 View on ai.google.dev
|
 Run in Google Colab
|
 View source on GitHub
|
If you want to quickly try out the PaLM API, you can use curl commands to call the methods in the REST API. The following examples show calls for each API method. For each curl command, you must specify the model name and your API key. See the get an API key page if you don't already have one.
"""
Add your API key and run this cell to set the PALM_API_KEY variable if
running in Colab.
(In a terminal you can just run PALM_API_KEY=<your API key>)
"""
import os
os.environ['PALM_API_KEY'] = ""
Generate text
Use the generate_text method
to generate a response from the model given an input message.
!curl https://generativelanguage.googleapis.com/v1beta2/models/text-bison-001:generateText?key=$PALM_API_KEY \
-H 'Content-Type: application/json' \
-X POST \
-d '{ \
"prompt": { \
"text": "Write a story about a magic backpack." \
} \
}'
{
"candidates": [
{
"output": "Once upon a time, there was a young girl named Lily who loved to explore. She would spend hours wandering through the forest near her house, looking for new and exciting things. One day, Lily was exploring a particularly dense part of the forest when she came across a strange backpack. It was sitting on a rock, and it looked like it had been abandoned.\n\nLily picked up the backpack and examined it. It was made of a strange, silvery material that she had never seen before. There were no zippers or buttons, but the backpack seemed to be open. Lily reached inside, and she was surprised to find that it was full of all sorts of amazing things.\n\nThere were books that could tell her anything she wanted to know, tools that could fix anything she broke, and clothes that could keep her warm or cool no matter what the weather. Lily was amazed by the backpack's contents, and she knew that she had found something truly special.\n\nLily decided to take the backpack home with her, and she quickly discovered that it was even more magical than she had thought. The backpack could transport her to any place she wanted to go, and it could provide her with anything she needed. Lily used the backpack to travel the world, and she had many amazing adventures.\n\nOne day, Lily was using the backpack to explore a new planet when she came across a group of people who were in trouble. The people were being held captive by a cruel wizard, and Lily knew that she had to help them. She used the backpack to create a distraction, and then she rescued the people and led them to safety.\n\nThe people were so grateful to Lily for saving them, and they told her that she was a true hero. Lily was happy to have helped, and she knew that she would continue to use the backpack to help others in need.\n\nLily's magic backpack continued to accompany her on many more adventures. She used it to help people in need, to explore new worlds, and to learn about all sorts of amazing things. Lily's backpack was a constant source of joy and inspiration, and she was grateful for the many ways that it had enriched her life.",
"safetyRatings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"probability": "NEGLIGIBLE"
}
]
}
]
}
The following example specifies values for all the parameters of
the generate_text method.
!curl https://generativelanguage.googleapis.com/v1beta2/models/text-bison-001:generateText?key=$PALM_API_KEY \
-H 'Content-Type: application/json' \
-X POST \
-d '{ \
"prompt": { \
"text": "Give an example of a title for a story about a magic backpack. \
Title: " \
}, \
"safetySettings": [ \
{ \
"category": "HARM_CATEGORY_TOXICITY", \
"threshold": "BLOCK_ONLY_HIGH" \
} \
], \
"stopSequences": [ \
"Title" \
], \
"temperature": 1.0, \
"candidate_count": 3, \
"maxOutputTokens": 800, \
"topP": 0.8, \
"topK": 10 \
}'
{
"candidates": [
{
"output": "The Backpack of Holding",
"safetyRatings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"probability": "NEGLIGIBLE"
}
]
},
{
"output": "The Backpack of Holding",
"safetyRatings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"probability": "NEGLIGIBLE"
}
]
},
{
"output": "The Backpack of Holding",
"safetyRatings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"probability": "NEGLIGIBLE"
}
]
}
]
}
The parameter values have the following meaning:
safetySettings: Lower the safety threshold for toxicity to only block highly probable toxic outputsstopSequences: Use the stop sequence to generate only 1 title by stopping the output at the next instance ofTitle.temperature: Set the temperature to 1.0 when generating responses.candidate_count: Return only 3 candidate responses.maxOutputTokens: Return a maximum of 800 tokens.topP: Consider only the top 80% of the tokens returned by the model.topK: Consider the top 10 tokens when sampling.
