servifai 0.1.1

A mini framework built on top of Langchain and Llamaindex to provide LLM powered Autonomous Agents as a simplified service to assist users with their tasks

ServifAI

A mini framework built on top of Langchain and Llamaindex to provide LLM powered Autonomous Agents as a simplified service to assist users with their tasks.

Overview

ServifAI (Task-based Agent) = LLM + Memory + Planning + Toolbox

Instead of feeding all kinds of tools to a single agent and confusing it while selection, ServifAI narrows down the selection by combining only necessary tools on basis of the task at hand.

Read this article to understand How Agents works.

By default, ServifAI can chat while browsing internet and solving common math problems.

Current Toolbox Status:

• Default (DuckDuckgo + LLM Math + PAL Math)
• QA Knowledge Base (Vector Index + Knowledge Graphs)

Installation

Works best with Poetry

poetry add servifai

With pip, you might have to install dependencies manually

pip install servifai

Usage

Create a .env file

OPENAI_API_KEY='sk-...'

Run Python Code

from servifai import ServifAI
myagent = ServifAI()

while True:
    text_input = input("Me: ")
    response = myagent.query(text_input)
    print(f'ServifAI: {response}\n')

Output

Me: Hi, How are you?
ServifAI: Hi, I'm an AI language model, so I don't have feelings, but I'm here to help you with any questions or tasks you have!

Me: What is the current weather of Bengaluru?
ServifAI: The current weather in Bengaluru is mostly cloudy with a temperature of 81°F (27°C). The wind is coming from the north at 3 mph (5 km/h). Tomorrow's temperature is expected to be nearly the same as today.

Data Creation Recipe for Local Knowledge Extraction Tasks

Consider the example of Uber 10Q filings.

• Download the quaterly reports for year 2022 and 2023 as pdf and save it locally in a directory (here reports).
• Create a config YAML file uber_10q.yaml inside a configs dir and fill details as:

task: 'qa_knowledge_base'

vectordb:
  dir: "uber_10q"

data:
  dir: "reports"
  about: "Uber 10Q Filing"

text:
  max_input_size: 2500
  num_outputs: 1000
  max_chunk_overlap: 0.05
  chunk_size_limit: 1000

llm:
  org: openai
  temperature: 0
  model_name: "gpt-3.5-turbo"

• As the task is to extract information from these pdfs, so task chosen is qa_knowledge_base. Based on these tasks we choose our toolbox which contains specific tools required for task completion. We will be adding more toolbox.
• To achieve optimum results, its recommended to rename your pdfs as few words description. For example, we rename quarterly 10Q reports as Q1-23.pdf, Q4-22.pdf etc. Do not add blank spaces between words, instead use hyphen -.
• Also in config file in data.about, provide a concise common summary of all these multiple pdfs. For example, here we write this as Uber 10Q Filing.
• Run Python Code

from servifai import ServifAI
myagent = ServifAI('configs/uber_10q.yaml')

while True:
    text_input = input("Me: ")
    response = myagent.query(text_input)
    print(f'ServifAI: {response}\n')

• Output

Me: Analyze the revenue growth of Uber across quarters
ServifAI: Based on the provided context information, the revenue growth of Uber across quarters can be summarized as follows:

- Q3 2022: Revenue growth of 72% year-over-year or 81% on a constant currency basis.
- Q4 2022: Revenue growth of 59% on a constant currency basis, significantly outpacing the 19% growth in Gross Bookings.
- Q1 2023: Revenue of $8.8 billion is mentioned, but the growth rate for this quarter is not provided.

Therefore, the specific revenue growth rate for Q1 2023 cannot be determined with the given information.

Me: How much cash did Uber have in last quarter of 2022?
ServifAI: Based on the provided context, the cash balance for Uber at the end of Q4 2022 was $4.3 billion.

TODO:

• Add other task based tools
• Add support for Local LLMs
• Add support for other VectorDBs
• Add support for other unstructured data
• Add support for structured data
• OpenAI funcs