reconify 2.0.0

A package for sending data to the Reconify platform

Reconify PIP module

The Reconify module is used for sending data to the Reconify platform at www.reconify.com.

Currently the module supports processing and analyzing Chats, Completions, and Images from OpenAI and the following foundational models using Amazon Bedrock: AI21 Jurassic, Anthropic Claude, Cohere Command, and Stability Stable Diffusion.

Support for additional actions and providers will be added.

Get started

The first step is to create an account at app.reconify.com.

Generate API and APP Keys

In the Reconify console, add an Application to your account. This will generate both an API_KEY and an APP_KEY which will be used in the code below to send data to Reconify.

Install the module

pip install reconify

Integrate the module with OpenAI

The following instructions are for OpenAI's Python SDK v1 or later (released in Nov 2023). For earlier versions of OpenAI's SDK, follow the legacy instructions

Import the module

from reconify import reconifyOpenAIHandler

Initialize the module

Prior to initializing the Reconify module, make sure to import the OpenaAI module.

from openai import OpenAI
openai_client = OpenAI(api_key = 'YOUR_OPENAI_KEY')

Configure the instance of Reconify passing the OpenAi instance along with the Reconify API_KEY and APP_KEY created above.

reconifyOpenAIHandler.config(openai_client, 
   appKey = 'Your_App_Key', 
   apiKey = 'Your_Api_Key'
)

This is all that is needed for a basic integration. The module takes care of sending the correct data to Reconify when you call openai_client.completions.create, openai_client.chat.completions.create, openai_client.images.generate.

Optional Config Parameters

There are additional optional parameters that can be passed in to the handler.

• debug: (default False) Enable/Disable console logging
• trackImages: (default True) Turn on/off tracking of createImage

For example:

reconifyOpenAIHandler.config(openai_client, 
   appKey = 'Your_App_Key', 
   apiKey = 'Your_Api_Key',
   debug = True
)

Optional methods

You can optionally pass in a user object or session ID to be used in the analytics reporting. The session ID will be used to group interactions together in the same session transcript.

Set a user

The user JSON should include a unique userId, all the other fields are optional. Without a unique userId, each user will be treated as a new user.

reconifyOpenAIHandler.setUser ({
   "userId": "ABC123",
   "isAuthenticated": 1,
   "firstName": "Francis",
   "lastName": "Smith",
   "email": "",
   "phone": "",
   "gender": "female"
})

Set a Session ID

The Session ID is an alphanumeric string.

reconifyOpenAIHandler.setSession('MySessionId')

Set Session Timeout

Set the session timeout in minutes to override the default

reconifyOpenAIHandler.setSessionTimeout(15)

Integrate the module with Amazon Bedrock Runtime

Import the module

from reconify import reconifyBedrockRuntimeHandler

Initialize the module

Prior to initializing the Reconify module, make sure to import the Amazon boto3 module.

import boto3
bedrock = boto3.client('bedrock-runtime')

Configure the instance of Reconify passing the Bedrock Runtime instance along with the Reconify API_KEY and APP_KEY created above.

reconifyBedrockRuntimeHandler.config(bedrock, 
   appKey = 'Your_App_Key', 
   apiKey = 'Your_Api_Key'
)

This is all that is needed for a basic integration. The module takes care of sending the correct data to Reconify when you call bedrock.invoke_model().

Response handling

When using the Reconify module, the response body from invoke_model will be converted from botocore.response.StreamingBody to JSON and saved in the response as parsedBody. See the examples below for more info.

Optional Config Parameters

There are additional optional parameters that can be passed in to the handler.

• debug: (default False) Enable/Disable console logging
• trackImages: (default True) Turn on/off tracking of createImage

For example:

reconifyBedrockRuntimeHandler.config(bedrock, 
   appKey = 'Your_App_Key', 
   apiKey = 'Your_Api_Key',
   debug = True
)

Optional methods

You can optionally pass in a user object or session ID to be used in the analytics reporting. The session ID will be used to group interactions together in the same session transcript.

