diff --git a/README.rst b/README.rst index ecd53a6c..66670ab9 100644 --- a/README.rst +++ b/README.rst @@ -54,6 +54,7 @@ It provides support for the following machine learning frameworks and packages: * sklearn-crfsuite_. ELI5 allows to check weights of sklearn_crfsuite.CRF models. +* OpenAI_ python client. ELI5 allows to explain LLM predictions with token probabilities. ELI5 also implements several algorithms for inspecting black-box models (see `Inspecting Black-Box Estimators`_): @@ -81,6 +82,7 @@ and formatting on a client. .. _Catboost: https://github.com/catboost/catboost .. _Permutation importance: https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html .. _Inspecting Black-Box Estimators: https://eli5.readthedocs.io/en/latest/blackbox/index.html +.. _OpenAI: https://github.com/openai/openai-python License is MIT. diff --git a/docs/source/_notebooks/explain_llm_logprobs.rst b/docs/source/_notebooks/explain_llm_logprobs.rst new file mode 100644 index 00000000..6efa805e --- /dev/null +++ b/docs/source/_notebooks/explain_llm_logprobs.rst @@ -0,0 +1,553 @@ +Visualize Token Probabilities and Model Confidence in LLM Predictions +===================================================================== + +In this tutorial we show how :func:`eli5.explain_prediction` can be used to +visualize LLM predictions, highlighting tokens proportionally to the log +probability. In many cases, this can help to see where LLM is uncertain +about its predictions: + +.. figure:: ../static/llm-explain-logprobs.png + :alt: LLM token probabilities visualized with eli5.explain_prediction + + LLM token probabilities visualized with eli5.explain_prediction + +To follow this tutorial you need the ``openai`` library installed and +working. + +.. code:: ipython3 + + import eli5 + import openai + + client = openai.OpenAI() + +First let’s define our task: we’ll be extracting specific product +properties from a free-form product description: + +.. code:: ipython3 + + product_description = """\ + Stay is designed by Danish Maria Berntsen and is functional and beautiful lighting + in one and the same design. Stay has several nice features, where the lamp's flexible + swivel mechanism makes it possible to direct the light and create optimal lighting + conditions. Furthermore, the switch is discreetly placed behind the lamp head, + making it easy and convenient to turn the light on and off. Thus, Stay combines great + flexibility and elegant appearance in a highly convincing way. + + Stay table lamp is highly functional, as the arm and head of the lamp can be adjusted + according to wishes and needs, which make it ideal as task lighting in the office. + Furthermore, the silky matte grey metal support the modern and minimalistic expression.\ + """ + prompt = f"""\ + Product description: + {product_description.strip()} + + Extract the following properties from product description: + 'materials' (list of strings), + 'type' (string, e.g. 'LED'), + 'color' (string), + 'price' (non-zero float with 2 decimal places), + 'summary' (string, a very short summary). + Respond with JSON dict with above keys. Always make sure to return the price or it's estimate. + """ + completion = client.chat.completions.create( + messages=[{"role": "user", "content": prompt}], + model="gpt-4o", + logprobs=True, + temperature=0, + ) + print(completion.choices[0].message.content.strip('`')) + + +.. parsed-literal:: + + json + { + "materials": ["metal"], + "type": "table lamp", + "color": "silky matte grey", + "price": 150.00, + "summary": "Stay is a flexible and elegant table lamp designed by Maria Berntsen." + } + + + +If you examine the prompt and description above, you can see that not +all attributes are equally clear: - “material” is quite clear and +explicitly mentioned - “color” is also clear, but its mention is more +ambiguous - “type” is underspecified, we don’t extractly specify what we +want, - “summary” is quite clear, but there is no single correct +summary, - “price” is not present at all, but we ask a model to make a +guess. + +You may have noticed that above we included ``logprobs=True,`` in a call +to the model, this allows us to get a log-probability for each token, +and then we can visualize them with :func:`eli5.explain_prediction`: + +.. code:: ipython3 + + eli5.explain_prediction(completion) + + + + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

```json + { + "materials": ["metal"], + "type": "table lamp", + "color": "silky matte grey", + "price": 150.00, + "summary": "Stay is a flexible and elegant table lamp designed by Maria Berntsen." + } + ```

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +We can clearly see that the model is very confident in the material – if +you hover over the prediction, you can see a probability for each token +– and less confident about type and color. The confidence in price is a +lot lower, while summary, being a longer piece of text, is harder to +interpret – we can see that some words follow more obviously. + +We can also obtain the same result by passing ``client`` and ``prompt`` +to :func:`eli5.explain_prediction`, in this case it would call the client, +and we can pass extra keyword arguments – here we’ll pass ``n=2`` to +obtain two different predictions, and would leave temperature at +default. + +.. code:: ipython3 + + explanation = eli5.explain_prediction(client, prompt, model='gpt-4o', n=2) + explanation + + + + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

```json + { + "materials": ["metal"], + "type": "", + "color": "silky matte grey", + "price": 199.99, + "summary": "Stay table lamp with flexible swivel mechanism for optimal task lighting." + } + ```

+ + + + + +

```json + { + "materials": ["metal"], + "type": "table lamp", + "color": "silky matte grey", + "price": 165.00, + "summary": "A functional and elegant adjustable table lamp designed by Maria Berntsen." + } + ```

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +We can obtain the original prediction from the explanation object via +``explanation.targets[0].target``, e.g. use +``explanation.targets[0].target.message.content`` to get the prediction +text. + +Limitations +----------- + +Even though above the model confidence matched our expectations, it’s +not always the case. For example, if we use “Chain of Thought” +(https://arxiv.org/abs/2201.11903) reasoning, asking the model first to +think about the price estimate, it would be much more confident in the +price in its final output, but that does not reflect the real confidence +of the model, as it’s smeared over CoT: + +.. code:: ipython3 + + prompt_cot = prompt + """ + Before outputting the JSON with extracted results, provide analysis of the most likely price. + """ + eli5.explain_prediction(client, prompt_cot, model='gpt-4o', temperature=0) + + + + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

To determine the most likely price for the Stay table lamp, we need to consider several factors: + + 1. **Design and Brand**: The lamp is designed by Danish designer Maria Berntsen, which suggests a focus on high-quality design and potentially a higher price point due to the designer's reputation. + + 2. **Functionality and Features**: The lamp has several advanced features, such as a flexible swivel mechanism and adjustable arm and head, which add to its functionality and likely increase its cost. + + 3. **Materials and Finish**: The description mentions a "silky matte grey metal," indicating the use of quality materials that contribute to a modern and minimalistic aesthetic. This choice of materials can also influence the price. + + 4. **Market Positioning**: Given the emphasis on both functionality and elegant appearance, the lamp is likely positioned in the mid to high-end market segment. + + Based on these factors, a reasonable estimate for the price of the Stay table lamp would be in the range of $150.00 to $300.00. For the purpose of this exercise, I will choose a mid-point estimate of $225.00. + + Now, I will provide the JSON with the extracted properties: + + ```json + { + "materials": ["metal"], + "type": "table lamp", + "color": "silky matte grey", + "price": 225.00, + "summary": "A functional and elegant table lamp with adjustable features." + } + ```

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +We can see that the model has already committed to a specific price +point as part of it’s analysis, and it’s condfidence is very high in a +particular prediction, but this is not indicative of the true +confidence. + +Finally, an interesting point is that if we leave the temperature at its +default value of 1, the analysis would show up a lot of less condfient +predictions, which is expected given the sampling performed at non-zero +temperatures: + +.. code:: ipython3 + + eli5.explain_prediction(client, prompt_cot, model='gpt-4o') + + + + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Analysis of Price: + + The product description does not provide an explicit price for the Stay table lamp. To estimate the price, it's important to consider factors such as the design origin (Danish design by Maria Berntsen), functionality (flexible swivel mechanism and adjustable arm and head), material quality (silky matte grey metal), and intended use (task lighting in modern, minimalistic settings). + + Based on these characteristics, the Stay table lamp is likely positioned as a mid-to-high-end product in the market. Danish-designed lighting products known for combining aesthetics with functionality often range from approximately $150 to $400. Given these aspects, a reasonable estimate for the price could be around $250.00. + + Now, I will provide the JSON dict with all extracted information: + + ```json + { + "materials": ["metal"], + "type": "table lamp", + "color": "silky matte grey", + "price": 250.00, + "summary": "A flexible and elegant table lamp designed by Maria Berntsen." + } + ```

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/source/libraries/index.rst b/docs/source/libraries/index.rst index f7bd9943..84f5cdd2 100644 --- a/docs/source/libraries/index.rst +++ b/docs/source/libraries/index.rst @@ -12,5 +12,5 @@ Supported Libraries catboost lightning sklearn_crfsuite + openai keras - diff --git a/docs/source/libraries/openai.rst b/docs/source/libraries/openai.rst new file mode 100644 index 00000000..7c87e1d9 --- /dev/null +++ b/docs/source/libraries/openai.rst @@ -0,0 +1,71 @@ +.. _library-openai: + +OpenAI +====== + +OpenAI_ provides a client library for calling Large Language Models (LLMs). + +.. _OpenAI: https://github.com/openai/openai-python + +eli5 supports :func:`eli5.explain_prediction` for +``ChatCompletion``, ``ChoiceLogprobs`` and ``openai.Client`` objects, +highlighting tokens proportionally to the log probability, +which can help to see where model is less confident in it's predictions. +More likely tokens are highlighted in green, +while unlikely tokens are highlighted in red: + +.. image:: ../static/llm-explain-logprobs.png + :alt: LLM token probabilities visualized + +Explaining with a client, invoking the model with ``logprobs`` enabled: +:: + + import eli5 + import opeanai + client = openai.Client() + prompt = 'some string' # or [{"role": "user", "content": "some string"}] + explanation = eli5.explain_prediction(client, prompt, model='gpt-4o') + explanation + +You may pass any extra keyword arguments to :func:`eli5.explain_prediction`, +they would be passed to the ``client.chat.completions.create``, +e.g. you may pass ``n=2`` to get multiple responses +and see explanations for each of them. + +You'd normally want to run it in a Jupyter notebook to see the explanation +formatted as HTML. + +You can access the ``Choice`` object on the ``explanation.targets[0].target``: +:: + + explanation.targets[0].target.message.content + +If you have already obtained a chat completion with ``logprobs`` from OpenAI client, +you may call :func:`eli5.explain_prediction` with +``ChatCompletion`` or ``ChoiceLogprobs`` like this: +:: + + chat_completion = client.chat.completions.create( + messages=[{"role": "user", "content": prompt}], + model="gpt-4o", + logprobs=True, + ) + eli5.explain_prediction(chat_completion) # or + eli5.explain_prediction(chat_completion.choices[0].logprobs) + + +See the :ref:`tutorial ` for a more detailed usage +example. + +.. note:: + While token probabilities reflect model uncertainty in many cases, + they are not always indicative, + e.g. in case of `Chain of Thought `_ + preceding the final response. + +.. note:: + Top-level :func:`eli5.explain_prediction` calls are dispatched + to :func:`eli5.llm.explain_prediction.explain_prediction_openai_client` + or :func:`eli5.llm.explain_prediction.explain_prediction_openai_completion` + or :func:`eli5.llm.explain_prediction.explain_prediction_openai_logprobs` + . diff --git a/docs/source/overview.rst b/docs/source/overview.rst index b8fa5db5..6d612244 100644 --- a/docs/source/overview.rst +++ b/docs/source/overview.rst @@ -50,6 +50,8 @@ following machine learning frameworks and packages: * :ref:`library-sklearn-crfsuite`. ELI5 allows to check weights of sklearn_crfsuite.CRF models. +* :ref:`library-openai`. ELI5 allows to explain LLM predictions with token probabilities. + ELI5 also implements several algorithms for inspecting black-box models (see :ref:`eli5-black-box`): diff --git a/docs/source/static/llm-explain-logprobs.png b/docs/source/static/llm-explain-logprobs.png new file mode 100644 index 00000000..452d9219 Binary files /dev/null and b/docs/source/static/llm-explain-logprobs.png differ diff --git a/docs/source/tutorials/explain_llm_logprobs.rst