packages. To use llama-index's one click, you must install the small integration first: +llama-index 0.10 introduced modular sub-packages. To use llama-index's one click, you must install the small integration first: ```bash pip install 'llama-index-callbacks-arize-phoenix>0.1.3' @@ -102,5 +102,4 @@ llama_index.set_global_handler("arize_phoenix") By adding the callback to the callback manager of LlamaIndex, we've created a one-way data connection between your LLM application and Phoenix Server. -For a fully working example of tracing with LlamaIndex, checkout our colab notebook. - +For a fully working example of tracing with LlamaIndex, checkout our colab notebook above. From 6abb82dfd842fdc923934e18f380302e4790d264 Mon Sep 17 00:00:00 2001 From: Parker Stafford Date: Thu, 24 Oct 2024 21:42:22 +0000 Subject: [PATCH 05/46] docs: docs(js): add documentation for js otel core functionality (GITBOOK-911) --- docs/SUMMARY.md | 1 + docs/tracing/how-to-tracing/README.md | 3 +- .../how-to-tracing/customize-spans/README.md | 133 +++++++++++++++++- ...g-prompt-templates-and-prompt-variables.md | 31 ++++ .../masking-span-attributes.md | 59 ++++++++ .../integrations-tracing/langchain.js.md | 6 + .../integrations-tracing/openai-node-sdk.md | 6 +- .../integrations-tracing/vercel-ai-sdk.md | 12 +- 8 files changed, 240 insertions(+), 11 deletions(-) create mode 100644 docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md index 6939ea21c7..69efab12b9 100644 --- a/docs/SUMMARY.md +++ b/docs/SUMMARY.md @@ -55,6 +55,7 @@ * [Customize Traces](tracing/how-to-tracing/customize-traces.md) * [Customize Spans](tracing/how-to-tracing/customize-spans/README.md) * [Instrumenting Prompt Templates and Prompt Variables](tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md) + * [Masking Span Attributes](tracing/how-to-tracing/customize-spans/masking-span-attributes.md) * [Auto Instrumentation](tracing/how-to-tracing/instrumentation/README.md) * [Auto Instrument: Python](tracing/how-to-tracing/instrumentation/auto-instrument-python.md) * [Auto Instrument: TS](tracing/how-to-tracing/instrumentation/auto-instrument-ts.md) diff --git a/docs/tracing/how-to-tracing/README.md b/docs/tracing/how-to-tracing/README.md index d83f29de1e..db5d0f8d00 100644 --- a/docs/tracing/how-to-tracing/README.md +++ b/docs/tracing/how-to-tracing/README.md @@ -31,6 +31,7 @@ How to set custom attributes and semantic attributes to child spans and spans cr * [Setting a user](customize-spans/#using\_user) * [Setting prompt template attributes](customize-spans/#specifying-the-prompt-template) * [How to read attributes from context](customize-spans/#using\_attributes) +* [Masking attributes on spans](customize-spans/masking-span-attributes.md) ## [Auto Instrumentation](./#auto-instrumentation) @@ -98,4 +99,4 @@ How to log evaluation results to annotate traces with evals * [How to instrument an application](trace-a-deployed-app.md#how-to-instrument-an-application) * [How to deploy a Phoenix server (collector)](../../deployment/deploying-phoenix.md) -* [How to use Arize as a collector ](trace-a-deployed-app.md#exporting-traces-to-arize) +* [How to use Arize as a collector](trace-a-deployed-app.md#exporting-traces-to-arize) diff --git a/docs/tracing/how-to-tracing/customize-spans/README.md b/docs/tracing/how-to-tracing/customize-spans/README.md index 91aba17bbf..3f35868033 100644 --- a/docs/tracing/how-to-tracing/customize-spans/README.md +++ b/docs/tracing/how-to-tracing/customize-spans/README.md @@ -22,19 +22,25 @@ Supported Context Attributes include: ### Install Core Instrumentation Package {% tabs %} -{% tab title="undefined" %} +{% tab title="Python" %} Install the core instrumentation package: ```sh pip install openinference-instrumentation ``` {% endtab %} + +{% tab title="JS" %} +```bash +npm install --save @arizeai/openinference-core @opentelemetry/api +``` +{% endtab %} {% endtabs %} ### Specifying a session {% hint style="warning" %} -Sessions are not currently supported in Phoenix and only supported via the Arize OTel collector ([https://docs.arize.com/arize/large-language-models/sessions-and-users#what-are-sessions](https://docs.arize.com/arize/large-language-models/sessions-and-users#what-are-sessions)). Support for sessions in Phoenix is coming in an upcoming release. +Sessions are not currently supported in Phoenix and only supported via the [Arize OTel collector](https://docs.arize.com/arize/large-language-models/sessions-and-users#what-are-sessions). Support for sessions in Phoenix is coming in an upcoming release. {% endhint %} {% tabs %} @@ -60,6 +66,22 @@ def call_fn(*args, **kwargs): ... ``` {% endtab %} + +{% tab title="JS" %} +We provide a `setSession` function which allows you to set a sessionId on context. You can use this utility in conjunction with [`context.with`](https://opentelemetry.io/docs/languages/js/context/#set-active-context) to set the active context. OpenInference [auto instrumentations](../../integrations-tracing/#javascript) will then pick up these attributes and add them to any spans created within the `context.with` callback. + +
import { context } from "@opentelemetry/api"
+import { setSession } from "@openinference-core"
+
+context.with(
+  setSession(context.active(), { sessionId: "session-id" }),
+  () => {
+      // Calls within this block will generate spans with the attributes:
+      // "session.id" = "session-id"
+  }
+)
+
+{% endtab %} {% endtabs %} ### Specifying users @@ -86,6 +108,23 @@ def call_fn(*args, **kwargs): ... ``` {% endtab %} + +{% tab title="JS" %} +We provide a `setUser` function which allows you to set a userId on context. You can use this utility in conjunction with [`context.with`](https://opentelemetry.io/docs/languages/js/context/#set-active-context) to set the active context. OpenInference [auto instrumentations](../../integrations-tracing/#javascript) will then pick up these attributes and add them to any spans created within the `context.with` callback. + +```typescript +import { context } from "@opentelemetry/api" +import { setUser } from "@openinference-core" + +context.with( + setUser(context.active(), { userId: "user-id" }), + () => { + // Calls within this block will generate spans with the attributes: + // "user.id" = "user-id" + } +) +``` +{% endtab %} {% endtabs %} ### Specifying Metadata @@ -117,13 +156,30 @@ def call_fn(*args, **kwargs): ... ``` {% endtab %} + +{% tab title="JS" %} +We provide a `setMetadata` function which allows you to set a metadata attributes on context. You can use this utility in conjunction with [`context.with`](https://opentelemetry.io/docs/languages/js/context/#set-active-context) to set the active context. OpenInference [auto instrumentations](../../integrations-tracing/#javascript) will then pick up these attributes and add them to any spans created within the `context.with` callback. Metadata attributes will be serialized to a JSON string when stored on context and will be propagated to spans in the same way. + +```typescript +import { context } from "@opentelemetry/api" +import { setMetadata } from "@openinference-core" + +context.with( + setMetadata(context.active(), { key1: "value1", key2: "value2" }), + () => { + // Calls within this block will generate spans with the attributes: + // "metadata" = '{"key1": "value1", "key2": "value2"}' + } +) +``` +{% endtab %} {% endtabs %} ### Specifying Tags {% tabs %} {% tab title="Python" %} -We provide a `using_tags` context manager to add tags to the current OpenTelemetry Context. OpenInference auto [instrumentators](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation) will read this Context and pass the tags as a span attribute, following the OpenInference [semantic conventions](https://github.com/Arize-ai/openinference/tree/main/python/openinference-semantic-conventions). ts input, the tag list, must be a list of strings. +We provide a `using_tags` context manager to add tags to the current OpenTelemetry Context. OpenInference auto [instrumentators](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation) will read this Context and pass the tags as a span attribute, following the OpenInference [semantic conventions](https://github.com/Arize-ai/openinference/tree/main/python/openinference-semantic-conventions). The