From 1cb685cf2b343096a6f2378138f8a084bd488289 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Sun, 3 Nov 2024 21:15:04 +0100 Subject: [PATCH 01/14] docs(app): AGE-1107 docs drafts obs --- .../observability/instrumenting-workflows.mdx | 142 +++++++++++++ docs/docs/observability/overview.mdx | 54 +++++ docs/docs/observability/quickstart.mdx | 197 +++++++----------- 3 files changed, 274 insertions(+), 119 deletions(-) create mode 100644 docs/docs/observability/instrumenting-workflows.mdx create mode 100644 docs/docs/observability/overview.mdx diff --git a/docs/docs/observability/instrumenting-workflows.mdx b/docs/docs/observability/instrumenting-workflows.mdx new file mode 100644 index 0000000000..0b29b560f1 --- /dev/null +++ b/docs/docs/observability/instrumenting-workflows.mdx @@ -0,0 +1,142 @@ +--- +title: Instrumenting Workflows +--- + +## 1. Create an application in **agenta** + +To start, we need to create an application in **agenta**. You can do this using the command from the CLI: + +```python +agenta init +``` + +This command creates a new application in **agenta** and a `config.toml` file with all the information about the application. + +## 2. Initialize **agenta** + +```python +import agenta as ag +# Option 1 + +ag.init(api_key="", app_id="") + +# Option 2 +os.environ["AGENTA_API_KEY"] = "" +os.environ["AGENTA_APP_ID"] = "" +ag.init() + +# Option 3 +ag.init(config_fname="config.toml") # using the config.toml generated by agenta init + +``` + +You can find the API Key under the Setting view in **agenta**. + +The app id can be found in the `config.toml` file if you have created the application from the CLI. + +Note that if you are serving your application to the **agenta** cloud, **agenta** will automatically populate all the information in the environment variable. Therefore, you only need to use `ag.init()`. + +## 3. Instrument with the decorator + +Add the `@ag.instrument()` decorator to the functions you want to instrument. This decorator will trace all input and output information for the functions. + +:::caution + {" "} + Make sure the `instrument` decorator is the first decorator in the function.{" "} +::: + +```python +@ag.instrument(spankind="llm") +def myllmcall(country:str): + prompt = f"What is the capital of {country}" + response = client.chat.completions.create( + model='gpt-4', + messages=[ + {'role': 'user', 'content': prompt}, + ], + ) + return response.choices[0].text + +@ag.instrument() +def generate(country:str): + return myllmcall(country) + +``` + +## 4. Modify a span's metadata + +You can modify a span's metadata to add additional information using `ag.tracing.set_span_attributes()`. This function will access the active span and add the key-value pairs to the metadata.: + +```python +@ag.instrument(spankind="llm") +def myllmcall(country:str): + prompt = f"What is the capital of {country}" + response = client.chat.completions.create( + model='gpt-4', + messages=[ + {'role': 'user', 'content': prompt}, + ], + ) + ag.tracing.set_span_attributes({"model": "gpt-4"}) + return response.choices[0].text + +``` + +## 5. Putting it all together + +Here's how our code would look if we combine everything: + +```python +import agenta as ag + +os.environ["AGENTA_API_KEY"] = "" +os.environ["AGENTA_APP_ID"] = "" +ag.init() + +@ag.instrument(spankind="llm") +def myllmcall(country:str): + prompt = f"What is the capital of {country}" + response = client.chat.completions.create( + model='gpt-4', + messages=[ + {'role': 'user', 'content': prompt}, + ], + ) + ag.tracing.set_span_attributes({"model": "gpt-4"}) + return response.choices[0].text + +@ag.instrument() +def generate(country:str): + return myllmcall(country) + +``` + +# Setting up telemetry for apps hosted in agenta + +If you're creating an application to serve to agenta, not much changes. You just need to add the entrypoint decorator, ensuring it comes _before_ the instrument decorator. + +```python +import agenta as ag + +ag.init() +ag.config.register_default(prompt=ag.TextParam("What is the capital of {country}")) + +@ag.instrument(spankind="llm") +def myllmcall(country:str): + response = client.chat.completions.create( + model='gpt-4', + messages=[ + {'role': 'user', 'content': ag.config.prompt.format(country=country)}, + ], + ) + ag.tracing.set_span_attributes({"model": "gpt-4"}) + return response.choices[0].text + +@ag.entrypoint +@ag.instrument() +def generate(country:str): + return myllmcall(country) + +``` + +The advantage of this approach is that the configuration you use is automatically instrumented along with the other data. diff --git a/docs/docs/observability/overview.mdx b/docs/docs/observability/overview.mdx new file mode 100644 index 0000000000..797633979a --- /dev/null +++ b/docs/docs/observability/overview.mdx @@ -0,0 +1,54 @@ +--- +title: Overview +--- + +# Observability Overview + +Building applications with Large Language Models (LLMs) can be challenging. To ensure your app runs smoothly and efficiently, you need visibility into its inner workings. That's where observability comes in. + +## Why Observability Matters + +Observability lets you monitor all inputs, outputs, and metadata of your application, whether it's in development or production. By instrumenting your app—logging detailed data—you can: + +- **Debug Effectively**: See the exact prompts sent and contexts retrieved. When issues arise, especially with multiple calls, you can identify the root cause quickly. +- **Optimize Performance**: Spot where latency spikes or costs increase. Understand performance bottlenecks to make your app faster and more cost-effective. +- **Enhance Test Sets**: Use real-world inputs and outputs to enrich your test cases, making them more robust and reflective of actual user interactions. +- **Track Costs and Latency Over Time**: Monitor how your app's expenses and response times change, helping you plan and budget effectively. +- **Compare App Versions**: Evaluate different versions of your application to see which performs better, aiding in decision-making for updates. + +## How Agenta Helps with Observability + +Agenta makes it easy to instrument your LLM application. Here's what it offers: + +- **Data Capture**: Collect all inputs, outputs, and relevant metadata from your app. +- **Performance Dashboards**: Access dashboards that display key metrics like request counts, average latency, and costs over time. +- **Flexible Environment**: Run your application in your own environment and still send data to Agenta—you don't need to host it within Agenta. + +### OpenTelemetry Compatibility + +Agenta's observability features are built on OpenTelemetry (OTel), an open-source standard for application observability. This provides several advantages: + +- **Wide Library Support**: Leverage many supported libraries right out of the box. [See the full list here](https://opentelemetry.io/docs/instrumentation/supported-libraries/). +- **Vendor Neutrality**: Send your traces to other platforms like New Relic or Datadog without changing your code. Switch vendors whenever you like. +- **Proven Reliability**: Use a mature and actively maintained SDK that's trusted in the industry. +- **Ease of Integration**: If you're familiar with OTel, you already know how to instrument your app with Agenta. +- **Simplicity**: No new concepts or syntax to learn—Agenta uses familiar OTel concepts like traces and spans. +- **Automatic Instrumentation**: All calls from the Agenta playground are automatically instrumented for you. + +## Next Steps + +Ready to enhance your application's observability with Agenta? Here's how to get started: + +1. **Quick Start Guide**: Learn how to instrument your application in just a few minutes. +2. **Instrument Your Workflows**: Discover how to use decorators and instrument your functions for detailed tracing. +3. **Use the SDK for Tracing**: Explore how to utilize Agenta's SDK for advanced observability features. +4. **Custom Workflow Instrumentation**: Find out how to instrument custom workflows tailored to your application's needs. +5. **Explore Integrations with Auto-Instrumentation**: + - OpenAI + - LiteLLM + - Anthropic + - Bedrock + - LangChain + - LLamaIndex + - Instructor + - Vercel AI diff --git a/docs/docs/observability/quickstart.mdx b/docs/docs/observability/quickstart.mdx index 35d3d2b378..861404060c 100644 --- a/docs/docs/observability/quickstart.mdx +++ b/docs/docs/observability/quickstart.mdx @@ -2,164 +2,123 @@ title: Quick Start --- -# Setting up telemetry +Agenta enables you to capture all inputs, outputs, and metadata from your Large Language Model (LLM) applications, whether they're hosted within Agenta or running in your own environment. This guide will walk you through setting up observability for an OpenAI application running locally. -You can configure Agenta to capture all inputs, outputs, and other metadata from your LLM applications, regardless of whether they are hosted in Agenta or in your environment. +## Why Instrument Your Application? -Post instrumentation, Agenta provides a dashboard that offers an overview of your app's performance metrics over time, including request counts, average latency, and costs. +Instrumenting your application allows you to monitor and debug its behavior in detail. You'll gain insights into how your app processes requests, helping you identify issues and optimize performance. -We also provide a table detailing all the requests made to your LLM application. This table can be filtered and used to enrich your test sets, debug your applications, or fine-tune them. +### Key Concepts -:::tip -Concepts of Telemetry: +**Traces**: A trace represents the complete journey of a request through your application. In our context, a trace corresponds to a single request to your LLM application. -**Traces:** A trace represents the entire journey of a request or operation as it moves through a system. In our context, a trace represents one request to the LLM application. +**Spans**: A span is a unit of work within a trace. Spans can be nested, forming a tree-like structure. The root span represents the overall operation, while child spans represent sub-operations. Agenta enriches each span with cost information and metadata when you make LLM calls. -**Spans:** A span represents a unit of work within a trace. Spans are nested to form a tree-like structure, with the root span representing the overall operation, and child spans representing sub-operations. In Agenta, we enrich each span with cost information and metadata in the event of an LLM call. +> **Note**: If you create an application through the Agenta UI, tracing is enabled by default. No additional setup is required—simply go to the observability view to see all your requests. -::: +## Step-by-Step Guide -:::note - When creating an application from the UI, tracing is enabled by default. No - setup is required. Simply navigate to the observability view to see all - requests. -::: +### 1. Install Required Packages -## 1. Create an application in **agenta** +First, install the Agenta SDK, OpenAI, and the OpenTelemetry instrumentor for OpenAI: -To start, we need to create an application in **agenta**. You can do this using the command from the CLI: - -```python -agenta init +```bash +pip install -U agenta openai opentelemetry-instrumentation-openai ``` -This command creates a new application in **agenta** and a `config.toml` file with all the information about the application. - -## 2. Initialize **agenta** - -```python -import agenta as ag -# Option 1 - -ag.init(api_key="", app_id="") - -# Option 2 -os.environ["AGENTA_API_KEY"] = "" -os.environ["AGENTA_APP_ID"] = "" -ag.init() - -# Option 3 -ag.init(config_fname="config.toml") # using the config.toml generated by agenta init - -``` +### 2. Obtain an API Key (Cloud and Enterprise Editions Only) -You can find the API Key under the Setting view in **agenta**. +If you're using Agenta Cloud or Enterprise Edition, you'll need an API key: -The app id can be found in the `config.toml` file if you have created the application from the CLI. +1. Visit the [Agenta API Keys page](https://cloud.agenta.ai/settings?tab=apiKeys). +2. Click on **Create New API Key** and follow the prompts. -Note that if you are serving your application to the **agenta** cloud, **agenta** will automatically populate all the information in the environment variable. Therefore, you only need to use `ag.init()`. +### 3. Configure Environment Variables -## 3. Instrument with the decorator +Set up the environment variables for your API key and host. -Add the `@ag.instrument()` decorator to the functions you want to instrument. This decorator will trace all input and output information for the functions. - -:::caution - {" "} - Make sure the `instrument` decorator is the first decorator in the function.{" "} -::: +For **Cloud and Enterprise Editions**: ```python -@ag.instrument(spankind="llm") -def myllmcall(country:str): - prompt = f"What is the capital of {country}" - response = client.chat.completions.create( - model='gpt-4', - messages=[ - {'role': 'user', 'content': prompt}, - ], - ) - return response.choices[0].text - -@ag.instrument() -def generate(country:str): - return myllmcall(country) +import os +os.environ["AGENTA_API_KEY"] = "YOUR_API_KEY_HERE" +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" ``` -## 4. Modify a span's metadata - -You can modify a span's metadata to add additional information using `ag.tracing.set_span_attributes()`. This function will access the active span and add the key-value pairs to the metadata.: +For **Open Source Version (OSS) Running Locally**: ```python -@ag.instrument(spankind="llm") -def myllmcall(country:str): - prompt = f"What is the capital of {country}" - response = client.chat.completions.create( - model='gpt-4', - messages=[ - {'role': 'user', 'content': prompt}, - ], - ) - ag.tracing.set_span_attributes({"model": "gpt-4"}) - return response.choices[0].text +import os +# No API key is needed for OSS +os.environ["AGENTA_HOST"] = "http://localhost" ``` -## 5. Putting it all together +### 4. Instrument Your Application -Here's how our code would look if we combine everything: +Below is a sample script to instrument an OpenAI application: ```python import agenta as ag +from opentelemetry.instrumentation.openai import OpenAIInstrumentor +import openai -os.environ["AGENTA_API_KEY"] = "" -os.environ["AGENTA_APP_ID"] = "" +# Initialize Agenta ag.init() -@ag.instrument(spankind="llm") -def myllmcall(country:str): - prompt = f"What is the capital of {country}" - response = client.chat.completions.create( - model='gpt-4', - messages=[ - {'role': 'user', 'content': prompt}, - ], - ) - ag.tracing.set_span_attributes({"model": "gpt-4"}) - return response.choices[0].text - -@ag.instrument() -def generate(country:str): - return myllmcall(country) +# Instrument OpenAI +OpenAIInstrumentor().instrument() + +# Set your OpenAI API key +openai.api_key = "YOUR_OPENAI_API_KEY" +# Make a chat completion request +response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Write a short story about AI Engineering."