# JinaAI Python SDK
The JinaAI Python SDK is an efficient instrument that smoothly brings the power of JinaAI's products — [SceneXplain](https://scenex.jina.ai), [PromptPerfect](https://promptperfect.jina.ai/), [Rationale](https://rationale.jina.ai/), [BestBanner](https://bestbanner.jina.ai/), and [JinaChat](https://chat.jina.ai/) — into Python applications. Acting as a sturdy interface for JinaAI's APIs, this SDK lets you effortlessly formulate and fine-tune prompts, thus streamlining application development.
## Installing
### Package manager
Using pip:
```bash
$ pip install jinaai
```
## API secrets
To generate an API secret, you need to authenticate on each respective platform's API tab:
- [SceneXplain API](https://scenex.jina.ai/api)
- [PromptPerfect API](https://promptperfect.jina.ai/api)
- [Rationale API](https://rationale.jina.ai/api)
- [JinaChat API](https://chat.jina.ai/api)
- [BestBanner API](https://bestbanner.jina.ai/api)
> **Note:** Each secret is product-specific and cannot be interchanged. If you're planning to use multiple products, you'll need to generate a separate secret for each.
## Example usage
Import the SDK and instantiate a new client with your authentication secrets:
```python
from jinaai import JinaAI
jinaai = JinaAI(
secrets = {
'promptperfect-secret': 'XXXXXX',
'scenex-secret': 'XXXXXX',
'rationale-secret': 'XXXXXX',
'jinachat-secret': 'XXXXXX',
'bestbanner-secret': 'XXXXXX',
}
)
```
Describe images:
```python
descriptions = jinaai.describe(
'https://picsum.photos/200'
)
```
Evaluate situations:
```python
decisions = jinaai.decide(
'Going to Paris this summer',
{ 'analysis': 'proscons' }
)
```
Optimize prompts:
```python
prompts = jinaai.optimize(
'Write an Hello World function in Python'
)
```
Generate complex answers:
```python
output = jinaai.generate(
'Give me a recipe for a pizza with pineapple'
)
```
Create images from text:
```python
output = jinaai.imagine(
'A controversial fusion of sweet pineapple and savory pizza.'
)
```
Use APIs together:
```python
situations = [toBase64(img) for img in [
'factory-1.png',
'factory-2.png',
'factory-3.png',
'factory-4.png',
]]
descriptions = jinaai.describe(situations)
prompt1 = [
'Do any of those situations present a danger?',
'Reply with [YES] or [NO] and explain why',
*['SITUATION:\n' + desc['output'] for i, desc in enumerate(descriptions['results'])]
]
analysis = jinaai.generate('\n'.join(prompt1))
prompt2 = [
'What should be done first to make those situations safer?',
'I only want the most urgent situation',
*['SITUATION:\n' + desc['output'] for i, desc in enumerate(descriptions['results'])]
]
recommendation = jinaai.generate('\n'.join(propmt2))
swot = jinaai.decide(
recommendation['output'],
{ 'analysis': 'swot' }
)
banners = jinaai.imagine(
*[desc['output'] for i, desc in enumerate(descriptions['results'])]
)
```
## Raw Output
You can retrieve the raw output of each APIs by passing `raw: True` in the options:
```python
descriptions = jinaai.describe(
'https://picsum.photos/200',
{ 'raw': True }
)
print(descriptions['raw'])
```
## Custom Base Urls
Custom base Urls can be passed directly in the client's constructor:
```python
jinaai = JinaAI(
baseUrls={
'promptperfect': 'https://promptperfect-customurl.jina.ai',
'scenex': 'https://scenex-customurl.jina.ai',
'rationale': 'https://rationale-customurl.jina.ai',
'jinachat': 'https://jinachat-customurl.jina.ai',
'bestbanner': 'https://bestbanner-customurl.jina.ai',
}
)
```
## API Documentation
### JinaAi.describe
```python
output = JinaAI.describe(input, options)
```
- Input
>| VARIABLE | TYPE | VALUE
>|---------------------------------------|-------------------|----------
>| input | str / str array | Image URL or Base64
- Options
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| options | dict |
>| options['algorithm'] | None / str | Aqua / Bolt / Comet / Dune / Ember / Flash / Glide / Hearth / Inception / Jelly
>| options['features'] | None / str array | high_quality, question_answer, tts, opt-out, json
>| options['languages'] | None / str array | en, cn, de, fr, it...