input, the tag list, must be a list of strings. ```python from openinference.instrumentation import using_tags @@ -144,6 +200,23 @@ def call_fn(*args, **kwargs): ... ``` {% endtab %} + +{% tab title="JS" %} +We provide a `setTags` function which allows you to set a list of string tags on context. You can use this utility in conjunction with [`context.with`](https://opentelemetry.io/docs/languages/js/context/#set-active-context) to set the active context. OpenInference [auto instrumentations](../../integrations-tracing/#javascript) will then pick up these attributes and add them to any spans created within the `context.with` callback. Tags, like metadata, will be serialized to a JSON string when stored on context and will be propagated to spans in the same way. + +```typescript +import { context } from "@opentelemetry/api" +import { setTags } from "@openinference-core" + +context.with( + setTags(context.active(), ["value1", "value2"]), + () => { + // Calls within this block will generate spans with the attributes: + // "tag.tags" = '["value1", "value2"]' + } +) +``` +{% endtab %} {% endtabs %} ### Customizing Attributes @@ -232,6 +305,60 @@ def call_fn(*args, **kwargs): ... ``` {% endtab %} + +{% tab title="JS" %} +We provide a `setAttributes` function which allows you to add a set of attributes to context. You can use this utility in conjunction with [`context.with`](https://opentelemetry.io/docs/languages/js/context/#set-active-context) to set the active context. OpenInference [auto instrumentations](../../integrations-tracing/#javascript) will then pick up these attributes and add them to any spans created within the `context.with` callback. Attributes set on context using `setAttributes` must be valid span [attribute values](https://opentelemetry.io/docs/specs/otel/common/#attribute). + +
import { context } from "@opentelemetry/api"
+import { setAttributes } from "@openinference-core"
+
+context.with(
+  setAttributes(context.active(), { myAttribute: "test" }),
+  () => {
+      // Calls within this block will generate spans with the attributes:
+      // "myAttribute" = "test"
+  }
+)
+
+ +You can also use multiple setters at the same time to propagate multiple attributes to the span below. Since each setter function returns a new context, they can be used together as follows. + +```typescript +import { context } from "@opentelemetry/api" +import { setAttributes } from "@openinference-core" + +context.with( + setAttributes( + setSession(context.active(), { sessionId: "session-id"}), + { myAttribute: "test" } + ), + () => { + // Calls within this block will generate spans with the attributes: + // "myAttribute" = "test" + // "session.id" = "session-id" + } +) +``` + +You can also use `setAttributes` in conjunction with the [OpenInference Semantic Conventions](https://github.com/Arize-ai/openinference/blob/main/spec/semantic\_conventions.md) to set OpenInference attributes manually. + +```typescript +import { context } from "@opentelemetry/api" +import { setAttributes } from "@openinference-core" +import { SemanticConventions } from "@arizeai/openinference-semantic-conventions"; + + +context.with( + setAttributes( + { [SemanticConventions.SESSION_ID: "session-id" } + ), + () => { + // Calls within this block will generate spans with the attributes: + // "session.id" = "session-id" + } +) +``` +{% endtab %} {% endtabs %} diff --git a/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md b/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md index 42d0ec1876..df3e288bfd 100644 --- a/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md +++ b/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md @@ -45,4 +45,35 @@ def call_fn(*args, **kwargs): ... ``` {% endtab %} + +{% tab title="JS" %} +We provide a `setPromptTemplate` function which allows you to set a template, version, and variables on context. You can use this utility in conjunction with [`context.with`](https://opentelemetry.io/docs/languages/js/context/#set-active-context) to set the active context. OpenInference [auto instrumentations](../../integrations-tracing/#javascript) will then pick up these attributes and add them to any spans created within the `context.with` callback. The components of a prompt template are: + +* template - a string with templated variables ex. `"hello {{name}}"` +* variables - an object with variable names and their values ex. `{name: "world"}` +* version - a string version of the template ex. `v1.0` + +All of these are optional. Application of variables to a template will typically happen before the call to an llm and may not be picked up by auto instrumentation. So, this can be helpful to add to ensure you can see the templates and variables while troubleshooting. + +
mport { context } from "@opentelemetry/api"
+import { setSession } from "@openinference-core"
+
+context.with(
+  setPromptTemplate(
+    context.active(),
+    { 
+      template: "hello {{name}}",
+      variables: { name: "world" },
+      version: "v1.0"
+    }
+  ),
+  () => {
+      // Calls within this block will generate spans with the attributes:
+      // "llm.prompt_template.template" = "hello {{name}}"
+      // "llm.prompt_template.version" = "v1.0"
+      // "llm.prompt_template.variables" = '{ "name": "world" }'
+  }
+)
+
+{% endtab %} {% endtabs %} diff --git a/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md b/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md new file mode 100644 index 0000000000..aa1b2399f3 --- /dev/null +++ b/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md @@ -0,0 +1,59 @@ +# Masking Span Attributes + +In some situations, you may need to modify the observability level of your tracing. For instance, you may want to keep sensitive information from being logged for security reasons, or you may want to limit the size of the base64 encoded images logged to reduced payload size. + +The OpenInference Specification defines a set of environment variables you can configure to suit your observability needs. In addition, the OpenInference auto-instrumentors accept a trace config which allows you to set these value in code without having to set environment variables, if that's what you prefer + +The possible settings are: + +
Environment Variable NameEffectTypeDefault
OPENINFERENCE_HIDE_INPUTSHides input value, all input messages & embedding input textboolFalse
OPENINFERENCE_HIDE_OUTPUTSHides output value & all output messagesboolFalse
OPENINFERENCE_HIDE_INPUT_MESSAGESHides all input messages & embedding input textboolFalse
OPENINFERENCE_HIDE_OUTPUT_MESSAGESHides all output messagesboolFalse
PENINFERENCE_HIDE_INPUT_IMAGESHides images from input messagesboolFalse
OPENINFERENCE_HIDE_INPUT_TEXTHides text from input messages & input embeddingsboolFalse
OPENINFERENCE_HIDE_OUTPUT_TEXTHides text from output messagesboolFalse
OPENINFERENCE_HIDE_EMBEDDING_VECTORSHides returned embedding vectorsboolFalse
OPENINFERENCE_BASE64_IMAGE_MAX_LENGTHLimits characters of a base64 encoding of an imageint32,000
+ +To set up this configuration you can either: + +* Set environment variables as specified above +* Define the configuration in code as shown below +* Do nothing and fall back to the default values +* Use a combination of the three, the order of precedence is: + * Values set in the `TraceConfig` in code + * Environment variables + * default values + +Below is an example of how to set these values in code using our OpenAI Python and JavaScript instrumentors, however, the config is respected by all of our auto-instrumentors. + +{% tabs %} +{% tab title="Python" %} +```python +from openinference.instrumentation import TraceConfig +config = TraceConfig( + hide_inputs=..., + hide_outputs=..., + hide_input_messages=..., + hide_output_messages=..., + hide_input_images=..., + hide_input_text=..., + hide_output_text=..., + hide_embedding_vectors=..., + base64_image_max_length=..., +) + +from openinference.instrumentation.openai import OpenAIInstrumentor +OpenAIInstrumentor().instrument( + tracer_provider=tracer_provider, + config=config, +) +``` +{% endtab %} + +{% tab title="JS" %} +```typescript +/** + * Everything left out of here will fallback to + * environment variables then defaults + */ +const traceConfig = { hideInputs: true } + +const instrumentation = new OpenAIInstrumentation({ traceConfig }) +``` +{% endtab %} +{% endtabs %} + diff --git a/docs/tracing/integrations-tracing/langchain.js.md b/docs/tracing/integrations-tracing/langchain.js.md index c021027727..024c2b58e0 100644 --- a/docs/tracing/integrations-tracing/langchain.js.md +++ b/docs/tracing/integrations-tracing/langchain.js.md @@ -31,6 +31,12 @@ lcInstrumentation.manuallyInstrument(CallbackManagerModule); ``` +## Support + +Instrumentation version >1.0.0 supports both [attribute masking](../how-to-tracing/customize-spans/masking-span-attributes.md) and [context attribute propagation](../how-to-tracing/customize-spans/) to spans. + +
