fix(generative-ai): temperature is not defined in the sample (GoogleCloudPlatform#11790)

gericdong · web-flow · commit a773f8081ec3 · 2024-05-24T09:19:21.000-05:00
diff --git a/generative_ai/summarization.py b/generative_ai/summarization.py
@@ -14,64 +14,60 @@
 
 
 def text_summarization(
-    temperature: float,
     project_id: str,
-    location: str,
 ) -> str:
     """Summarization Example with a Large Language Model"""
     # [START aiplatform_sdk_summarization]
     import vertexai
     from vertexai.language_models import TextGenerationModel
 
-    # TODO(developer): update project_id, location & temperature
-    vertexai.init(project=project_id, location=location)
+    # TODO(developer): Update and un-comment below line
+    # project_id = "PROJECT_ID"
+
+    vertexai.init(project=project_id, location="us-central1")
+
     parameters = {
-        "temperature": temperature,  # Temperature controls the degree of randomness in token selection.
-        "max_output_tokens": 256,  # Token limit determines the maximum amount of text output.
-        "top_p": 0.95,  # Tokens are selected from most probable to least until the sum of their probabilities equals the top_p value.
-        "top_k": 40,  # A top_k of 1 means the selected token is the most probable among all tokens.
+        "temperature": 0,
+        "max_output_tokens": 256,
+        "top_p": 0.95,
+        "top_k": 40,
     }
 
     model = TextGenerationModel.from_pretrained("text-bison@002")
     response = model.predict(
         """Provide a summary with about two sentences for the following article:
-The efficient-market hypothesis (EMH) is a hypothesis in financial \
-economics that states that asset prices reflect all available \
-information. A direct implication is that it is impossible to \
-"beat the market" consistently on a risk-adjusted basis since market \
-prices should only react to new information. Because the EMH is \
-formulated in terms of risk adjustment, it only makes testable \
-predictions when coupled with a particular model of risk. As a \
-result, research in financial economics since at least the 1990s has \
-focused on market anomalies, that is, deviations from specific \
-models of risk. The idea that financial market returns are difficult \
-to predict goes back to Bachelier, Mandelbrot, and Samuelson, but \
-is closely associated with Eugene Fama, in part due to his \
-influential 1970 review of the theoretical and empirical research. \
-The EMH provides the basic logic for modern risk-based theories of \
-asset prices, and frameworks such as consumption-based asset pricing \
-and intermediary asset pricing can be thought of as the combination \
-of a model of risk with the EMH. Many decades of empirical research \
-on return predictability has found mixed evidence. Research in the \
-1950s and 1960s often found a lack of predictability (e.g. Ball and \
-Brown 1968; Fama, Fisher, Jensen, and Roll 1969), yet the \
-1980s-2000s saw an explosion of discovered return predictors (e.g. \
-Rosenberg, Reid, and Lanstein 1985; Campbell and Shiller 1988; \
-Jegadeesh and Titman 1993). Since the 2010s, studies have often \
-found that return predictability has become more elusive, as \
-predictability fails to work out-of-sample (Goyal and Welch 2008), \
-or has been weakened by advances in trading technology and investor \
-learning (Chordia, Subrahmanyam, and Tong 2014; McLean and Pontiff \
-2016; Martineau 2021).
-Summary:
-""",
+        The efficient-market hypothesis (EMH) is a hypothesis in financial \
+        economics that states that asset prices reflect all available \
+        information. A direct implication is that it is impossible to \
+        "beat the market" consistently on a risk-adjusted basis since market \
+        prices should only react to new information. Because the EMH is \
+        formulated in terms of risk adjustment, it only makes testable \
+        predictions when coupled with a particular model of risk. As a \
+        result, research in financial economics since at least the 1990s has \
+        focused on market anomalies, that is, deviations from specific \
+        models of risk. The idea that financial market returns are difficult \
+        to predict goes back to Bachelier, Mandelbrot, and Samuelson, but \
+        is closely associated with Eugene Fama, in part due to his \
+        influential 1970 review of the theoretical and empirical research. \
+        The EMH provides the basic logic for modern risk-based theories of \
+        asset prices, and frameworks such as consumption-based asset pricing \
+        and intermediary asset pricing can be thought of as the combination \
+        of a model of risk with the EMH. Many decades of empirical research \
+        on return predictability has found mixed evidence. Research in the \
+        1950s and 1960s often found a lack of predictability (e.g. Ball and \
+        Brown 1968; Fama, Fisher, Jensen, and Roll 1969), yet the \
+        1980s-2000s saw an explosion of discovered return predictors (e.g. \
+        Rosenberg, Reid, and Lanstein 1985; Campbell and Shiller 1988; \
+        Jegadeesh and Titman 1993). Since the 2010s, studies have often \
+        found that return predictability has become more elusive, as \
+        predictability fails to work out-of-sample (Goyal and Welch 2008), \
+        or has been weakened by advances in trading technology and investor \
+        learning (Chordia, Subrahmanyam, and Tong 2014; McLean and Pontiff \
+        2016; Martineau 2021).
+        Summary:""",
         **parameters,
     )
     print(f"Response from Model: {response.text}")
     # [END aiplatform_sdk_summarization]
 
     return response.text
-
-
-if __name__ == "__main__":
-    text_summarization()
diff --git a/generative_ai/summarization_test.py b/generative_ai/summarization_test.py
@@ -21,15 +21,12 @@
 
 
 _PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
-_LOCATION = "us-central1"
 
 
 expected_response = """The efficient-market hypothesis"""
 
 
 @backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
 def test_text_summarization() -> None:
-    content = summarization.text_summarization(
-        temperature=0, project_id=_PROJECT_ID, location=_LOCATION
-    )
+    content = summarization.text_summarization(project_id=_PROJECT_ID)
     assert expected_response in content