Set a user

The user JSON should include a unique userId, all the other fields are optional. Without a unique userId, each user will be treated as a new user.

reconifyBedrockRuntimeHandler.setUser ({
   "userId": "ABC123",
   "isAuthenticated": 1,
   "firstName": "Francis",
   "lastName": "Smith",
   "email": "",
   "phone": "",
   "gender": "female"
})

Set a Session ID

The Session ID is an alphanumeric string.

reconifyBedrockRuntimeHandler.setSession('MySessionId')

Set Session Timeout

Set the session timeout in minutes to override the default

reconifyBedrockRuntimeHandler.setSessionTimeout(15)

Examples with OpenAI

Chat Example

from openai import OpenAI
from reconify import reconifyOpenAIHandler

openai_client = OpenAI(api_key = 'YOUR_OPENAI_KEY')

reconifyOpenAIHandler.config(openai_client, 'Your_App_Key', 'Your_Api_Key')

reconifyOpenAIHandler.setUser({
   "userId": "12345",
   "isAuthenticated": 1,
   "firstName": "Jim",
   "lastName": "Stand",
   "gender": "male"
})

response = openai_client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are an expert on commedians."},
        {"role": "user", "content": "Tell a joke about cats"},
    ],
    temperature=0,
)

Completion Example

from openai import OpenAI
from reconify import reconifyOpenAIHandler

openai_client = OpenAI(api_key = 'YOUR_OPENAI_KEY')

reconifyOpenAIHandler.config(openai_client, 'Your_App_Key', 'Your_Api_Key')

reconifyOpenAIHandler.setUser({
   "userId": "12345",
   "isAuthenticated": 1,
   "firstName": "Jim",
   "lastName": "Stand",
   "gender": "male"
})

response = openai_client.completions.create(
   model = "text-davinci-003",
   prompt = "write a haiku about cats",
   max_tokens = 100,
   temperature = 0,
)

Image Example

from openai import OpenAI
from reconify import reconifyOpenAIHandler

openai_client = OpenAI(api_key = 'YOUR_OPENAI_KEY')

reconifyOpenAIHandler.config(openai_client, 'Your_App_Key', 'Your_Api_Key')

reconifyOpenAIHandler.setUser({
   "userId": "12345",
   "isAuthenticated": 1,
   "firstName": "Jim",
   "lastName": "Stand",
   "gender": "male"
})

response = openai_client.images.generate(
   model = "dall-e-3",
   prompt = "a cat on the moon",
   n = 1,
   size = "1024x1024",
   quality="standard",
   response_format = "url"
)

Examples with Amazon Bedrock Runtime

Anthropic Claude example

import boto3
from reconify import reconifyBedrockRuntimeHandler

bedrock = boto3.client('bedrock-runtime')

reconifyBedrockRuntimeHandler.config(bedrock, 'Your_App_Key', 'Your_Api_Key')

reconifyOpenAIHandler.setUser({
   "userId": "12345",
   "firstName": "Jane",
   "lastName": "Smith"
})

response = bedrock.invoke_model(
    modelId = "anthropic.claude-instant-v1",
    contentType = "application/json",
    accept = "application/json",
    body = "{\"prompt\":\"\\n\\nHuman: Tell a cat joke.\\n\\nAssistant:\",\"max_tokens_to_sample\":300,\"temperature\":1,\"top_k\":250,\"top_p\":0.999,\"stop_sequences\":[\"\\n\\nHuman:\"],\"anthropic_version\":\"bedrock-2023-05-31\"}"
)

#The botocore.response.StreamingBody object will be converted to JSON and saved in parsedBody
print(response.get("parsedBody"))

Stable Diffusion image example

import boto3
from reconify import reconifyBedrockRuntimeHandler

bedrock = boto3.client('bedrock-runtime')

reconifyBedrockRuntimeHandler.config(bedrock, 'Your_App_Key', 'Your_Api_Key')

reconifyOpenAIHandler.setUser({
   "userId": "12345",
   "firstName": "Jane",
   "lastName": "Smith"
})

response = bedrock.invoke_model(
    modelId = "stability.stable-diffusion-xl-v0",
    contentType = "application/json",
    accept = "application/json",
    body = "{\"text_prompts\":[{\"text\":\"a cat drinking boba tea\"}],\"cfg_scale\":10,\"seed\":0,\"steps\":50}"
)
#The botocore.response.StreamingBody object will be converted to JSON and saved in parsedBody
#The following will print out the image result in JSON base64
print(response.get("parsedBody"))