langchain-ai · agola11 · May 6, 2024 · May 6, 2024 · May 6, 2024 · May 6, 2024
diff --git a/docs/evaluation/faq/regression-testing.mdx b/docs/evaluation/faq/regression-testing.mdx
@@ -37,4 +37,4 @@ Click on the regressions or improvements buttons on the top of each column to fi
 
 ## Try it out
 
-To get started with regression testing, try [running a no-code experiment in our prompt playground](experiments-app) or check out the [Evaluation Quick Start Guide](/evaluation/quickstart) to get started with the SDK.
+To get started with regression testing, try [running a no-code experiment in our prompt playground](experiments-app) or check out the [Evaluation Quick Start Guide](../quickstart) to get started with the SDK.
diff --git a/docs/evaluation/index.mdx b/docs/evaluation/index.mdx
@@ -129,14 +129,14 @@ The evaluator itself can be any arbitrary function. There are a few different ty
   which would be considered a **ground truth** evaluator because it compares the output to a reference. See [How to create custom evaluators](evaluation/faq/custom-evaluators).
 - **LLM-as-judge**: An LLM-as-judge evaluator uses an LLM to score system output. For example, you might want to check whether your system is outputting
   offensive content. This is **reference-free**, as there is no comparison to an example output. You might also want to check whether the system output has the same
-  meaning as the example output, which would be a **ground truth** evaluator. To get started with LLM-as-a-judge, try out LangSmith's [off-the-shelf evaluators](https://docs.smith.langchain.com/evaluation/faq/evaluator-implementations)!
+  meaning as the example output, which would be a **ground truth** evaluator. To get started with LLM-as-a-judge, try out LangSmith's [off-the-shelf evaluators](evaluation/faq/evaluator-implementations)!
 - **Human**: You can also evaluate your runs manually. This can be done in LangSmith [via the SDK](tracing/faq/logging_feedback#capturing-feedback-programmatically),
   or [in the LangSmith UI](http://localhost:3000/tracing/faq/logging_feedback#annotating-traces-with-feedback).
 
 ## Next steps
 
-To get started with code, check out the [Quick Start Guide](/evaluation/quickstart).
+To get started with code, check out the [Quick Start Guide](evaluation/quickstart).
 
-If you want to learn how to accomplish a particular task, check out our comprehensive [How-To Guides](/evaluation/faq)
+If you want to learn how to accomplish a particular task, check out our comprehensive [How-To Guides](evaluation/faq)
 
-For a higher-level set of recommendations on how to think about testing and evaluating your LLM app, check out the [evaluation recommendations](/evaluation/recommendations) page.
+For a higher-level set of recommendations on how to think about testing and evaluating your LLM app, check out the [evaluation recommendations](evaluation/recommendations) page.
diff --git a/docs/index.mdx b/docs/index.mdx
@@ -61,11 +61,9 @@ To create an API key head to the [Settings page](https://smith.langchain.com/set
 
 ## Log your first trace
 
-<p>
-  We provide multiple ways to log traces to LangSmith. Below, we'll highlight
-  how to use <code>traceable</code>. See more on the{" "}
-  <a href="./tracing/integrations">Integrations</a> page.
-</p>
+We provide multiple ways to log traces to LangSmith. Below, we'll highlight
+how to use `traceable`. See more on the [Integrations](./tracing/integrations/index.mdx) page.
+
 <CodeTabs
   tabs={[
     {
@@ -85,11 +83,11 @@ To create an API key head to the [Settings page](https://smith.langchain.com/set
 />
 
 - View a [sample output trace](https://smith.langchain.com/public/b37ca9b1-60cd-4a2a-817e-3c4e4443fdc0/r).
-- Learn more about tracing on the [tracing page](/tracing).
+- Learn more about tracing on the [tracing page](./tracing/index.mdx).
 
 ## Create your first evaluation
 
-Evalution requires a system to test, [data](evaluation/faq) to serve as test cases, and optionally evaluators to grade the results. Here we use a built-in accuracy evaluator.
+Evalution requires a system to test, [data](./evaluation/faq/index.mdx) to serve as test cases, and optionally evaluators to grade the results. Here we use a built-in accuracy evaluator.
 