}, + ], +) + +# Print the assistant's reply +print(response.choices[0].message.content) ``` -# Setting up telemetry for apps hosted in agenta +**Explanation**: -If you're creating an application to serve to agenta, not much changes. You just need to add the entrypoint decorator, ensuring it comes _before_ the instrument decorator. +- **Import Libraries**: Import Agenta, OpenAI, and the OpenTelemetry instrumentor. +- **Initialize Agenta**: Call `ag.init()` to initialize the Agenta SDK. +- **Instrument OpenAI**: Use `OpenAIInstrumentor().instrument()` to enable tracing for OpenAI calls. +- **Set OpenAI API Key**: Replace `"YOUR_OPENAI_API_KEY"` with your actual OpenAI API key. +- **Make a Request**: Send a chat completion request to the OpenAI API. +- **View Output**: The assistant's response is printed to the console. -```python -import agenta as ag +### 5. View Traces in the Agenta UI -ag.init() -ag.config.register_default(prompt=ag.TextParam("What is the capital of {country}")) - -@ag.instrument(spankind="llm") -def myllmcall(country:str): - response = client.chat.completions.create( - model='gpt-4', - messages=[ - {'role': 'user', 'content': ag.config.prompt.format(country=country)}, - ], - ) - ag.tracing.set_span_attributes({"model": "gpt-4"}) - return response.choices[0].text - -@ag.entrypoint -@ag.instrument() -def generate(country:str): - return myllmcall(country) +After running your application, you can view the captured traces in Agenta: -``` +1. Log in to your Agenta dashboard. +2. Navigate to the **Observability** section. +3. You'll see a list of traces corresponding to your application's requests. + +_Note: Replace `path_to_screenshot_image` with the actual path to your screenshot._ + +## Next Steps + +Now that you've instrumented a simple OpenAI call, you can: -The advantage of this approach is that the configuration you use is automatically instrumented along with the other data. +- **Instrument Specific Applications**: Specify application IDs to monitor particular apps within Agenta. +- **Instrument Complex Workflows**: Use decorators and instrument functions in more intricate workflows. +- **Add Metadata**: Enrich your spans with custom metadata for deeper insights. +- **Explore Integrations**: Auto-instrument other libraries and frameworks supported by Agenta: + - OpenAI + - LiteLLM + - Anthropic + - Bedrock + - LangChain + - LLamaIndex + - Instructor + - Vercel AI + 0` From 7ff8b7b21d161e482b8c76282a8d226e39bf5018 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Tue, 5 Nov 2024 19:46:36 +0100 Subject: [PATCH 02/14] docs(app): AGE-1107 updating structure docs --- .../{overview.mdx => 01-overview.mdx} | 14 +++++++++++++- .../{quickstart.mdx => 02-quickstart.mdx} | 0 ...lows.mdx => 03-instrumenting-workflows.mdx} | 0 .../observability/integrations/_category_.json | 4 ++++ .../images/observability/observability.png | Bin 0 -> 923018 bytes 5 files changed, 17 insertions(+), 1 deletion(-) rename docs/docs/observability/{overview.mdx => 01-overview.mdx} (91%) rename docs/docs/observability/{quickstart.mdx => 02-quickstart.mdx} (100%) rename docs/docs/observability/{instrumenting-workflows.mdx => 03-instrumenting-workflows.mdx} (100%) create mode 100644 docs/docs/observability/integrations/_category_.json create mode 100644 docs/static/images/observability/observability.png diff --git a/docs/docs/observability/overview.mdx b/docs/docs/observability/01-overview.mdx similarity index 91% rename from docs/docs/observability/overview.mdx rename to docs/docs/observability/01-overview.mdx index 797633979a..e0fbf9ed93 100644 --- a/docs/docs/observability/overview.mdx +++ b/docs/docs/observability/01-overview.mdx @@ -2,10 +2,22 @@ title: Overview --- -# Observability Overview +```mdx-code-block +import DocCard from '@theme/DocCard'; +import clsx from 'clsx'; +import Image from "@theme/IdealImage"; + +``` Building applications with Large Language Models (LLMs) can be challenging. To ensure your app runs smoothly and efficiently, you need visibility into its inner workings. That's where observability comes in. +Illustration of observability + ## Why Observability Matters Observability lets you monitor all inputs, outputs, and metadata of your application, whether it's in development or production. By instrumenting your app—logging detailed data—you can: diff --git a/docs/docs/observability/quickstart.mdx b/docs/docs/observability/02-quickstart.mdx similarity index 100% rename from docs/docs/observability/quickstart.mdx rename to docs/docs/observability/02-quickstart.mdx diff --git a/docs/docs/observability/instrumenting-workflows.mdx b/docs/docs/observability/03-instrumenting-workflows.mdx similarity index 100% rename from docs/docs/observability/instrumenting-workflows.mdx rename to docs/docs/observability/03-instrumenting-workflows.mdx diff --git a/docs/docs/observability/integrations/_category_.json b/docs/docs/observability/integrations/_category_.json new file mode 100644 index 0000000000..10e5757b16 --- /dev/null +++ b/docs/docs/observability/integrations/_category_.json @@ -0,0 +1,4 @@ +{ + "position": 4, + "label": "Integrations" +} diff --git a/docs/static/images/observability/observability.png b/docs/static/images/observability/observability.png new file mode 100644 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z{LI?<`>F3H+{N6LYVd~#!X3kloy~Wlf5af`Z}(3a-)xb1C*IfonW_TWp7yV+%Ty+L#*$f>_7Tc8>Le9vJpD;)7Ep zmM>;<20kePM5o1r&}?8X%{7BX+@To&YxTty-NHydn$o1K#l~TPfr^$KNu< zaLJK+=I+vTPQJGN%q(r*k2Ukv>(7z#zefkFBk}l_c=?r^-Nnnl#m91_^XXQ>rJul{G-$Rl6Ay(JzK5A5v`3X>X)o%6DbHrvsMEOZ1R_QW#T zmH85tu8+f`$2tLJUhVRaV{%wPA6@RKM3y7FUS}u@WMDqaP*nsd3O- z0xDxTf=gZLIrHpL|Bz0KO9qZj8=vv4xLzLEIg@3_qlkx%Bb4bk#Z+r|q#Y z+5ztINZO|p-kHCT)6jaY6SEWQbyxFb{MdYT=AFp#wrYfdnyO}JclfdMBYC_bwEVZ*kE+hnM2*Z*v#mu^1hyeq4G zyRldbVx^#PCkLA>zDSMA<6uXxI0000 Date: Tue, 5 Nov 2024 19:47:07 +0100 Subject: [PATCH 03/14] docs(app): AGE-1107 added openai integration docs --- .../observability/integrations/01-openai.mdx | 198 ++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 docs/docs/observability/integrations/01-openai.mdx diff --git a/docs/docs/observability/integrations/01-openai.mdx b/docs/docs/observability/integrations/01-openai.mdx new file mode 100644 index 0000000000..84000d407b --- /dev/null +++ b/docs/docs/observability/integrations/01-openai.mdx @@ -0,0 +1,198 @@ +--- +title: OpenAI +--- + +```mdx-code-block +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +``` + +OpenAI calls can be automatically instrumented with Agenta using the `opentelemetry-instrumentation-openai` package. This guide shows how to set it up. + +## Installation + +Install the required packages: + +```bash +pip install -U agenta openai opentelemetry-instrumentation-openai +``` + +--- + +## Instrument OpenAI API Calls + +### 1. Configure Environment Variables + + + + +```python +import os + +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" +``` + + + + +```python +import os + +os.environ["AGENTA_HOST"] = "http://localhost" +``` + + + + +### 2. Initialize Agenta and Instrument OpenAI + +```python +# highlight-start +import agenta as ag +from opentelemetry.instrumentation.openai import OpenAIInstrumentor +# highlight-end +import openai + +# highlight-start +ag.init() +OpenAIInstrumentor().instrument() +# highlight-end + +response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Write a short story about AI."}, + ], +) + +print(response.choices[0].message.content) +``` + +After running this code, Agenta will automatically capture the details of this API call. + +## Instrumenting a Workflow with a Parent Span + +If you have a function or workflow with multiple calls you want to monitor as a single trace, you can use the `@ag.instrument` decorator. + +### Example + +```python +# highlight-start +import agenta as ag +from opentelemetry.instrumentation.openai import OpenAIInstrumentor +# highlight-end + +import openai +import asyncio + +# highlight-start +ag.init() +OpenAIInstrumentor().instrument() +# highlight-end + +# highlight-next-line +@ag.instrument(spankind="TASK") +async def generate_story_prompt(topic: str, genre: str): + return f"Write a {genre} story about {topic}." + +# highlight-next-line +@ag.instrument(spankind="WORKFLOW") +async def generate_story(topic: str, genre: str): + prompt = await generate_story_prompt(topic, genre) + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": prompt, + }, + ], + ) + return response.choices[0].message.content + +if __name__ == "__main__": + asyncio.run(generate_story(topic="the future", genre="sci-fi")) +``` + +## Associating Traces with Applications + +In the previous examples, the traces are instrumented in the global project scope. They are not associated with a specific application or variant or environment. +To link traces to specific parts of your Agenta projects, you can store references inside your instrumented functions. + +```python +# highlight-next-line +@ag.instrument(spankind="WORKFLOW") +async def generate_story(topic: str, genre: str): + # highlight-start + # Associate with a specific application and variant + ag.tracing.store_refs({ + "application.id": "YOUR_APPLICATION_ID", + "variant.id": "YOUR_VARIANT_ID", + "environment.slug": "production", + }) + # highlight-end + + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": f"Write a {genre} story about {topic}.", + }, + ], + ) + return response.choices[0].message.content +``` + +**Note**: You need to have the `@ag.instrument` decorator to use `ag.tracing.store_refs`. + +## Complete Example + +Here's the full code putting it all together: + +```python +import os +import agenta as ag +from opentelemetry.instrumentation.openai import OpenAIInstrumentor +import openai +import asyncio + +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" # Skip if using OSS locally +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" # Use "http://localhost" for OSS + +# highlight-next-line +ag.init() + +# Set your OpenAI API key +openai.api_key = "YOUR_OPENAI_API_KEY" + +# highlight-next-line +OpenAIInstrumentor().instrument() + +# highlight-next-line +@ag.instrument(spankind="WORKFLOW") +async def generate_story(topic: str, genre: str): + # highlight-start + # Associate with application and variant + ag.tracing.store_refs({ + "application.id": "YOUR_APPLICATION_ID", + "variant.id": "YOUR_VARIANT_ID", + }) + ag.tracing.store_refs({"environment.slug": "production"}) + # highlight-end + # Make the OpenAI API call + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": f"Write a {genre} story about {topic}.", + }, + ], + ) + return response.choices[0].message.content + +if __name__ == "__main__": + asyncio.run(generate_story(topic="the future", genre="sci-fi")) +``` From b62288110cbf37352b016b7404d42487447efef1 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Tue, 5 Nov 2024 20:08:36 +0100 Subject: [PATCH 04/14] docs(app): AGE-1107 instructor integration docs --- .../integrations/04-instructor.mdx | 104 ++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 docs/docs/observability/integrations/04-instructor.mdx diff --git a/docs/docs/observability/integrations/04-instructor.mdx b/docs/docs/observability/integrations/04-instructor.mdx new file mode 100644 index 0000000000..20fe4f129f --- /dev/null +++ b/docs/docs/observability/integrations/04-instructor.mdx @@ -0,0 +1,104 @@ +--- +title: Instructor +description: Learn how to implement and monitor Instructor traces with Agenta for enhanced LLM observability. This comprehensive guide covers setup, configuration, and best practices for tracking structured data extraction using Instructor and OpenAI models. +--- + +```mdx-code-block +import Image from "@theme/IdealImage"; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +``` + +[Instructor](https://python.useinstructor.com/) is a library that helps you extract structured data from natural language using LLMs. By instrumenting Instructor with Agenta, you can monitor and debug your applications more effectively. + +This guide shows you how to instrument Instructor when using OpenAI models, you can use the same approach for other LLM providers like Anthropic, Google, etc. You just need to use the correct instrumentation library for the LLM provider. + +Illustration of instructor instrumented trace + +## Installation + +Install the required packages: + +```bash +pip install -U agenta openai opentelemetry-instrumentation-openai instructor +``` + +## Configure Environment Variables + + + + +```python +import os + +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" +``` + + + + +```python +import os + +os.environ["AGENTA_HOST"] = "http://localhost" +``` + + + + +## Code Example + +```python +# highlight-next-line +import agenta as ag +import openai +import instructor +from pydantic import BaseModel +# highlight-next-line +from opentelemetry.instrumentation.openai import OpenAIInstrumentor + +# highlight-next-line +ag.init() + +# highlight-start +# Instrument OpenAI before creating the Instructor client +OpenAIInstrumentor().instrument() +# highlight-end + +class UserInfo(BaseModel): + name: str + age: int + +# highlight-next-line +@ag.instrument(spankind="WORKFLOW") +def instructor_workflow(): + # Create an Instructor client using the instrumented OpenAI model + client = instructor.from_openai(openai.OpenAI()) + + # Extract structured data from natural language + user_info = client.chat.completions.create( + model="gpt-3.5-turbo", + response_model=UserInfo, + messages=[{"role": "user", "content": "John Doe is 30 years old."}], + ) + return user_info + +user_info = instructor_workflow() +print(user_info) +``` + +:::warning +**Order Matters**: Call `OpenAIInstrumentor().instrument()` **before** creating the Instructor client with `instructor.from_openai(openai.OpenAI())`. Both modify the OpenAI library, so the order ensures proper instrumentation. +::: + +## Explanation + +- **Initialize Agenta**: `ag.init()` sets up the Agenta SDK. +- **Instrument OpenAI**: `OpenAIInstrumentor().instrument()` instruments the OpenAI library for tracing. This must come **before** creating the Instructor client. +- **Instrument the Workflow**: The `@ag.instrument(spankind="WORKFLOW")` decorator creates a parent span. This is optional, but it's a good practice to instrument the main function of your application. From 9987b67dd5c1c9ac47ecb037c08c76728fb7c955 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Tue, 5 Nov 2024 20:08:56 +0100 Subject: [PATCH 05/14] docs(app): AGE-1107 formatting overview --- docs/docs/observability/01-overview.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/observability/01-overview.mdx b/docs/docs/observability/01-overview.mdx index e0fbf9ed93..f3260cbc6a 100644 --- a/docs/docs/observability/01-overview.mdx +++ b/docs/docs/observability/01-overview.mdx @@ -36,7 +36,7 @@ Agenta makes it easy to instrument your LLM application. Here's what it offers: - **Performance Dashboards**: Access dashboards that display key metrics like request counts, average latency, and costs over time. - **Flexible Environment**: Run your application in your own environment and still send data to Agenta—you don't need to host it within Agenta. -### OpenTelemetry Compatibility +## OpenTelemetry Compatibility Agenta's observability features are built on OpenTelemetry (OTel), an open-source standard for application observability. This provides several advantages: From fe214b854402b9c4504c0101bb5acdbe9b747f7e Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Tue, 5 Nov 2024 20:26:43 +0100 Subject: [PATCH 06/14] docs(app): AGE-1107 langchain integration --- .../integrations/02-langchain.mdx | 116 ++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 docs/docs/observability/integrations/02-langchain.mdx diff --git a/docs/docs/observability/integrations/02-langchain.mdx b/docs/docs/observability/integrations/02-langchain.mdx new file mode 100644 index 0000000000..3a196a52b0 --- /dev/null +++ b/docs/docs/observability/integrations/02-langchain.mdx @@ -0,0 +1,116 @@ +--- +title: LangChain +description: Learn how to instrument LangChain traces with Agenta for enhanced LLM observability. This guide covers setup, configuration, and best practices for monitoring structured data extraction using LangChain and OpenAI models. +--- + +```mdx-code-block +import Image from "@theme/IdealImage"; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +``` + +[LangChain](https://python.langchain.com/) **is a framework for developing applications powered by large language models (LLMs)**. By instrumenting LangChain with Agenta, you can monitor and debug your applications more effectively, gaining insights into each step of your workflows. + +This guide shows you how to instrument LangChain applications using Agenta's observability features. + +## Installation + +Install the required packages: + +```bash +pip install -U agenta openai opentelemetry-instrumentation-langchain langchain langchain_community +``` + +## Configure Environment Variables + + + + +```python +import os + +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" +``` + + + + +```python +import os + +os.environ["AGENTA_HOST"] = "http://localhost" +``` + + + + +## Code Example + +```python +# highlight-next-line +import agenta as ag +# highlight-next-line +from opentelemetry.instrumentation.langchain import LangchainInstrumentor +from langchain.schema import SystemMessage, HumanMessage +from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate +from langchain_community.chat_models import ChatOpenAI +from langchain.chains import LLMChain, SequentialChain, TransformChain + +# highlight-next-line +ag.init() +# highlight-next-line +LangchainInstrumentor().instrument() + + +def langchain_app(): + # Initialize the chat model + chat = ChatOpenAI(temperature=0) + + # Define a transformation chain to create the prompt + transform = TransformChain( + input_variables=["subject"], + output_variables=["prompt"], + transform=lambda inputs: {"prompt": f"Tell me a joke about {inputs['subject']}."