servifai 0.1.0

A mini framework built on top of Langchain and Llamaindex to simplify creation of LLM powered Autonomous Agents

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.

Agent = LLM + memory + planning skills + tool use

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

Installation

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 support for Local LLMs
• Add support for other VectorDBs
• Add support for other unstructured data
• Add support for structured data