Instrumentation VersionLangChain ^0.3.0LangChain ^0.2.0LangChain ^0.1.0
>1.0.0
>0.2.0
>0.1.0
+ ## Resources * [Example project](https://github.com/Arize-ai/openinference/blob/main/js/packages/openinference-instrumentation-langchain/examples) diff --git a/docs/tracing/integrations-tracing/openai-node-sdk.md b/docs/tracing/integrations-tracing/openai-node-sdk.md index e1149134fd..e6849255e8 100644 --- a/docs/tracing/integrations-tracing/openai-node-sdk.md +++ b/docs/tracing/integrations-tracing/openai-node-sdk.md @@ -29,10 +29,12 @@ registerInstrumentations({ }); ``` +## Support + +Instrumentation version >1.0.0 supports both [attribute masking](../how-to-tracing/customize-spans/masking-span-attributes.md) and [context attribute propagation](../how-to-tracing/customize-spans/) to spans. + ## Resources * [Example project](https://github.com/Arize-ai/openinference/tree/main/js/examples/openai) * [OpenInference package](https://github.com/Arize-ai/openinference/blob/main/js/packages/openinference-instrumentation-openai) * [Working examples](https://github.com/Arize-ai/openinference/blob/main/js/examples/openai) - -\ diff --git a/docs/tracing/integrations-tracing/vercel-ai-sdk.md b/docs/tracing/integrations-tracing/vercel-ai-sdk.md index 9c847d7390..805e31eebe 100644 --- a/docs/tracing/integrations-tracing/vercel-ai-sdk.md +++ b/docs/tracing/integrations-tracing/vercel-ai-sdk.md @@ -26,7 +26,7 @@ npm i @opentelemetry/api @vercel/otel @opentelemetry/exporter-trace-otlp-proto @ To process your Vercel AI SDK Spans add a `OpenInferenceSimpleSpanProcessor` or `OpenInferenceBatchSpanProcessor` to your OpenTelemetry configuration. -> Note: The `OpenInferenceSpanProcessor` does not handle the exporting of spans so you will pass it an [exporter](https://opentelemetry.io/docs/languages/js/exporters/) as a parameter. +> Note: The `OpenInferenceSpanProcessor` does not handle the exporting of spans so you will need to pass it an [exporter](https://opentelemetry.io/docs/languages/js/exporters/) as a parameter. ```typescript import { registerOTel } from "@vercel/otel"; @@ -39,20 +39,22 @@ import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; import { SEMRESATTRS_PROJECT_NAME } from "@arizeai/openinference-semantic-conventions"; // For troubleshooting, set the log level to DiagLogLevel.DEBUG +// This is not required and should not be added in a production setting diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.DEBUG); export function register() { registerOTel({ serviceName: "phoenix-next-app", attributes: { - // This is not required but it will + // This is not required but it will allow you to send traces to a specific + // project in phoenix [SEMRESATTRS_PROJECT_NAME]: "your-next-app", }, spanProcessors: [ new OpenInferenceSimpleSpanProcessor({ exporter: new OTLPTraceExporter({ headers: { - // API key if you are sending it ot Phoenix + // API key if you're sending it to Phoenix api_key: process.env["PHOENIX_API_KEY"], }, url: @@ -60,7 +62,7 @@ export function register() { "https://app.phoenix.arize.com/v1/traces", }), spanFilter: (span) => { - // Only export spans that are OpenInference to negate non-generative spans + // Only export spans that are OpenInference to remove non-generative spans // This should be removed if you want to export all spans return isOpenInferenceSpan(span); }, @@ -86,6 +88,6 @@ For details on Vercel AI SDK telemetry see the [Vercel AI SDK Telemetry document ### Examples -To see an example go to the [Next.js OpenAI Telemetry Example](https://github.com/Arize-ai/openinference/tree/main/js/examples/next-openai-telemetry-app) in the examples directory of this repo. +To see an example go to the [Next.js OpenAI Telemetry Example](https://github.com/Arize-ai/openinference/tree/main/js/examples/next-openai-telemetry-app) in the [OpenInference repo](https://github.com/Arize-ai/openinference/tree/main/js). For more information on Vercel OpenTelemetry support see the [Vercel AI SDK Telemetry documentation](https://sdk.vercel.ai/docs/ai-sdk-core/telemetry). From 6774ffce23df68dd6db73666c9e78b5a5a05d54c Mon Sep 17 00:00:00 2001 From: Parker Stafford Date: Fri, 25 Oct 2024 04:22:10 +0000 Subject: [PATCH 06/46] docs: docs: parker fixes for js (GITBOOK-912) --- .../instrumenting-prompt-templates-and-prompt-variables.md | 4 ++-- .../how-to-tracing/customize-spans/masking-span-attributes.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md b/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md index df3e288bfd..3feedfc8c9 100644 --- a/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md +++ b/docs/tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md @@ -55,8 +55,8 @@ We provide a `setPromptTemplate` function which allows you to set a template, ve All of these are optional. Application of variables to a template will typically happen before the call to an llm and may not be picked up by auto instrumentation. So, this can be helpful to add to ensure you can see the templates and variables while troubleshooting. -
mport { context } from "@opentelemetry/api"
-import { setSession } from "@openinference-core"
+
import { context } from "@opentelemetry/api"
+import { setPromptTemplate } from "@openinference-core"
 
 context.with(
   setPromptTemplate(
diff --git a/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md b/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md
index aa1b2399f3..d24b7f315d 100644
--- a/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md
+++ b/docs/tracing/how-to-tracing/customize-spans/masking-span-attributes.md
@@ -6,7 +6,7 @@ The OpenInference Specification defines a set of environment variables you can c
 
 The possible settings are:
 
-
Environment Variable NameEffectTypeDefault
OPENINFERENCE_HIDE_INPUTSHides input value, all input messages & embedding input textboolFalse
OPENINFERENCE_HIDE_OUTPUTSHides output value & all output messagesboolFalse
OPENINFERENCE_HIDE_INPUT_MESSAGESHides all input messages & embedding input textboolFalse
OPENINFERENCE_HIDE_OUTPUT_MESSAGESHides all output messagesboolFalse
PENINFERENCE_HIDE_INPUT_IMAGESHides images from input messagesboolFalse
OPENINFERENCE_HIDE_INPUT_TEXTHides text from input messages & input embeddingsboolFalse
OPENINFERENCE_HIDE_OUTPUT_TEXTHides text from output messagesboolFalse
OPENINFERENCE_HIDE_EMBEDDING_VECTORSHides returned embedding vectorsboolFalse
OPENINFERENCE_BASE64_IMAGE_MAX_LENGTHLimits characters of a base64 encoding of an imageint32,000
+
Environment Variable NameEffectTypeDefault
OPENINFERENCE_HIDE_INPUTSHides input value, all input messages & embedding input textboolFalse
OPENINFERENCE_HIDE_OUTPUTSHides output value & all output messagesboolFalse
OPENINFERENCE_HIDE_INPUT_MESSAGESHides all input messages & embedding input textboolFalse
OPENINFERENCE_HIDE_OUTPUT_MESSAGESHides all output messagesboolFalse
PENINFERENCE_HIDE_INPUT_IMAGESHides images from input messagesboolFalse
OPENINFERENCE_HIDE_INPUT_TEXTHides text from input messages & input embeddingsboolFalse
OPENINFERENCE_HIDE_OUTPUT_TEXTHides text from output messagesboolFalse
OPENINFERENCE_HIDE_EMBEDDING_VECTORSHides returned embedding vectorsboolFalse
OPENINFERENCE_BASE64_IMAGE_MAX_LENGTHLimits characters of a base64 encoding of an imageint32,000