}, + ) + + # Define the first LLM chain to generate a joke + first_prompt_messages = [ + SystemMessage(content="You are a funny sarcastic nerd."), + HumanMessage(content="{prompt}"), + ] + first_prompt_template = ChatPromptTemplate.from_messages(first_prompt_messages) + first_chain = LLMChain(llm=chat, prompt=first_prompt_template, output_key="joke") + + # Define the second LLM chain to translate the joke + second_prompt_messages = [ + SystemMessage(content="You are an Elf."), + HumanMessagePromptTemplate.from_template( + "Translate the joke below into Sindarin language:\n{joke}" + ), + ] + second_prompt_template = ChatPromptTemplate.from_messages(second_prompt_messages) + second_chain = LLMChain(llm=chat, prompt=second_prompt_template) + + # Chain everything together in a sequential workflow + workflow = SequentialChain( + chains=[transform, first_chain, second_chain], + input_variables=["subject"], + ) + + # Execute the workflow and print the result + result = workflow({"subject": "OpenTelemetry"}) + print(result) + +# Run the LangChain application +langchain_app() +``` + +## Explanation + +- **Initialize Agenta**: `ag.init()` sets up the Agenta SDK. +- **Instrument LangChain**: `LangchainInstrumentor().instrument()` instruments LangChain for tracing. This must be called **before** running your application to ensure all components are traced. + +## Using Workflows + +You can optionally use the `@ag.instrument(spankind="WORKFLOW")` decorator to create a parent span for your workflow. This is optional, but it's a good practice to instrument the main function of your application. From a5a5cbd99b38001b49ba7e6cbd3318b19ebf90f2 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 15:15:11 +0100 Subject: [PATCH 07/14] docs(app): AGE-1107 updated observability --- docs/docs/observability/01-overview.mdx | 53 +++++++++---------------- 1 file changed, 19 insertions(+), 34 deletions(-) diff --git a/docs/docs/observability/01-overview.mdx b/docs/docs/observability/01-overview.mdx index f3260cbc6a..dc3237e3eb 100644 --- a/docs/docs/observability/01-overview.mdx +++ b/docs/docs/observability/01-overview.mdx @@ -1,5 +1,6 @@ --- title: Overview +description: Learn how to instrument your application with Agenta for enhanced observability. This guide covers the benefits of observability, how Agenta helps, and how to get started. --- ```mdx-code-block @@ -9,7 +10,16 @@ import Image from "@theme/IdealImage"; ``` -Building applications with Large Language Models (LLMs) can be challenging. To ensure your app runs smoothly and efficiently, you need visibility into its inner workings. That's where observability comes in. +## Why Observability? + +Observability is the practice of monitoring and understanding the behavior of your LLM application. With Agenta, you can add a few lines of code to start tracking all inputs, outputs, and metadata of your application. +This allows you to: + +- **Debug Effectively**: View exact prompts sent and contexts retrieved. For complex workflows like agents, you can trace the data flow and quickly identify root causes. +- **Bootstrap Test Sets**: Track real-world inputs and outputs and use them to bootstrap test sets in which you can continuously iterate. +- **Find Edge Cases**: Identify latency spikes and cost increases. Understand performance bottlenecks to optimize your app's speed and cost-effectiveness. +- **Track Costs and Latency Over Time**: Monitor how your app's expenses and response times change. +- **Compare App Versions**: Compare the behavior in productions of different versions of your application to see which performs better. -## Why Observability Matters - -Observability lets you monitor all inputs, outputs, and metadata of your application, whether it's in development or production. By instrumenting your app—logging detailed data—you can: - -- **Debug Effectively**: See the exact prompts sent and contexts retrieved. When issues arise, especially with multiple calls, you can identify the root cause quickly. -- **Optimize Performance**: Spot where latency spikes or costs increase. Understand performance bottlenecks to make your app faster and more cost-effective. -- **Enhance Test Sets**: Use real-world inputs and outputs to enrich your test cases, making them more robust and reflective of actual user interactions. -- **Track Costs and Latency Over Time**: Monitor how your app's expenses and response times change, helping you plan and budget effectively. -- **Compare App Versions**: Evaluate different versions of your application to see which performs better, aiding in decision-making for updates. - ## How Agenta Helps with Observability -Agenta makes it easy to instrument your LLM application. Here's what it offers: - -- **Data Capture**: Collect all inputs, outputs, and relevant metadata from your app. -- **Performance Dashboards**: Access dashboards that display key metrics like request counts, average latency, and costs over time. -- **Flexible Environment**: Run your application in your own environment and still send data to Agenta—you don't need to host it within Agenta. - -## OpenTelemetry Compatibility - -Agenta's observability features are built on OpenTelemetry (OTel), an open-source standard for application observability. This provides several advantages: +Agenta's observability features are built on **OpenTelemetry (OTel)**, an open-source standard for application observability. This provides several advantages: -- **Wide Library Support**: Leverage many supported libraries right out of the box. [See the full list here](https://opentelemetry.io/docs/instrumentation/supported-libraries/). -- **Vendor Neutrality**: Send your traces to other platforms like New Relic or Datadog without changing your code. Switch vendors whenever you like. +- **Wide Library Support**: Use many supported libraries right out of the box. +- **Vendor Neutrality**: Send your traces to platforms like New Relic or Datadog without code changes. Switch vendors at will. - **Proven Reliability**: Use a mature and actively maintained SDK that's trusted in the industry. -- **Ease of Integration**: If you're familiar with OTel, you already know how to instrument your app with Agenta. -- **Simplicity**: No new concepts or syntax to learn—Agenta uses familiar OTel concepts like traces and spans. -- **Automatic Instrumentation**: All calls from the Agenta playground are automatically instrumented for you. +- **Ease of Integration**: If you're familiar with OTel, you already know how to instrument your app with Agenta. No new concepts or syntax to learn—Agenta uses familiar OTel concepts like traces and spans. ## Next Steps -Ready to enhance your application's observability with Agenta? Here's how to get started: +Here's how to get started: 1. **Quick Start Guide**: Learn how to instrument your application in just a few minutes. -2. **Instrument Your Workflows**: Discover how to use decorators and instrument your functions for detailed tracing. -3. **Use the SDK for Tracing**: Explore how to utilize Agenta's SDK for advanced observability features. -4. **Custom Workflow Instrumentation**: Find out how to instrument custom workflows tailored to your application's needs. -5. **Explore Integrations with Auto-Instrumentation**: +2. **Use the SDK for Tracing**: Explore advanced concepts from the SDK to use Agenta's SDK for advanced observability features. +3. **Observability in the UI**: Learn how to use the observability features in the UI. +4. **Explore Integrations with Auto-Instrumentation**: - OpenAI - LiteLLM - - Anthropic - - Bedrock - LangChain - LLamaIndex - - Instructor - - Vercel AI From a26fa2c708d0d93331d43a18719b1e633aec17dd Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 16:24:16 +0100 Subject: [PATCH 08/14] docs(app): AGE-1107 ovserview page update --- docs/docs/observability/01-overview.mdx | 87 ++++++++++++++++++++++--- 1 file changed, 77 insertions(+), 10 deletions(-) diff --git a/docs/docs/observability/01-overview.mdx b/docs/docs/observability/01-overview.mdx index dc3237e3eb..0f97d1812b 100644 --- a/docs/docs/observability/01-overview.mdx +++ b/docs/docs/observability/01-overview.mdx @@ -28,7 +28,7 @@ This allows you to: loading="lazy" /> -## How Agenta Helps with Observability +## Observability in Agenta Agenta's observability features are built on **OpenTelemetry (OTel)**, an open-source standard for application observability. This provides several advantages: @@ -39,13 +39,80 @@ Agenta's observability features are built on **OpenTelemetry (OTel)**, an open-s ## Next Steps -Here's how to get started: +