 <CodeTabs
   tabs={[
@@ -176,19 +174,19 @@ await runOnDataset(
   groupId="client-language"
 />
 
-- See more on the [evaluation quick start page](/evaluation/quickstart).
+- See more on the [evaluation quick start page](./evaluation/quickstart.mdx).
 
 ## Next Steps
 
 Check out the following sections to learn more about LangSmith:
 
-- **[User Guide](/user_guide)**: Learn about the workflows LangSmith supports at each stage of the LLM application lifecycle.
+- **[User Guide](./user_guide.mdx)**: Learn about the workflows LangSmith supports at each stage of the LLM application lifecycle.
 - **[Pricing](/pricing)**: Learn about the pricing model for LangSmith.
 - **[Self-Hosting](/category/self-hosting)**: Learn about self-hosting options for LangSmith.
-- **[Proxy](/category/proxy)**: Learn about the proxy capabilities of LangSmith.
-- **[Tracing](/tracing)**: Learn about the tracing capabilities of LangSmith.
-- **[Evaluation](/evaluation)**: Learn about the evaluation capabilities of LangSmith.
-- **[Prompt Hub](/category/prompt-hub)** Learn about the Prompt Hub, a prompt management tool built into LangSmith.
+- **[Proxy](/1.0/category/proxy)**: Learn about the proxy capabilities of LangSmith.
+- **[Tracing](./tracing/index.mdx)**: Learn about the tracing capabilities of LangSmith.
+- **[Evaluation](./evaluation/index.mdx)**: Learn about the evaluation capabilities of LangSmith.
+- **[Prompt Hub](/1.0/category/prompt-hub)** Learn about the Prompt Hub, a prompt management tool built into LangSmith.
 
 ## Additional Resources
 
@@ -202,7 +200,7 @@ Check out the following sections to learn more about LangSmith:
 
 ### How do I migrate projects between organizations?
 
-Currently we do not support project migration betwen organizations. While you can manually imitate this by reading and writing runs and datasets using the SDK (see the querying runs and traces guide [here](/tracing/faq/querying_traces)), it will be fastest to create a new project within your organization and go from there.
+Currently we do not support project migration betwen organizations. While you can manually imitate this by reading and writing runs and datasets using the SDK (see the querying runs and traces guide [here](./tracing/faq/querying_traces.mdx)), it will be fastest to create a new project within your organization and go from there.
 
 ### Why aren't my runs aren't showing up in my project?
 

diff --git a/docs/monitoring/concepts.mdx b/docs/monitoring/concepts.mdx
@@ -7,7 +7,7 @@ table_of_contents: true
 # Concepts
 
 In this guide we will go over some of the concepts that are important to understand when thinking about production logging and automations in LangSmith.
-A lot of these concepts build off of tracing concepts - it is recommended to read the [Tracing Concepts](/tracing/concepts) documentation before.
+A lot of these concepts build off of tracing concepts - it is recommended to read the [Tracing Concepts](../tracing/concepts) documentation before.
 
 ## Runs
 
@@ -29,7 +29,7 @@ A `Thread` is a sequence of traces representing a conversation. Each response is
 
 You can track threads by attaching a special metadata key to runs (one of `session_id`, `thread_id` or `conversation_id`).
 
-See [this documentation](/tracing/faq/customizing_trace_attributes#adding-metadata-and-tags-to-traces) for how to add metadata keys to a trace.
+See [this documentation](../tracing/faq/customizing_trace_attributes#adding-metadata-and-tags-to-traces) for how to add metadata keys to a trace.
 