b/docs/source/tutorials/explain_llm_logprobs.rst new file mode 100644 index 00000000..d2124ef6 --- /dev/null +++ b/docs/source/tutorials/explain_llm_logprobs.rst @@ -0,0 +1,9 @@ +.. _explain-llm-logprobs-tutorial: + +.. note:: + + This tutorial can be run as an IPython notebook_. + +.. _notebook: https://github.com/eli5-org/eli5/blob/master/notebooks/explain_llm_logprobs.ipynb + +.. include:: ../_notebooks/explain_llm_logprobs.rst diff --git a/docs/update-notebooks.sh b/docs/update-notebooks.sh index 899fa8d5..c32fe594 100755 --- a/docs/update-notebooks.sh +++ b/docs/update-notebooks.sh @@ -78,4 +78,17 @@ rm -r source/_notebooks/keras-image-classifiers_files mv ../notebooks/keras-image-classifiers_files/ \ source/_notebooks/ sed -i 's&.. image:: keras-image-classifiers_files/&.. image:: ../_notebooks/keras-image-classifiers_files/&g' \ - source/_notebooks/keras-image-classifiers.rst \ No newline at end of file + source/_notebooks/keras-image-classifiers.rst + + +# LLM logprobs explain prediction tutorial +jupyter nbconvert \ + --to rst \ + --stdout \ + '../notebooks/explain_llm_logprobs.ipynb' \ + > source/_notebooks/explain_llm_logprobs.rst + +sed -i '' 's/``eli5.explain_prediction``/:func:`eli5.explain_prediction`/g' \ + source/_notebooks/explain_llm_logprobs.rst +sed -i '' 's/\/docs\/source//g' \ + source/_notebooks/explain_llm_logprobs.rst diff --git a/eli5/__init__.py b/eli5/__init__.py index 1bc4586e..7a005c88 100644 --- a/eli5/__init__.py +++ b/eli5/__init__.py @@ -93,3 +93,12 @@ except ImportError: # keras is not available pass + +try: + from .llm.explain_prediction import ( + explain_prediction_openai_logprobs, + explain_prediction_openai_client + ) +except ImportError: + # openai not available + pass diff --git a/eli5/base.py b/eli5/base.py index 44834a6d..f10293ad 100644 --- a/eli5/base.py +++ b/eli5/base.py @@ -1,4 +1,4 @@ -from typing import Union, Optional +from typing import Union, Optional, Sequence import numpy as np @@ -135,7 +135,7 @@ def __init__(self, WeightedSpan = tuple[ Feature, list[tuple[int, int]], # list of spans (start, end) for this feature - float, # feature weight + float, # feature weight or probability ] @@ -147,16 +147,20 @@ class DocWeightedSpans: :document:). :preserve_density: determines how features are colored when doing formatting - it is better set to True for char features and to False for word features. + :with_probabilities: would interpret weights as probabilities from 0 to 1, + using a more suitable color scheme. """ def __init__(self, document: str, - spans: list[WeightedSpan], + spans: Sequence[WeightedSpan], preserve_density: Optional[bool] = None, + with_probabilities: Optional[bool] = None, vec_name: Optional[str] = None, ): self.document = document self.spans = spans self.preserve_density = preserve_density + self.with_probabilities = with_probabilities self.vec_name = vec_name diff --git a/eli5/formatters/__init__.py b/eli5/formatters/__init__.py index 27a49c6d..a21200f8 100644 --- a/eli5/formatters/__init__.py +++ b/eli5/formatters/__init__.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Functions to convert explanations to human-digestible formats. @@ -24,4 +23,4 @@ ) except ImportError: # Pillow or matplotlib not available - pass \ No newline at end of file + pass diff --git a/eli5/formatters/html.py b/eli5/formatters/html.py index 2f601ec5..9057e3bd 100644 --- a/eli5/formatters/html.py +++ b/eli5/formatters/html.py @@ -4,6 +4,7 @@ import numpy as np from jinja2 import Environment, PackageLoader +from scipy.special import betainc from eli5 import _graphviz from eli5.base import (Explanation, TargetExplanation, FeatureWeights, @@ -164,7 +165,8 @@ def render_weighted_spans(pws: PreparedWeightedSpans) -> str: return ''.join( _colorize(''.join(t for t, _ in tokens_weights), weight, - pws.weight_range) + pws.weight_range, + pws.doc_weighted_spans.with_probabilities) for weight, tokens_weights in groupby( zip(pws.doc_weighted_spans.document, pws.char_weights), key=lambda x: x[1])) @@ -173,12 +175,24 @@ def render_weighted_spans(pws: PreparedWeightedSpans) -> str: def _colorize(token: str, weight: float, weight_range: float, + with_probabilities: Optional[bool], ) -> str: """ Return token wrapped in a span with some styles (calculated from weight and weight_range) applied. """ token = html_escape(token) - if np.isclose(weight, 0.): + if with_probabilities: + return ( + '{token}'.format( + color=format_hsl( + probability_color_hsl(weight, weight_range)), + weight=weight, + token=token) + ) + elif np.isclose(weight, 0.): return ( ' _ return hue, saturation, lightness +def probability_color_hsl(probability: float, probability_range: float) -> _HSL_COLOR: + """ Return HSL color components for given probability, + where the max absolute probability is given by probability_range + (should always be 1 at the moment). + """ + hue = transformed_probability(probability / probability_range) * 120 + saturation = 1 + lightness = 0.5 + return hue, saturation, lightness + + +def transformed_probability(prob: float, alpha: float = 0.4) -> float: + """ + Transforms a probability in [0, 1] using the Beta(alpha, alpha) CDF. + This function is symmetric about (0.5, 0.5) and raises sharply near 0 and 1, + highlighting differences in low and high probabilities. + The parameter 'alpha' controls the steepness. + """ + prob = max(0.0, min(1.0, prob)) + return betainc(alpha, alpha, prob) + + def format_hsl(hsl_color: _HSL_COLOR) -> str: """ Format hsl color as css color string. """ diff --git a/eli5/llm/__init__.py b/eli5/llm/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/eli5/llm/explain_prediction.py b/eli5/llm/explain_prediction.py new file mode 100644 index 00000000..8d078be0 --- /dev/null +++ b/eli5/llm/explain_prediction.py @@ -0,0 +1,99 @@ +import math +from typing import Union + +import openai +from openai.types.chat.chat_completion import ChoiceLogprobs, ChatCompletion + +from eli5.base import Explanation, TargetExplanation, WeightedSpans, DocWeightedSpans +from eli5.explain import explain_prediction + + +LOGPROBS_ESTIMATOR = 'llm_logprobs' + + +@explain_prediction.register(ChoiceLogprobs) +def explain_prediction_openai_logprobs(logprobs: ChoiceLogprobs, doc=None): + """ Creates an explanation of the logprobs + (available as ``.choices[idx].logprobs`` on a ChatCompletion object), + highlighting them proportionally to the log probability. + More likely tokens are highlighted in green, + while unlikely tokens are highlighted in red. + ``doc`` argument is ignored. + """ + if logprobs.content is None: + raise ValueError('Predictions must be obtained with logprobs enabled') + text = ''.join(x.token for x in logprobs.content) + spans = [] + idx = 0 + for lp in logprobs.content: + token_len = len(lp.token) + spans.append(( + f'{idx}-{lp.token}', # each token is a unique feature with it's own weight + [(idx, idx + token_len)], + math.exp(lp.logprob))) + idx += token_len + weighted_spans = WeightedSpans([ + DocWeightedSpans( + document=text, + spans=spans, + preserve_density=False, + with_probabilities=True, + ) + ]) + target_explanation = TargetExplanation(target=text, weighted_spans=weighted_spans) + return Explanation( + estimator=LOGPROBS_ESTIMATOR, + targets=[target_explanation], + ) + + +@explain_prediction.register(ChatCompletion) +def explain_prediction_openai_completion( + chat_completion: ChatCompletion, doc=None): + """ Creates an explanation of the ChatCompletion's logprobs + highlighting them proportionally to the log probability. + More likely tokens are highlighted in green, + while unlikely tokens are highlighted in red. + ``doc`` argument is ignored. + """ + targets = [] + for choice in chat_completion.choices: + if choice.logprobs is None: + raise ValueError('Predictions must be obtained with logprobs enabled') + target, = explain_prediction_openai_logprobs(choice.logprobs).targets + target.target = choice + targets.append(target) + explanation = Explanation( + estimator=LOGPROBS_ESTIMATOR, + targets=targets, + ) + return explanation + + +@explain_prediction.register(openai.Client) +def explain_prediction_openai_client( + client: openai.Client, + doc: Union[str, list[dict]], + *, + model: str, + **kwargs, + ): + """ + Calls OpenAI client, obtaining response for ``doc`` (a string, or a list of messages), + with logprobs enabled, and explains the prediction, + highlighting tokens proportionally to the log probability. + More likely tokens are highlighted in green, + while unlikely tokens are highlighted in red. + . Other keyword arguments are passed to OpenAI client, with + ``model`` keyword argument required. + """ + if isinstance(doc, str): + messages = [{"role": "user", "content": doc}] + else: + messages = doc + kwargs['logprobs'] = True + chat_completion = client.chat.completions.create( + messages=messages, # type: ignore + model=model, + **kwargs) + return explain_prediction_openai_completion(chat_completion) diff --git a/eli5/templates/weighted_spans.html b/eli5/templates/weighted_spans.html index 98d30c6b..2dc4dfa7 100644 --- a/eli5/templates/weighted_spans.html +++ b/eli5/templates/weighted_spans.html @@ -6,7 +6,5 @@ {% endif %} {% if rendered_ws %} -

- {{ rendered_ws }} -

+

{{ rendered_ws }}

{% endif %} diff --git a/notebooks/explain_llm_logprobs.ipynb b/notebooks/explain_llm_logprobs.ipynb new file mode 100644 index 00000000..d697eadd --- /dev/null +++ b/notebooks/explain_llm_logprobs.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7c2ae8b9-9525-4b24-884e-3dc3bd9b8b8a", + "metadata": {}, + "source": [ + "# Visualize Token Probabilities and Model Confidence in LLM Predictions\n", + "\n", + "In this tutorial we show how ``eli5.explain_prediction`` can be used to visualize LLM predictions,\n", + "highlighting tokens proportionally to the log probability. In many cases, this can help to see where\n", + "LLM is uncertain about its predictions:\n", + "\n", + "![LLM token probabilities visualized with eli5.explain_prediction](../docs/source/static/llm-explain-logprobs.png)\n", + "\n", + "To follow this tutorial you need the ``openai`` library installed and working." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71c34fb4-503a-40ea-b166-63164ae22ca2", + "metadata": {}, + "outputs": [], + "source": [ + "import eli5\n", + "import openai\n", + "\n", + "client = openai.OpenAI()" + ] + }, + { + "cell_type": "markdown", + "id": "9a4cacc8-b0e1-45bf-ba48-ebe352da9ead", + "metadata": {}, + "source": [ + "First let's define our task: we'll be extracting specific product properties from a free-form product description:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "355d48c3-e24a-4731-a7bb-5aface7d969c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "json\n", + "{\n", + " \"materials\": [\"metal\"],\n", + " \"type\": \"table lamp\",\n", + " \"color\": \"silky matte grey\",\n", + " \"price\": 150.00,\n", + " \"summary\": \"Stay is a flexible and elegant table lamp designed by Maria Berntsen.\"\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "product_description = \"\"\"\\\n", + "Stay is designed by Danish Maria Berntsen and is functional and beautiful lighting\n", + "in one and the same design. Stay has several nice features, where the lamp's flexible\n", + "swivel mechanism makes it possible to direct the light and create optimal lighting\n", + "conditions. Furthermore, the switch is discreetly placed behind the lamp head,\n", + "making it easy and convenient to turn the light on and off. Thus, Stay combines great\n", + "flexibility and elegant appearance in a highly convincing way.\n", + "\n", + "Stay table lamp is highly functional, as the arm and head of the lamp can be adjusted\n", + "according to wishes and needs, which make it ideal as task lighting in the office.\n", + "Furthermore, the silky matte grey metal support the modern and minimalistic expression.\\\n", + "\"\"\"\n", + "prompt = f\"\"\"\\\n", + "Product description:\n", + "{product_description.strip()}\n", + "\n", + "Extract the following properties from product description:\n", + "'materials' (list of strings),\n", + "'type' (string, e.g. 'LED'),\n", + "'color' (string),\n", + "'price' (non-zero float with 2 decimal places),\n", + "'summary' (string, a very short summary).\n", + "Respond with JSON dict with above keys. Always make sure to return the price or it's estimate.\n", + "\"\"\"\n", + "completion = client.chat.completions.create(\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + " model=\"gpt-4o\",\n", + " logprobs=True,\n", + " temperature=0,\n", + ")\n", + "print(completion.choices[0].message.content.strip('`'))" + ] + }, + { + "cell_type": "markdown", + "id": "b3f870b8-8b15-4fd5-a5ee-6836c3e12d76", + "metadata": {}, + "source": [ + "If you examine the prompt and description above, you can see that not all attributes are equally clear:\n", + "- \"material\" is quite clear and explicitly mentioned\n", + "- \"color\" is also clear, but its mention is more ambiguous\n", + "- \"type\" is underspecified, we don't extractly specify what we want,\n", + "- \"summary\" is quite clear, but there is no single correct summary,\n", + "- \"price\" is not present at all, but we ask a model to make a guess.\n", + "\n", + "You may have noticed that above we included ``logprobs=True,`` in a call to the model,\n", + "this allows us to get a log-probability for each token, and then we can visualize them with ``eli5.explain_prediction``:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "17bf32f3-cedb-4aa6-9998-e6a4bdd57407", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "

```json\n", + "{\n", + " "materials": ["metal"],\n", + " "type": "table lamp",\n", + " "color": "silky matte grey",\n", + " "price": 150.00,\n", + " "summary": "Stay is a flexible and elegant table lamp designed by Maria Berntsen."\n", + "}\n", + "```

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The confidence in price is a lot lower, while summary, being a longer piece of text, is harder to interpret -- we can see that some words follow more obviously.\n", + "\n", + "We can also obtain the same result by passing ``client`` and ``prompt`` to ``eli5.explain_prediction``,\n", + "in this case it would call the client, and we can pass extra keyword arguments -- here we'll pass ``n=2`` to obtain two different predictions, and would leave temperature at default." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "868651b1-0998-4d92-a029-5259cb11e046", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + "\n", + "

```json\n", + "{\n", + " "materials": ["metal"],\n", + " "type": "",\n", + " "color": "silky matte grey",\n", + " "price": 199.99,\n", + " "summary": "Stay table lamp with flexible swivel mechanism for optimal task lighting."\n", + "}\n", + "```

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```json\n", + "{\n", + " "materials": ["metal"],\n", + " "type": "table lamp",\n", + " "color": "silky matte grey",\n", + " "price": 165.00,\n", + " "summary": "A functional and elegant adjustable table lamp designed by Maria Berntsen."\n", + "}\n", + "```

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[\"metal\"],\\n \"type\": \"table lamp\",\\n \"color\": \"silky matte grey\",\\n \"price\": 165.00,\\n \"summary\": \"A functional and elegant adjustable table lamp designed by Maria Berntsen.\"\\n}\\n```', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None)), feature_weights=None, proba=None, score=None, weighted_spans=WeightedSpans(docs_weighted_spans=[DocWeightedSpans(document='```json\\n{\\n \"materials\": [\"metal\"],\\n \"type\": \"table lamp\",\\n \"color\": \"silky matte grey\",\\n \"price\": 165.00,\\n \"summary\": \"A functional and elegant adjustable table lamp designed by Maria Berntsen.\"\\n}\\n```', spans=[('0-```', [(0, 3)], 0.9999827604127778), ('3-json', [(3, 7)], 1.0), ('7-\\n', [(7, 8)], 1.0), ('8-{\\n', [(8, 10)], 0.9999984951481292), ('10- ', [(10, 13)], 0.9398849290249089), ('13- \"', [(13, 15)], 1.0), ('15-materials', [(15, 24)], 0.9999998063873693), ('24-\":', [(24, 26)], 1.0), ('26- [\"', [(26, 29)], 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1.0), ('212-```', [(212, 215)], 0.9999723899264568)], preserve_density=False, with_probabilities=True, vec_name=None)], other=None), heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explanation = eli5.explain_prediction(client, prompt, model='gpt-4o', n=2)\n", + "explanation" + ] + }, + { + "cell_type": "markdown", + "id": "82b223cd-f23c-4737-a12a-873d71fa9b48", + "metadata": {}, + "source": [ + "We can obtain the original prediction from the explanation object via ``explanation.targets[0].target``,\n", + "e.g. use ``explanation.targets[0].target.message.content`` to get the prediction text." + ] + }, + { + "cell_type": "markdown", + "id": "a136a3c3-f840-403a-9a3a-9b7cd07065f4", + "metadata": {}, + "source": [ + "## Limitations\n", + "\n", + "Even though above the model confidence matched our expectations, it's not always the case.\n", + "For example, if we use \"Chain of Thought\" (https://arxiv.org/abs/2201.11903) reasoning,\n", + "asking the model first to think about the price estimate, it would be much more confident in the price in its final output,\n", + "but that does not reflect the real confidence of the model, as it's smeared over CoT:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "622fcc1f-fe36-47bf-a02c-ed5e62a25e6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "

To determine the most likely price for the Stay table lamp, we need to consider several factors:\n", + "\n", + "1. **Design and Brand**: The lamp is designed by Danish designer Maria Berntsen, which suggests a focus on high-quality design and potentially a higher price point due to the designer's reputation.\n", + "\n", + "2. **Functionality and Features**: The lamp has several advanced features, such as a flexible swivel mechanism and adjustable arm and head, which add to its functionality and likely increase its cost.\n", + "\n", + "3. **Materials and Finish**: The description mentions a "silky matte grey metal," indicating the use of quality materials that contribute to a modern and minimalistic aesthetic. This choice of materials can also influence the price.\n", + "\n", + "4. **Market Positioning**: Given the emphasis on both functionality and elegant appearance, the lamp is likely positioned in the mid to high-end market segment.\n", + "\n", + "Based on these factors, a reasonable estimate for the price of the Stay table lamp would be in the range of $150.00 to $300.00. For the purpose of this exercise, I will choose a mid-point estimate of $225.00.\n", + "\n", + "Now, I will provide the JSON with the extracted properties:\n", + "\n", + "```json\n", + "{\n", + " "materials": ["metal"],\n", + " "type": "table lamp",\n", + " "color": "silky matte grey",\n", + " "price": 225.00,\n", + " "summary": "A functional and elegant table lamp with adjustable features."\n", + "}\n", + "```

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logprob=-0.19027188420295715, top_logprobs=[]), ChatCompletionTokenLogprob(token=' likely', bytes=[32, 108, 105, 107, 101, 108, 121], logprob=-0.0009278989746235311, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-5.466968650580384e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' for', bytes=[32, 102, 111, 114], logprob=-0.4798658788204193, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.009040074422955513, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Stay', bytes=[32, 83, 116, 97, 121], logprob=-0.2057265341281891, top_logprobs=[]), ChatCompletionTokenLogprob(token=' table', bytes=[32, 116, 97, 98, 108, 101], logprob=-0.004193079192191362, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.137336865067482, top_logprobs=[]), ChatCompletionTokenLogprob(token=' we', bytes=[32, 119, 101], logprob=-0.36271974444389343, top_logprobs=[]), ChatCompletionTokenLogprob(token=' need', bytes=[32, 110, 101, 101, 100], logprob=-0.7786848545074463, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-0.0005338519695214927, top_logprobs=[]), ChatCompletionTokenLogprob(token=' consider', bytes=[32, 99, 111, 110, 115, 105, 100, 101, 114], logprob=-0.04549144208431244, top_logprobs=[]), ChatCompletionTokenLogprob(token=' several', bytes=[32, 115, 101, 118, 101, 114, 97, 108], logprob=-0.6360411047935486, top_logprobs=[]), ChatCompletionTokenLogprob(token=' factors', bytes=[32, 102, 97, 99, 116, 111, 114, 115], logprob=-0.037374746054410934, top_logprobs=[]), ChatCompletionTokenLogprob(token=':\\n\\n', bytes=[58, 10, 10], logprob=-1.1546510457992554, top_logprobs=[]), ChatCompletionTokenLogprob(token='1', bytes=[49], logprob=-0.00035929924342781305, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' **', bytes=[32, 42, 42], logprob=-0.018017534166574478, top_logprobs=[]), ChatCompletionTokenLogprob(token='Design', bytes=[68, 101, 115, 105, 103, 110], logprob=-0.25278693437576294, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.33452877402305603, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Brand', bytes=[32, 66, 114, 97, 110, 100], logprob=-0.514303982257843, top_logprobs=[]), ChatCompletionTokenLogprob(token='**', bytes=[42, 42], logprob=-0.18277929723262787, top_logprobs=[]), ChatCompletionTokenLogprob(token=':', bytes=[58], logprob=-0.0037415295373648405, top_logprobs=[]), ChatCompletionTokenLogprob(token=' The', bytes=[32, 84, 104, 101], logprob=-0.18502825498580933, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-0.10684743523597717, top_logprobs=[]), ChatCompletionTokenLogprob(token=' is', bytes=[32, 105, 115], logprob=-0.001284875557757914, top_logprobs=[]), ChatCompletionTokenLogprob(token=' designed', bytes=[32, 100, 101, 115, 105, 103, 110, 101, 100], logprob=-0.003955867141485214, top_logprobs=[]), ChatCompletionTokenLogprob(token=' by', bytes=[32, 98, 121], logprob=-9.014684110297821e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Danish', bytes=[32, 68, 97, 110, 105, 115, 104], logprob=-0.5383610129356384, top_logprobs=[]), ChatCompletionTokenLogprob(token=' designer', bytes=[32, 100, 101, 115, 105, 103, 110, 101, 114], logprob=-0.020705843344330788, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Maria', bytes=[32, 77, 97, 114, 105, 97], logprob=-5.6100132496794686e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Ber', bytes=[32, 66, 101, 114], logprob=-1.3856492842023727e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='nt', bytes=[110, 116], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='sen', bytes=[115, 101, 110], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.24715355038642883, top_logprobs=[]), ChatCompletionTokenLogprob(token=' which', bytes=[32, 119, 104, 105, 99, 104], logprob=-0.5647792220115662, top_logprobs=[]), ChatCompletionTokenLogprob(token=' suggests', bytes=[32, 115, 117, 103, 103, 101, 115, 116, 115], logprob=-0.6731479167938232, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.7282735705375671, top_logprobs=[]), ChatCompletionTokenLogprob(token=' focus', bytes=[32, 102, 111, 99, 117, 115], logprob=-0.7995526194572449, top_logprobs=[]), ChatCompletionTokenLogprob(token=' on', bytes=[32, 111, 110], logprob=-1.2664456789934775e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' high', bytes=[32, 104, 105, 103, 104], logprob=-0.5208733677864075, top_logprobs=[]), ChatCompletionTokenLogprob(token='-quality', bytes=[45, 113, 117, 97, 108, 105, 116, 121], logprob=-0.07403000444173813, top_logprobs=[]), ChatCompletionTokenLogprob(token=' design', bytes=[32, 100, 101, 115, 105, 103, 110], logprob=-0.32639598846435547, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.2514616847038269, top_logprobs=[]), ChatCompletionTokenLogprob(token=' potentially', bytes=[32, 112, 111, 116, 101, 110, 116, 105, 97, 108, 108, 121], logprob=-1.488715410232544, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.5769942998886108, top_logprobs=[]), ChatCompletionTokenLogprob(token=' higher', bytes=[32, 104, 105, 103, 104, 101, 114], logprob=-0.6764459609985352, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-0.025465071201324463, top_logprobs=[]), ChatCompletionTokenLogprob(token=' point', bytes=[32, 112, 111, 105, 110, 116], logprob=-0.16209593415260315, top_logprobs=[]), ChatCompletionTokenLogprob(token=' due', bytes=[32, 100, 117, 101], logprob=-0.42360591888427734, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-2.4391956685576588e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.27825748920440674, top_logprobs=[]), ChatCompletionTokenLogprob(token=' designer', bytes=[32, 100, 101, 115, 105, 103, 110, 101, 114], logprob=-0.29129624366760254, top_logprobs=[]), ChatCompletionTokenLogprob(token=\"'s\", bytes=[39, 115], logprob=-0.2024281769990921, top_logprobs=[]), ChatCompletionTokenLogprob(token=' reputation', bytes=[32, 114, 101, 112, 117, 116, 97, 116, 105, 111, 110], logprob=-0.2312728762626648, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\\n\\n', bytes=[46, 10, 10], logprob=-0.3100598454475403, top_logprobs=[]), ChatCompletionTokenLogprob(token='2', bytes=[50], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' **', bytes=[32, 42, 42], logprob=-4.320199877838604e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='Function', bytes=[70, 117, 110, 99, 116, 105, 111, 110], logprob=-1.0028660297393799, top_logprobs=[]), ChatCompletionTokenLogprob(token='ality', bytes=[97, 108, 105, 116, 121], logprob=-0.0005564896273426712, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.6350793838500977, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Features', bytes=[32, 70, 101, 97, 116, 117, 114, 101, 115], logprob=-0.011311288923025131, top_logprobs=[]), ChatCompletionTokenLogprob(token='**', bytes=[42, 42], logprob=-9.014684110297821e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=':', bytes=[58], logprob=-0.0005530327325686812, top_logprobs=[]), ChatCompletionTokenLogprob(token=' The', bytes=[32, 84, 104, 101], logprob=-0.02379845269024372, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-0.1298796534538269, top_logprobs=[]), ChatCompletionTokenLogprob(token=' has', bytes=[32, 104, 97, 115], logprob=-1.265273094177246, top_logprobs=[]), ChatCompletionTokenLogprob(token=' several', bytes=[32, 115, 101, 118, 101, 114, 97, 108], logprob=-0.7500738501548767, top_logprobs=[]), ChatCompletionTokenLogprob(token=' advanced', bytes=[32, 97, 100, 118, 97, 110, 99, 101, 100], logprob=-0.8393288254737854, top_logprobs=[]), ChatCompletionTokenLogprob(token=' features', bytes=[32, 102, 101, 97, 116, 117, 114, 101, 115], logprob=-0.00549177173525095, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.48477548360824585, top_logprobs=[]), ChatCompletionTokenLogprob(token=' such', bytes=[32, 115, 117, 99, 104], logprob=-0.296755850315094, top_logprobs=[]), ChatCompletionTokenLogprob(token=' as', bytes=[32, 97, 115], logprob=-6.704273118884885e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.016932565718889236, top_logprobs=[]), ChatCompletionTokenLogprob(token=' flexible', bytes=[32, 102, 108, 101, 120, 105, 98, 108, 101], logprob=-0.002565906848758459, top_logprobs=[]), ChatCompletionTokenLogprob(token=' swivel', bytes=[32, 115, 119, 105, 118, 101, 108], logprob=-1.0445127372804563e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' mechanism', bytes=[32, 