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@@ -11,4 +11,5 @@ compare_embeddings
 compare_llms
 custom_prompts
 use_prompt_adaptation
+tracing
 ```
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+
+# Logging and tracing
+
+Logging and tracing results from llm are important for any language model-based application. This is a tutorial on how to do tracing with Ragas. Ragas provides `callbacks` functionality which allows you to hook various tracers like Langmsith, wandb, etc easily.  In this notebook, I will be using Langmith for tracing
+
+To set up Langsmith, we need to set some environment variables that it needs. For more information, you can refer to the [docs](https://docs.smith.langchain.com/)
+
+```{code-block} bash
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+Now we have to import the required tracer from langchain, here we are using `LangChainTracer` but you can similarly use any tracer supported by langchain like [WandbTracer](https://python.langchain.com/docs/integrations/providers/wandb_tracing)
+
+```{code-block} python
+# langsmith
+from langchain.callbacks.tracers import LangChainTracer
+
+tracer = LangChainTracer(project_name="callback-experiments")
+```
+
+We now pass the tracer to the `callbacks` parameter when calling `evaluate`
+
+```{code-block} python
+from datasets import load_dataset
+from ragas.metrics import context_precision
+from ragas import evaluate
+
+dataset = load_dataset("explodinggradients/amnesty_qa","english")
+evaluate(dataset["train"],metrics=[context_precision],callbacks=[tracer])
+```
+
+```
+{'context_precision': 1.0000}
+```
+![](./../../_static/imgs/trace-langsmith.png)
+ 
+
+You can also write your own custom callbacks using langchain’s `BaseCallbackHandler`, refer [here](https://www.notion.so/Docs-logging-and-tracing-6f21cde9b3cb4d499526f48fd615585d?pvs=21) to read more about it.
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