 ## Monitoring Dashboard
 
@@ -43,7 +43,7 @@ An example of a rule could be, in plain English, "Run a 'vagueness' evaluator on
 
 ## Datasets
 
-Datasets are a way to collect examples, which are input-output pairs. You can use datasets for evaluation, as well as fine-tuning and few-shot prompting. For more information, see [here](/evaluation)
+Datasets are a way to collect examples, which are input-output pairs. You can use datasets for evaluation, as well as fine-tuning and few-shot prompting. For more information, see [here](../evaluation)
 
 ## Annotation Queues
 

diff --git a/docs/monitoring/faq/filter.mdx b/docs/monitoring/faq/filter.mdx
@@ -22,7 +22,7 @@ You can also define a filter from the `Filter Shortcuts` on the sidebar. This co
 ## How to filter for sub runs
 
 In order to filter for sub runs, you first need to remove the default filter of `IsRoot` is `true`. After that, you can apply any filter you wish. A common way to do this is to filter by name for sub runs.
-This relies on good naming for all parts of your pipeline - see [here](/tracing/faq/customizing_trace_attributes#customizing-the-run-name) for more details on how to do that.
+This relies on good naming for all parts of your pipeline - see [here](../../tracing/faq/customizing_trace_attributes#customizing-the-run-name) for more details on how to do that.
 
 ## How to filter for sub runs whose parent traces have some attribute
 

diff --git a/docs/monitoring/faq/monitoring.mdx b/docs/monitoring/faq/monitoring.mdx
@@ -16,7 +16,7 @@ You can view monitors over differing time periods. This can be controlled by the
 By default, the monitor tab shows results for all runs. However, you can group runs in order to see how different subsets perform.
 This can be useful to compare how two different prompts or models are performing.
 
-In order to do this, you first need to make sure you are [attaching appropriate tags or metadata](/tracing/faq/customizing_trace_attributes#adding-metadata-and-tags-to-traces) to these runs when logging them.
+In order to do this, you first need to make sure you are [attaching appropriate tags or metadata](../../tracing/faq/customizing_trace_attributes#adding-metadata-and-tags-to-traces) to these runs when logging them.
 After that, you can click the `Tag` or `Metadata` tab at the top to group runs accordingly.
 
 ![Subsets Monitor](../static/subsets_monitor.png)

diff --git a/docs/monitoring/faq/online_evaluation.mdx b/docs/monitoring/faq/online_evaluation.mdx
@@ -11,7 +11,7 @@ Currently, we provide support for specifying a prompt template, a model, and a s
 
 ## How to set up online evaluation
 
-The way to configure online evaluation is to first set up an [automation](/monitoring/faq/automations).
+The way to configure online evaluation is to first set up an [automation](../../monitoring/faq/automations).
 
 ![Subsets Monitor](../static/filter_rule.png)
 

diff --git a/docs/monitoring/index.mdx b/docs/monitoring/index.mdx
@@ -11,10 +11,10 @@ It's also crucial to get a high-level overview of application performance with r
 In order to facilitate this, LangSmith supports a series of workflows to support production monitoring and automations.
 This includes support for easily exploring and visualizing key production metrics, as well as support for defining automations to process the data.
 
-To get started, check out the [Quick Start Guide](/monitoring/quickstart).
+To get started, check out the [Quick Start Guide](monitoring/quickstart).
 
-After that, peruse the [Concepts Section](/monitoring/concepts) to better understand the different components involved with monitoring and automations.
+After that, peruse the [Concepts Section](monitoring/concepts) to better understand the different components involved with monitoring and automations.
 
-If you want to learn how to accomplish a particular task, check out our comprehensive [How-To Guides](/monitoring/faq)
+If you want to learn how to accomplish a particular task, check out our comprehensive [How-To Guides](monitoring/faq)
 
-For example use cases, check out the [Use Cases](/monitoring/use_cases) page.
+For example use cases, check out the [Use Cases](monitoring/use_cases) page.
diff --git a/docs/monitoring/quickstart.mdx b/docs/monitoring/quickstart.mdx
@@ -6,13 +6,13 @@ table_of_contents: true
 
 # Quick Start
 
-Production monitoring starts by configuring tracing for your application. See the [tracing section](/tracing) for details on how to do that.
+Production monitoring starts by configuring tracing for your application. See the [tracing section](../tracing) for details on how to do that.
 
