ClickUp Operator
by noahvanhart
- .venv
- Lib
- site-packages
- anthropic
- resources
- __pycache__
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Create a Message.
Send a structured list of input messages with text and/or image content, and the
model will generate the next message in the conversation.
The Messages API can be used for for either single queries or stateless
multi-turn conversations.
Args:
max_tokens: The maximum number of tokens to generate before stopping.
Note that our models may stop _before_ reaching this maximum. This parameter
only specifies the absolute maximum number of tokens to generate.
Different models have different maximum values for this parameter. See
[models](https://docs.anthropic.com/claude/docs/models-overview) for details.
messages: Input messages.
Our models are trained to operate on alternating `user` and `assistant`
conversational turns. When creating a new `Message`, you specify the prior
conversational turns with the `messages` parameter, and the model then generates
the next `Message` in the conversation.
Each input message must be an object with a `role` and `content`. You can
specify a single `user`-role message, or you can include multiple `user` and
`assistant` messages. The first message must always use the `user` role.
If the final message uses the `assistant` role, the response content will
continue immediately from the content in that message. This can be used to
constrain part of the model's response.
Example with a single `user` message:
```json
[{ "role": "user", "content": "Hello, Claude" }]
```
Example with multiple conversational turns:
```json
[
{ "role": "user", "content": "Hello there." },
{ "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" },
{ "role": "user", "content": "Can you explain LLMs in plain English?" }
]
```
Example with a partially-filled response from Claude:
```json
[
{
"role": "user",
"content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"
},
{ "role": "assistant", "content": "The best answer is (" }
]
```
Each input message `content` may be either a single `string` or an array of
content blocks, where each block has a specific `type`. Using a `string` for
`content` is shorthand for an array of one content block of type `"text"`. The
following input messages are equivalent:
```json
{ "role": "user", "content": "Hello, Claude" }
```
```json
{ "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] }
```
Starting with Claude 3 models, you can also send image content blocks:
```json
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": "/9j/4AAQSkZJRg..."
}
},
{ "type": "text", "text": "What is in this image?" }
]
}
```
We currently support the `base64` source type for images, and the `image/jpeg`,
`image/png`, `image/gif`, and `image/webp` media types.
See [examples](https://docs.anthropic.com/claude/reference/messages-examples)
for more input examples.
Note that if you want to include a
[system prompt](https://docs.anthropic.com/claude/docs/system-prompts), you can
use the top-level `system` parameter — there is no `"system"` role for input
messages in the Messages API.
model: The model that will complete your prompt.
See [models](https://docs.anthropic.com/claude/docs/models-overview) for
additional details and options.
metadata: An object describing metadata about the request.
stop_sequences: Custom text sequences that will cause the model to stop generating.
Our models will normally stop when they have naturally completed their turn,
which will result in a response `stop_reason` of `"end_turn"`.
If you want the model to stop generating when it encounters custom strings of
text, you can use the `stop_sequences` parameter. If the model encounters one of
the custom sequences, the response `stop_reason` value will be `"stop_sequence"`
and the response `stop_sequence` value will contain the matched stop sequence.
stream: Whether to incrementally stream the response using server-sent events.
See [streaming](https://docs.anthropic.com/claude/reference/messages-streaming)
for details.
system: System prompt.
A system prompt is a way of providing context and instructions to Claude, such
as specifying a particular goal or role. See our
[guide to system prompts](https://docs.anthropic.com/claude/docs/system-prompts).
temperature: Amount of randomness injected into the response.
Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0`
for analytical / multiple choice, and closer to `1.0` for creative and
generative tasks.
Note that even with `temperature` of `0.0`, the results will not be fully
deterministic.
top_k: Only sample from the top K options for each subsequent token.
Used to remove "long tail" low probability responses.
[Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277).
Recommended for advanced use cases only. You usually only need to use
`temperature`.
top_p: Use nucleus sampling.
In nucleus sampling, we compute the cumulative distribution over all the options
for each subsequent token in decreasing probability order and cut it off once it
reaches a particular probability specified by `top_p`. You should either alter
`temperature` or `top_p`, but not both.
Recommended for advanced use cases only. You usually only need to use
`temperature`.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
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Create a Message.