>| options['question'] | None / str | Question related to the picture(s)
>| options['style'] | None / str | default / concise / prompt
>| options['output_length'] | None / number |
>| options['json_schema'] | None / dict |
>| options['callback_url'] | None / string |
- Output
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| output | dict |
>| output['results'] | dict array |
>| results[0]['output'] | str | The picture description
>| results[0]['i18n'] | dict | Contains one key for each item in languages
>| ...i18n['cn'] | str | The translated picture description
>| ...i18n['cn'] | dict array | Only for Hearth algorithm
>| ...i18n['cn'][0] | dict |
>| ...i18n['cn'][0]['message'] | str |
>| ...i18n['cn'][0]['isNarrator'] | boolean |
>| ...i18n['cn'][0]['name'] | str |
>| ...i18n['cn'] | dict array | Only for Inception algorithm
>| ...i18n['cn'][0] | dict |
>| ...i18n['cn'][0]['summary'] | str |
>| ...i18n['cn'][0]['events'] | dict array |
>| ...['events']['description'] | str |
>| ...['events']['timestamp'] | str |
>| results[0]['tts'] | dict | Only for Hearth algorithm
>| ...tts['cn'] | str | Contains the url to the tts file
>| results[0]['ssml'] | dict | Only for Hearth algorithm
>| ...ssml['cn'] | str | Contains the url to the ssml file
<br/>
### JinaAi.optimize
```python
output = JinaAI.optimize(input, options)
```
- Input
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| input | str / str array | Image URL or Base64 / prompt to optimize
- Options
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| options | dict |
>| options['targetModel'] | None / str | chatgpt / gpt-4 / stablelm-tuned-alpha-7b / claude / cogenerate / text-davinci-003 / dalle / sd / midjourney / kandinsky / lexica
>| options['features'] | None / str array | preview, no_spam, shorten, bypass_ethics, same_language, always_en, high_quality, redo_original_image, variable_subs, template_run
>| options['iterations'] | None / number | Default: 1
>| options['previewSettings'] | None / dict | Contains the settings for the preview
>| ...previewSettings['temperature'] | number | Example: 0.9
>| ...previewSettings['topP'] | number | Example: 0.9
>| ...previewSettings['topK'] | number | Example: 0
>| ...previewSettings['frequencyPenalty'] | number | Example: 0
>| ...previewSettings['presencePenalty'] | number | Example: 0
>| options['previewVariables'] | None / dict | Contains one key for each variables in the prompt
>| ...previewVariables['var1'] | str | The value of the variable
>| options['timeout'] | Number | Default: 20000
>| options['target_language'] | None / str | en / cn / de / fr / it...
- Output
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| output | dict |
>| output['results'] | dict array |
>| results[0]['output'] | str | The optimized prompt
<br/>
### JinaAi.decide
```python
output = JinaAI.decide(input, options)
```
- Input
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| input | str / str array | Decision to evaluate
- Options
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| options | dict |
>| options['analysis'] | None / str | proscons / swot / multichoice / outcomes
>| options['style'] | None / str | concise / professional / humor / sarcastic / childish / genZ
>| options['profileId'] | None / str | The id of the Personas you want to use
- Output
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| output | dict |
>| output['results'] | dict array |
>| results[0]['proscons'] | None / dict |
>| ...proscons['pros'] | dict | Contains one key for each pros
>| ...proscons['pros']['pros1'] | str | The explanation of the pros
>| ...proscons['cons'] | dict | Contains one key for each cons
>| ...proscons['cons']['cons1'] | str | The explanation of the cons
>| ...proscons['bestChoice'] | str |
>| ...proscons['conclusion'] | str |
>| ...proscons['confidenceScore'] | number |
>| results[0]['swot'] | None / dict |
>| ...swot['strengths'] | dict | Contains one key for each strength
>| ...swot['strengths']['str1'] | str | The explanation of the strength
>| ...swot['weaknesses'] | dict | Contains one key for each weakness