109, 101, 99, 104, 97, 110, 105, 115, 109], logprob=-1.3663626305060461e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.6078916788101196, top_logprobs=[]), ChatCompletionTokenLogprob(token=' adjustable', bytes=[32, 97, 100, 106, 117, 115, 116, 97, 98, 108, 101], logprob=-0.3469345271587372, top_logprobs=[]), ChatCompletionTokenLogprob(token=' arm', bytes=[32, 97, 114, 109], logprob=-0.07993956655263901, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.0018137017032131553, top_logprobs=[]), ChatCompletionTokenLogprob(token=' head', bytes=[32, 104, 101, 97, 100], logprob=-0.001930301426909864, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.43073713779449463, top_logprobs=[]), ChatCompletionTokenLogprob(token=' which', bytes=[32, 119, 104, 105, 99, 104], logprob=-0.4856716990470886, top_logprobs=[]), ChatCompletionTokenLogprob(token=' add', bytes=[32, 97, 100, 100], logprob=-1.4381940364837646, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-0.1545192003250122, top_logprobs=[]), ChatCompletionTokenLogprob(token=' its', bytes=[32, 105, 116, 115], logprob=-0.07364879548549652, top_logprobs=[]), ChatCompletionTokenLogprob(token=' functionality', bytes=[32, 102, 117, 110, 99, 116, 105, 111, 110, 97, 108, 105, 116, 121], logprob=-0.27764394879341125, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.3953467905521393, top_logprobs=[]), ChatCompletionTokenLogprob(token=' likely', bytes=[32, 108, 105, 107, 101, 108, 121], logprob=-1.4487597942352295, top_logprobs=[]), ChatCompletionTokenLogprob(token=' increase', bytes=[32, 105, 110, 99, 114, 101, 97, 115, 101], logprob=-0.11556302756071091, top_logprobs=[]), ChatCompletionTokenLogprob(token=' its', bytes=[32, 105, 116, 115], logprob=-0.43220198154449463, top_logprobs=[]), ChatCompletionTokenLogprob(token=' cost', bytes=[32, 99, 111, 115, 116], logprob=-1.0745759010314941, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\\n\\n', bytes=[46, 10, 10], logprob=-0.1987321674823761, top_logprobs=[]), ChatCompletionTokenLogprob(token='3', bytes=[51], logprob=-5.512236498361744e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' **', bytes=[32, 42, 42], logprob=-1.981667537620524e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='Materials', bytes=[77, 97, 116, 101, 114, 105, 97, 108, 115], logprob=-0.3177346885204315, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.7049893140792847, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Finish', bytes=[32, 70, 105, 110, 105, 115, 104], logprob=-0.9479977488517761, top_logprobs=[]), ChatCompletionTokenLogprob(token='**', bytes=[42, 42], logprob=-6.2729995988775045e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=':', bytes=[58], logprob=-1.4855663721391466e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' The', bytes=[32, 84, 104, 101], logprob=-0.179951474070549, top_logprobs=[]), ChatCompletionTokenLogprob(token=' description', bytes=[32, 100, 101, 115, 99, 114, 105, 112, 116, 105, 111, 110], logprob=-0.9577102661132812, top_logprobs=[]), ChatCompletionTokenLogprob(token=' mentions', bytes=[32, 109, 101, 110, 116, 105, 111, 110, 115], logprob=-0.1118556335568428, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.3255186975002289, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-0.1179909035563469, top_logprobs=[]), ChatCompletionTokenLogprob(token='sil', bytes=[115, 105, 108], logprob=-3.786196975852363e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token='ky', bytes=[107, 121], logprob=-4.0722858102526516e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' matte', bytes=[32, 109, 97, 116, 116, 101], logprob=-1.0280383548888494e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' grey', bytes=[32, 103, 114, 101, 121], logprob=-1.5451681974809617e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' metal', bytes=[32, 109, 101, 116, 97, 108], logprob=-0.0052392068319022655, top_logprobs=[]), ChatCompletionTokenLogprob(token=',\"', bytes=[44, 34], logprob=-0.4425547420978546, top_logprobs=[]), ChatCompletionTokenLogprob(token=' indicating', bytes=[32, 105, 110, 100, 105, 99, 97, 116, 105, 110, 103], logprob=-0.6408705711364746, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.9325041770935059, top_logprobs=[]), ChatCompletionTokenLogprob(token=' use', bytes=[32, 117, 115, 101], logprob=-0.005283680744469166, top_logprobs=[]), ChatCompletionTokenLogprob(token=' of', bytes=[32, 111, 102], logprob=-4.246537173457909e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' quality', bytes=[32, 113, 117, 97, 108, 105, 116, 121], logprob=-0.41946297883987427, top_logprobs=[]), ChatCompletionTokenLogprob(token=' materials', bytes=[32, 109, 97, 116, 101, 114, 105, 97, 108, 115], logprob=-0.0028609856963157654, top_logprobs=[]), ChatCompletionTokenLogprob(token=' that', bytes=[32, 116, 104, 97, 116], logprob=-0.8825982213020325, top_logprobs=[]), ChatCompletionTokenLogprob(token=' contribute', bytes=[32, 99, 111, 110, 116, 114, 105, 98, 117, 116, 101], logprob=-1.0108187198638916, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-0.0030215606093406677, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.11164658516645432, top_logprobs=[]), ChatCompletionTokenLogprob(token=' modern', bytes=[32, 109, 111, 100, 101, 114, 110], logprob=-0.07743553817272186, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.07459418475627899, top_logprobs=[]), ChatCompletionTokenLogprob(token=' minimal', bytes=[32, 109, 105, 110, 105, 109, 97, 108], logprob=-0.16620802879333496, top_logprobs=[]), ChatCompletionTokenLogprob(token='istic', bytes=[105, 115, 116, 105, 99], logprob=-0.0004605783324223012, top_logprobs=[]), ChatCompletionTokenLogprob(token=' aesthetic', bytes=[32, 97, 101, 115, 116, 104, 101, 116, 105, 99], logprob=-1.272208333015442, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.34661105275154114, top_logprobs=[]), ChatCompletionTokenLogprob(token=' This', bytes=[32, 84, 104, 105, 115], logprob=-0.5294309854507446, top_logprobs=[]), ChatCompletionTokenLogprob(token=' choice', bytes=[32, 99, 104, 111, 105, 99, 101], logprob=-0.6454590559005737, top_logprobs=[]), ChatCompletionTokenLogprob(token=' of', bytes=[32, 111, 102], logprob=-0.007842617109417915, top_logprobs=[]), ChatCompletionTokenLogprob(token=' materials', bytes=[32, 109, 97, 116, 101, 114, 105, 97, 108, 115], logprob=-0.5893488526344299, top_logprobs=[]), ChatCompletionTokenLogprob(token=' can', bytes=[32, 99, 97, 110], logprob=-1.3226159811019897, top_logprobs=[]), ChatCompletionTokenLogprob(token=' also', bytes=[32, 97, 108, 115, 111], logprob=-0.7278075814247131, top_logprobs=[]), ChatCompletionTokenLogprob(token=' influence', bytes=[32, 105, 110, 102, 108, 117, 101, 110, 99, 101], logprob=-1.14788019657135, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.05610622465610504, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-0.1288062334060669, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\\n\\n', bytes=[46, 10, 10], logprob=-0.6564987301826477, top_logprobs=[]), ChatCompletionTokenLogprob(token='4', bytes=[52], logprob=-0.0038703777827322483, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-3.128163257315464e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' **', bytes=[32, 42, 42], logprob=-1.1637164789135568e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token='Market', bytes=[77, 97, 114, 107, 101, 116], logprob=-0.13202142715454102, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Position', bytes=[32, 80, 111, 115, 105, 116, 105, 111, 110], logprob=-0.6024223566055298, top_logprobs=[]), ChatCompletionTokenLogprob(token='ing', bytes=[105, 110, 103], logprob=-0.6018260717391968, top_logprobs=[]), ChatCompletionTokenLogprob(token='**', bytes=[42, 42], logprob=-0.01463598944246769, top_logprobs=[]), ChatCompletionTokenLogprob(token=':', bytes=[58], logprob=-1.4259644558478612e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Given', bytes=[32, 71, 105, 118, 101, 110], logprob=-1.009255051612854, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.42789575457572937, top_logprobs=[]), ChatCompletionTokenLogprob(token=' emphasis', bytes=[32, 101, 109, 112, 104, 97, 115, 105, 115], logprob=-0.9409775733947754, top_logprobs=[]), ChatCompletionTokenLogprob(token=' on', bytes=[32, 111, 110], logprob=-1.3306015716807451e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' both', bytes=[32, 98, 111, 116, 104], logprob=-0.6611020565032959, top_logprobs=[]), ChatCompletionTokenLogprob(token=' functionality', bytes=[32, 102, 117, 110, 99, 116, 105, 111, 110, 97, 108, 105, 116, 121], logprob=-0.45676717162132263, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.00039659533649683, top_logprobs=[]), ChatCompletionTokenLogprob(token=' elegant', bytes=[32, 101, 108, 101, 103, 97, 110, 116], logprob=-1.149277925491333, top_logprobs=[]), ChatCompletionTokenLogprob(token=' appearance', bytes=[32, 97, 112, 112, 101, 97, 114, 97, 110, 99, 101], logprob=-0.4769769608974457, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.005541327875107527, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.6089863777160645, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-0.389377236366272, top_logprobs=[]), ChatCompletionTokenLogprob(token=' is', bytes=[32, 105, 115], logprob=-0.43932950496673584, top_logprobs=[]), ChatCompletionTokenLogprob(token=' likely', bytes=[32, 108, 105, 107, 101, 108, 121], logprob=-0.06283710896968842, top_logprobs=[]), ChatCompletionTokenLogprob(token=' positioned', bytes=[32, 112, 111, 115, 105, 116, 105, 111, 110, 101, 100], logprob=-0.04982662573456764, top_logprobs=[]), ChatCompletionTokenLogprob(token=' in', bytes=[32, 105, 110], logprob=-0.6074603199958801, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.10160448402166367, top_logprobs=[]), ChatCompletionTokenLogprob(token=' mid', bytes=[32, 109, 105, 100], logprob=-0.30626994371414185, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-0.47068873047828674, top_logprobs=[]), ChatCompletionTokenLogprob(token=' high', bytes=[32, 104, 105, 103, 104], logprob=-0.1460588276386261, top_logprobs=[]), ChatCompletionTokenLogprob(token='-end', bytes=[45, 101, 110, 100], logprob=-0.020427661016583443, top_logprobs=[]), ChatCompletionTokenLogprob(token=' market', bytes=[32, 109, 97, 114, 107, 101, 116], logprob=-0.2641751170158386, top_logprobs=[]), ChatCompletionTokenLogprob(token=' segment', bytes=[32, 115, 101, 103, 109, 101, 110, 116], logprob=-0.4092211127281189, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\\n\\n', bytes=[46, 10, 10], logprob=-0.32233819365501404, top_logprobs=[]), ChatCompletionTokenLogprob(token='Based', bytes=[66, 97, 115, 101, 100], logprob=-0.7752827405929565, top_logprobs=[]), ChatCompletionTokenLogprob(token=' on', bytes=[32, 111, 110], logprob=-1.9504606825648807e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' these', bytes=[32, 116, 104, 101, 115, 101], logprob=-0.08208250999450684, top_logprobs=[]), ChatCompletionTokenLogprob(token=' factors', bytes=[32, 102, 97, 99, 116, 111, 114, 115], logprob=-0.7664922475814819, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.22567568719387054, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.9796017408370972, top_logprobs=[]), ChatCompletionTokenLogprob(token=' reasonable', bytes=[32, 114, 101, 97, 115, 111, 110, 97, 98, 108, 101], logprob=-0.12573164701461792, top_logprobs=[]), ChatCompletionTokenLogprob(token=' estimate', bytes=[32, 101, 115, 116, 105, 109, 97, 116, 101], logprob=-0.4213482737541199, top_logprobs=[]), ChatCompletionTokenLogprob(token=' for', bytes=[32, 102, 111, 114], logprob=-0.011164778843522072, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.08224985748529434, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-0.10537097603082657, top_logprobs=[]), ChatCompletionTokenLogprob(token=' of', bytes=[32, 111, 102], logprob=-0.17389218509197235, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.3174235224723816, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Stay', bytes=[32, 83, 116, 97, 121], logprob=-0.021571654826402664, top_logprobs=[]), ChatCompletionTokenLogprob(token=' table', bytes=[32, 116, 97, 98, 108, 101], logprob=-0.0016024599317461252, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-9.088346359931165e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' would', bytes=[32, 119, 111, 117, 108, 100], logprob=-0.6761465072631836, top_logprobs=[]), ChatCompletionTokenLogprob(token=' be', bytes=[32, 98, 101], logprob=-0.5130773782730103, top_logprobs=[]), ChatCompletionTokenLogprob(token=' in', bytes=[32, 105, 110], logprob=-0.6392275094985962, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.016515925526618958, top_logprobs=[]), ChatCompletionTokenLogprob(token=' range', bytes=[32, 114, 97, 110, 103, 101], logprob=-0.020542005077004433, top_logprobs=[]), ChatCompletionTokenLogprob(token=' of', bytes=[32, 111, 102], logprob=-0.01836426928639412, top_logprobs=[]), ChatCompletionTokenLogprob(token=' $', bytes=[32, 36], logprob=-0.09238871186971664, top_logprobs=[]), ChatCompletionTokenLogprob(token='150', bytes=[49, 53, 48], logprob=-0.10822010785341263, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.5784227848052979, top_logprobs=[]), ChatCompletionTokenLogprob(token='00', bytes=[48, 48], logprob=-9.610702363715973e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-0.001433324534446001, top_logprobs=[]), ChatCompletionTokenLogprob(token=' $', bytes=[32, 36], logprob=-1.8624639324116288e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='300', bytes=[51, 48, 