aibridge-test 0.0.1

Bridge for LLM"s

AIBridge 0.0.1

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

Requirement

• Python 3

Install The Test package

pip install aibridge-test

Set config.yaml

• Setting the the config.yaml at your desire choice
• In .env file set the path for file.

Example in bash we set:
export AIBRIDGE_CONFIG=C:/Users/Admin/aibridge/config.yaml
variable name for env:AIBRIDGE_CONFIG

starter file.
group_name: consumer gorup name
message_queue: redis
no_of_threads: 1
open_ai:
- key: Api_key
  priority: equal
- key: APi_key
  priority: equal
redis_host: localhost
redis_port: port _for redis
stream_name: redis topic

Configuration

• With the help of the AIBridge we can svae the prompts in your sql/nosql data bases by default the :sqllite on the disc space
• To configure the database you have add the data in config file.

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))

• If you want use the sqllite on the disc no need to configure these for db support
• Currently AI bridge support only the OPEN_AI api:

from AIBridge import SetConfig
SetConfig.set_api_key(ai_service="open_ai",key="YOUR_API_KEY",priority="high")
#priority:high/medium/low/equal

Prompt save

• Prompt save mechanism is used the save the reusable and extraordinary prompts that give you the exceptional result from LLM

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)

variables

• why variables?-> variables are the specific data used the get the desired and with context from ai LLM's.
• These is the example of the self consistance prompts Ref:https://www.promptingguide.ai/techniques/consistency
• Varibales methods:

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)

Get Response

• LLm=open_ai
• default_model="gpt-3.4-turbo-oo3"
• Max_toke count - 3500
• temprature set to 0.5 methods

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",
    }
}

Message Queue

• default Queue=redis,

Configure redis

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
)

• To use the Queue service set message_queue = True

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'}

• Message queue is for increasing scalibilty
• for APplication server you have turn on the consumer when application getting started.

from AIBridge import MessageQ

# to start the consumer in background 
MessageQ.mq_deque()

• In the non application environmen:
  • you can run set message queue=True
• Run the below function to procees the Stream data in consumer

from AIBridge import MessageQ
# these for testingthe redis env in local on single page file
data = MessageQ.local_process()
print(data)