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-1. **Quick Start Guide**: Learn how to instrument your application in just a few minutes. -2. **Use the SDK for Tracing**: Explore advanced concepts from the SDK to use Agenta's SDK for advanced observability features. -3. **Observability in the UI**: Learn how to use the observability features in the UI. -4. **Explore Integrations with Auto-Instrumentation**: - - OpenAI - - LiteLLM - - LangChain - - LLamaIndex + +
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From 322de036abfe97a3131d1e90ff75d26e28eef66b Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 16:24:33 +0100 Subject: [PATCH 09/14] docs(app): AGE-1107 added litellm page --- .../observability/integrations/05-litellm.mdx | 90 +++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 docs/docs/observability/integrations/05-litellm.mdx diff --git a/docs/docs/observability/integrations/05-litellm.mdx b/docs/docs/observability/integrations/05-litellm.mdx new file mode 100644 index 0000000000..3c02e7f797 --- /dev/null +++ b/docs/docs/observability/integrations/05-litellm.mdx @@ -0,0 +1,90 @@ +--- +title: LiteLLM +description: Learn how to instrument LiteLLM traces with Agenta for enhanced LLM observability. This guide covers setup, configuration, and best practices for monitoring API calls and performance using LiteLLM and OpenAI models. +--- + +```mdx-code-block +import Image from "@theme/IdealImage"; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +``` + +[LiteLLM](https://www.litellm.ai/) is Python SDK that allows you to **call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq]**. + +This guide shows you how to instrument LiteLLM applications using Agenta's observability features. + +## Installation + +Install the required packages: + +```bash +pip install -U agenta litellm +``` + +## Configure Environment Variables + + + + +```python +import os + +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" +``` + + + + +```python +import os + +os.environ["AGENTA_HOST"] = "http://localhost" +``` + + + + +## Code Example + +```python +# highlight-next-line +import agenta as ag +import litellm +import asyncio + +# highlight-next-line +ag.init() + +# highlight-next-line +@ag.instrument() +async def agenerate_completion(): + # highlight-next-line + litellm.callbacks = [ag.callbacks.litellm_handler()] + + # Define the messages for the chat completion + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Write a short story about AI Engineering."}, + ] + temperature = 0.2 + max_tokens = 100 + + # Make an asynchronous chat completion request + chat_completion = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + ) + print(chat_completion) + +# Run the asynchronous function +asyncio.run(agenerate_completion()) +``` + +## Explanation + +- **Initialize Agenta**: `ag.init()` sets up the Agenta SDK. +- **Instrument the Function**: The `@ag.instrument()` decorator wraps the `agenerate_completion` function, creating a parent span in Agenta. +- **Set Up Callback**: `litellm.callbacks = [ag.callbacks.litellm_handler()]` sets the Agenta callback handler for LiteLLM. This enables LiteLLM to send trace data to Agenta. From 39fabf1caa99602f30872c2cdd997b9bd2cb3149 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 16:24:55 +0100 Subject: [PATCH 10/14] docs(app): AGE-1107 renaming and updating observability sdk page --- ...ng-workflows.mdx => 03-observability-sdk.mdx} | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) rename docs/docs/observability/{03-instrumenting-workflows.mdx => 03-observability-sdk.mdx} (83%) diff --git a/docs/docs/observability/03-instrumenting-workflows.mdx b/docs/docs/observability/03-observability-sdk.mdx similarity index 83% rename from docs/docs/observability/03-instrumenting-workflows.mdx rename to docs/docs/observability/03-observability-sdk.mdx index 0b29b560f1..c9e41d6036 100644 --- a/docs/docs/observability/03-instrumenting-workflows.mdx +++ b/docs/docs/observability/03-observability-sdk.mdx @@ -1,7 +1,17 @@ --- -title: Instrumenting Workflows +title: Observability SDK --- +This guide shows you how to use the Agenta observability SDK to instrument complex workflows for your application. + +If you are using solely a framework like [LangChain](/observability/integrations/langchain) or [LlamaIndex](/observability/integrations/llamaindex), you can use their auto-instrumentation packages to automatically instrument your application. + +However, if you need more flexibility, you can use the SDK to: + +- Instrument your own functions part of the workflow +- Add additional **metadata** to the spans +- Link your traces to **applications**, **variants**, and **environments** in Agenta + ## 1. Create an application in **agenta** To start, we need to create an application in **agenta**. You can do this using the command from the CLI: @@ -41,8 +51,8 @@ Note that if you are serving your application to the **agenta** cloud, **agenta* Add the `@ag.instrument()` decorator to the functions you want to instrument. This decorator will trace all input and output information for the functions. :::caution - {" "} - Make sure the `instrument` decorator is the first decorator in the function.{" "} +{" "} +Make sure the `instrument` decorator is the first decorator in the function.{" "} ::: ```python From 90401c14d7b4d6e57478e2ca4486a3e840c6dc2d Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 17:47:18 +0100 Subject: [PATCH 11/14] docs(app): AGE-1107 minor update to obs overview --- docs/docs/observability/01-overview.mdx | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/docs/observability/01-overview.mdx b/docs/docs/observability/01-overview.mdx index 0f97d1812b..4441a582fa 100644 --- a/docs/docs/observability/01-overview.mdx +++ b/docs/docs/observability/01-overview.mdx @@ -37,6 +37,12 @@ Agenta's observability features are built on **OpenTelemetry (OTel)**, an open-s - **Proven Reliability**: Use a mature and actively maintained SDK that's trusted in the industry. - **Ease of Integration**: If you're familiar with OTel, you already know how to instrument your app with Agenta. No new concepts or syntax to learn—Agenta uses familiar OTel concepts like traces and spans. +## Key Concepts + +**Traces**: A trace represents the complete journey of a request through your application. In our context, a trace corresponds to a single request to your LLM application. + +**Spans**: A span is a unit of work within a trace. Spans can be nested, forming a tree-like structure. The root span represents the overall operation, while child spans represent sub-operations. Agenta enriches each span with cost information and metadata when you make LLM calls. + ## Next Steps
