AIBridge is the python package with the support of the Multiple LLM's,User can utilised the Formatters ,prompts, varibales to get most of the LLM's Through the AIBridge
pip install aibridge-test
Example in bash we set:
export AIBRIDGE_CONFIG=C:/Users/Admin/aibridge/aibridge_config.yaml
variable name for env:AIBRIDGE_CONFIG
starter file.
&id001
data: *id001
group_name: my_consumer_group
message_queue: redis
no_of_threads: 1
open_ai:
- key: API KEY HERE
priority: equal
palm_api:
- key: API KEY HERE
priority: equal
stable_diffusion:
- key: API KEY HERE
priority: equal
redis_host: localhost
redis_port: Loacal Host
stream_name: my_stream
database: nosql
database_name: aibridge
database_uri: mongodb://localhost:27017
per_page: 10
from AIBridge impprt Setconfig
#call the config method
Setconfig.set_db_confonfig(database=sql,database_name=None,database_uri=None)
#parameters:
#database-sql/nosql
#database_uri: url of the databse of your choice(all sql support for no sql(Mongo))
from AIBridge import SetConfig
SetConfig.set_api_key(ai_service="open_ai",key="YOUR_API_KEY",priority="high")
#priority:high/medium/low/equal
from AIBridge import PromptInsertion
# save prompt
data = PromptInsertion.save_prompt(
prompt="your prompt:{{data}},context:{{context}}",
name="first_prompt",
prompt_data={"data": "what is purpose of the ozone here"},
variables={"context": "environment_context"},
)
print(data)
# parameters: prompt_data: is used to manipulatre the same prompt with diffrent context at realtime
# variables: is used to manipulate the prompt with fixed context as varibales is a specific data
#update prompt can see the prompt_data and variables, is used the get diffrent output from same prompt
data = PromptInsertion.update_prompt(
id="prompt_id",
name="updated_prompt",
prompt_data={"data": "write abouts the plastic pollution"},
variables={"context": "ocean_pollution"},
)
print(data)
#Get prompt from id
data = PromptInsertion.get_prompt(id="prompt_id")
print(data)
# pagination support for getting the all prompt
data = PromptInsertion.get_all_prompt(page=1)
print(data)
from AIBridge import VariableInsertion
# save varibales
# parameters: var_key: key for the varibales
# var_value: list of the string for the context
data = VariableInsertion.save_variables(
var_key="ochean_context",
var_value=[
"Ocean pollution is a significant environmental issue that poses a threat to marine life and ecosystems"
],
)
print(data)
# update the variables
data = VariableInsertion.update_variables(
id="variable_id",
var_key="updated_string",
var_value=["updated senetece about topics"],
)
print(data)
# get Variables from id
data = VariableInsertion.get_variable(id="variable_id")
# get all Variables pagination
data = VariableInsertion.get_all_variable(page=1)
from AIBridge import OpenAIService
import json
json_schema = json.dumps({"animal": ["list of animals"]})
xml_schema = "<animals><category>animal name</category></animals>"
csv = "name,category,species,age,weight,color,habitat"
data = OpenAIService.generate(
prompts=["name of the animals in the {{jungle}}"],
prompt_ids=None,
prompt_data=[{"jungle": "jungle"}],
variables=None,
output_format=["json"],
format_strcture=[json_schema],
model="gpt-3.5-turbo",
variation_count=1,
max_tokens=3500,
temperature=0.5,
message_queue=False,
)
print(data)
# Prameters
# prompts= list of the string that need to executed in session where output id dependant on each other,
# promts_ids= prompt id's list and so at a time ids will execute or prompts,
# prompt_data=[data of the every prompt id they required],
# variables=[ varibale dict of the prompt],
# output_format=["xml/json/csv/sql/"],
# format_strcture=[out put strcture of the prompt],
# model="gpt-3.5-turbo", model for completion api of the gpt
# variation_count = 1, n of the output require
# max_tokens = 3500, maximut token per out put
# temperature = 0.5, data consistecy
# message_queue=False, scalability purpose
output = {
"items": {
"response": [
{
"data": [
'{"animal": ["lion", "tiger", "elephant", "monkey", "snake", "gorilla", "leopard", "crocodile", "jaguar", "giraffe"]}'