Send a structured list of input messages with text and/or image content, and the
model will generate the next message in the conversation.
The Messages API can be used for for either single queries or stateless
multi-turn conversations.
Args:
max_tokens: The maximum number of tokens to generate before stopping.
Note that our models may stop _before_ reaching this maximum. This parameter
only specifies the absolute maximum number of tokens to generate.
Different models have different maximum values for this parameter. See
[models](https://docs.anthropic.com/claude/docs/models-overview) for details.
messages: Input messages.
Our models are trained to operate on alternating `user` and `assistant`
conversational turns. When creating a new `Message`, you specify the prior
conversational turns with the `messages` parameter, and the model then generates
the next `Message` in the conversation.
Each input message must be an object with a `role` and `content`. You can
specify a single `user`-role message, or you can include multiple `user` and
`assistant` messages. The first message must always use the `user` role.
If the final message uses the `assistant` role, the response content will
continue immediately from the content in that message. This can be used to
constrain part of the model's response.
Example with a single `user` message:
```json
[{ "role": "user", "content": "Hello, Claude" }]
```
Example with multiple conversational turns:
```json
[
{ "role": "user", "content": "Hello there." },
{ "role": "assistant", "content": "Hi, I'm Claude. How can I help you?" },
{ "role": "user", "content": "Can you explain LLMs in plain English?" }
]
```
Example with a partially-filled response from Claude:
```json
[
{
"role": "user",
"content": "What's the Greek name for Sun? (A) Sol (B) Helios (C) Sun"
},
{ "role": "assistant", "content": "The best answer is (" }
]
```
Each input message `content` may be either a single `string` or an array of
content blocks, where each block has a specific `type`. Using a `string` for
`content` is shorthand for an array of one content block of type `"text"`. The
following input messages are equivalent:
```json
{ "role": "user", "content": "Hello, Claude" }
```
```json
{ "role": "user", "content": [{ "type": "text", "text": "Hello, Claude" }] }
```
Starting with Claude 3 models, you can also send image content blocks:
```json
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": "/9j/4AAQSkZJRg..."
}
},
{ "type": "text", "text": "What is in this image?" }
]
}
```
We currently support the `base64` source type for images, and the `image/jpeg`,
`image/png`, `image/gif`, and `image/webp` media types.
See [examples](https://docs.anthropic.com/claude/reference/messages-examples)
for more input examples.
Note that if you want to include a
[system prompt](https://docs.anthropic.com/claude/docs/system-prompts), you can
use the top-level `system` parameter — there is no `"system"` role for input
messages in the Messages API.
model: The model that will complete your prompt.
See [models](https://docs.anthropic.com/claude/docs/models-overview) for
additional details and options.
stream: Whether to incrementally stream the response using server-sent events.
See [streaming](https://docs.anthropic.com/claude/reference/messages-streaming)
for details.
metadata: An object describing metadata about the request.
stop_sequences: Custom text sequences that will cause the model to stop generating.
Our models will normally stop when they have naturally completed their turn,
which will result in a response `stop_reason` of `"end_turn"`.
If you want the model to stop generating when it encounters custom strings of
text, you can use the `stop_sequences` parameter. If the model encounters one of
the custom sequences, the response `stop_reason` value will be `"stop_sequence"`
and the response `stop_sequence` value will contain the matched stop sequence.
system: System prompt.
A system prompt is a way of providing context and instructions to Claude, such
as specifying a particular goal or role. See our
[guide to system prompts](https://docs.anthropic.com/claude/docs/system-prompts).
temperature: Amount of randomness injected into the response.
Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0`
for analytical / multiple choice, and closer to `1.0` for creative and
generative tasks.
Note that even with `temperature` of `0.0`, the results will not be fully
deterministic.
top_k: Only sample from the top K options for each subsequent token.
Used to remove "long tail" low probability responses.
[Learn more technical details here](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277).
Recommended for advanced use cases only. You usually only need to use
`temperature`.
top_p: Use nucleus sampling.
In nucleus sampling, we compute the cumulative distribution over all the options
for each subsequent token in decreasing probability order and cut it off once it
reaches a particular probability specified by `top_p`. You should either alter
`temperature` or `top_p`, but not both.
Recommended for advanced use cases only. You usually only need to use
`temperature`.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
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