 Compared to tracing while prototyping applications, you want to pay attention to a few particular points:
 
-- [Sampling](/tracing/faq/logging_and_viewing#setting-a-sampling-rate-for-tracing): When logging production workloads, you may only want to log a subset of the datapoints flowing through your system.
-- [Adding Metadata](/tracing/faq/customizing_trace_attributes#adding-metadata-and-tags-to-traces): As we'll see with automations, attaching relevant metadata to runs is particularly important to enable filtering and grouping your data.
-- [Feedback](/tracing/faq/logging_feedback): When an application is in production you can't always look at all datapoints. Capturing user feedback is helpful to draw your attention to particular datapoints.
+- [Sampling](../tracing/faq/logging_and_viewing#setting-a-sampling-rate-for-tracing): When logging production workloads, you may only want to log a subset of the datapoints flowing through your system.
+- [Adding Metadata](../tracing/faq/customizing_trace_attributes#adding-metadata-and-tags-to-traces): As we'll see with automations, attaching relevant metadata to runs is particularly important to enable filtering and grouping your data.
+- [Feedback](../tracing/faq/logging_feedback): When an application is in production you can't always look at all datapoints. Capturing user feedback is helpful to draw your attention to particular datapoints.
 
 So - now you've got your logs flowing into LangSmith. What can you do with that data?
 

diff --git a/docs/tracing/concepts.mdx b/docs/tracing/concepts.mdx
@@ -54,7 +54,7 @@ Collecting feedback on runs can be done in three main ways:
 
 1. [In the UI](faq/logging_feedback#annotating-traces-with-feedback) - Annotate runs directly in the UI
 2. [With the SDK](faq/logging_feedback#capturing-feedback-programmatically) - Programmatically log feedback to LangSmith
-3. [With Online Evaluators](/monitoring/faq/online_evaluation) - Automatically run evaluators on a sample of your production traces to generate feedback in realtime
+3. [With Online Evaluators](../monitoring/faq/online_evaluation) - Automatically run evaluators on a sample of your production traces to generate feedback in realtime
 
 ![Feedback](static/concepts/feedback.png)
 

diff --git a/docs/tracing/faq/customizing_trace_attributes.mdx b/docs/tracing/faq/customizing_trace_attributes.mdx
@@ -134,7 +134,7 @@ await chain.invoke(
 ### Adding metadata and tags to traces
 
 LangSmith supports sending arbitrary metadata and tags along with traces. This is useful for associating additional information with a trace, such as the environment in which it was executed, or the user who initiated it.
-For more information on metadata and tags, see the [Concepts](/tracing/concepts) page. For information on how to query traces and runs by metadata and tags, see the [Querying Traces](/tracing/faq/querying_traces) page.
+For more information on metadata and tags, see the [Concepts](../../tracing/concepts) page. For information on how to query traces and runs by metadata and tags, see the [Querying Traces](../../tracing/faq/querying_traces) page.
 
 <CodeTabs
   tabs={[

diff --git a/docs/tracing/faq/logging_and_viewing.mdx b/docs/tracing/faq/logging_and_viewing.mdx
@@ -28,7 +28,7 @@ The `LANGCHAIN_TRACING_V2` environment variable must be set to `'true'` in order
 Additionally, you will need to set the `LANGCHAIN_API_KEY` environment variable to your API key (see [Setup](/) for more information).
 
 By default, the traces will be logged to a project named `default`.
-To log traces to a different project, see [this section](/tracing/faq/customizing_trace_attributes#logging-to-a-specific-project).
+To log traces to a different project, see [this section](customizing_trace_attributes#logging-to-a-specific-project).
 :::
 
 <CodeTabs
@@ -117,7 +117,7 @@ The `LANGCHAIN_TRACING_V2` environment variable must be set to `'true'` in order
 Additionally, you will need to set the `LANGCHAIN_API_KEY` environment variable to your API key (see [Setup](/) for more information).
 
