architecture.py

# -*- coding: utf-8 -*-
"""Untitled4.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1eV4-E7VDAHekOoakymatKd3grGwUSTAC
"""

from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

class LegalLLMArchitecture:
    def __init__(self):
        # Document existing architecture choices
        self.base_model = ChatGroq(model="llama-3.1-70b-versatile", temperature=0)
        self.embeddings = HuggingFaceEmbeddings()

    def architecture_modifications(self):
        """
        Document our architectural modifications:
        1. RAG Integration
        - Added vector store for legal document retrieval
        - Implemented similarity search

        2. Custom Memory Management
        - Implemented ConversationBufferMemory for context retention

        3. Chain Architecture
        - Added ConversationalRetrievalChain for document retrieval
        - Custom prompt templates for legal domain
        """
        return {
            "rag_enabled": True,
            "memory_type": "ConversationBufferMemory",
            "chain_type": "ConversationalRetrievalChain",
            "modifications": [
                "Legal domain-specific prompt engineering",
                "Vector store integration",
                "Similarity search implementation",
                "Conversation memory management"
            ]
        }