Logs
Logs are essential to monitor and improve your LLM app in production. Literal AI provides a flexible and composable SDK to log your LLM app at different levels of granularity.
Semantics
Log Hierarchy on Literal AI
Literal AI approaches LLM logging at three levels:
- Generation: Log of a single LLM call. (Generations are Steps.)
- Step: Log of a regular function execution, which is usually an intermediate step in an LLM system. Possible
type
s are:tool
,embedding
,retrieval
,rerank
,undefined
, etc. Steps can be considered as Spans. - Run: Trace of an Agent/Chain run, including its intermediate steps. Can contain one or multiple generations.
- Thread: A collection of Runs that are part of a single conversation.
A Thread containing a run and intermediate steps in Literal AI
You can log a generation only (typically for extraction use cases), or log a run only (typically for task automation), or combine them in threads (typically for chatbots).
Log an LLM Generation
Generations are logged by integrations with LLM providers. They capture the prompt, completion, settings, and token latency.
Here is an example with OpenAI:
Check out the TypeScript client to learn more about the wrap
function.
Multimodal LLM
You can leverage multimodal capabilities on Literal AI in two ways:
- Simple logging on API calls to Multimodal LLM APIs, like
gpt-4o
- Save multimodal files as Attachments. Image, videos, audio and other files are shown as
Attachment
in the Literal AI platform, which can be accessed and downloaded via aStep
.
Example of a logged multimodal LLM call
Log a Run
A Run represents a trace of an Agent or Chain execution, capturing all intermediate steps and actions.
Runs can be logged manually using decorators or through framework integrations such as Llama Index or LangChain.
Log a Run with Intermediate Steps
Here’s how you can log a Run with intermediate steps using Python and TypeScript:
Add Metadata and Tags to Steps
Tags and Metadata can be added to both Runs and Steps to provide additional context and facilitate filtering and categorization.
Add Attachments to Steps
You can attach files to a Run or any of its intermediate steps, which is particularly useful for multimodal use cases.
Example of attachments
Learn More
The intermediate steps and the agent itself are logged using the Step
class. You can learn more about the Step API in the following references:
Python Step API reference
Learn how to use the Python Step API.
TypeScript Step API reference
Learn how to use the TypeScript Step API.
Log a Thread
You can interact with an example Thread in the platform here.
It is up to the application to keep track of the thread ID and pass it to the Literal AI client. Every run logged with the same thread ID will be part of the same conversation.
Here is an example:
You can learn more about the Thread API in the following references:
Python Thread API reference
Learn how to use the Python Thread API.
TypeScript Thread API reference
Learn how to use the TypeScript Thread API.
Bind a Thread to a User
You can bind a thread to a user to track the user’s activity. This is useful for chatbots or any other conversational AI.
To do so, you need to use a common identifier for the user, such as an email or a user ID:
Log to a Specific Environment
Literal AI supports logging to different environments, which allows you to separate your development, staging, and production data: dev
, staging
, prod
.
This is particularly useful for managing your LLM application lifecycle.
To specify an environment when initializing the LiteralClient, you can use the environment
parameter:
Log with a Release
Literal AI supports pairing your logs to a release, a release is a version of your deployed code to help you identify new issues and regressions.
This is particularly useful for managing your LLM application once in production. The value can be arbitrary, but we recommend Semantic Versioning, Calendar Versioning, or the Git commit SHA.
To specify a release when initializing the LiteralClient, you can use the release
parameter:
Your logs will have a new key release
in the metadata.
Log a Distributed Trace
Distributed Tracing Cookbook
Learn how to log distributed traces with Literal AI.
Add a Score
Scores can be human generated (human feedback, like a thump up or down), or AI generated (hallucination evaluation for instance).
They can be visualized on the dashboard charts and used as filters.
Add a User Feedback
Add a Product-Related Metric
Correlate your LLM system to a product metric, such as conversion, churn, upsell, etc. This can be done by:
- Adding a specific product-related score on Literal AI.
- Sending the logged run id to your analytics system, such as PostHog or Amplitude.
Add an AI Evaluation Result
Refer to Evaluation
Fetch Existing Logs
You can fetch existing logs using the SDKs. Here is an example to fetch the last 5 threads where a user participated:
More generally, you can fetch any Literal AI object. Check out the SDKs and API reference to learn how.
On Literal AI
Filter logs
Leverage the powerful filters on Literal AI. Use these same filters to export your data using the SDKs.
Filter on logs
Debug logged LLM generations
Replay a logged LLM generation in the Playground
Add Tags and Scores from the UI
You can add tags and scores directly from the user interface.
Add a Tag to a Thread
Conclusion
Logging with Literal AI is composable and unopinionated. It can be done at different levels depending on your use case.
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