To set up this configuration you can either: From 364699fc03f1d14c911215be3a70df61db54b47a Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Mon, 28 Oct 2024 20:14:13 +0000 Subject: [PATCH 07/46] docs: No subject (GITBOOK-913) --- docs/SUMMARY.md | 10 +- .../generating-embeddings.md | 2 +- docs/setup/configuration.md | 2 +- .../concepts-tracing/what-are-traces.md | 15 +- docs/tracing/how-to-tracing/README.md | 17 +- .../how-to-tracing/configure-tracing.md | 14 -- .../how-to-tracing/customize-spans/README.md | 8 +- .../customize-spans/filtering-spans.md | 150 ++++++++++++++++++ .../how-to-tracing/customize-traces.md | 112 ------------- .../setup-tracing-python/README.md | 115 +++++++++++++- .../how-to-tracing/trace-a-deployed-app.md | 8 +- .../how-to-tracing/tracing-core-concepts.md | 83 ---------- 12 files changed, 286 insertions(+), 250 deletions(-) delete mode 100644 docs/tracing/how-to-tracing/configure-tracing.md create mode 100644 docs/tracing/how-to-tracing/customize-spans/filtering-spans.md delete mode 100644 docs/tracing/how-to-tracing/customize-traces.md delete mode 100644 docs/tracing/how-to-tracing/tracing-core-concepts.md diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md index 69efab12b9..4a2a31c8aa 100644 --- a/docs/SUMMARY.md +++ b/docs/SUMMARY.md @@ -51,11 +51,10 @@ * [Setup Tracing: Python](tracing/how-to-tracing/setup-tracing/setup-tracing-python/README.md) * [Using OTEL Python Directly](tracing/how-to-tracing/setup-tracing/setup-tracing-python/using-otel-python-directly.md) * [Setup Tracing: TS](tracing/how-to-tracing/setup-tracing/setup-tracing-ts.md) - * [Tracing Core Concepts](tracing/how-to-tracing/tracing-core-concepts.md) - * [Customize Traces](tracing/how-to-tracing/customize-traces.md) - * [Customize Spans](tracing/how-to-tracing/customize-spans/README.md) + * [Customize Tracing](tracing/how-to-tracing/customize-spans/README.md) * [Instrumenting Prompt Templates and Prompt Variables](tracing/how-to-tracing/customize-spans/instrumenting-prompt-templates-and-prompt-variables.md) * [Masking Span Attributes](tracing/how-to-tracing/customize-spans/masking-span-attributes.md) + * [Filtering Spans](tracing/how-to-tracing/customize-spans/filtering-spans.md) * [Auto Instrumentation](tracing/how-to-tracing/instrumentation/README.md) * [Auto Instrument: Python](tracing/how-to-tracing/instrumentation/auto-instrument-python.md) * [Auto Instrument: TS](tracing/how-to-tracing/instrumentation/auto-instrument-ts.md) @@ -63,13 +62,12 @@ * [Instrument: Python](tracing/how-to-tracing/manual-instrumentation/custom-spans.md) * [Instrument: TS](tracing/how-to-tracing/manual-instrumentation/javascript.md) * [Multimodal Tracing](tracing/how-to-tracing/multimodal-tracing.md) - * [Querying Spans](tracing/how-to-tracing/extract-data-from-spans.md) * [Log Evaluation Results](tracing/how-to-tracing/llm-evaluations.md) + * [Capture Feedback](tracing/how-to-tracing/capture-feedback.md) + * [Querying Spans](tracing/how-to-tracing/extract-data-from-spans.md) * [Importing Existing Traces](tracing/how-to-tracing/importing-existing-traces.md) * [Save and Load Traces](tracing/how-to-tracing/save-and-load-traces.md) - * [Capture Feedback](tracing/how-to-tracing/capture-feedback.md) * [Trace a Deployed App](tracing/how-to-tracing/trace-a-deployed-app.md) - * [Configure Tracing](tracing/how-to-tracing/configure-tracing.md) * [Use Cases: Tracing](tracing/use-cases-tracing/README.md) * [Evaluate RAG](tracing/use-cases-tracing/rag-evaluation.md) * [Structured Data Extraction](tracing/use-cases-tracing/structured-extraction.md) diff --git a/docs/inferences/how-to-inferences/generating-embeddings.md b/docs/inferences/how-to-inferences/generating-embeddings.md index 355f1857f7..a1327391a2 100644 --- a/docs/inferences/how-to-inferences/generating-embeddings.md +++ b/docs/inferences/how-to-inferences/generating-embeddings.md @@ -6,7 +6,7 @@ But what if I don't have embeddings handy? Well, that is not a problem. The mode ### What are Auto-Embeddings? -Generating embeddings is likely another problem to solve, on top of ensuring your model is performing properly. With our Python [SDK](https://app.gitbook.com/s/-MAlgpMyBRcl2qFZRQ67/api-reference/python-sdk/arize.pandas/autoembeddings#the-embeddinggenerator-class), you can offload that task to the SDK and we will generate the embeddings for you. We use large, pre-trained [models](https://github.com/Arize-ai/phoenix/blob/main/docs/inferences/how-to-inferences/broken-reference/README.md) that will capture information from your inputs and encode it into embedding vectors. +Generating embeddings is likely another problem to solve, on top of ensuring your model is performing properly. With our Python [SDK](https://app.gitbook.com/s/-MAlgpMyBRcl2qFZRQ67/machine-learning/api-reference-ml/python-sdk/arize.pandas/autoembeddings#the-embeddinggenerator-class), you can offload that task to the SDK and we will generate the embeddings for you. We use large, pre-trained [models](https://github.com/Arize-ai/phoenix/blob/main/docs/inferences/how-to-inferences/broken-reference/README.md) that will capture information from your inputs and encode it into embedding vectors. We support generating embeddings for you for the following types of data: diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md index 678229b758..e9ca1fefbd 100644 --- a/docs/setup/configuration.md +++ b/docs/setup/configuration.md @@ -51,5 +51,5 @@ The following environment variables will control your client or notebook environ * **PHOENIX\_NOTEBOOK\_ENV:** The notebook environment. Typically you do not need to set this but it can be set explicitly (e.x. `sagemaker`) * **PHOENIX\_COLLECTOR\_ENDPOINT:** The endpoint traces and evals are sent to. This must be set if the Phoenix server is running on a remote instance. For example if phoenix is running at `http://125.2.3.5:4040` , this environment variable must be set where your LLM application is running and being traced. Note that the endpoint should not contain trailing slashes or slugs. -* **PHOENIX\_PROJECT\_NAME:** The project under which traces will be sent. See [projects](../tracing/how-to-tracing/customize-traces.md#log-to-a-specific-project). +* **PHOENIX\_PROJECT\_NAME:** The project under which traces will be sent. See [projects](../tracing/how-to-tracing/setup-tracing/setup-tracing-python/#log-to-a-specific-project). * **PHOENIX\_CLIENT\_HEADERS:** The headers to set when talking to the phoenix server. This might be things like authentication headers for hosted phoenix. diff --git a/docs/tracing/concepts-tracing/what-are-traces.md b/docs/tracing/concepts-tracing/what-are-traces.md index c8d59c09ac..4357c010d6 100644 --- a/docs/tracing/concepts-tracing/what-are-traces.md +++ b/docs/tracing/concepts-tracing/what-are-traces.md @@ -2,7 +2,7 @@ description: A deep dive into the details of a trace --- -# What are Traces? +# Traces ## Spans @@ -57,15 +57,13 @@ Tracing makes debugging and understanding LLM applications less daunting by brea A trace is made of one or more spans. The first span represents the root span. Each root span represents a request from start to finish. The spans underneath the parent provide a more in-depth context of what occurs during a request (or what steps make up a request). - - ## Projects -A `project` is a collection of traces. You can think of a project as a container for all the traces that are related to a single application or service. You can have multiple projects, and each project can have multiple traces. Projects can be useful for various use-cases such as separating out environments, logging traces for evaluation runs, etc. To learn more about how to setup projects, see the [how-to guide.](../how-to-tracing/customize-traces.md#log-to-a-specific-project) +A `project` is a collection of traces. You can think of a project as a container for all the traces that are related to a single application or service. You can have multiple projects, and each project can have multiple traces. Projects can be useful for various use-cases such as separating out environments, logging traces for evaluation runs, etc. To learn more about how to setup projects, see the [how-to guide](../how-to-tracing/setup-tracing/setup-tracing-python/#log-to-a-specific-project) ## Span Kind -When a span is created, it is created as one of the following: Chain, Retriever, Reranker, LLM, Embedding, Agent, or Tool. +When a span is created, it is created as one of the following: Chain, Retriever, Reranker, LLM, Embedding, Agent, or Tool.

