classification optimization example

docs/monitoring/use_cases/classification.mdx

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+---
+sidebar_label: Optimizing a Classifier
+sidebar_position: 2
+table_of_contents: true
+---
+
+# Optimizing a Classifier
+
+This tutorial walks through optimizing a classifier based on user a feedback.
+Classifiers are great to optimize because its generally pretty simple to collect the desired output, which makes it easy to create few shot examples based on user feedback.
+That is exactly what we will do in this example.
+
+## The objective
+
+In this example, we will build a bot that classify GitHub issues based on their title.
+It will take in a title and classify it into one of many different classes.
+Then, we will start to collect user feedback and use that to shape how this classifier performs.
+
+## Getting started
+
+To get started, we will first set it up so that we send all traces to a specific project.
+We can do this by setting an environment variable:
+
+```python
+import os
+os.environ["LANGCHAIN_PROJECT"] = "classifier"
+```
+
+We can then create our initial application. This will be a really simple function that just takes in a GitHub issue title and tries to label it.
+
+```python
+import openai
+from langsmith import traceable, Client
+import uuid
+
+client = openai.Client()
+
+available_topics = [
+    "bug",
+    "improvement",
+    "new_feature",
+    "documentation",
+    "integration",
+]
+
+prompt_template = """Classify the type of the issue as one of {topics}.
+
+Issue: {text}"""
+
+@traceable(
+    run_type="chain",
+    name="Classifier",
+)
+def topic_classifier(
+    topic: str
+):
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        temperature=0,
+        messages=[
+            {
+                "role": "user",
+                "content": prompt_template.format(
+                    topics=','.join(available_topics),
+                    text=topic,
+                )
+            }
+        ],
+    ).choices[0].message.content
+```
+
+We can then start to interact with it.
+When interacting with it, we will generate the LangSmith run id ahead of time and pass that into this function.
+We do this so we can attach feedback later on.
+
+Here's how we can invoke the application:
+
+```python
+run_id = uuid.uuid4()
+topic_classifier(
+    "fix bug in LCEL",
+    langsmith_extra={"run_id": run_id}
+)
+```
+
+Here's how we can attach feedback after.
+We can collect feedback in two forms.
+
+First, we can collect "positive" feedback - this is for examples that the model got right.
+
+```python
+ls_client = Client()
+run_id = uuid.uuid4()
+
+topic_classifier(
+    "fix bug in LCEL",
+    langsmith_extra={"run_id": run_id}
+)
+
+ls_client.create_feedback(
+    run_id,
+    key="user-score",
+    score=1.0,
+)
+```
+
+Next, we can focus on collecting feedback that corresponds to a "correction" to the generation.
+In this example the model will classify it as a bug, whereas I really want this to be classified as documentation.
+
+```python
+ls_client = Client()
+
+run_id = uuid.uuid4()
+topic_classifier(
+    "fix bug in documentation",
+    langsmith_extra={"run_id": run_id}
+)
+
+ls_client.create_feedback(
+    run_id,
+    key="correction",
+    correction="documentation"
+)
+```
+
+## Set up automations
+
+We can now set up automations to move examples with feedback of some form into a dataset.
+We will set up two automations, one for positive feedback and the other for negative feedback.
+
+The first will take all runs with positive feedback and automatically add them to a dataset.
+The logic behind this is that any run with positive feedback we can use as a good example in future iterations.
+Let's create a dataset called `classifier-github-issues` to add this data to.
+
+![Optimization Positive](../static/class-optimization-pos.png)
+
+The second will take all runs with a correction and use a webhook to add them to a dataset.
+When creating this webhook, we will select the option to "Use Corrections".
+This option will make it so that when creating a dataset from a run, rather than using the output of the run
+as the gold-truth output of the datapoint, it will use the correction.
+
+![Optimization Negative](../static/class-optimization-neg.png)
+
+## Updating the application
+
+We can now update our code to pull down the dataset we are sending runs to.