From 662b16b8efda5f00935670103e46a3d368000d4c Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 17:47:36 +0100 Subject: [PATCH 12/14] docs(app): AGE-1107 final version quickstart ovs --- docs/docs/observability/02-quickstart.mdx | 83 +++++++++-------------- 1 file changed, 33 insertions(+), 50 deletions(-) diff --git a/docs/docs/observability/02-quickstart.mdx b/docs/docs/observability/02-quickstart.mdx index 861404060c..1e00398f51 100644 --- a/docs/docs/observability/02-quickstart.mdx +++ b/docs/docs/observability/02-quickstart.mdx @@ -2,19 +2,20 @@ title: Quick Start --- -Agenta enables you to capture all inputs, outputs, and metadata from your Large Language Model (LLM) applications, whether they're hosted within Agenta or running in your own environment. This guide will walk you through setting up observability for an OpenAI application running locally. +```mdx-code-block +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from "@theme/IdealImage"; -## Why Instrument Your Application? - -Instrumenting your application allows you to monitor and debug its behavior in detail. You'll gain insights into how your app processes requests, helping you identify issues and optimize performance. - -### Key Concepts +``` -**Traces**: A trace represents the complete journey of a request through your application. In our context, a trace corresponds to a single request to your LLM application. +Agenta enables you to capture all inputs, outputs, and metadata from your LLM applications, **whether they're hosted within Agenta or running in your own environment**. -**Spans**: A span is a unit of work within a trace. Spans can be nested, forming a tree-like structure. The root span represents the overall operation, while child spans represent sub-operations. Agenta enriches each span with cost information and metadata when you make LLM calls. +This guide will walk you through setting up observability for an OpenAI application running locally. -> **Note**: If you create an application through the Agenta UI, tracing is enabled by default. No additional setup is required—simply go to the observability view to see all your requests. +:::note +If you create an application through the Agenta UI, tracing is enabled by default. No additional setup is required—simply go to the observability view to see all your requests. +::: ## Step-by-Step Guide @@ -26,54 +27,53 @@ First, install the Agenta SDK, OpenAI, and the OpenTelemetry instrumentor for Op pip install -U agenta openai opentelemetry-instrumentation-openai ``` -### 2. Obtain an API Key (Cloud and Enterprise Editions Only) +### 2. Configure Environment Variables + + If you're using Agenta Cloud or Enterprise Edition, you'll need an API key: 1. Visit the [Agenta API Keys page](https://cloud.agenta.ai/settings?tab=apiKeys). 2. Click on **Create New API Key** and follow the prompts. -### 3. Configure Environment Variables - -Set up the environment variables for your API key and host. - -For **Cloud and Enterprise Editions**: - ```python import os -os.environ["AGENTA_API_KEY"] = "YOUR_API_KEY_HERE" +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" ``` -For **Open Source Version (OSS) Running Locally**: + + ```python import os -# No API key is needed for OSS os.environ["AGENTA_HOST"] = "http://localhost" ``` -### 4. Instrument Your Application + + + +### 3. Instrument Your Application Below is a sample script to instrument an OpenAI application: ```python +# highlight-start import agenta as ag from opentelemetry.instrumentation.openai import OpenAIInstrumentor import openai +# highlight-end -# Initialize Agenta +# highlight-start ag.init() +# highlight-end -# Instrument OpenAI +# highlight-start OpenAIInstrumentor().instrument() +# highlight-end -# Set your OpenAI API key -openai.api_key = "YOUR_OPENAI_API_KEY" - -# Make a chat completion request response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ @@ -82,7 +82,6 @@ response = openai.ChatCompletion.create( ], ) -# Print the assistant's reply print(response.choices[0].message.content) ``` @@ -91,11 +90,8 @@ print(response.choices[0].message.content) - **Import Libraries**: Import Agenta, OpenAI, and the OpenTelemetry instrumentor. - **Initialize Agenta**: Call `ag.init()` to initialize the Agenta SDK. - **Instrument OpenAI**: Use `OpenAIInstrumentor().instrument()` to enable tracing for OpenAI calls. -- **Set OpenAI API Key**: Replace `"YOUR_OPENAI_API_KEY"` with your actual OpenAI API key. -- **Make a Request**: Send a chat completion request to the OpenAI API. -- **View Output**: The assistant's response is printed to the console. -### 5. View Traces in the Agenta UI +### 4. View Traces in the Agenta UI After running your application, you can view the captured traces in Agenta: @@ -103,22 +99,9 @@ After running your application, you can view the captured traces in Agenta: 2. Navigate to the **Observability** section. 3. You'll see a list of traces corresponding to your application's requests. -_Note: Replace `path_to_screenshot_image` with the actual path to your screenshot._ - -## Next Steps - -Now that you've instrumented a simple OpenAI call, you can: - -- **Instrument Specific Applications**: Specify application IDs to monitor particular apps within Agenta. -- **Instrument Complex Workflows**: Use decorators and instrument functions in more intricate workflows. -- **Add Metadata**: Enrich your spans with custom metadata for deeper insights. -- **Explore Integrations**: Auto-instrument other libraries and frameworks supported by Agenta: - - OpenAI - - LiteLLM - - Anthropic - - Bedrock - - LangChain - - LLamaIndex - - Instructor - - Vercel AI - 0` +Illustration of observability From a675cbd21d8f29a8af753ab5631523b24f4a108d Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 17:47:49 +0100 Subject: [PATCH 13/14] docs(app): AGE-1107 observability sdk docs --- .../observability/03-observability-sdk.mdx | 183 +++++++++++------- 1 file changed, 110 insertions(+), 73 deletions(-) diff --git a/docs/docs/observability/03-observability-sdk.mdx b/docs/docs/observability/03-observability-sdk.mdx index c9e41d6036..2ebe100ff7 100644 --- a/docs/docs/observability/03-observability-sdk.mdx +++ b/docs/docs/observability/03-observability-sdk.mdx @@ -2,61 +2,72 @@ title: Observability SDK --- -This guide shows you how to use the Agenta observability SDK to instrument complex workflows for your application. +```mdx-code-block +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from "@theme/IdealImage"; -If you are using solely a framework like [LangChain](/observability/integrations/langchain) or [LlamaIndex](/observability/integrations/llamaindex), you can use their auto-instrumentation packages to automatically instrument your application. +``` + +This guide shows you how to use the Agenta observability SDK to instrument workflows in your application. + +If you're exclusively using a framework like [LangChain](/observability/integrations/langchain), you can use the auto-instrumentation packages to automatically instrument your application. However, if you need more flexibility, you can use the SDK to: -- Instrument your own functions part of the workflow +- Instrument custom functions in your workflow - Add additional **metadata** to the spans -- Link your traces to **applications**, **variants**, and **environments** in Agenta +- Link traces to **applications**, **variants**, and **environments** in Agenta -## 1. Create an application in **agenta** +## Setup -To start, we need to create an application in **agenta**. You can do this using the command from the CLI: +**1. Install the Agenta SDK** -```python -agenta init +```bash +pip install -U agenta ``` -This command creates a new application in **agenta** and a `config.toml` file with all the information about the application. +**2. Set environment variables** + + + -## 2. Initialize **agenta** +1. Visit the [Agenta API Keys page](https://cloud.agenta.ai/settings?tab=apiKeys). +2. Click on **Create New API Key** and follow the prompts. ```python -import agenta as ag -# Option 1 +import os -ag.init(api_key="", app_id="") +os.environ["AGENTA_API_KEY"] = "YOUR_AGENTA_API_KEY" +os.environ["AGENTA_HOST"] = "https://cloud.agenta.ai" +``` -# Option 2 -os.environ["AGENTA_API_KEY"] = "" -os.environ["AGENTA_APP_ID"] = "" -ag.init() + + -# Option 3 -ag.init(config_fname="config.toml") # using the config.toml generated by agenta init +```python +import os +os.environ["AGENTA_HOST"] = "http://localhost" ``` -You can find the API Key under the Setting view in **agenta**. + + -The app id can be found in the `config.toml` file if you have created the application from the CLI. +## Instrumenting functions with the decorator -Note that if you are serving your application to the **agenta** cloud, **agenta** will automatically populate all the information in the environment variable. Therefore, you only need to use `ag.init()`. +To instrument a function, add the `@ag.instrument()` decorator. This automatically captures all input and output data. -## 3. Instrument with the decorator +The decorator has a `spankind` argument to categorize each span in the UI. Available types are: -Add the `@ag.instrument()` decorator to the functions you want to instrument. This decorator will trace all input and output information for the functions. +`agent`, `chain`, `workflow`, `tool`, `embedding`, `query`, `completion`, `chat`, `rerank` :::caution -{" "} -Make sure the `instrument` decorator is the first decorator in the function.