The SpanKinds supported by OpenInference Tracing

@@ -95,8 +93,7 @@ A Tool is a span that represents a call to an external tool such as a calculator **AGENT** -A span that encompasses calls to LLMs and Tools. An agent describes a reasoning block that acts on tools using the guidance of an LLM.\ - +A span that encompasses calls to LLMs and Tools. An agent describes a reasoning block that acts on tools using the guidance of an LLM.\\ ## Span Attributes @@ -108,7 +105,3 @@ Attributes have the following rules: * Keys must be non-null string values * Values must be a non-null string, boolean, floating point value, integer, or an array of these values Additionally, there are Semantic Attributes, which are known naming conventions for metadata that is typically present in common operations. It's helpful to use semantic attribute naming wherever possible so that common kinds of metadata are standardized across systems. See [semantic conventions](https://github.com/Arize-ai/openinference/blob/main/spec/semantic\_conventions.md) for more information. - -## - -\ diff --git a/docs/tracing/how-to-tracing/README.md b/docs/tracing/how-to-tracing/README.md index db5d0f8d00..9a9215433e 100644 --- a/docs/tracing/how-to-tracing/README.md +++ b/docs/tracing/how-to-tracing/README.md @@ -9,23 +9,14 @@ description: Guides on how to use traces * Setup Tracing in [Python](setup-tracing/setup-tracing-python/) or [TypeScript](setup-tracing/setup-tracing-ts.md) * Add Integrations via [Auto Intstrumentation](./#auto-instrumentation) -## [Tracing Core Concepts](tracing-core-concepts.md) +## [Customize Traces & Spans](customize-spans/) -* [How to log traces](tracing-core-concepts.md#how-to-log-traces) -* [How to turn off tracing](tracing-core-concepts.md#how-to-turn-off-tracing) - -## [Customize Traces](customize-traces.md) +How to set custom attributes and semantic attributes to child spans and spans created by auto-instrumentors. -* [How to log to a specific project](customize-traces.md#log-to-a-specific-project) -* [How to switch projects in a notebook](customize-traces.md#switching-projects-in-a-notebook) +* [How to log to a specific project](setup-tracing/setup-tracing-python/#log-to-a-specific-project) +* [How to switch projects in a notebook](setup-tracing/setup-tracing-python/#switching-projects-in-a-notebook) * [How to add auto-instrumentation](instrumentation/) * [How to create custom spans](manual-instrumentation/custom-spans.md) -* [How to add custom metadata](customize-traces.md#adding-custom-metadata-to-spans) - -### [Customize Spans](./#customizing-spans) - -How to set custom attributes and semantic attributes to child spans and spans created by auto-instrumentors. - * [Setting metadata](customize-spans/#using\_metadata) * [Setting tags](customize-spans/#specifying-tags) * [Setting a user](customize-spans/#using\_user) diff --git a/docs/tracing/how-to-tracing/configure-tracing.md b/docs/tracing/how-to-tracing/configure-tracing.md deleted file mode 100644 index fec37c50c9..0000000000 --- a/docs/tracing/how-to-tracing/configure-tracing.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -description: How to customize OpenTelemetry and OpenInference for your setup ---- - -# Configure Tracing - -### Span Processing - -The tutorials and code snippets in these docs default to the `SimpleSpanProcessor.` A `SimpleSpanProcessor` processes and exports spans as they are created. This means that if you create 5 spans, each will be processed and exported before the next span is created in code. This can be helpful in scenarios where you do not want to risk losing a batch, or if you’re experimenting with OpenTelemetry in development. However, it also comes with potentially significant overhead, especially if spans are being exported over a network - each time a call to create a span is made, it would be processed and sent over a network before your app’s execution could continue. - - The `BatchSpanProcessor` processes spans in batches before they are exported. This is usually the right processor to use for an application in production but it does mean spans may take some time to show up in Phoenix. - -In production we recommend the `BatchSpanProcessor` over `SimpleSpanProcessor`\ -when deployed and the `SimpleSpanProcessor` when developing. diff --git a/docs/tracing/how-to-tracing/customize-spans/README.md b/docs/tracing/how-to-tracing/customize-spans/README.md index 3f35868033..b8ff1119f8 100644 --- a/docs/tracing/how-to-tracing/customize-spans/README.md +++ b/docs/tracing/how-to-tracing/customize-spans/README.md @@ -1,4 +1,4 @@ -# Customize Spans +# Customize Tracing ### Using context to customize spans @@ -361,5 +361,11 @@ context.with( {% endtab %} {% endtabs %} +### Span Processing +The tutorials and code snippets in these docs default to the `SimpleSpanProcessor.` A `SimpleSpanProcessor` processes and exports spans as they are created. This means that if you create 5 spans, each will be processed and exported before the next span is created in code. This can be helpful in scenarios where you do not want to risk losing a batch, or if you’re experimenting with OpenTelemetry in development. However, it also comes with potentially significant overhead, especially if spans are being exported over a network - each time a call to create a span is made, it would be processed and sent over a network before your app’s execution could continue. +The `BatchSpanProcessor` processes spans in batches before they are exported. This is usually the right processor to use for an application in production but it does mean spans may take some time to show up in Phoenix. + +In production we recommend the `BatchSpanProcessor` over `SimpleSpanProcessor`\ +when deployed and the `SimpleSpanProcessor` when developing. diff --git a/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md b/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md new file mode 100644 index 0000000000..2dfff0a330 --- /dev/null +++ b/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md @@ -0,0 +1,150 @@ +# Filtering Spans + +Because Phoenix is built on OpenTelemetry, you can run into situations where other OpenTelemetry traces unrelated to your LLM calls are logged into Phoenix. You can also run into cases where Phoenix traces are sent to other OTEL endpoints. + +You have a few options to avoid these cases: + +1. Disable instrumentation on the offending package +2. Filtering spans based on specific criteria +3. Selectively suppressing instrumentation on specific calls +4. Defining your own FilterProcessor + +## Disabling Instrumentation + +Certain libraries will automatically enable instrumentation if they detect the presence of the OTEL libraries. For example, `google-cloud-bigquery` displays this behavior. + +If you simply want to suppress traces from one of these libraries, most libraries offer an option to suppress their traces. + +For example, BigQuery traces can be surpressed with: + +```python +import google.cloud.bigquery.opentelemetry_tracing + +google.cloud.bigquery.opentelemetry_tracing.HAS_OPENTELEMETRY = False +``` + +### Disabling Phoenix Instrumentation + +Phoenix does not automatically instrument any libraries unless you've called one of our [auto-instrumentors](../instrumentation/). To disable tracing of one of those auto-instrumentors, see [#disabling-instrumentation](filtering-spans.md#disabling-instrumentation "mention") + +## Filtering all Spans that meet a criteria + +A good option if you want to keep both instrumentations running simultaneously is to define a custom ConditionalSpanProcessor. This custom processor can use conditions you define to selectively distribute spans. + +This example uses a ConsoleSpanExporter as the second exporter alongside Phoenix. You could replace this with another exporter of a different kind. + +```python +from opentelemetry.sdk.trace.export import SpanExporter, SpanProcessor + +# Custom SpanProcessor that only exports spans based on a condition +class ConditionalSpanProcessor(SpanProcessor): + def __init__(self, exporter: SpanExporter, condition: callable): + self.exporter = exporter + self.condition = condition + + def on_start(self, span, parent_context): + pass + + def on_end(self, span): + # Only export spans that meet the condition + if self.condition(span): + self.exporter.export([span]) + + def shutdown(self): + self.exporter.shutdown() + + def force_flush(self, timeout_millis=None): + self.exporter.force_flush(timeout_millis) + +# Define conditions for sending spans to specific exporters +def console_condition(span): + return "console" in span.name # Example: send to Console if "console" is in the span name + +def phoenix_condition(span): + # return "phoenix" in span.name # Example: send to Phoenix if "phoenix" is in the span name + return not console_condition(span) # Example: send to Phoenix if "console" is not in the span name +``` + +You can set your conditions to be related to any criteria on your spans. + +With this defined, you can now create instances for each of your destinations: + +```python +import os + +from opentelemetry.sdk.trace.export import ConsoleSpanExporter +from opentelemetry import trace as trace_api +from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter +from opentelemetry.sdk.trace import TracerProvider + +from dotenv import load_dotenv +load_dotenv() + +from openinference.instrumentation.openai import OpenAIInstrumentor + +def instrument(): + # Add Phoenix API Key to the headers for tracing and API access + PHOENIX_API_KEY = os.getenv("PHOENIX_API_KEY") + os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}" + + tracer_provider = TracerProvider() + + # Create the Console exporter + console_exporter = ConsoleSpanExporter() + + # Add the Console exporter to the tracer provider + tracer_provider.add_span_processor( + ConditionalSpanProcessor(console_exporter, console_condition) + ) + + # Create the Phoenix exporter. Replace endpoint with your endpoint if self-hosting + otlp_exporter = OTLPSpanExporter(endpoint="https://app.phoenix.arize.com/v1/traces") + + # Add the Phoenix exporter to the tracer provider + tracer_provider.add_span_processor( + ConditionalSpanProcessor(otlp_exporter, phoenix_condition) + ) + + # Set the tracer provider + trace_api.set_tracer_provider(tracer_provider) + + # Auto-instrumentors can still be used + OpenAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True) +``` + +From here, spans will be filtered based on the criteria you've set: + +
import openai
+
+def run_app():
+    # Create a tracer
+    tracer = trace_api.get_tracer(__name__)
+    
+    # Example of creating and exporting spans
+    with tracer.start_as_current_span("console-span"):
+        print("This span will be exported to Console only.")