]
}
],
"token_used": 85,
"created_at": 1689323114.9568439,
"ai_service": "open_ai",
}
}
from AIBridge import SetConfig
# set redis configuration
SetConfig.redis_config(
redis_host="localhost",
redis_port="port _for redis",
group_name="consumer gorup name",
stream_name="redis topic",
no_of_threads=1,#concurrent thread ypu want run for your application
)
from AIBridge import OpenAIService
import json
json_schema = json.dumps({"animal": ["list of animals"]})
data = OpenAIService.generate(
prompts=["name of the animals in the {{jungle}}"],
prompt_ids=None,
prompt_data=[{"jungle": "jungle"}],
variables=None,
output_format=["json"],
format_strcture=[json_schema],
message_queue=True# to activate message queue service
)
# to use the Queue service use the name set the message queue prameter = True
print(data)
*Response for above function is the id of the response stored in the databse
{ "response_id": "eaa61944-3216-4ba1-bec5-05842fb86d86" }
from AIBridge import MessageQ
# to start the consumer in background
MessageQ.mq_deque()
from AIBridge import MessageQ
# these for testingthe redis env in local on single page file
data = MessageQ.local_process()
print(data)
###Dalle image genration###
from AIBridge.ai_services.openai_images import OpenAIImage
images = OpenAIImage.generate(
prompts=["A sunlit indoor lounge area with a pool containing a flamingo"],
image_data=["image loacation or image url"],
mask_image=["image loacation or image url"],
variation_count=1,
process_type="edit",
)
print(images)
# prompts: is list string how many diffrent image we have to genearte
# image_data: is the lacation of the image in file or the image url
# mask_image: is the mask image with transpernet patch in the image where we want edit the images
# variation_count: is the number of image we want to generate
# prcess type : create, edit, variation,
# create is for genrating new images
# edit the image with the mask mask is compulsary to edi the images
# variation is for genrating new images of sama type
palm_api:
- key: AIz****************************QkkA(your-api_key)
priority: equal
from AIBridge import SetConfig
SetConfig.set_api_key(ai_service="palm_api",key="YOUR_API_KEY",priority="high")
#priority:high/medium/low/equal
```python
from AIBridge import PalmText
prompt = """
write paragraph about the {{prompting}}in ai and let user know what is the{{prompting}} and how the {{prompting}} works in genrative AI
"""
json_format = """{"text": "paragraph here"}"""
data = PalmText.generate(
prompts=[prompt],
prompt_data=[{"prompting": "model training"}],
output_format=["json"],
format_strcture=[json_format],
message_queue=True,
)
print(data)
# Prameters
# prompts= list of the string that need to executed in session where output id dependant on each other,
# promts_ids= prompt id's list and so at a time ids will execute or prompts,
# prompt_data=[data of the every prompt id they required],
# variables=[ varibale dict of the prompt],
# output_format=["xml/json/csv/sql/"],
# format_strcture=[out put strcture of the prompt],
# model="models/text-bison-001", model for generate api of the palm
# variation_count = 1-8, n of the output require
# max_tokens = default 10000, maximut token per out put, no limit for token"
# temperature = default-0.5, data consistecy
# message_queue=False, scalability purpose
from AIBridge import PalmChat
# An array of "ideal" interactions between the user and the model
examples = [
(
"What's up?",
"What isn't up?? The sun rose another day, the world is bright, anything is possible!",
),
(
"I'm kind of bored",
"How can you be bored when there are so many fun, exciting, beautiful experiences to be had in the world?",
),
]
data = PalmChat.generate(
messages="give the protype for the stack on c++",
context="carreer or growth advides",
variation_count=3,
message_queue=True,
)
print(data)
# mesages: text provided to chat
# context: on the what basis do you want start the chat
# examples: demo for the LLm to undersat the tone and what do you reaaly want(few shot prompt type)
# variation_count: how many variations of the context should be used
# message_queue: if true, the chat will return the messages in a queue
# temperature = default-0.5, data consistecy
stable_diffusion:
