An orchestration framework for all your AI needs.
Installation
bash
pip install empire_chain
RAG Example
python
from empire_chain.vector_stores import QdrantVectorStore
from empire_chain.embeddings import OpenAIEmbeddings
from empire_chain.llms import OpenAILLM
from empire_chain.file_reader import DocumentReader
import os
from dotenv import load_dotenv
def main():
load_dotenv()
vector_store = QdrantVectorStore(":memory:")
embeddings = OpenAIEmbeddings("text-embedding-3-small")
llm = OpenAILLM("gpt-4o-mini")
reader = DocumentReader()
file_path = "input.pdf"
text = reader.read(file_path)
text_embedding = embeddings.embed(text)
vector_store.add(text, text_embedding)
query = "What is the main topic of this document?"
query_embedding = embeddings.embed(query)
relevant_texts = vector_store.query(query_embedding, k=3)
context = "\n".join(relevant_texts)
prompt = f"Based on the following context, {query}\n\nContext: {context}"
response = llm.generate(prompt)
print(f"Query: {query}")
print(f"Response: {response}")
if __name__ == "__main__":
main()
PhiData Agents
python
from empire_chain.phidata_agents import PhiWebAgent, PhiFinanceAgent
from dotenv import load_dotenv
load_dotenv()
web_agent = PhiWebAgent()
web_agent.generate("What is the recent news about Tesla with sources?")
finance_agent = PhiFinanceAgent()
finance_agent.generate("What is the price of Tesla?")
Simple Chatbot
python
from empire_chain.streamlit import Chatbot
from empire_chain.llms import OpenAILLM
chatbot = Chatbot(llm=OpenAILLM("gpt-4o-mini"), title="Empire Chain Chatbot")
chatbot.chat()
Vision Chatbot
python
from empire_chain.streamlit import VisionChatbot
chatbot = VisionChatbot(title="Empire Chain Chatbot")
chatbot.chat()
PDF Chatbot
python
from empire_chain.streamlit import PDFChatbot
from empire_chain.llms import OpenAILLM
from empire_chain.vector_stores import QdrantVectorStore
from empire_chain.embeddings import OpenAIEmbeddings
pdf_chatbot = PDFChatbot(title="PDF Chatbot", llm=OpenAILLM("gpt-4o-mini"), vector_store=QdrantVectorStore(":memory:"), embeddings=OpenAIEmbeddings("text-embedding-3-small"))
pdf_chatbot.chat()
Visualizer
python
from empire_chain.visualizer import DataAnalyzer, ChartFactory
data = """
Empire chain has secured a $100M Series A funding round from Sequoia Capital in 2024 and a $10M Series B funding round from Tiger Global in 2025.
...
"""
analyzer = DataAnalyzer()
analyzed_data = analyzer.analyze(data)
chart = ChartFactory.create_chart('Bar Graph', analyzed_data)
chart.show()
Docling
python
from empire_chain.docling import Docling
docling = Docling()