+
+    # This request will only be exported to Phoenix
+    client = openai.OpenAI()
+    client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": "Hello, world!"}])
+
+ +## Selectively suppress individual calls + +If you instead want to selectively suppress instrumentation of individual calls, you can do so using the \_SUPPRESS\_INSTRUMENTATION\_KEY: + +```python +from opentelemetry.context import _SUPPRESS_INSTRUMENTATION_KEY, attach, detach, set_value +``` + +```python +token = attach(set_value(_SUPPRESS_INSTRUMENTATION_KEY, True)) + +# your method call(s) that you want suppressed + +detach(token) +``` + +## Defining your own Filter Processor + +If you've defined your own OTEL collector, you can instead set up app-wide criteria to block traces that meet certain criteria using a [Filter Processor](https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/processor/filterprocessor/README.md). This approach is more involved, as it requires defining your own OTEL span collector. diff --git a/docs/tracing/how-to-tracing/customize-traces.md b/docs/tracing/how-to-tracing/customize-traces.md deleted file mode 100644 index 5f6234c1bc..0000000000 --- a/docs/tracing/how-to-tracing/customize-traces.md +++ /dev/null @@ -1,112 +0,0 @@ ---- -description: Oftentimes you want to customize various aspects of traces you log to Phoenix ---- - -# Customize Traces - -## Log to a specific project - -Phoenix uses [projects](../concepts-tracing/#projects) to group traces. If left unspecified, all traces are sent to a default project. - -{% embed url="https://www.youtube.com/watch?v=GPno92s9WFM" %} - -{% tabs %} -{% tab title="Notebook" %} -In the notebook, you can set the `PHOENIX_PROJECT_NAME` environment variable **before** adding instrumentation or running any of your code. - -In python this would look like: - -```python -import os - -os.environ['PHOENIX_PROJECT_NAME'] = "" -``` - -{% hint style="warning" %} -Note that setting a project via an environment variable only works in a notebook and must be done **BEFORE** instrumentation is initialized. If you are using OpenInference Instrumentation, see the Server tab for how to set the project name in the Resource attributes. -{% endhint %} -{% endtab %} - -{% tab title="Server (Python)" %} -If you are using Phoenix as a collector and running your application separately, you can set the project name in the `Resource` attributes for the trace provider. - -```python -from openinference.semconv.resource import ResourceAttributes -from openinference.instrumentation.llama_index import LlamaIndexInstrumentor -from opentelemetry import trace as trace_api -from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter -from opentelemetry.sdk import trace as trace_sdk -from opentelemetry.sdk.resources import Resource -from opentelemetry.sdk.trace.export import SimpleSpanProcessor - -resource = Resource(attributes={ - ResourceAttributes.PROJECT_NAME: '' -}) -tracer_provider = trace_sdk.TracerProvider(resource=resource) -span_exporter = OTLPSpanExporter(endpoint="http://phoenix:6006/v1/traces") -span_processor = SimpleSpanProcessor(span_exporter=span_exporter) -tracer_provider.add_span_processor(span_processor=span_processor) -trace_api.set_tracer_provider(tracer_provider=tracer_provider) -# Add any auto-instrumentation you want -LlamaIndexInstrumentor().instrument() -``` -{% endtab %} -{% endtabs %} - -Projects work by setting something called the **Resource** attributes (as seen in the Server example above). The phoenix server uses the project name attribute to group traces into the appropriate project. - -## Switching projects in a notebook - -Typically you want traces for an LLM app to all be grouped in one project. However, while working with Phoenix inside a notebook, we provide a utility to temporarily associate spans with different projects. You can use this to trace things like evaluations. - -{% tabs %} -{% tab title="Notebook" %} -```python -from phoenix.trace import using_project - -# Switch project to run evals -with using_project("my-eval-project"): - # all spans created within this context will be associated with - # the "my-eval-project" project. - # Run evaluations here... -``` -{% endtab %} -{% endtabs %} - -## Adding custom metadata to spans - -Spans produced by [auto-instrumentation](instrumentation/) can get you very far. However at some point you may want to track `metadata` - things like account or user info. \\ - -{% tabs %} -{% tab title="LangChain" %} -With LangChain, you can provide metadata directly via the chain or to to an invocation of a chain. - -```python -# Pass metadata into the chain -llm = LLMChain(llm=OpenAI(), prompt=prompt, metadata={"category": "jokes"}) - -# Pass metadata into the invocation -completion = llm.predict(adjective="funny", metadata={"variant": "funny"}) -print(completion) -``` -{% endtab %} - -{% tab title="DSPy" %} -To add metadata to a span, you will have to use OpenTelemetry's trace\_api. - -```python -import dspy -from openinference.semconv.trace import SpanAttributes -from opentelemetry import trace as trace_api - -class QuestionClassifier(dspy.Module): - def __init__(self): - super().__init__() - ... - def forward(self, question: str) -> tuple[str,str]: - current_span = trace_api.get_current_span() - current_span.set_attribute(SpanAttributes.METADATA, "{ 'foo': 'bar' }") - ... -``` -{% endtab %} -{% endtabs %} diff --git a/docs/tracing/how-to-tracing/setup-tracing/setup-tracing-python/README.md b/docs/tracing/how-to-tracing/setup-tracing/setup-tracing-python/README.md index f3701b3e84..9a6053a019 100644 --- a/docs/tracing/how-to-tracing/setup-tracing/setup-tracing-python/README.md +++ b/docs/tracing/how-to-tracing/setup-tracing/setup-tracing-python/README.md @@ -4,7 +4,7 @@ description: How to configure OpenTelemetry and connect to the Phoenix server # Setup Tracing: Python -Phoenix uses OTLP (OpenTelemetry Language Protocol) to receive traces from your phoenix instance. To make this process as simple as possible, we've created a python package called `arize-phoenix-otel` for python. +Phoenix uses OTLP (OpenTelemetry Language Protocol) to receive traces from your phoenix instance. To make this process as simple as possible, we've created a python package called `arize-phoenix-otel` for python. {% hint style="info" %} Note that you do not need to use arize-phoenix-otel to setup OpenTelemetry. If you wold like to use pure OpenTelemetry, see [using-otel-python-directly.md](using-otel-python-directly.md "mention") @@ -19,15 +19,15 @@ pip install arize-phoenix-otel If you have specified endpoints, headers, and project names as [environment variables](../../../../setup/configuration.md#environment-variables), setting up OTEL can be as simple as:
from phoenix.otel import register
-
-# Configuration is picked up from your environment variables
+
+# Configuration is picked up from your environment variables
 tracer_provider = register()
 
 # Initialize Instrumentors and pass in the tracer_provider
 # E.x. OpenAIInstrumentor.instrument(tracer_provider=tracer_provider)
 
-And setup is done! You are ready to setup [integrations](../../../integrations-tracing/) and instrumentation. Read further for more advanced configuration options. +And setup is done! You are ready to setup [integrations](../../../integrations-tracing/) and instrumentation. Read further for more advanced configuration options. ## Setup Endpoints, Projects, etc. @@ -52,5 +52,112 @@ When using the `endpoint` argument, we must pass in the fully qualified OTel end **phoenix.otel** can be further configured for things like batch span processing and specifying resources. For the full details of how to configure **phoenix.otel,** please consult the package repository ([https://github.com/Arize-ai/phoenix/tree/main/packages/phoenix-otel](https://github.com/Arize-ai/phoenix/tree/main/packages/phoenix-otel)) +## Log to a specific project +Phoenix uses [projects](../../../concepts-tracing/#projects) to group traces. If left unspecified, all traces are sent to a default project. +{% embed url="https://www.youtube.com/watch?v=GPno92s9WFM" %} + +{% tabs %} +{% tab title="Using Phoenix Wrappers" %} +In the notebook, you can set the `PHOENIX_PROJECT_NAME` environment variable **before** adding instrumentation or running any of your code. + +In python this would look