48], logprob=-0.19296149909496307, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='00', bytes=[48, 48], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.13142132759094238, top_logprobs=[]), ChatCompletionTokenLogprob(token=' For', bytes=[32, 70, 111, 114], logprob=-0.570335865020752, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.2939893901348114, top_logprobs=[]), ChatCompletionTokenLogprob(token=' purpose', bytes=[32, 112, 117, 114, 112, 111, 115, 101], logprob=-0.37512460350990295, top_logprobs=[]), ChatCompletionTokenLogprob(token=' of', bytes=[32, 111, 102], logprob=-3.8889261304575484e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' this', bytes=[32, 116, 104, 105, 115], logprob=-0.2848525643348694, top_logprobs=[]), ChatCompletionTokenLogprob(token=' exercise', bytes=[32, 101, 120, 101, 114, 99, 105, 115, 101], logprob=-0.8632019758224487, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.023284848779439926, top_logprobs=[]), ChatCompletionTokenLogprob(token=' I', bytes=[32, 73], logprob=-1.3395649194717407, top_logprobs=[]), ChatCompletionTokenLogprob(token=' will', bytes=[32, 119, 105, 108, 108], logprob=-0.005582586862146854, top_logprobs=[]), ChatCompletionTokenLogprob(token=' choose', bytes=[32, 99, 104, 111, 111, 115, 101], logprob=-1.483843207359314, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.20085424184799194, top_logprobs=[]), ChatCompletionTokenLogprob(token=' mid', bytes=[32, 109, 105, 100], logprob=-1.272668719291687, top_logprobs=[]), ChatCompletionTokenLogprob(token='-point', bytes=[45, 112, 111, 105, 110, 116], logprob=-0.6398953199386597, top_logprobs=[]), ChatCompletionTokenLogprob(token=' estimate', bytes=[32, 101, 115, 116, 105, 109, 97, 116, 101], logprob=-0.31783151626586914, top_logprobs=[]), ChatCompletionTokenLogprob(token=' of', bytes=[32, 111, 102], logprob=-0.5164365768432617, top_logprobs=[]), ChatCompletionTokenLogprob(token=' $', bytes=[32, 36], logprob=-0.008019437082111835, top_logprobs=[]), ChatCompletionTokenLogprob(token='225', bytes=[50, 50, 53], logprob=-0.05688877031207085, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-4.723352049040841e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='00', bytes=[48, 48], logprob=-0.002398482523858547, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\\n\\n', bytes=[46, 10, 10], logprob=-0.4111771583557129, top_logprobs=[]), ChatCompletionTokenLogprob(token='Now', bytes=[78, 111, 119], logprob=-0.44121289253234863, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.010635088197886944, top_logprobs=[]), ChatCompletionTokenLogprob(token=' I', bytes=[32, 73], logprob=-1.21943998336792, top_logprobs=[]), ChatCompletionTokenLogprob(token=' will', bytes=[32, 119, 105, 108, 108], logprob=-0.0025467700324952602, top_logprobs=[]), ChatCompletionTokenLogprob(token=' provide', bytes=[32, 112, 114, 111, 118, 105, 100, 101], logprob=-0.2521243095397949, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.018794996663928032, top_logprobs=[]), ChatCompletionTokenLogprob(token=' JSON', bytes=[32, 74, 83, 79, 78], logprob=-0.33000504970550537, top_logprobs=[]), ChatCompletionTokenLogprob(token=' with', bytes=[32, 119, 105, 116, 104], logprob=-0.8988541960716248, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.16055524349212646, top_logprobs=[]), ChatCompletionTokenLogprob(token=' extracted', bytes=[32, 101, 120, 116, 114, 97, 99, 116, 101, 100], logprob=-0.0017182581359520555, top_logprobs=[]), ChatCompletionTokenLogprob(token=' properties', bytes=[32, 112, 114, 111, 112, 101, 114, 116, 105, 101, 115], logprob=-0.5211189985275269, top_logprobs=[]), ChatCompletionTokenLogprob(token=':\\n\\n', bytes=[58, 10, 10], logprob=-0.23949961364269257, top_logprobs=[]), ChatCompletionTokenLogprob(token='```', bytes=[96, 96, 96], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='json', bytes=[106, 115, 111, 110], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='\\n', bytes=[10], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='{\\n', bytes=[123, 10], logprob=-1.5048530030981055e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=-0.5760262608528137, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='materials', bytes=[109, 97, 116, 101, 114, 105, 97, 108, 115], logprob=-3.128163257315464e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' [\"', bytes=[32, 91, 34], logprob=-0.000488811288960278, top_logprobs=[]), ChatCompletionTokenLogprob(token='metal', bytes=[109, 101, 116, 97, 108], logprob=-0.014525678008794785, top_logprobs=[]), ChatCompletionTokenLogprob(token='\"],\\n', bytes=[34, 93, 44, 10], logprob=-0.0014908155426383018, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='type', bytes=[116, 121, 112, 101], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-0.15258395671844482, top_logprobs=[]), ChatCompletionTokenLogprob(token='table', bytes=[116, 97, 98, 108, 101], logprob=-0.4851100742816925, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-0.005258422344923019, top_logprobs=[]), ChatCompletionTokenLogprob(token='\",\\n', bytes=[34, 44, 10], logprob=-0.00018816311785485595, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='color', bytes=[99, 111, 108, 111, 114], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='sil', bytes=[115, 105, 108], logprob=-0.2694111466407776, top_logprobs=[]), ChatCompletionTokenLogprob(token='ky', bytes=[107, 121], logprob=-0.000431861262768507, top_logprobs=[]), ChatCompletionTokenLogprob(token=' matte', bytes=[32, 109, 97, 116, 116, 101], logprob=-1.0280383548888494e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' grey', bytes=[32, 103, 114, 101, 121], logprob=-2.935296834039036e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='\",\\n', bytes=[34, 44, 10], logprob=-4.4849443838757e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='price', bytes=[112, 114, 105, 99, 101], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='225', bytes=[50, 50, 53], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='00', bytes=[48, 48], logprob=-5.512236498361744e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=',\\n', bytes=[44, 10], logprob=-6.2729995988775045e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='summary', bytes=[115, 117, 109, 109, 97, 114, 121], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='A', bytes=[65], logprob=-1.227637529373169, top_logprobs=[]), ChatCompletionTokenLogprob(token=' functional', bytes=[32, 102, 117, 110, 99, 116, 105, 111, 110, 97, 108], logprob=-0.41765180230140686, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.0053070466965436935, top_logprobs=[]), ChatCompletionTokenLogprob(token=' elegant', bytes=[32, 101, 108, 101, 103, 97, 110, 116], logprob=-0.419668585062027, top_logprobs=[]), ChatCompletionTokenLogprob(token=' table', bytes=[32, 116, 97, 98, 108, 101], logprob=-0.03026655875146389, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-1.2664456789934775e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' with', bytes=[32, 119, 105, 116, 104], logprob=-0.8116578459739685, top_logprobs=[]), ChatCompletionTokenLogprob(token=' adjustable', bytes=[32, 97, 100, 106, 117, 115, 116, 97, 98, 108, 101], logprob=-0.3303641378879547, top_logprobs=[]), ChatCompletionTokenLogprob(token=' features', bytes=[32, 102, 101, 97, 116, 117, 114, 101, 115], logprob=-0.2633365988731384, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\"\\n', bytes=[46, 34, 10], logprob=-0.8170788288116455, top_logprobs=[]), ChatCompletionTokenLogprob(token='}\\n', bytes=[125, 10], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='```', bytes=[96, 96, 96], logprob=-0.00861456897109747, top_logprobs=[])], refusal=None), message=ChatCompletionMessage(content='To determine the most likely price for the Stay table lamp, we need to consider several factors:\\n\\n1. **Design and Brand**: The lamp is designed by Danish designer Maria Berntsen, which suggests a focus on high-quality design and potentially a higher price point due to the designer\\'s reputation.\\n\\n2. **Functionality and Features**: The lamp has several advanced features, such as a flexible swivel mechanism and adjustable arm and head, which add to its functionality and likely increase its cost.\\n\\n3. **Materials and Finish**: The description mentions a \"silky matte grey metal,\" indicating the use of quality materials that contribute to a modern and minimalistic aesthetic. This choice of materials can also influence the price.\\n\\n4. **Market Positioning**: Given the emphasis on both functionality and elegant appearance, the lamp is likely positioned in the mid to high-end market segment.\\n\\nBased on these factors, a reasonable estimate for the price of the Stay table lamp would be in the range of $150.00 to $300.00. For the purpose of this exercise, I will choose a mid-point estimate of $225.00.\\n\\nNow, I will provide the JSON with the extracted properties:\\n\\n```json\\n{\\n \"materials\": [\"metal\"],\\n \"type\": \"table lamp\",\\n \"color\": \"silky matte grey\",\\n \"price\": 225.00,\\n \"summary\": \"A functional and elegant table lamp with adjustable features.\"\\n}\\n```', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None)), feature_weights=None, proba=None, score=None, weighted_spans=WeightedSpans(docs_weighted_spans=[DocWeightedSpans(document='To determine the most likely price for the Stay table lamp, we need to consider several factors:\\n\\n1. **Design and Brand**: The lamp is designed by Danish designer Maria Berntsen, which suggests a focus on high-quality design and potentially a higher price point due to the designer\\'s reputation.\\n\\n2. **Functionality and Features**: The lamp has several advanced features, such as a flexible swivel mechanism and adjustable arm and head, which add to its functionality and likely increase its cost.\\n\\n3. **Materials and Finish**: The description mentions a \"silky matte grey metal,\" indicating the use of quality materials that contribute to a modern and minimalistic aesthetic. This choice of materials can also influence the price.\\n\\n4. **Market Positioning**: Given the emphasis on both functionality and elegant appearance, the lamp is likely positioned in the mid to high-end market segment.\\n\\nBased on these factors, a reasonable estimate for the price of the Stay table lamp would be in the range of $150.00 to $300.00. For the purpose of this exercise, I will choose a mid-point estimate of $225.00.\\n\\nNow, I will provide the JSON with the extracted properties:\\n\\n```json\\n{\\n \"materials\": [\"metal\"],\\n \"type\": \"table lamp\",\\n \"color\": \"silky matte grey\",\\n \"price\": 225.00,\\n \"summary\": \"A functional and elegant table lamp with adjustable features.\"\\n}\\n```', spans=[('0-To', [(0, 2)], 0.6146111216564284), ('2- determine', [(2, 12)], 0.5640915076225902), ('12- the', [(12, 16)], 0.8720717410202131), ('16- most', [(16, 21)], 0.8267343273804134), ('21- likely', [(21, 28)], 0.9990725313905079), ('28- price', [(28, 34)], 0.9999453318078543), ('34- for', [(34, 38)], 0.6188663893302934), ('38- the', [(38, 42)], 0.9910006641973534), ('42- Stay', [(42, 47)], 0.8140556622880858), ('47- table', [(47, 53)], 0.995815699490175), ('53- lamp', [(53, 58)], 1.0), ('58-,', [(58, 59)], 0.871676539292237), ('59- we', [(59, 62)], 0.6957814027811717), ('62- need', [(62, 67)], 0.45900927850987233), ('67- to', [(67, 70)], 0.9994662905040868), ('70- consider', [(70, 79)], 0.9555277798631413), ('79- several', [(79, 87)], 0.5293840570390912), ('87- factors', [(87, 95)], 0.9633150691778885), ('95-:\\n\\n', [(95, 98)], 0.31516749673874944), ('98-1', [(98, 99)], 0.9996407652968153), ('99-.', [(99, 100)], 1.0), ('100- **', [(100, 103)], 0.9821438111341367), ('103-Design', [(103, 109)], 0.7766333380635712), ('109- and', [(109, 113)], 0.7156752516754369), ('113- Brand', [(113, 119)], 0.5979166103934268), ('119-**', [(119, 121)], 0.832951970257082), ('121-:', [(121, 122)], 0.9962654612627951), ('122- The', [(122, 126)], 0.8310808013441299), ('126- lamp', [(126, 131)], 0.8986627668051506), ('131- is', [(131, 134)], 0.9987159495414205), ('134- designed', [(134, 143)], 0.996051946993646), ('143- by', [(143, 146)], 0.9999909853565219), ('146- Danish', [(146, 153)], 0.5837041523612825), ('153- designer', [(153, 162)], 0.9795070507143964), ('162- Maria', [(162, 168)], 0.9999439014410862), ('168- Ber', [(168, 172)], 0.9999986143516758), ('172-nt', [(172, 174)], 1.0), ('174-sen', [(174, 177)], 1.0), ('177-,', [(177, 178)], 0.7810207582843168), ('178- which', [(178, 184)], 0.5684856420202337), ('184- suggests', [(184, 193)], 0.5101002944601217), ('193- a', [(193, 195)], 0.4827416905613928), ('195- focus', [(195, 201)], 0.44953003012622045), ('201- on', [(201, 204)], 0.9999987335551229), ('204- high', [(204, 209)], 0.5940015395508474), ('209--quality', [(209, 217)], 0.9286438299835773), ('217- design', [(217, 224)], 0.7215194174885157), ('224- and', [(224, 228)], 0.7776632534368243), ('228- potentially', [(228, 240)], 0.22566235297795414), ('240- a', [(240, 242)], 0.5615837848101857), ('242- higher', [(242, 249)], 0.50842073229768), ('249- price', [(249, 255)], 0.9748564289350192), ('255- point', [(255, 261)], 0.8503596220972705), ('261- due', [(261, 265)], 0.6546818288250628), ('265- to', [(265, 268)], 0.9999756083407958), ('268- the', [(268, 272)], 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[(1286, 1288)], 1.0), ('1288- \"', [(1288, 1290)], 0.9999998063873693), ('1290-A', [(1290, 1291)], 0.2929839266343186), ('1291- functional', [(1291, 1302)], 0.6585915085526952), ('1302- and', [(1302, 1306)], 0.9947070107968573), ('1306- elegant', [(1306, 1314)], 0.6572646110337294), ('1314- table', [(1314, 1320)], 0.9701868872724675), ('1320- lamp', [(1320, 1325)], 0.9999987335551229), ('1325- with', [(1325, 1330)], 0.4441211710669766), ('1330- adjustable', [(1330, 1341)], 0.7186619937195989), ('1341- features', [(1341, 1350)], 0.7684831831986731), ('1350-.\"\\n', [(1350, 1353)], 0.44172011175144665), ('1353-}\\n', [(1353, 1355)], 0.9999998063873693), ('1355-```', [(1355, 1358)], 0.991422430108248)], preserve_density=False, with_probabilities=True, vec_name=None)], other=None), heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prompt_cot = prompt + \"\"\"\n", + "Before outputting the JSON with extracted results, provide analysis of the most likely price.\n", + "\"\"\"\n", + "eli5.explain_prediction(client, prompt_cot, model='gpt-4o', temperature=0)" + ] + }, + { + "cell_type": "markdown", + "id": "6f0adca2-4729-41b8-8d76-a91cf7d7082f", + "metadata": {}, + "source": [ + "We can see that the model has already committed to a specific price point as part of it's analysis,\n", + "and it's condfidence is very high in a particular prediction, but this is not indicative of the true confidence.\n", + "\n", + "Finally, an interesting point is that if we leave the temperature at its default value of 1, the analysis would show up a lot of less condfient predictions, which is expected given the sampling performed at non-zero temperatures:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3caaeca2-1d58-406a-ba22-3fb15942ffe8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "

Analysis of Price:\n", + "\n", + "The product description does not provide an explicit price for the Stay table lamp. To estimate the price, it's important to consider factors such as the design origin (Danish design by Maria Berntsen), functionality (flexible swivel mechanism and adjustable arm and head), material quality (silky matte grey metal), and intended use (task lighting in modern, minimalistic settings).\n", + "\n", + "Based on these characteristics, the Stay table lamp is likely positioned as a mid-to-high-end product in the market. Danish-designed lighting products known for combining aesthetics with functionality often range from approximately $150 to $400. Given these aspects, a reasonable estimate for the price could be around $250.00.\n", + "\n", + "Now, I will provide the JSON dict with all extracted information:\n", + "\n", + "```json\n", + "{\n", + " "materials": ["metal"],\n", + " "type": "table lamp",\n", + " "color": "silky matte grey",\n", + " "price": 250.00,\n", + " "summary": "A flexible and elegant table lamp designed by Maria Berntsen."\n", + "}\n", + "```

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logprob=-0.4149337112903595, top_logprobs=[]), ChatCompletionTokenLogprob(token='The', bytes=[84, 104, 101], logprob=-0.1381458044052124, top_logprobs=[]), ChatCompletionTokenLogprob(token=' product', bytes=[32, 112, 114, 111, 100, 117, 99, 116], logprob=-0.0961286872625351, top_logprobs=[]), ChatCompletionTokenLogprob(token=' description', bytes=[32, 100, 101, 115, 99, 114, 105, 112, 116, 105, 111, 110], logprob=-0.05404112488031387, top_logprobs=[]), ChatCompletionTokenLogprob(token=' does', bytes=[32, 100, 111, 101, 115], logprob=-0.7267906665802002, top_logprobs=[]), ChatCompletionTokenLogprob(token=' not', bytes=[32, 110, 111, 116], logprob=-8.41866585687967e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' provide', bytes=[32, 112, 114, 111, 118, 105, 100, 101], logprob=-0.8855213522911072, top_logprobs=[]), ChatCompletionTokenLogprob(token=' an', bytes=[32, 97, 110], logprob=-1.4068679809570312, top_logprobs=[]), ChatCompletionTokenLogprob(token=' explicit', bytes=[32, 101, 120, 112, 108, 105, 99, 105, 116], logprob=-0.016328897327184677, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-0.0034691598266363144, top_logprobs=[]), ChatCompletionTokenLogprob(token=' for', bytes=[32, 102, 111, 114], logprob=-0.14249683916568756, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.00014883324911352247, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Stay', bytes=[32, 83, 116, 97, 121], logprob=-0.20818568766117096, top_logprobs=[]), ChatCompletionTokenLogprob(token=' table', bytes=[32, 116, 97, 98, 108, 101], logprob=-0.0077700018882751465, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.046941064298152924, top_logprobs=[]), ChatCompletionTokenLogprob(token=' To', bytes=[32, 84, 111], logprob=-1.4835844039916992, top_logprobs=[]), ChatCompletionTokenLogprob(token=' estimate', bytes=[32, 101, 115, 116, 105, 109, 97, 116, 101], logprob=-0.0776246190071106, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.6596805453300476, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-0.09459269791841507, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.016335463151335716, top_logprobs=[]), ChatCompletionTokenLogprob(token=\" it's\", bytes=[32, 105, 116, 39, 115], logprob=-4.817591190338135, top_logprobs=[]), ChatCompletionTokenLogprob(token=' important', bytes=[32, 105, 109, 112, 111, 114, 116, 97, 110, 116], logprob=-0.6164944171905518, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-3.523948907968588e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' consider', bytes=[32, 99, 111, 110, 115, 105, 100, 101, 114], logprob=-0.00870734453201294, top_logprobs=[]), ChatCompletionTokenLogprob(token=' factors', bytes=[32, 102, 97, 99, 116, 111, 114, 115], logprob=-1.8121271133422852, top_logprobs=[]), ChatCompletionTokenLogprob(token=' such', bytes=[32, 115, 117, 99, 104], logprob=-0.13879328966140747, top_logprobs=[]), ChatCompletionTokenLogprob(token=' as', bytes=[32, 97, 115], logprob=-9.088346359931165e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.42100611329078674, top_logprobs=[]), ChatCompletionTokenLogprob(token=' design', bytes=[32, 100, 101, 115, 105, 103, 110], logprob=-0.9122723340988159, top_logprobs=[]), ChatCompletionTokenLogprob(token=' origin', bytes=[32, 111, 114, 105, 103, 105, 110], logprob=-0.8151019811630249, top_logprobs=[]), ChatCompletionTokenLogprob(token=' (', bytes=[32, 40], logprob=-0.9830595254898071, top_logprobs=[]), ChatCompletionTokenLogprob(token='D', bytes=[68], logprob=-0.11297745257616043, top_logprobs=[]), ChatCompletionTokenLogprob(token='anish', bytes=[97, 110, 105, 115, 104], logprob=-4.320199877838604e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' design', bytes=[32, 100, 101, 115, 105, 103, 110], logprob=-0.5272119641304016, top_logprobs=[]), ChatCompletionTokenLogprob(token=' by', bytes=[32, 98, 121], logprob=-0.2019457370042801, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Maria', bytes=[32, 77, 97, 114, 105, 97], logprob=-0.001441899803467095, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Ber', bytes=[32, 66, 101, 114], logprob=-1.5048530030981055e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='nt', bytes=[110, 116], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='sen', bytes=[115, 101, 110], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='),', bytes=[41, 44], logprob=-0.005479914136230946, top_logprobs=[]), ChatCompletionTokenLogprob(token=' functionality', bytes=[32, 102, 117, 110, 99, 116, 105, 111, 110, 97, 108, 105, 116, 121], logprob=-2.8578741550445557, top_logprobs=[]), ChatCompletionTokenLogprob(token=' (', bytes=[32, 40], logprob=-0.3838485777378082, top_logprobs=[]), ChatCompletionTokenLogprob(token='flex', bytes=[102, 108, 101, 120], logprob=-0.6801329255104065, top_logprobs=[]), ChatCompletionTokenLogprob(token='ible', bytes=[105, 98, 108, 101], logprob=-0.01604997180402279, top_logprobs=[]), ChatCompletionTokenLogprob(token=' swivel', bytes=[32, 115, 119, 105, 118, 101, 108], logprob=-0.015337996184825897, top_logprobs=[]), ChatCompletionTokenLogprob(token=' mechanism', bytes=[32, 109, 101, 99, 104, 97, 110, 105, 115, 109], logprob=-0.0020417715422809124, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-1.8754100799560547, top_logprobs=[]), ChatCompletionTokenLogprob(token=' adjustable', bytes=[32, 97, 100, 106, 117, 115, 116, 97, 98, 108, 101], logprob=-0.05741465464234352, top_logprobs=[]), ChatCompletionTokenLogprob(token=' arm', bytes=[32, 97, 114, 109], logprob=-0.07884681969881058, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.06441891938447952, top_logprobs=[]), ChatCompletionTokenLogprob(token=' head', bytes=[32, 104, 101, 97, 100], logprob=-0.003179259831085801, top_logprobs=[]), ChatCompletionTokenLogprob(token='),', bytes=[41, 44], logprob=-0.03575617074966431, top_logprobs=[]), ChatCompletionTokenLogprob(token=' material', bytes=[32, 109, 97, 116, 101, 114, 105, 97, 108], logprob=-1.3489422798156738, top_logprobs=[]), ChatCompletionTokenLogprob(token=' quality', bytes=[32, 113, 117, 97, 108, 105, 116, 121], logprob=-0.98789381980896, top_logprobs=[]), ChatCompletionTokenLogprob(token=' (', bytes=[32, 40], logprob=-0.0027631523553282022, top_logprobs=[]), ChatCompletionTokenLogprob(token='sil', bytes=[115, 105, 108], logprob=-0.046541180461645126, top_logprobs=[]), ChatCompletionTokenLogprob(token='ky', bytes=[107, 121], logprob=-0.0009175319573841989, top_logprobs=[]), ChatCompletionTokenLogprob(token=' matte', bytes=[32, 109, 97, 116, 116, 101], logprob=-1.4259644558478612e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' grey', bytes=[32, 103, 114, 101, 121], logprob=-0.015260986983776093, top_logprobs=[]), ChatCompletionTokenLogprob(token=' metal', bytes=[32, 109, 101, 116, 97, 108], logprob=-0.00024382755509577692, top_logprobs=[]), ChatCompletionTokenLogprob(token='),', bytes=[41, 44], logprob=-0.031130028888583183, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.01216877344995737, top_logprobs=[]), ChatCompletionTokenLogprob(token=' intended', bytes=[32, 105, 110, 116, 101, 110, 100, 101, 100], logprob=-1.7925525903701782, top_logprobs=[]), ChatCompletionTokenLogprob(token=' use', bytes=[32, 117, 115, 101], logprob=-0.11203160881996155, top_logprobs=[]), ChatCompletionTokenLogprob(token=' (', bytes=[32, 40], logprob=-0.020648395642638206, top_logprobs=[]), ChatCompletionTokenLogprob(token='task', bytes=[116, 97, 115, 107], logprob=-0.4322299659252167, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lighting', bytes=[32, 108, 105, 103, 104, 116, 105, 110, 103], logprob=-1.4855663721391466e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' in', bytes=[32, 105, 110], logprob=-0.6959487199783325, top_logprobs=[]), ChatCompletionTokenLogprob(token=' modern', bytes=[32, 109, 111, 100, 101, 114, 110], logprob=-2.7436435222625732, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-1.7304409742355347, top_logprobs=[]), ChatCompletionTokenLogprob(token=' minimal', bytes=[32, 109, 105, 110, 105, 109, 97, 108], logprob=-0.18132318556308746, top_logprobs=[]), ChatCompletionTokenLogprob(token='istic', bytes=[105, 115, 116, 105, 99], logprob=-0.0023766038939356804, top_logprobs=[]), ChatCompletionTokenLogprob(token=' settings', bytes=[32, 115, 101, 116, 116, 105, 110, 103, 115], logprob=-0.2906411588191986, top_logprobs=[]), ChatCompletionTokenLogprob(token=').\\n\\n', bytes=[41, 46, 10, 10], logprob=-1.7185794115066528, top_logprobs=[]), ChatCompletionTokenLogprob(token='Based', bytes=[66, 97, 115, 101, 100], logprob=-2.036790609359741, top_logprobs=[]), ChatCompletionTokenLogprob(token=' on', bytes=[32, 111, 110], logprob=-2.2723104848410003e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' these', bytes=[32, 116, 104, 101, 115, 101], logprob=-0.6593130826950073, top_logprobs=[]), ChatCompletionTokenLogprob(token=' characteristics', bytes=[32, 99, 104, 97, 114, 97, 99, 116, 101, 114, 105, 115, 116, 105, 99, 115], logprob=-2.027945041656494, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.31632760167121887, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.8887858390808105, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Stay', bytes=[32, 83, 116, 97, 121], logprob=-0.46331724524497986, top_logprobs=[]), ChatCompletionTokenLogprob(token=' table', bytes=[32, 116, 97, 98, 108, 101], logprob=-0.048772674053907394, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-2.2200749754119897e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' is', bytes=[32, 105, 115], logprob=-1.146453619003296, top_logprobs=[]), ChatCompletionTokenLogprob(token=' likely', bytes=[32, 108, 105, 107, 101, 108, 121], logprob=-0.19884903728961945, top_logprobs=[]), ChatCompletionTokenLogprob(token=' positioned', bytes=[32, 112, 111, 115, 105, 116, 105, 111, 110, 101, 100], logprob=-0.16601024568080902, top_logprobs=[]), ChatCompletionTokenLogprob(token=' as', bytes=[32, 97, 115], logprob=-0.6005222797393799, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.0025139504577964544, top_logprobs=[]), ChatCompletionTokenLogprob(token=' mid', bytes=[32, 109, 105, 100], logprob=-1.7088682651519775, top_logprobs=[]), ChatCompletionTokenLogprob(token='-to', bytes=[45, 116, 111], logprob=-1.050847053527832, top_logprobs=[]), ChatCompletionTokenLogprob(token='-high', bytes=[45, 104, 105, 103, 104], logprob=-0.02905040979385376, top_logprobs=[]), ChatCompletionTokenLogprob(token='-end', bytes=[45, 101, 110, 100], logprob=-0.18556329607963562, top_logprobs=[]), ChatCompletionTokenLogprob(token=' product', bytes=[32, 112, 114, 111, 100, 117, 99, 116], logprob=-0.22854799032211304, top_logprobs=[]), ChatCompletionTokenLogprob(token=' in', bytes=[32, 105, 110], logprob=-0.9800060987472534, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.039520345628261566, top_logprobs=[]), ChatCompletionTokenLogprob(token=' market', bytes=[32, 109, 97, 114, 107, 101, 