+Once we pull it down, we can create a string with the examples in it.
+We can then put this string as part of the prompt!
+
+```python
+
+### NEW CODE ###
+
+# Initialize the LangSmith Client so we can use to get the dataset
+ls_client = Client()
+
+# Create a function that will take in a list of examples and format them into a string
+def create_example_string(examples):
+    final_strings = []
+    for e in examples:
+        final_strings.append(f"Input: {e.inputs['topic']}\n> {e.outputs['output']}")
+    return "\n\n".join(final_strings)
+
+### NEW CODE ###
+
+
+client = openai.Client()
+
+available_topics = [
+    "bug",
+    "improvement",
+    "new_feature",
+    "documentation",
+    "integration",
+]
+
+prompt_template = """Classify the type of the issue as one of {topics}.
+
+Here are some examples:
+
+{examples}
+
+Begin!
+
+Issue: {text}
+>"""
+
+@traceable(
+    run_type="chain",
+    name="Classifier",
+)
+def topic_classifier(
+    topic: str
+):
+    # We can now pull down the examples from the dataset
+    # We do this inside the function so it always get the most up-to-date examples,
+    # But this can be done outside and cached for speed if desired
+    examples = list(ls_client.list_examples(dataset_name="classifier-github-issues"))  # <- New Code
+    example_string = create_example_string(examples)
+
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        temperature=0,
+        messages=[
+            {
+                "role": "user",
+                "content": prompt_template.format(
+                    topics=','.join(available_topics),
+                    text=topic,
+                    examples=example_string,
+                )
+            }
+        ],
+    ).choices[0].message.content
+```
+
+If now run the application with a similar input as before, we can see that it correctly learns that anything related to docs (even if a bug) should be classified as `documentation`
+
+```python
+ls_client = Client()
+
+run_id = uuid.uuid4()
+topic_classifier(
+    "fix bug in streaming docs",
+    langsmith_extra={"run_id": run_id}
+)
+```
+
+## Semantic Search Over Examples
+
+One additional thing we can do is only use the most semantically similar examples.
+This is useful when you start to build up a lot of examples.
+
+In order to do this, we can first define an example to find the `k` most similar examples:
+
+```python
+import numpy as np
+
+def find_similar(examples, topic, k=5):
+    inputs = [e.inputs['topic'] for e in examples] + [topic]
+    embedds = client.embeddings.create(input=inputs, model="text-embedding-3-small")
+    embedds = [e.embedding for e in embedds.data]
+    embedds = np.array(embedds)
+    args = np.argsort(-embedds.dot(embedds[-1])[:-1])[:5]
+    examples = [examples[i] for i in args]
+    return examples
+```
+
+We can then use that in the application
+
+```python
+ls_client = Client()
+
+def create_example_string(examples):
+    final_strings = []
+    for e in examples:
+        final_strings.append(f"Input: {e.inputs['topic']}\n> {e.outputs['output']}")
+    return "\n\n".join(final_strings)
+
+
+
+client = openai.Client()
+
+available_topics = [
+    "bug",
+    "improvement",
+    "new_feature",
+    "documentation",
+    "integration",
+]
+
+prompt_template = """Classify the type of the issue as one of {topics}.
+
+Here are some examples:
+
+{examples}
+
+Begin!
+
+Issue: {text}
+>"""
+
+@traceable(
+    run_type="chain",
+    name="Classifier",
+)
+def topic_classifier(
+    topic: str
+):
+    examples = list(ls_client.list_examples(dataset_name="classifier-github-issues"))
+    examples = find_similar(examples, topic)
+    example_string = create_example_string(examples)
+
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        temperature=0,
+        messages=[
+            {
+                "role": "user",
+                "content": prompt_template.format(
+                    topics=','.join(available_topics),
+                    text=topic,
+                    examples=example_string,
+                )
+            }
+        ],
+    ).choices[0].message.content
+```