 By default, the traces will be logged to a project named `default`.
-To log traces to a different project, see [this section](/tracing/faq/customizing_trace_attributes#logging-to-a-specific-project).
+To log traces to a different project, see [this section](customizing_trace_attributes#logging-to-a-specific-project).
 :::
 
 <CodeTabs
@@ -343,7 +343,7 @@ This setting works both with LangChain and the LangSmith SDK, in both Python and
 
 ## Getting the run ID of a logged run
 
-The example below shows how to get the run ID of a logged run using the LangSmith SDK. **To get the run ID of a run using LangChain, you can follow the guide [here](/tracing/faq/langchain_specific_guides#getting-a-run-id-from-a-langchain-call).**
+The example below shows how to get the run ID of a logged run using the LangSmith SDK. **To get the run ID of a run using LangChain, you can follow the guide [here](langchain_specific_guides#getting-a-run-id-from-a-langchain-call).**
 
 <CodeTabs
   tabs={[

diff --git a/docs/tracing/faq/logging_feedback.mdx b/docs/tracing/faq/logging_feedback.mdx
@@ -15,12 +15,12 @@ import {
 # How to Collect Feedback for Traces
 
 Feedback allows you to understand how your users are experiencing your application and helps draw attention to problematic traces. LangSmith makes it easy to collect feedback for traces and view it in the context of the trace, as well as filter traces based on feedback.
-For more information on how to score your application programmatically using a testing workflow, see the [Evaluation](/evaluation) section.
+For more information on how to score your application programmatically using a testing workflow, see the [Evaluation](../../evaluation) section.
 
 ### Capturing feedback programmatically
 
 It's often helpful to expose a simple mechanism (such as a thumbs-up, thumbs-down button) to collect user feedback for your application responses. You can then use the LangSmith SDK or API to send feedback for a trace.
-To get the run_id of a logged run, see [this guide](/tracing/faq/logging_and_viewing#getting-the-run-id-of-a-logged-run).
+To get the run_id of a logged run, see [this guide](./logging_and_viewing#getting-the-run-id-of-a-logged-run).
 
 <CodeTabs
   tabs={[

diff --git a/docs/tracing/index.mdx b/docs/tracing/index.mdx
@@ -15,10 +15,10 @@ Tracing can help you track down issues like:
 - Why a chain was slower than expected
 - How many tokens an agent used at each step
 
-To get started, check out the [Quick Start Guide](/tracing/quick_start).
+To get started, check out the [Quick Start Guide](./tracing/quick_start).
 
-After that, peruse the [Concepts Section](/tracing/concepts) to better understand the different components involved with tracing.
+After that, peruse the [Concepts Section](./tracing/concepts) to better understand the different components involved with tracing.
 
-If you want to learn how to accomplish a particular task, check out our comprehensive [How-To Guides](/tracing/faq)
+If you want to learn how to accomplish a particular task, check out our comprehensive [How-To Guides](./tracing/faq)
 
-For example use cases, check out the [Use Cases](/tracing/use_cases) page.
+For example use cases, check out the [Use Cases](./tracing/use_cases) page.
diff --git a/docs/tracing/integrations/python.mdx b/docs/tracing/integrations/python.mdx
@@ -54,7 +54,7 @@ client.chat.completions.create(
 
 Oftentimes, you use the OpenAI client inside of other functions.
 You can get nested traces by using this wrapped client and decorating those functions with `@traceable`.
-See [this documentation](/tracing/faq/logging_and_viewing) for more documentation how to use this decorator
+See [this documentation](../faq/logging_and_viewing.mdx) for more documentation how to use this decorator
 
 ```python
 from langsmith import traceable
@@ -124,7 +124,7 @@ user = client.chat.completions.create(
 
 Oftentimes, you use `instructor` inside of other functions.
 You can get nested traces by using this wrapped client and decorating those functions with `@traceable`.
-See [this documentation](/tracing/faq/logging_and_viewing) for more documentation how to use this decorator
+See [this documentation](../faq/logging_and_viewing.mdx) for more documentation how to use this decorator
 
 ```python
 # You can customize the run name with the `name` keyword argument
Original file line number	Diff line number	Diff line change
Expand Up		@@ -37,4 +37,4 @@ Click on the regressions or improvements buttons on the top of each column to fi

		## Try it out

		To get started with regression testing, try [running a no-code experiment in our prompt playground](experiments-app) or check out the [Evaluation Quick Start Guide](/evaluation/quickstart) to get started with the SDK.
		To get started with regression testing, try [running a no-code experiment in our prompt playground](experiments-app) or check out the [Evaluation Quick Start Guide](../quickstart) to get started with the SDK.