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zN~P5S)%lW_4o3gmt#o(at;*ZNz=`3%m%pYRMGOUK0Mlvfqlb>p2i#p78)6PJBsIo| z+kfmk_50&V*mRFeeap%N46j?>q=@oy{lDvW0+I-x_Ba4%vP<6I^JU}T7i(iABtpP%`D0-j6m4fNnsU{zdnWB%f~kyX1-o)EGu zGmBo9e`>2rFT {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} +) +``` \ No newline at end of file From 173d8a3cba25a10e9de66b6d7c666e7db76c324a Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Mon, 22 Apr 2024 20:18:55 -0700 Subject: [PATCH 2/3] cr --- docs/monitoring/use_cases/classification.mdx | 81 ++++++++++++++++++++ 1 file changed, 81 insertions(+) diff --git a/docs/monitoring/use_cases/classification.mdx b/docs/monitoring/use_cases/classification.mdx index 560681f9..36835e48 100644 --- a/docs/monitoring/use_cases/classification.mdx +++ b/docs/monitoring/use_cases/classification.mdx @@ -226,4 +226,85 @@ 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 ``` \ No newline at end of file From 7d3d3c6c62b2ff677aa1c4b1df44444831b3befb Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Thu, 25 Apr 2024 17:47:51 -0700 Subject: [PATCH 3/3] x --- docs/monitoring/use_cases/classification.mdx | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/monitoring/use_cases/classification.mdx b/docs/monitoring/use_cases/classification.mdx index 36835e48..2c6221da 100644 --- a/docs/monitoring/use_cases/classification.mdx +++ b/docs/monitoring/use_cases/classification.mdx @@ -88,7 +88,6 @@ 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() @@ -142,7 +141,6 @@ 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. @@ -307,4 +305,4 @@ def topic_classifier( } ], ).choices[0].message.content -``` \ No newline at end of file +```