- key: API Key here
priority: equal
from AIBridge import StableDiffusion
data = StableDiffusion.generate(
prompts=["cat sitting on bench"],# prompts is the list for how many images you have to genrate per request
action="text2img",
)
print(data)
from AIBridge import StableDiffusion
init_image = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
data = StableDiffusion.generate(
prompts=["cat sitting on bench"],
image_data=[init_image], #image data is the list followed by the prompts for each prompt there should be one image data
action="img2img",
)
print(data)
from AIBridge import StableDiffusion
mask_image = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
data = StableDiffusion.generate(
prompts=["cat sitting on bench"],
image_data=[init_image],
mask_image=[mask_image],
action="inpaint",
)
print(data)
from AIBridge import StableDiffusion
data=StableDiffusion.fetch_image(id="image_id here")
print(Data)
{
"status": "success",
"id": 12202888,
"output": [
"https://pub-8b49af329fae499aa563997f5d4068a4.r2.dev/generations/e5cd86d3-7305-47fc-82c1-7d1a3b130fa4-0.png"
]
}
from AIBridge import StableDiffusion
data=StableDiffusion.system_load(id="image_id here")
print(Data)
{
"queue_num": 0,
"queue_time": 0,
"status": "ok"
}
cohere_api:
- key: API Key here
priority: equal
from AIBridge import SetConfig
SetConfig.set_api_key(ai_service="cohere_api",key="YOUR_API_KEY",priority="high")
#priority:high/medium/low/equal
from AIBridge import CohereApi
json_str = """{"text": "text here"}"""
csv_string = "name,animal_type,prey,predators"
xml_string = """<data><animal>animal information here</animal></data>"""
data = CohereApi.generate(
prompts=["give the me the more information about the {{animals}}"],
prompt_data=[{"animals": "animals"}],
format_strcture=[xml_string],
output_format=["xml"],
)
print(data)
# Prameters
# prompts= list of the string that need to executed in session where output id dependant on each other,
# promts_ids= prompt id's list and so at a time ids will execute or prompts,
# prompt_data=[data of the every prompt id they required],
# variables=[ varibale dict of the prompt],
# output_format=["xml/json/csv/sql/"],
# format_strcture=[out put strcture of the prompt],
# model="models/text-bison-001", model for generate api of the palm
# variation_count = the output require
# max_tokens = default 10000, maximut token per out put, no limit for token"
# temperature = default-0.5, data consistecy
# message_queue=False, scalability purpose
ai21_api:
- key: API Key here
priority: equal
from AIBridge import SetConfig
SetConfig.set_api_key(ai_service="ai21_api",key="YOUR_API_KEY",priority="high")
#priority:high/medium/low/equal
from AIBridge import AI21labsText
json_str = """{"text": "text here"}"""
csv_string = "name,animal_type,prey,predators"
xml_string = """<?xml version="1.0" encoding="UTF-8" ?><data><animal>animal information here</animal></data>"""
data = AI21labsText.generate(
prompts=["give the me the more information about the {{animals}}"],
prompt_data=[{"animals": "tigers"}],
format_strcture=[csv_string],
output_format=["csv"],
)
print(data)
# Prameters
# prompts= list of the string that need to executed in session where output id dependant on each other,
# promts_ids= prompt id's list and so at a time ids will execute or prompts,
# prompt_data=[data of the every prompt id they required],
# variables=[ varibale dict of the prompt],
# output_format=["xml/json/csv/sql/"],
# format_strcture=[out put strcture of the prompt],
# model="models/text-bison-001", model for generate api of the palm
# variation_count = the output require
# max_tokens = default 10000, maximut token per out put, no limit for token"
# temperature = default-0.5, data consistecy
# message_queue=False, scalability purpose
FAQs
Bridge for LLM"s
We found that aibridge-test demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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