Learn more about these parameters in the API reference
for the generateText
method.
Generate message
Use the generateMessage
method to generate a response from the model
given an input MessagePrompt.
!curl https://generativelanguage.googleapis.com/v1beta2/models/chat-bison-001:generateMessage?key=$PALM_API_KEY \
-H 'Content-Type: application/json' \
-X POST \
-d '{ \
"prompt": {"messages": [{"content":"hi"}]} \
}'
{
"candidates": [
{
"author": "1",
"content": "Hi! How can I help you today?"
}
],
"messages": [
{
"author": "0",
"content": "hi"
}
]
}
The following example shows a call with different values for the parameters.
!curl https://generativelanguage.googleapis.com/v1beta2/models/chat-bison-001:generateMessage?key=$PALM_API_KEY \
-H 'Content-Type: application/json' \
-X POST \
-d '{ \
"prompt": {"messages": [{"content":"hi"}]}, \
"temperature": 0.1, \
"candidate_count": 1, \
"topP": 0.8, \
"topK": 10}'
{
"candidates": [
{
"author": "1",
"content": "Hi! How can I help you today?"
}
],
"messages": [
{
"author": "0",
"content": "hi"
}
]
}
The parameter values have the following meaning:
temperature: Set the temperature to 0.1 when generating responses.candidate_count: Return only 1 candidate response.topP: Consider only the top 80% of the tokens returned by the model.topK: Consider the top 10 tokens when sampling.
Learn more about these parameters in the API reference
for the generateMessage
method.
Embed text
Use the embedText method to
generate an embedding from the model given an input message.
!curl https://generativelanguage.googleapis.com/v1beta2/models/embedding-gecko-001:embedText?key=$PALM_API_KEY \
-H 'Content-Type: application/json' \
-X POST \
-d '{"text": "say something nice!"}'
{
"embedding": {
"value": [
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}
}
Count message tokens
Use the
countMessageTokens
method to run a model's tokenizer on a message
prompt string and get a token count.
!curl https://generativelanguage.googleapis.com/v1beta2/models/chat-bison-001:countMessageTokens?key=$PALM_API_KEY \
-H 'Content-Type: application/json' \
-X POST \
-d '{ \
"prompt": { \
"messages": [ \
{"content":"How many tokens?"}, \
{"content":"For this whole conversation?"} \
] \
} \
}'
{
"tokenCount": 23
}
Get model
Use the get method to get information about a specific model such as
version, display name, input token limit, etc.
curl https://generativelanguage.googleapis.com/v1beta2/models/text-bison-001?key=$PALM_API_KEY
{
"name": "models/text-bison-001",
"version": "001",
"displayName": "Text Bison",
"description": "Model targeted for text generation.",
"inputTokenLimit": 8196,
"outputTokenLimit": 1024,
"supportedGenerationMethods": [
"generateText"
],
"temperature": 0.7,
"topP": 0.95,
"topK": 40
}
List models
Use the list method to list all of the models available through the
PaLM API.
curl https://generativelanguage.googleapis.com/v1beta2/models?key=$PALM_API_KEY
{
"models": [
{
"name": "models/chat-bison-001",
"version": "001",
"displayName": "Chat Bison",
"description": "Chat-optimized generative language model.",
"inputTokenLimit": 4096,
"outputTokenLimit": 1024,
"supportedGenerationMethods": [
"generateMessage",
"countMessageTokens"
],
"temperature": 0.25,
"topP": 0.95,
"topK": 40
},
{
"name": "models/text-bison-001",
"version": "001",
"displayName": "Text Bison",
"description": "Model targeted for text generation.",
"inputTokenLimit": 8196,
"outputTokenLimit": 1024,
"supportedGenerationMethods": [
"generateText"
],
"temperature": 0.7,
"topP": 0.95,
"topK": 40
},
{
"name": "models/embedding-gecko-001",
"version": "001",
"displayName": "Embedding Gecko",
"description": "Obtain a distributed representation of a text.",
"inputTokenLimit": 1024,
"outputTokenLimit": 1,
"supportedGenerationMethods": [
"embedText"
]
}
]
}