>| ...swot['weaknesses']['weak1'] | str | The explanation of the weakness
>| ...swot['opportunities'] | dict | Contains one key for each opportunity
>| ...swot['opportunities']['opp1'] | str | The explanation of the opportunity
>| ...swot['threats'] | dict | Contains one key for each threat
>| ...swot['threats']['thre1'] | str | The explanation of the threat
>| ...swot['bestChoice'] | str |
>| ...swot['conclusion'] | str |
>| ...swot['confidenceScore'] | number |
>| results[0]['multichoice'] | None / dict | Contains one key for each choice
>| ...multichoice['choice1'] | str | The value of the choice
>| results[0]['outcomes'] | None / dict array |
>| ...outcomes[0]['children'] | None / dict array | a recursive array of results['outcomes']
>| ...outcomes[0]['label'] | str |
>| ...outcomes[0]['sentiment'] | str |
<br/>
### JinaAi.generate
```python
output = JinaAI.generate(input, options)
```
- Input
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|------------------------|----------
>| input | str / str array | Image URL or Base64 / prompt
- Options
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|------------------------|----------
>| options | dict |
>| options['role'] | None / str | user / assistant
>| options['name'] | None / str | The name of the author of this message
>| options['chatId'] | None / str | The id of the conversation to continue
>| options['stream'] | None / boolean | Whether to stream back partial progress, Default: false
>| options['temperature'] | None / number | Default: 1
>| options['top_p'] | None / str | Default: 1
>| options['stop'] | None / str / str array | Up to 4 sequences where the API will stop generating further tokens
>| options['max_tokens'] | None / number | Default: infinite
>| options['presence_penalty'] | None / number | Number between -2.0 and 2.0, Default: 0
>| options['frequency_penalty'] | None / number | Number between -2.0 and 2.0, Default: 0
>| options['logit_bias'] | None / dict | The likelihood for a token to appear in the completion
>| ...logit_bias['tokenId'] | number | Bias value from -100 to 100
>| options['image'] | str | The attached image of the message. The image can be either a URL or a base64-encoded string
- Output
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| output | dict |
>| output['output'] | str | The generated answer
>| output['chatId'] | str | The chatId to continue the conversation
<br/>
### JinaAi.imagine
```python
output = JinaAI.imagine(input, options)
```
- Input
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|------------------------|----------
>| input | str / str array | Prompt
- Options
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|------------------------|----------
>| options | dict |
>| options['style'] | None / str | default / photographic / minimalist / flat
- Output
>| VARIABLE | TYPE | VALUE
>|----------------------------------------|-------------------|----------
>| output | dict |
>| output['results'] | dict array |
>| results[0]['output'] | array | array of 4 image urls
<br/>
### JinaAi.utils
```python
outout = JinaAI.utils.image_to_base64(input)
```
>| VARIABLE | TYPE | VALUE
>|---------------------------------------|-------------------|----------
>| input | str | Image path on disk
>| output | str | Base64 image
```python
outout = JinaAI.utils.is_url(input)
```
>| VARIABLE | TYPE | VALUE
>|---------------------------------------|-------------------|----------
>| input | str |
>| output | boolean |
```python
outout = JinaAI.utils.is_base64(input)
```
>| VARIABLE | TYPE | VALUE
>|---------------------------------------|-------------------|----------
>| input | str |
>| output | boolean |
Raw data
{
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"name": "jinaai",
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"requires_python": "",
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"author": "Jina AI",
"author_email": "guillaume.roncari@jina.ai",
"download_url": "https://files.pythonhosted.org/packages/3a/5a/3c5062541d0cddf33d3295a2ec696aa45ee199e61341c831e96cdde96d16/jinaai-0.2.10.tar.gz",
"platform": null,