116], logprob=-0.8923746943473816, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.1461196094751358, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Danish', bytes=[32, 68, 97, 110, 105, 115, 104], logprob=-1.4255707263946533, top_logprobs=[]), ChatCompletionTokenLogprob(token='-designed', bytes=[45, 100, 101, 115, 105, 103, 110, 101, 100], logprob=-1.3932698965072632, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lighting', bytes=[32, 108, 105, 103, 104, 116, 105, 110, 103], logprob=-0.3129201829433441, top_logprobs=[]), ChatCompletionTokenLogprob(token=' products', bytes=[32, 112, 114, 111, 100, 117, 99, 116, 115], logprob=-0.8966972827911377, top_logprobs=[]), ChatCompletionTokenLogprob(token=' known', bytes=[32, 107, 110, 111, 119, 110], logprob=-4.667842864990234, top_logprobs=[]), ChatCompletionTokenLogprob(token=' for', bytes=[32, 102, 111, 114], logprob=-1.1637164789135568e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' combining', bytes=[32, 99, 111, 109, 98, 105, 110, 105, 110, 103], logprob=-3.5495126247406006, top_logprobs=[]), ChatCompletionTokenLogprob(token=' aesthetics', bytes=[32, 97, 101, 115, 116, 104, 101, 116, 105, 99, 115], logprob=-1.151599407196045, top_logprobs=[]), ChatCompletionTokenLogprob(token=' with', bytes=[32, 119, 105, 116, 104], logprob=-0.7611759305000305, top_logprobs=[]), ChatCompletionTokenLogprob(token=' functionality', bytes=[32, 102, 117, 110, 99, 116, 105, 111, 110, 97, 108, 105, 116, 121], logprob=-0.03216484934091568, top_logprobs=[]), ChatCompletionTokenLogprob(token=' often', bytes=[32, 111, 102, 116, 101, 110], logprob=-1.234926700592041, top_logprobs=[]), ChatCompletionTokenLogprob(token=' range', bytes=[32, 114, 97, 110, 103, 101], logprob=-1.017974615097046, top_logprobs=[]), ChatCompletionTokenLogprob(token=' from', bytes=[32, 102, 114, 111, 109], logprob=-0.47531887888908386, top_logprobs=[]), ChatCompletionTokenLogprob(token=' approximately', bytes=[32, 97, 112, 112, 114, 111, 120, 105, 109, 97, 116, 101, 108, 121], logprob=-1.0552136898040771, top_logprobs=[]), ChatCompletionTokenLogprob(token=' $', bytes=[32, 36], logprob=-0.02605084888637066, top_logprobs=[]), ChatCompletionTokenLogprob(token='150', bytes=[49, 53, 48], logprob=-0.3372423052787781, top_logprobs=[]), ChatCompletionTokenLogprob(token=' to', bytes=[32, 116, 111], logprob=-0.0553513802587986, top_logprobs=[]), ChatCompletionTokenLogprob(token=' $', bytes=[32, 36], logprob=-0.004928105045109987, top_logprobs=[]), ChatCompletionTokenLogprob(token='400', bytes=[52, 48, 48], logprob=-0.8789671659469604, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.9540044069290161, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Given', bytes=[32, 71, 105, 118, 101, 110], logprob=-0.39220085740089417, top_logprobs=[]), ChatCompletionTokenLogprob(token=' these', bytes=[32, 116, 104, 101, 115, 101], logprob=-3.0127131938934326, top_logprobs=[]), ChatCompletionTokenLogprob(token=' aspects', bytes=[32, 97, 115, 112, 101, 99, 116, 115], logprob=-3.4703633785247803, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.22813519835472107, top_logprobs=[]), ChatCompletionTokenLogprob(token=' a', bytes=[32, 97], logprob=-0.5484052896499634, top_logprobs=[]), ChatCompletionTokenLogprob(token=' reasonable', bytes=[32, 114, 101, 97, 115, 111, 110, 97, 98, 108, 101], logprob=-0.062040284276008606, top_logprobs=[]), ChatCompletionTokenLogprob(token=' estimate', bytes=[32, 101, 115, 116, 105, 109, 97, 116, 101], logprob=-0.6718961596488953, top_logprobs=[]), ChatCompletionTokenLogprob(token=' for', bytes=[32, 102, 111, 114], logprob=-0.032594285905361176, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.062239013612270355, top_logprobs=[]), ChatCompletionTokenLogprob(token=' price', bytes=[32, 112, 114, 105, 99, 101], logprob=-0.5471123456954956, top_logprobs=[]), ChatCompletionTokenLogprob(token=' could', bytes=[32, 99, 111, 117, 108, 100], logprob=-2.299600124359131, top_logprobs=[]), ChatCompletionTokenLogprob(token=' be', bytes=[32, 98, 101], logprob=-0.0175681971013546, top_logprobs=[]), ChatCompletionTokenLogprob(token=' around', bytes=[32, 97, 114, 111, 117, 110, 100], logprob=-0.2351464182138443, top_logprobs=[]), ChatCompletionTokenLogprob(token=' $', bytes=[32, 36], logprob=-0.022104566916823387, top_logprobs=[]), ChatCompletionTokenLogprob(token='250', bytes=[50, 53, 48], logprob=-0.08193402737379074, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=-0.021702788770198822, top_logprobs=[]), ChatCompletionTokenLogprob(token='00', bytes=[48, 48], logprob=-0.0022797922138124704, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\\n\\n', bytes=[46, 10, 10], logprob=-0.25367406010627747, top_logprobs=[]), ChatCompletionTokenLogprob(token='Now', bytes=[78, 111, 119], logprob=-1.1820061206817627, top_logprobs=[]), ChatCompletionTokenLogprob(token=',', bytes=[44], logprob=-0.021479368209838867, top_logprobs=[]), ChatCompletionTokenLogprob(token=' I', bytes=[32, 73], logprob=-1.3105272054672241, top_logprobs=[]), ChatCompletionTokenLogprob(token=' will', bytes=[32, 119, 105, 108, 108], logprob=-0.004302287939935923, top_logprobs=[]), ChatCompletionTokenLogprob(token=' provide', bytes=[32, 112, 114, 111, 118, 105, 100, 101], logprob=-0.27623647451400757, top_logprobs=[]), ChatCompletionTokenLogprob(token=' the', bytes=[32, 116, 104, 101], logprob=-0.02480592206120491, top_logprobs=[]), ChatCompletionTokenLogprob(token=' JSON', bytes=[32, 74, 83, 79, 78], logprob=-0.22695498168468475, top_logprobs=[]), ChatCompletionTokenLogprob(token=' dict', bytes=[32, 100, 105, 99, 116], logprob=-1.3176720142364502, top_logprobs=[]), ChatCompletionTokenLogprob(token=' with', bytes=[32, 119, 105, 116, 104], logprob=-0.03229481726884842, top_logprobs=[]), ChatCompletionTokenLogprob(token=' all', bytes=[32, 97, 108, 108], logprob=-7.828518867492676, top_logprobs=[]), ChatCompletionTokenLogprob(token=' extracted', bytes=[32, 101, 120, 116, 114, 97, 99, 116, 101, 100], logprob=-0.7467106580734253, top_logprobs=[]), ChatCompletionTokenLogprob(token=' information', bytes=[32, 105, 110, 102, 111, 114, 109, 97, 116, 105, 111, 110], logprob=-1.9947469234466553, top_logprobs=[]), ChatCompletionTokenLogprob(token=':\\n\\n', bytes=[58, 10, 10], logprob=-0.6308677196502686, top_logprobs=[]), ChatCompletionTokenLogprob(token='```', bytes=[96, 96, 96], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='json', bytes=[106, 115, 111, 110], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='\\n', bytes=[10], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='{\\n', bytes=[123, 10], logprob=-6.2729995988775045e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32, 32, 32], logprob=-0.474151074886322, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='materials', bytes=[109, 97, 116, 101, 114, 105, 97, 108, 115], logprob=-4.320199877838604e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' [\"', bytes=[32, 91, 34], logprob=-0.0010337610729038715, top_logprobs=[]), ChatCompletionTokenLogprob(token='metal', bytes=[109, 101, 116, 97, 108], logprob=-0.028111685067415237, top_logprobs=[]), ChatCompletionTokenLogprob(token='\"],\\n', bytes=[34, 93, 44, 10], logprob=-0.000873347744345665, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32, 32, 32], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='type', bytes=[116, 121, 112, 101], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=-3.128163257315464e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-0.09915617108345032, top_logprobs=[]), ChatCompletionTokenLogprob(token='table', bytes=[116, 97, 98, 108, 101], logprob=-0.38992148637771606, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-0.005958187393844128, top_logprobs=[]), ChatCompletionTokenLogprob(token='\",\\n', bytes=[34, 44, 10], logprob=-0.0003460712905507535, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32, 32, 32], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='color', bytes=[99, 111, 108, 111, 114], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='sil', bytes=[115, 105, 108], logprob=-0.2278972715139389, top_logprobs=[]), ChatCompletionTokenLogprob(token='ky', bytes=[107, 121], logprob=-0.00029744720086455345, top_logprobs=[]), ChatCompletionTokenLogprob(token=' matte', bytes=[32, 109, 97, 116, 116, 101], logprob=-9.088346359931165e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' grey', bytes=[32, 103, 114, 101, 121], logprob=-2.696889623621246e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token='\",\\n', bytes=[34, 44, 10], logprob=-9.253090865968261e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32, 32, 32], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='price', bytes=[112, 114, 105, 99, 101], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32], logprob=-1.9361264946837764e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='250', bytes=[50, 53, 48], logprob=-3.128163257315464e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='.', bytes=[46], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='00', bytes=[48, 48], logprob=-1.8624639324116288e-06, top_logprobs=[]), ChatCompletionTokenLogprob(token=',\\n', bytes=[44, 10], logprob=-3.130576806142926e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' ', bytes=[32, 32, 32], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='summary', bytes=[115, 117, 109, 109, 97, 114, 121], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='\":', bytes=[34, 58], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token=' \"', bytes=[32, 34], logprob=-4.320199877838604e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='A', bytes=[65], logprob=-1.41616690158844, top_logprobs=[]), ChatCompletionTokenLogprob(token=' flexible', bytes=[32, 102, 108, 101, 120, 105, 98, 108, 101], logprob=-1.1143524646759033, top_logprobs=[]), ChatCompletionTokenLogprob(token=' and', bytes=[32, 97, 110, 100], logprob=-0.18483828008174896, top_logprobs=[]), ChatCompletionTokenLogprob(token=' elegant', bytes=[32, 101, 108, 101, 103, 97, 110, 116], logprob=-0.42920824885368347, top_logprobs=[]), ChatCompletionTokenLogprob(token=' table', bytes=[32, 116, 97, 98, 108, 101], logprob=-1.285778522491455, top_logprobs=[]), ChatCompletionTokenLogprob(token=' lamp', bytes=[32, 108, 97, 109, 112], logprob=-4.143808109802194e-05, top_logprobs=[]), ChatCompletionTokenLogprob(token=' designed', bytes=[32, 100, 101, 115, 105, 103, 110, 101, 100], logprob=-0.31241145730018616, top_logprobs=[]), ChatCompletionTokenLogprob(token=' by', bytes=[32, 98, 121], logprob=-0.12911923229694366, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Maria', bytes=[32, 77, 97, 114, 105, 97], logprob=-0.07896524667739868, top_logprobs=[]), ChatCompletionTokenLogprob(token=' Ber', bytes=[32, 66, 101, 114], logprob=-4.320199877838604e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='nt', bytes=[110, 116], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='sen', bytes=[115, 101, 110], logprob=0.0, top_logprobs=[]), ChatCompletionTokenLogprob(token='.\"\\n', bytes=[46, 34, 10], logprob=-0.36576616764068604, top_logprobs=[]), ChatCompletionTokenLogprob(token='}\\n', bytes=[125, 10], logprob=-6.704273118884885e-07, top_logprobs=[]), ChatCompletionTokenLogprob(token='```', bytes=[96, 96, 96], logprob=-0.00861456897109747, top_logprobs=[])], refusal=None), message=ChatCompletionMessage(content='Analysis of Price:\\n\\nThe product description does not provide an explicit price for the Stay table lamp. To estimate the price, it\\'s important to consider factors such as the design origin (Danish design by Maria Berntsen), functionality (flexible swivel mechanism and adjustable arm and head), material quality (silky matte grey metal), and intended use (task lighting in modern, minimalistic settings).\\n\\nBased on these characteristics, the Stay table lamp is likely positioned as a mid-to-high-end product in the market. Danish-designed lighting products known for combining aesthetics with functionality often range from approximately $150 to $400. Given these aspects, a reasonable estimate for the price could be around $250.00.\\n\\nNow, I will provide the JSON dict with all extracted information:\\n\\n```json\\n{\\n \"materials\": [\"metal\"],\\n \"type\": \"table lamp\",\\n \"color\": \"silky matte grey\",\\n \"price\": 250.00,\\n \"summary\": \"A flexible and elegant table lamp designed by Maria Berntsen.\"\\n}\\n```', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None)), feature_weights=None, proba=None, score=None, weighted_spans=WeightedSpans(docs_weighted_spans=[DocWeightedSpans(document='Analysis of Price:\\n\\nThe product description does not provide an explicit price for the Stay table lamp. To estimate the price, it\\'s important to consider factors such as the design origin (Danish design by Maria Berntsen), functionality (flexible swivel mechanism and adjustable arm and head), material quality (silky matte grey metal), and intended use (task lighting in modern, minimalistic settings).\\n\\nBased on these characteristics, the Stay table lamp is likely positioned as a mid-to-high-end product in the market. Danish-designed lighting products known for combining aesthetics with functionality often range from approximately $150 to $400. Given these aspects, a reasonable estimate for the price could be around $250.00.\\n\\nNow, I will provide the JSON dict with all extracted information:\\n\\n```json\\n{\\n \"materials\": [\"metal\"],\\n \"type\": \"table lamp\",\\n \"color\": \"silky matte grey\",\\n \"price\": 250.00,\\n \"summary\": \"A flexible and elegant table lamp designed by Maria Berntsen.