{" "} +The instrument decorator should be the top-most decorator on a function (i.e. the last decorator before the function call). ::: ```python -@ag.instrument(spankind="llm") +@ag.instrument(spankind="task") def myllmcall(country:str): prompt = f"What is the capital of {country}" response = client.chat.completions.create( @@ -67,86 +78,112 @@ def myllmcall(country:str): ) return response.choices[0].text -@ag.instrument() +@ag.instrument(spankind="workflow") def generate(country:str): return myllmcall(country) ``` -## 4. Modify a span's metadata +Agenta automatically determines the parent span based on the function call and nests the spans accordingly. + +## Modify a span's metadata -You can modify a span's metadata to add additional information using `ag.tracing.set_span_attributes()`. This function will access the active span and add the key-value pairs to the metadata.: +You can add additional information to a span's metadata using `ag.tracing.store_meta()`. This function accesses the active span from the context and adds the key-value pairs to the metadata. ```python -@ag.instrument(spankind="llm") -def myllmcall(country:str): +@ag.instrument(spankind="task") +def compile_prompt(country:str): prompt = f"What is the capital of {country}" - response = client.chat.completions.create( - model='gpt-4', - messages=[ - {'role': 'user', 'content': prompt}, - ], - ) - ag.tracing.set_span_attributes({"model": "gpt-4"}) - return response.choices[0].text + + # highlight-next-line + ag.tracing.store_meta({"prompt_template": prompt}) + + formatted_prompt = prompt.format(country=country) + return formatted_prompt ``` -## 5. Putting it all together +Metadata is displayed in the span's raw data view. -Here's how our code would look if we combine everything: +## Linking spans to applications, variants, and environments -```python -import agenta as ag +You can link a span to an application, variant, and environment by calling `ag.tracing.store_refs()`. -os.environ["AGENTA_API_KEY"] = "" -os.environ["AGENTA_APP_ID"] = "" -ag.init() +Applications, variants, and environments can be referenced by their slugs, versions, and commit IDs (for specific versions). +You can link a span to an application and variant like this: -@ag.instrument(spankind="llm") -def myllmcall(country:str): +```python + +@ag.instrument(spankind="workflow") +def generate(country:str): prompt = f"What is the capital of {country}" - response = client.chat.completions.create( + + + formatted_prompt = prompt.format(country=country) + + completion = client.chat.completions.create( model='gpt-4', messages=[ - {'role': 'user', 'content': prompt}, + {'role': 'user', 'content': formatted_prompt}, ], ) - ag.tracing.set_span_attributes({"model": "gpt-4"}) - return response.choices[0].text -@ag.instrument() -def generate(country:str): - return myllmcall(country) + # highlight-start + ag.tracing.store_refs( + { + "application.slug": "capital-app", + "environment.slug": "production", + } + ) + # highlight-end + return completion.choices[0].message.content ``` -# Setting up telemetry for apps hosted in agenta +`ag.tracing.store_refs()` takes a dict with keys from `application.slug`, `application.id`, `application.version`, `variant.slug`, `variant.id`, `variant.version`, `environment.slug`, `environment.id` and `environment.commit_id`, with the values being the slug, id, version or commit id of the application, variant, and environment respectively. + +## Storing Internals + +Internals are additional data stored in the span. Compared to metadata, internals have the following differences: -If you're creating an application to serve to agenta, not much changes. You just need to add the entrypoint decorator, ensuring it comes _before_ the instrument decorator. +- Internals are saved within the span data and are searchable with plain text queries. +- Internals are shown by default in the span view in a collapsible section, while metadata is only shown as part of the JSON file with the raw data (i.e. better visibility with internals). +- **Internals can be used for evaluation**. For instance, you can save the retrieved context in the internals and then use it to evaluate the factuality of the response. + +As a rule of thumb, use metadata for additional information that is not used for evaluation and not elementary to understand the span, otherwise use internals. + +Internals can be stored similarly to metadata: ```python -import agenta as ag +@ag.instrument(spankind="workflow") +def rag_workflow(query:str): -ag.init() -ag.config.register_default(prompt=ag.TextParam("What is the capital of {country}")) + context = retrieve_context(query) -@ag.instrument(spankind="llm") -def myllmcall(country:str): - response = client.chat.completions.create( + # highlight-start + ag.tracing.store_internals({"context": context}) + # highlight-end + + prompt = f"Answer the following question {query} based on the context: {context}" + + completion = client.chat.completions.create( model='gpt-4', messages=[ - {'role': 'user', 'content': ag.config.prompt.format(country=country)}, + {'role': 'user', 'content': formatted_prompt}, ], ) - ag.tracing.set_span_attributes({"model": "gpt-4"}) - return response.choices[0].text - -@ag.entrypoint -@ag.instrument() -def generate(country:str): - return myllmcall(country) + return completion.choices[0].message.content ``` -The advantage of this approach is that the configuration you use is automatically instrumented along with the other data. +## Excluding Inputs/Outputs from Capture + +In some cases, you may want to exclude parts of the inputs or outputs due to privacy concerns or because the data is too large to be stored in the span. + +You can achieve this by setting the ignore_inputs and ignore_outputs arguments to True in the instrument decorator. + +```python +@ag.instrument(spankind="workflow", ignore_inputs=True, ignore_outputs=True) +def rag_workflow(query:str): + ... +``` From 7850fd5c49115bfc7e86ca72171e0c3e4cd46ec1 Mon Sep 17 00:00:00 2001 From: Mahmoud Mabrouk Date: Wed, 6 Nov 2024 17:54:15 +0100 Subject: [PATCH 14/14] docs(app): AGE-1107 link fixes and image Closes: AGE-1107 --- docs/docs/observability/01-overview.mdx | 2 +- .../images/observability/instructor.png | Bin 0 -> 1026116 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 docs/static/images/observability/instructor.png diff --git a/docs/docs/observability/01-overview.mdx b/docs/docs/observability/01-overview.mdx index 4441a582fa..4fe0a2a962 100644 --- a/docs/docs/observability/01-overview.mdx +++ b/docs/docs/observability/01-overview.mdx @@ -51,7 +51,7 @@ Agenta's observability features are built on **OpenTelemetry (OTel)**, an open-s k;0r>~RiUmOWZ6zJ<}X5nDvNoQ_lW9KZ!aMs$%Kxbzu z#-PKe%%$uqWo2t8@9%D<>96w1!r#F{$dUmljwR|V{FlJV%F~R_*U8b@L)cf0;a|MM zf7k!G0St8i3h{IhW6)Jrqmy!Rx1!_c4{ypG-r0M;?*gyTC6aCkoS{_#JQZ7z^EAsr8_uqYzbo`I?Z`0B4pXLZV znmOBuG5B&=T3MTUIeIdPYg)Kl{XL4Ui>D~ypLROh{fFaU*8e6E{a5FN|F50?o$)^| z2nn|06mp=f6sG|0}7IWK6lg(p9#Tl2TKUlA=>{cd@o}v_e9X zkNU2IuB$mrl4~%HiHt&!_}ZUP`WIT_>krs;?HE5_e?$7wmyZAGc>iap11Uk-K3il= 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