"description": "# JinaAI Python SDK\n\nThe JinaAI Python SDK is an efficient instrument that smoothly brings the power of JinaAI's products \u2014 [SceneXplain](https://scenex.jina.ai), [PromptPerfect](https://promptperfect.jina.ai/), [Rationale](https://rationale.jina.ai/), [BestBanner](https://bestbanner.jina.ai/), and [JinaChat](https://chat.jina.ai/) \u2014 into Python applications. Acting as a sturdy interface for JinaAI's APIs, this SDK lets you effortlessly formulate and fine-tune prompts, thus streamlining application development.\n\n## Installing\n\n### Package manager\n\nUsing pip:\n```bash\n$ pip install jinaai\n```\n\n## API secrets\n\nTo generate an API secret, you need to authenticate on each respective platform's API tab:\n\n- [SceneXplain API](https://scenex.jina.ai/api)\n- [PromptPerfect API](https://promptperfect.jina.ai/api)\n- [Rationale API](https://rationale.jina.ai/api)\n- [JinaChat API](https://chat.jina.ai/api)\n- [BestBanner API](https://bestbanner.jina.ai/api)\n\n> **Note:** Each secret is product-specific and cannot be interchanged. If you're planning to use multiple products, you'll need to generate a separate secret for each.\n\n## Example usage\n\n\nImport the SDK and instantiate a new client with your authentication secrets:\n\n```python\nfrom jinaai import JinaAI\n\njinaai = JinaAI(\n secrets = {\n 'promptperfect-secret': 'XXXXXX',\n 'scenex-secret': 'XXXXXX',\n 'rationale-secret': 'XXXXXX',\n 'jinachat-secret': 'XXXXXX',\n 'bestbanner-secret': 'XXXXXX',\n }\n)\n```\n\nDescribe images:\n\n```python\ndescriptions = jinaai.describe(\n 'https://picsum.photos/200'\n)\n```\n\nEvaluate situations:\n\n```python\ndecisions = jinaai.decide(\n 'Going to Paris this summer', \n { 'analysis': 'proscons' }\n)\n```\n\nOptimize prompts:\n\n```python\nprompts = jinaai.optimize(\n 'Write an Hello World function in Python'\n)\n```\n\nGenerate complex answers:\n\n```python\noutput = jinaai.generate(\n 'Give me a recipe for a pizza with pineapple'\n)\n```\n\nCreate images from text:\n\n```python\noutput = jinaai.imagine(\n 'A controversial fusion of sweet pineapple and savory pizza.'\n)\n```\n\nUse APIs together:\n\n```python\nsituations = [toBase64(img) for img in [\n 'factory-1.png',\n 'factory-2.png',\n 'factory-3.png',\n 'factory-4.png',\n]]\n\ndescriptions = jinaai.describe(situations)\n\nprompt1 = [\n 'Do any of those situations present a danger?',\n 'Reply with [YES] or [NO] and explain why',\n *['SITUATION:\\n' + desc['output'] for i, desc in enumerate(descriptions['results'])]\n]\n\nanalysis = jinaai.generate('\\n'.join(prompt1))\n\nprompt2 = [\n 'What should be done first to make those situations safer?',\n 'I only want the most urgent situation',\n *['SITUATION:\\n' + desc['output'] for i, desc in enumerate(descriptions['results'])]\n]\n\nrecommendation = jinaai.generate('\\n'.join(propmt2))\n\nswot = jinaai.decide(\n recommendation['output'],\n { 'analysis': 'swot' }\n)\n\nbanners = jinaai.imagine(\n *[desc['output'] for i, desc in enumerate(descriptions['results'])]\n)\n```\n\n## Raw Output\n\nYou can retrieve the raw output of each APIs by passing `raw: True` in the options:\n\n```python\ndescriptions = jinaai.describe(\n 'https://picsum.photos/200',\n { 'raw': True }\n)\n\nprint(descriptions['raw'])\n```\n\n## Custom Base Urls\n\nCustom base Urls can be passed directly in the client's constructor:\n\n```python\njinaai = JinaAI(\n baseUrls={\n 'promptperfect': 'https://promptperfect-customurl.jina.ai',\n 'scenex': 'https://scenex-customurl.jina.ai',\n 'rationale': 'https://rationale-customurl.jina.ai',\n 'jinachat': 'https://jinachat-customurl.jina.ai',\n 'bestbanner': 'https://bestbanner-customurl.jina.ai',\n }\n)\n```\n\n## API Documentation\n\n### JinaAi.describe\n\n```python\noutput = JinaAI.describe(input, options)\n```\n\n- Input\n\n>| VARIABLE | TYPE | VALUE \n>|---------------------------------------|-------------------|----------\n>| input | str / str array | Image URL or Base64\n\n- Options\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| options | dict | \n>| options['algorithm'] | None / str | Aqua / Bolt / Comet / Dune / Ember / Flash / Glide / Hearth / Inception / Jelly\n>| options['features'] | None / str array | high_quality, question_answer, tts, opt-out, json\n>| options['languages'] | None / str array | en, cn, de, fr, it...