\"\\n}\\n```', spans=[('0-Analysis', [(0, 8)], 0.031100326941917205), ('8- of', [(8, 11)], 0.9272939728288944), ('11- Price', [(11, 17)], 0.05589812648675302), ('17-:\\n\\n', [(17, 20)], 0.6603840552609779), ('20-The', [(20, 23)], 0.8709716909780277), ('23- product', [(23, 31)], 0.908347115843313), ('31- description', [(31, 43)], 0.9473931442709769), ('43- does', [(43, 48)], 0.4834580811626623), ('48- not', [(48, 52)], 0.99999158136958), ('52- provide', [(52, 60)], 0.4124990598790427), ('60- an', [(60, 63)], 0.24490914328368124), ('63- explicit', [(63, 72)], 0.9838036964324991), ('72- price', [(72, 78)], 0.9965368507557492), ('78- for', [(78, 82)], 0.8671902953327638), ('82- the', [(82, 86)], 0.9998511778260051), ('86- Stay', [(86, 91)], 0.8120562338884505), ('91- table', [(91, 97)], 0.992260106545069), ('97- lamp', [(97, 102)], 1.0), ('102-.', [(102, 103)], 0.9541436290577758), ('103- To', [(103, 106)], 0.22682320353831265), ('106- estimate', [(106, 115)], 0.9253117057852979), ('115- the', [(115, 119)], 0.517016471439654), ('119- price', [(119, 125)], 0.9097433993978243), ('125-,', [(125, 126)], 0.983797236971635), (\"126- it's\", [(126, 131)], 0.008086241915920705), ('131- important', [(131, 141)], 0.5398335556628256), ('141- to', [(141, 144)], 0.9999647611318239), ('144- consider', [(144, 153)], 0.991330454602796), ('153- factors', [(153, 161)], 0.1633063958780871), ('161- such', [(161, 166)], 0.8704079321820062), ('166- as', [(166, 169)], 0.999999091165777), ('169- the', [(169, 173)], 0.6563860887178772), ('173- design', [(173, 180)], 0.4016105929444318), ('180- origin', [(180, 187)], 0.44259418878962736), ('187- (', [(187, 189)], 0.3741645797743778), ('189-D', [(189, 190)], 0.8931707985895362), ('190-anish', [(190, 195)], 0.9999995679801056), ('195- design', [(195, 202)], 0.5902483112299441), ('202- by', [(202, 205)], 0.8171392671637929), ('205- Maria', [(205, 211)], 0.9985591392345983), ('211- Ber', [(211, 215)], 0.9999984951481292), ('215-nt', [(215, 217)], 1.0), ('217-sen', [(217, 220)], 1.0), ('220-),', [(220, 222)], 0.9945350732042957), ('222- functionality', [(222, 236)], 0.05739063426730033), ('236- (', [(236, 238)], 0.6812345734502243), ('238-flex', [(238, 242)], 0.5065496545188568), ('242-ible', [(242, 246)], 0.9840781426664655), ('246- swivel', [(246, 253)], 0.9847790317898051), ('253- mechanism', [(253, 263)], 0.9979603114553249), ('263- and', [(263, 267)], 0.15329209193961424), ('267- adjustable', [(267, 278)], 0.9442024702453381), ('278- arm', [(278, 282)], 0.9241814800005089), ('282- and', [(282, 286)], 0.9376121333490623), ('286- head', [(286, 291)], 0.9968257886638747), ('291-),', [(291, 293)], 0.964875529680294), ('293- material', [(293, 302)], 0.25951460936867965), ('302- quality', [(302, 310)], 0.37236012322321166), ('310- (', [(310, 312)], 0.9972406616364523), ('312-sil', [(312, 315)], 0.9545252519700747), ('315-ky', [(315, 317)], 0.9990828888463521), ('317- matte', [(317, 323)], 0.9999857404571098), ('323- grey', [(323, 328)], 0.984854871756404), ('328- metal', [(328, 334)], 0.9997562021684266), ('334-),', [(334, 336)], 0.969349521440105), ('336- and', [(336, 340)], 0.9879049666617071), ('340- intended', [(340, 349)], 0.16653453221588588), ('349- use', [(349, 353)], 0.8940159982630072), ('353- (', [(353, 355)], 0.9795633227595859), ('355-task', [(355, 359)], 0.6490600978153632), ('359- lighting', [(359, 368)], 0.9999851444466235), ('368- in', [(368, 371)], 0.49860119061551494), ('371- modern', [(371, 378)], 0.06433551147059785), ('378-,', [(378, 379)], 0.17720624934739607), ('379- minimal', [(379, 387)], 0.8341657248077662), ('387-istic', [(387, 392)], 0.9976262179931533), ('392- settings', [(392, 401)], 0.7477839655600889), ('401-).\\n\\n', [(401, 405)], 0.1793207079905138), ('405-Based', [(405, 410)], 0.13044669418316154), ('410- on', [(410, 413)], 0.9999772771533194), ('413- these', [(413, 419)], 0.5172064905849382), ('419- characteristics', [(419, 435)], 0.13160568766887565), ('435-,', [(435, 436)], 0.72882064819143), ('436- the', [(436, 440)], 0.4111546577327526), ('440- Stay', [(440, 445)], 0.629192992357393), ('445- table', [(445, 451)], 0.9523976097795025), ('451- lamp', [(451, 456)], 0.999997779927489), ('456- is', [(456, 459)], 0.3177616774982452), ('459- likely', [(459, 466)], 0.8196736241452497), ('466- positioned', [(466, 477)], 0.8470375556685125), ('477- as', [(477, 480)], 0.5485250777340852), ('480- a', [(480, 482)], 0.9974892068693135), ('482- mid', [(482, 486)], 0.18107060060975305), ('486--to', [(486, 489)], 0.3496414586109216), ('489--high', [(489, 494)], 0.9713674967971203), ('494--end', [(494, 498)], 0.8306362578982314), ('498- product', [(498, 506)], 0.7956881109594603), ('506- in', [(506, 509)], 0.37530880993084603), ('509- the', [(509, 513)], 0.9612503965484102), ('513- market', [(513, 520)], 0.4096817278210386), ('520-.', [(520, 521)], 0.8640543479165882), ('521- Danish', [(521, 528)], 0.24037123784304548), ('528--designed', [(528, 537)], 0.24826218429718366), ('537- lighting', [(537, 546)], 0.7313082811139549), ('546- products', [(546, 555)], 0.4079146642145345), ('555- known', [(555, 561)], 0.0093925085990811), ('561- for', [(561, 565)], 0.9999883629029224), ('565- combining', [(565, 575)], 0.028738642745044892), ('575- aesthetics', [(575, 586)], 0.31613074303053984), ('586- with', [(586, 591)], 0.46711680701534064), ('591- functionality', [(591, 605)], 0.9683469375664772), ('605- often', [(605, 611)], 0.2908560811650651), ('611- range', [(611, 617)], 0.36132602383397133), ('617- from', [(617, 622)], 0.6216867820635172), ('622- approximately', [(622, 636)], 0.3481180300838724), ('636- $', [(636, 638)], 0.9742855470140944), ('638-150', [(638, 641)], 0.7137358769780064), ('641- to', [(641, 644)], 0.9461526301807588), ('644- $', [(644, 646)], 0.9950840181416029), ('646-400', [(646, 649)], 0.4152115349076142), ('649-.', [(649, 650)], 0.3851954516350021), ('650- Given', [(650, 656)], 0.6755684074178104), ('656- these', [(656, 662)], 0.049158122143082114), ('662- aspects', [(662, 670)], 0.031105725454522743), ('670-,', [(670, 671)], 0.796016632421059), ('671- a', [(671, 673)], 0.5778706122255274), ('673- reasonable', [(673, 684)], 0.9398450250520831), ('684- estimate', [(684, 693)], 0.5107392159521865), ('693- for', [(693, 697)], 0.967931183260857), ('697- the', [(697, 701)], 0.9396582688316502), ('701- price', [(701, 707)], 0.5786182497626335), ('707- could', [(707, 713)], 0.10029894280897976), ('713- be', [(713, 716)], 0.9825852239159907), ('716- around', [(716, 723)], 0.7904551041308469), ('723- $', [(723, 725)], 0.9781379488338885), ('725-250', [(725, 728)], 0.9213327394318271), ('728-.', [(728, 729)], 0.9785310222449798), ('729-00', [(729, 731)], 0.9977228045387296), ('731-.\\n\\n', [(731, 734)], 0.7759446721579865), ('734-Now', [(734, 737)], 0.3066629182800305), ('737-,', [(737, 738)], 0.9787496706186609), ('738- I', [(738, 740)], 0.2696778432668547), ('740- will', [(740, 745)], 0.9957069536427563), ('745- provide', [(745, 753)], 0.7586335120570286), ('753- the', [(753, 757)], 0.9754992165350655), ('757- JSON', [(757, 762)], 0.7969566591287495), ('762- dict', [(762, 767)], 0.2677579135800676), ('767- with', [(767, 772)], 0.9682210916996221), ('772- all', [(772, 776)], 0.0003982148526756783), ('776- extracted', [(776, 786)], 0.47392288610067923), ('786- information', [(786, 798)], 0.1360480803897047), ('798-:\\n\\n', [(798, 801)], 0.5321298610817511), ('801-```', [(801, 804)], 1.0), ('804-json', [(804, 808)], 1.0), ('808-\\n', [(808, 809)], 1.0), ('809-{\\n', [(809, 811)], 0.9999937270200764), ('811- ', [(811, 814)], 0.6224132144588476), ('814- \"', [(814, 816)], 1.0), ('816-materials', [(816, 825)], 0.9999995679801056), ('825-\":', [(825, 827)], 1.0), ('827- [\"', [(827, 830)], 0.9989667730739981), ('830-metal', [(830, 835)], 0.9722797716052907), ('835-\"],\\n', [(835, 839)], 0.9991270335127975), ('839- ', [(839, 842)], 1.0), ('842- \"', [(842, 844)], 1.0), ('844-type', [(844, 848)], 0.9999998063873693), ('848-\":', [(848, 850)], 0.9999996871837232), ('850- \"', [(850, 852)], 0.9056012682481982), ('852-table', [(852, 857)], 0.6771100347727493), ('857- lamp', [(857, 862)], 0.9940595274045081), ('862-\",\\n', [(862, 865)], 0.999653988585211), ('865- ', [(865, 868)], 1.0), ('868- \"', [(868, 870)], 1.0), ('870-color', [(870, 875)], 0.9999998063873693), ('875-\":', [(875, 877)], 0.9999998063873693), ('877- \"', [(877, 879)], 0.9999998063873693), ('879-sil', [(879, 882)], 0.7962060486763332), ('882-ky', [(882, 884)], 0.9997025970321683), ('884- matte', [(884, 890)], 0.999999091165777), ('890- grey', [(890, 895)], 0.999997303114013), ('895-\",\\n', [(895, 898)], 0.9999907469519438), ('898- ', [(898, 901)], 0.9999998063873693), ('901- \"', [(901, 903)], 1.0), ('903-price', [(903, 908)], 0.9999998063873693), ('908-\":', [(908, 910)], 1.0), ('910- ', [(910, 911)], 0.9999998063873693), ('911-250', [(911, 914)], 0.9999996871837232), ('914-.', [(914, 915)], 1.0), ('915-00', [(915, 917)], 0.999998137537802), ('917-,\\n', [(917, 919)], 0.999968694721959), ('919- ', [(919, 922)], 1.0), ('922- \"', [(922, 924)], 1.0), ('924-summary', [(924, 931)], 1.0), ('931-\":', [(931, 933)], 1.0), ('933- \"', [(933, 935)], 0.9999995679801056), ('935-A', [(935, 936)], 0.24264230849118593), ('936- flexible', [(936, 945)], 0.3281276843885938), ('945- and', [(945, 949)], 0.8312387008376273), ('949- elegant', [(949, 957)], 0.6510243399908526), ('957- table', [(957, 963)], 0.27643528874717094), ('963- lamp', [(963, 968)], 0.9999585627774474), ('968- designed', [(968, 977)], 0.7316804110376031), ('977- by', [(977, 980)], 0.8788691697083658), ('980- Maria', [(980, 986)], 0.9240720384607087), ('986- Ber', [(986, 990)], 0.9999995679801056), ('990-nt', [(990, 992)], 1.0), ('992-sen', [(992, 995)], 1.0), ('995-.\"\\n', [(995, 998)], 0.6936649835669562), ('998-}\\n', [(998, 1000)], 0.9999993295729128), ('1000-```', [(1000, 1003)], 0.991422430108248)], preserve_density=False, with_probabilities=True, vec_name=None)], other=None), heatmap=None)], feature_importances=None, decision_tree=None, highlight_spaces=None, transition_features=None, image=None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eli5.explain_prediction(client, prompt_cot, model='gpt-4o')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/test_llm_explain_prediction.py b/tests/test_llm_explain_prediction.py new file mode 100644 index 00000000..a8faaee7 --- /dev/null +++ b/tests/test_llm_explain_prediction.py @@ -0,0 +1,106 @@ +import math +import pytest +from unittest.mock import Mock + +pytest.importorskip('openai') +from openai.types.chat.chat_completion import ( + ChoiceLogprobs, + ChatCompletion, + ChatCompletionMessage, + ChatCompletionTokenLogprob, + Choice, +) +from openai import Client + +import eli5 +from eli5.base import Explanation +from eli5.formatters.html import format_as_html + + +@pytest.fixture +def example_logprobs(): + return ChoiceLogprobs(content=[ + ChatCompletionTokenLogprob( + token="Hello", + logprob=math.log(0.9), + top_logprobs=[], + ), + ChatCompletionTokenLogprob( + token=" world", + logprob=math.log(0.2), + top_logprobs=[], + ), + ChatCompletionTokenLogprob( + token=" world", + logprob=math.log(0.4), + top_logprobs=[], + ), + ]) + + +@pytest.fixture +def example_completion(example_logprobs): + return ChatCompletion( + id='chatcmpl-x', + created=1743590849, + model='gpt-4o-2024-08-06', + object='chat.completion', + choices=[ + Choice( + logprobs=example_logprobs, + finish_reason='stop', + index=0, + message=ChatCompletionMessage( + content=''.join(x.token for x in example_logprobs.content), + role='assistant', + ), + ) + ], + ) + + +def _assert_explanation_structure_and_html(explanation: Explanation): + assert isinstance(explanation, Explanation) + assert explanation.targets + target = explanation.targets[0] + html = format_as_html(explanation) + + spans = target.weighted_spans.docs_weighted_spans[0].spans + assert len(spans) == 3 + assert spans[0][1:] == ([(0, 5)], 0.9) + assert spans[1][1:] == ([(5, 11)], 0.2) + assert spans[2][1:] == ([(11, 17)], 0.4) + + assert isinstance(target.target, (str, Choice)) + + assert "Hello" in html + assert "world" in html + assert "0.900" in html + assert "0.200" in html + assert "0.400" in html + + +def test_explain_prediction_choice_logprobs(example_logprobs): + explanation = eli5.explain_prediction(example_logprobs) + _assert_explanation_structure_and_html(explanation) + + +def test_explain_prediction_chat_completion(example_completion): + explanation = eli5.explain_prediction(example_completion) + _assert_explanation_structure_and_html(explanation) + + +class MockClient(Client): + def __init__(self, chat_return_value): + self.chat = Mock() + self.chat.completions = Mock() + self.chat.completions.create = Mock(return_value=chat_return_value) + + +def test_explain_prediction_openai_client(monkeypatch, example_completion): + client = MockClient(example_completion) + + explanation = eli5.explain_prediction(client, doc="Hello world", model="gpt-4o") + _assert_explanation_structure_and_html(explanation) + + client.chat.completions.create.assert_called_once() diff --git a/tox.ini b/tox.ini index d8893a98..d0717246 100644 --- a/tox.ini +++ b/tox.ini @@ -29,6 +29,7 @@ deps= ipython pandas sklearn-crfsuite + openai commands= pip install -e . py.test --doctest-modules \ @@ -87,6 +88,8 @@ commands= --ignore eli5/keras \ --ignore eli5/formatters/as_dataframe.py \ --ignore eli5/formatters/image.py \ + --ignore eli5/formatters/image.py \ + --ignore eli5/llm/explain_prediction.py \ --ignore tests/utils_image.py \ --cov=eli5 --cov-report=html --cov-report=term {posargs: eli5 tests}