like: + +```python +import os + +os.environ['PHOENIX_PROJECT_NAME'] = "" +``` + +{% hint style="warning" %} +Note that setting a project via an environment variable only works in a notebook and must be done **BEFORE** instrumentation is initialized. If you are using OpenInference Instrumentation, see the Server tab for how to set the project name in the Resource attributes. +{% endhint %} + +Alternatively, you can set the project name in your `register` function call: + +```python +from phoenix.otel import register + +tracer_provider = register( + project_name="my-project-name", + .... +) +``` +{% endtab %} + +{% tab title="Using OTEL Directly" %} +If you are using Phoenix as a collector and running your application separately, you can set the project name in the `Resource` attributes for the trace provider. + +```python +from openinference.semconv.resource import ResourceAttributes +from openinference.instrumentation.llama_index import LlamaIndexInstrumentor +from opentelemetry import trace as trace_api +from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter +from opentelemetry.sdk import trace as trace_sdk +from opentelemetry.sdk.resources import Resource +from opentelemetry.sdk.trace.export import SimpleSpanProcessor + +resource = Resource(attributes={ + ResourceAttributes.PROJECT_NAME: '' +}) +tracer_provider = trace_sdk.TracerProvider(resource=resource) +span_exporter = OTLPSpanExporter(endpoint="http://phoenix:6006/v1/traces") +span_processor = SimpleSpanProcessor(span_exporter=span_exporter) +tracer_provider.add_span_processor(span_processor=span_processor) +trace_api.set_tracer_provider(tracer_provider=tracer_provider) +# Add any auto-instrumentation you want +LlamaIndexInstrumentor().instrument() +``` +{% endtab %} +{% endtabs %} + +Projects work by setting something called the **Resource** attributes (as seen in the OTEL example above). The phoenix server uses the project name attribute to group traces into the appropriate project. + +## Switching projects in a notebook + +Typically you want traces for an LLM app to all be grouped in one project. However, while working with Phoenix inside a notebook, we provide a utility to temporarily associate spans with different projects. You can use this to trace things like evaluations. + +{% tabs %} +{% tab title="Notebook" %} +```python +from phoenix.trace import using_project + +# Switch project to run evals +with using_project("my-eval-project"): + # all spans created within this context will be associated with + # the "my-eval-project" project. + # Run evaluations here... +``` +{% endtab %} +{% endtabs %} + +## How to turn off tracing + +Tracing can be paused temporarily or disabled permanently. + +### Pause tracing using context manager + +If there is a section of your code for which tracing is not desired, e.g. the document chunking process, it can be put inside the `suppress_tracing` context manager as shown below. + +```python +from phoenix.trace import suppress_tracing + +with suppress_tracing(): + # Code running inside this block doesn't generate traces. + # For example, running LLM evals here won't generate additional traces. + ... +# Tracing will resume outside the block. +... +``` + +### Uninstrument the auto-instrumentors permanently + +Calling `.uninstrument()` on the auto-instrumentors will remove tracing permanently. Below is the examples for LangChain, LlamaIndex and OpenAI, respectively. + +```python +LangChainInstrumentor().uninstrument() +LlamaIndexInstrumentor().uninstrument() +OpenAIInstrumentor().uninstrument() +# etc. +``` diff --git a/docs/tracing/how-to-tracing/trace-a-deployed-app.md b/docs/tracing/how-to-tracing/trace-a-deployed-app.md index d640cd4294..b91828f42c 100644 --- a/docs/tracing/how-to-tracing/trace-a-deployed-app.md +++ b/docs/tracing/how-to-tracing/trace-a-deployed-app.md @@ -10,14 +10,14 @@ description: >- ## How to Instrument an Application -The same tracing capabilities you used during your experimentation in the notebook is available when you deploy your application. As illustrated in the image above, Phoenix is made up of **tracing** capabilities as well as **collecting** capabilities. Notably,`phoenix.trace` is in fact a wrapper around [OpenInference auto-instrumentation](https://github.com/Arize-ai/openinference) and the OpenTelemetry SDK. When you deploy your application, you only need to bring along the **instrumentation** parts. \ +The same tracing capabilities you used during your experimentation in the notebook is available when you deploy your application. As illustrated in the image above, Phoenix is made up of **tracing** capabilities as well as **collecting** capabilities. When you deploy your application, you only need to bring along the **instrumentation** parts.\ \ Let's take the following code in the notebook and look at how this might look on the server.\ \ **BEFORE** ```python -from phoenix.trace.openai import OpenAIInstrumentor +from openinference.instrumentation.openai import OpenAIInstrumentor OpenAIInstrumentor().instrument() ``` @@ -58,10 +58,10 @@ pip install openinference-instrumentation-openai openinference-semantic-conventi Note that instrumentation **MUST** be initialized **BEFORE** you use initialize any library or package that you are instrumenting. {% endhint %} -Once you've made the appropriate instrumentation, you can [deploy phoenix](../../deployment/deploying-phoenix.md) and the traces will be exported to the phoenix server (collector).\ +Once you've made the appropriate instrumentation, you can [deploy phoenix](../../deployment/deploying-phoenix.md) and the traces will be exported to the phoenix server (collector).\ \ For fully working Python examples, [check out our example apps](https://github.com/Arize-ai/openinference/tree/main/python/examples) ## Exporting Traces to Arize -Arize is an enterprise grade observability platform that supports the same capabilities as Phoenix. Note that you can export your traces to both Phoenix and Arize if you so desire (simply add two **exporters**). See the [Arize documentation](https://docs.arize.com/arize/llm-large-language-models/llm-traces) for details. +Arize is an enterprise grade observability platform that supports the same capabilities as Phoenix. Note that you can export your traces to both Phoenix and Arize if you so desire (simply add two **exporters**). See the [Arize documentation](https://docs.arize.com/arize/llm-large-language-models/llm-traces) for details. diff --git a/docs/tracing/how-to-tracing/tracing-core-concepts.md b/docs/tracing/how-to-tracing/tracing-core-concepts.md deleted file mode 100644 index aa8e4f86e4..0000000000 --- a/docs/tracing/how-to-tracing/tracing-core-concepts.md +++ /dev/null @@ -1,83 +0,0 @@ -# Tracing Core Concepts - -## How to log traces - -To log traces, you must instrument your application either [manually](manual-instrumentation/custom-spans.md) or [automatically](instrumentation/). To log to a remote instance of Phoenix, you must also configure the host and port where your traces will be sent. - -{% tabs %} -{% tab title="Local Phoenix" %} -When running running Phoenix locally on the default port of `6006`, no additional configuration is necessary. - -```python -import phoenix as px -from phoenix.trace import LangChainInstrumentor - -px.launch_app() - -LangChainInstrumentor().instrument() - -# run your LangChain application -``` -{% endtab %} - -{% tab title="Remote Phoenix" %} -If you are running a remote instance of Phoenix, you can configure your instrumentation to log to that instance using the `PHOENIX_HOST` and `PHOENIX_PORT` environment variables. - -```python -import os -from phoenix.trace import LangChainInstrumentor - -# assume phoenix is running at 162.159.135.42:6007 -os.environ["PHOENIX_HOST"] = "162.159.135.42" -os.environ["PHOENIX_PORT"] = "6007" - -LangChainInstrumentor().instrument() # logs to http://162.159.135.42:6007 - -# run your LangChain application -``` - -Alternatively, you can use the `PHOENIX_COLLECTOR_ENDPOINT` environment variable. - -```python -import os -from phoenix.trace import LangChainInstrumentor - -# assume phoenix is running at 162.159.135.42:6007 -os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "162.159.135.42:6007" - -LangChainInstrumentor().instrument() # logs to http://162.159.135.42:6007 - -# run your LangChain application -``` -{% endtab %} -{% endtabs %} - -## How to turn off tracing - -Tracing can be paused temporarily or disabled permanently. - -### Pause tracing using context manager - -If there is a section of your code for which tracing is not desired, e.g. the document chunking process, it can be put inside the `suppress_tracing` context manager as shown below. - -```python -from phoenix.trace import suppress_tracing - -with suppress_tracing(): - # Code running inside this block doesn't generate traces. - # For example, running LLM evals here won't generate additional traces. - ... -# Tracing will resume outside the block. -... -``` - -### Uninstrument the auto-instrumentors permanently - -Calling `.uninstrument()` on the auto-instrumentors will remove tracing permanently. Below is the examples for LangChain, LlamaIndex and OpenAI, respectively. - -```python -LangChainInstrumentor().uninstrument() -LlamaIndexInstrumentor().uninstrument() -OpenAIInstrumentor().uninstrument() -# etc. -``` From b47f4060280fb2fd10f04ac3680d74f57d551950 Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Mon, 28 Oct 2024 23:24:27 +0000 Subject: [PATCH 08/46] docs: No subject (GITBOOK-914) --- docs/tracing/how-to-tracing/customize-spans/filtering-spans.