\n>| options['question'] | None / str | Question related to the picture(s)\n>| options['style'] | None / str | default / concise / prompt\n>| options['output_length'] | None / number |\n>| options['json_schema'] | None / dict |\n>| options['callback_url'] | None / string |\n\n- Output\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| output | dict | \n>| output['results'] | dict array | \n>| results[0]['output'] | str | The picture description\n>| results[0]['i18n'] | dict | Contains one key for each item in languages\n>| ...i18n['cn'] | str | The translated picture description\n>| ...i18n['cn'] | dict array | Only for Hearth algorithm\n>| ...i18n['cn'][0] | dict | \n>| ...i18n['cn'][0]['message'] | str | \n>| ...i18n['cn'][0]['isNarrator'] | boolean | \n>| ...i18n['cn'][0]['name'] | str | \n>| ...i18n['cn'] | dict array | Only for Inception algorithm\n>| ...i18n['cn'][0] | dict | \n>| ...i18n['cn'][0]['summary'] | str | \n>| ...i18n['cn'][0]['events'] | dict array | \n>| ...['events']['description'] | str | \n>| ...['events']['timestamp'] | str | \n>| results[0]['tts'] | dict | Only for Hearth algorithm\n>| ...tts['cn'] | str | Contains the url to the tts file\n>| results[0]['ssml'] | dict | Only for Hearth algorithm\n>| ...ssml['cn'] | str | Contains the url to the ssml file\n\n<br/>\n\n### JinaAi.optimize\n\n```python\noutput = JinaAI.optimize(input, options)\n```\n\n- Input\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| input | str / str array | Image URL or Base64 / prompt to optimize\n\n- Options\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| options | dict | \n>| options['targetModel'] | None / str | chatgpt / gpt-4 / stablelm-tuned-alpha-7b / claude / cogenerate / text-davinci-003 / dalle / sd / midjourney / kandinsky / lexica\n>| options['features'] | None / str array | preview, no_spam, shorten, bypass_ethics, same_language, always_en, high_quality, redo_original_image, variable_subs, template_run\n>| options['iterations'] | None / number | Default: 1\n>| options['previewSettings'] | None / dict | Contains the settings for the preview\n>| ...previewSettings['temperature'] | number | Example: 0.9\n>| ...previewSettings['topP'] | number | Example: 0.9\n>| ...previewSettings['topK'] | number | Example: 0\n>| ...previewSettings['frequencyPenalty'] | number | Example: 0\n>| ...previewSettings['presencePenalty'] | number | Example: 0\n>| options['previewVariables'] | None / dict | Contains one key for each variables in the prompt\n>| ...previewVariables['var1'] | str | The value of the variable\n>| options['timeout'] | Number | Default: 20000\n>| options['target_language'] | None / str | en / cn / de / fr / it...\n\n- Output\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| output | dict | \n>| output['results'] | dict array | \n>| results[0]['output'] | str | The optimized prompt\n\n<br/>\n\n### JinaAi.decide\n\n```python\noutput = JinaAI.decide(input, options)\n```\n\n- Input\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| input | str / str array | Decision to evaluate\n\n- Options\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| options | dict | \n>| options['analysis'] | None / str | proscons / swot / multichoice / outcomes\n>| options['style'] | None / str | concise / professional / humor / sarcastic / childish / genZ\n>| options['profileId'] | None / str | The id of the Personas you want to use\n\n- Output\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| output | dict | \n>| output['results'] | dict array | \n>| results[0]['proscons'] | None / dict |\n>| ...proscons['pros'] | dict | Contains one key for each pros\n>| ...proscons['pros']['pros1'] | str | The explanation of the pros\n>| ...proscons['cons'] | dict | Contains one key for each cons\n>| ...proscons['cons']['cons1'] | str | The explanation of the cons\n>| ...proscons['bestChoice'] | str | \n>| ...proscons['conclusion'] | str | \n>| ...proscons['confidenceScore'] | number | \n>| results[0]['swot'] | None / dict |\n>| ...swot['strengths'] | dict | Contains one key for each strength\n>| ...swot['strengths']['str1'] | str | The explanation of the strength\n>| ...swot['weaknesses'] | dict | Contains one key for each weakness\n>| ...swot['weaknesses']['weak1'] | str | The explanation of the weakness\n>| ...swot['opportunities'] | dict | Contains one key for each opportunity\n>| ...swot['opportunities']['opp1'] | str | The explanation of the opportunity\n>| ...swot['threats'] | dict | Contains one key for each threat\n>| ...swot['threats']['thre1'] | str | The explanation of the threat\n>| ...swot['bestChoice'] | str | \n>| ...swot['conclusion'] | str | \n>| ...swot['confidenceScore'] | number | \n>| results[0]['multichoice'] | None / dict | Contains one key for each choice\n>| ...multichoice['choice1'] | str | The value of the choice\n>| results[0]['outcomes'] | None / dict array |\n>| ...outcomes[0]['children'] | None / dict array | a recursive array of results['outcomes']\n>| ...outcomes[0]['label'] | str | \n>| ...outcomes[0]['sentiment'] | str | \n\n<br/>\n\n### JinaAi.generate\n\n```python\noutput = JinaAI.generate(input, options)\n```\n\n- Input\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|------------------------|----------\n>| input | str / str array | Image URL or Base64 / prompt\n\n- Options\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|------------------------|----------\n>| options | dict | \n>| options['role'] | None / str | user / assistant\n>| options['name'] | None / str | The name of the author of this message\n>| options['chatId'] | None / str | The id of the conversation to continue\n>| options['stream'] | None / boolean | Whether to stream back partial progress, Default: false\n>| options['temperature'] | None / number | Default: 1\n>| options['top_p'] | None / str | Default: 1\n>| options['stop'] | None / str / str array | Up to 4 sequences where the API will stop generating further tokens\n>| options['max_tokens'] | None / number | Default: infinite\n>| options['presence_penalty'] | None / number | Number between -2.0 and 2.0, Default: 0\n>| options['frequency_penalty'] | None / number | Number between -2.0 and 2.0, Default: 0\n>| options['logit_bias'] | None / dict | The likelihood for a token to appear in the completion\n>| ...logit_bias['tokenId'] | number | Bias value from -100 to 100\n>| options['image'] | str | The attached image of the message. The image can be either a URL or a base64-encoded string\n\n- Output\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| output | dict | \n>| output['output'] | str | The generated answer\n>| output['chatId'] | str | The chatId to continue the conversation\n\n<br/>\n\n### JinaAi.imagine\n\n```python\noutput = JinaAI.imagine(input, options)\n```\n\n- Input\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|------------------------|----------\n>| input | str / str array | Prompt\n\n- Options\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|------------------------|----------\n>| options | dict | \n>| options['style'] | None / str | default / photographic / minimalist / flat\n\n- Output\n\n>| VARIABLE | TYPE | VALUE \n>|----------------------------------------|-------------------|----------\n>| output | dict | \n>| output['results'] | dict array |\n>| results[0]['output'] | array | array of 4 image urls\n\n<br/>\n\n### JinaAi.utils\n\n```python\noutout = JinaAI.utils.image_to_base64(input)\n```\n\n>| VARIABLE | TYPE | VALUE \n>|---------------------------------------|-------------------|----------\n>| input | str | Image path on disk\n>| output | str | Base64 image\n\n```python\noutout = JinaAI.utils.is_url(input)\n```\n\n>| VARIABLE | TYPE | VALUE \n>|---------------------------------------|-------------------|----------\n>| input | str | \n>| output | boolean | \n\n```python\noutout = JinaAI.utils.is_base64(input)\n```\n\n>| VARIABLE | TYPE | VALUE \n>|---------------------------------------|-------------------|----------\n>| input | str | \n>| output | boolean | \n\n\n",
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