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md b/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md index 2dfff0a330..36f312e6ff 100644 --- a/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md +++ b/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md @@ -29,6 +29,8 @@ Phoenix does not automatically instrument any libraries unless you've called one ## Filtering all Spans that meet a criteria +{% embed url="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/span_filtering_tutorial.ipynb" %} + A good option if you want to keep both instrumentations running simultaneously is to define a custom ConditionalSpanProcessor. This custom processor can use conditions you define to selectively distribute spans. This example uses a ConsoleSpanExporter as the second exporter alongside Phoenix. You could replace this with another exporter of a different kind. From 64a7b3cbb2a00cbfe1ae146c34c6eb07c7eeb745 Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Mon, 28 Oct 2024 23:27:18 +0000 Subject: [PATCH 09/46] docs: No subject (GITBOOK-915) --- .../customize-spans/filtering-spans.md | 42 +++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md b/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md index 36f312e6ff..a25ac9d12d 100644 --- a/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md +++ b/docs/tracing/how-to-tracing/customize-spans/filtering-spans.md @@ -4,29 +4,11 @@ Because Phoenix is built on OpenTelemetry, you can run into situations where oth You have a few options to avoid these cases: -1. Disable instrumentation on the offending package -2. Filtering spans based on specific criteria -3. Selectively suppressing instrumentation on specific calls +1. Filtering spans based on specific criteria +2. Selectively suppressing instrumentation on specific calls +3. Disable instrumentation on the offending package 4. Defining your own FilterProcessor -## Disabling Instrumentation - -Certain libraries will automatically enable instrumentation if they detect the presence of the OTEL libraries. For example, `google-cloud-bigquery` displays this behavior. - -If you simply want to suppress traces from one of these libraries, most libraries offer an option to suppress their traces. - -For example, BigQuery traces can be surpressed with: - -```python -import google.cloud.bigquery.opentelemetry_tracing - -google.cloud.bigquery.opentelemetry_tracing.HAS_OPENTELEMETRY = False -``` - -### Disabling Phoenix Instrumentation - -Phoenix does not automatically instrument any libraries unless you've called one of our [auto-instrumentors](../instrumentation/). To disable tracing of one of those auto-instrumentors, see [#disabling-instrumentation](filtering-spans.md#disabling-instrumentation "mention") - ## Filtering all Spans that meet a criteria {% embed url="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/span_filtering_tutorial.ipynb" %} @@ -147,6 +129,24 @@ token = attach(set_value(_SUPPRESS_INSTRUMENTATION_KEY, True)) detach(token) ``` +## Disabling Instrumentation + +Certain libraries will automatically enable instrumentation if they detect the presence of the OTEL libraries. For example, `google-cloud-bigquery` displays this behavior. + +If you simply want to suppress traces from one of these libraries, most libraries offer an option to suppress their traces. + +For example, BigQuery traces can be surpressed with: + +```python +import google.cloud.bigquery.opentelemetry_tracing + +google.cloud.bigquery.opentelemetry_tracing.HAS_OPENTELEMETRY = False +``` + +### Disabling Phoenix Instrumentation + +Phoenix does not automatically instrument any libraries unless you've called one of our [auto-instrumentors](../instrumentation/). To disable tracing of one of those auto-instrumentors, see [#disabling-instrumentation](filtering-spans.md#disabling-instrumentation "mention") + ## Defining your own Filter Processor If you've defined your own OTEL collector, you can instead set up app-wide criteria to block traces that meet certain criteria using a [Filter Processor](https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/processor/filterprocessor/README.md). This approach is more involved, as it requires defining your own OTEL span collector. From f36a038ab4b3181460f367d68c571195292c115e Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Tue, 29 Oct 2024 17:31:46 +0000 Subject: [PATCH 10/46] docs: Update tracing page for SEO (GITBOOK-916) --- docs/tracing/llm-traces.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tracing/llm-traces.md b/docs/tracing/llm-traces.md index 276a10baa4..9e5ac69ea2 100644 --- a/docs/tracing/llm-traces.md +++ b/docs/tracing/llm-traces.md @@ -4,6 +4,8 @@ description: Tracing the execution of LLM applications using Telemetry # Overview: Tracing +Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. + LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application. For example, when a user interacts with an LLM application, tracing can capture the sequence of operations, such as document retrieval, embedding generation, language model invocation, and response generation to provide a detailed timeline of the request's execution. Tracing is a helpful tool for understanding how your LLM application works. Phoenix offers comprehensive tracing capabilities that are not tied to any specific LLM vendor or framework. Phoenix accepts traces over the OpenTelemetry protocol (OTLP) and supports first-class instrumentation for a variety of frameworks ( [LlamaIndex](integrations-tracing/llamaindex.md), [LangChain](integrations-tracing/langchain.md),[ DSPy](integrations-tracing/dspy.md)), SDKs ([OpenAI](integrations-tracing/openai.md), [Bedrock](integrations-tracing/bedrock.md), [Mistral](integrations-tracing/mistralai.md), [Vertex](integrations-tracing/vertexai.md)), and Languages. (Python, Javascript, etc.) @@ -29,5 +31,3 @@ To get started, check out the [Quickstart guide](llm-traces-1.md) After that, read through the [Concepts Section](concepts-tracing/) to get and understanding of the different components. If you want to learn how to accomplish a particular task, check out the [How-To Guides.](how-to-tracing/) - -\\ From 0bdfb3b41f088a0ab5cdb050120e7819033a5b36 Mon Sep 17 00:00:00 2001 From: John Gilhuly Date: Tue, 29 Oct 2024 22:54:08 +0000 Subject: [PATCH 11/46] docs: New Navigation & Hosted Phoenix Changes (GITBOOK-908) --- ...t 2024-10-29 at 2.26.50\342\200\257PM.png" | Bin 0 -> 1350849 bytes ...t 2024-10-29 at 2.28.28\342\200\257PM.png" | Bin 0 -> 1068651 bytes ...t 2024-10-29 at 2.51.24\342\200\257PM.png" | Bin 0 -> 1045717 bytes ...t 2024-10-29 at 3.52.49\342\200\257PM.png" | Bin 0 -> 1272117 bytes docs/.gitbook/assets/image (32).png | Bin 0 -> 3391 bytes docs/README.md | 18 +- docs/SUMMARY.md | 25 +-- docs/api/client.md | 2 +- docs/api/inference-and-schema.md | 6 +- docs/api/session.md | 12 +- docs/deployment/README.md | 27 ++- docs/{setup => deployment}/authentication.md | 0 docs/{setup => deployment}/configuration.md | 18 ++ docs/deployment/deploying-phoenix.md | 8 +- 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