Spaces:
Runtime error
Runtime error
File size: 2,209 Bytes
1f891e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
"""
Tool for answering legal questions using a knowledge base.
"""
from langchain.tools import BaseTool
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from AI_core.config import LLM
class LegalQATool(BaseTool):
"""Tool to answer legal questions using a knowledge base."""
name: str = "legal_qa_tool"
description: str = "Answers legal questions using a knowledge base of laws and regulations."
memory: ConversationBufferMemory = None
def __init__(self):
"""Initialize the legal QA tool with conversation memory."""
super().__init__()
# Initialize memory in the constructor
self.memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
def _run(self, query: str) -> str:
"""
Answer legal questions using a knowledge base.
Args:
query: Legal question to answer
Returns:
str: Answer to the legal question
"""
# In production environment:
# 1. Load vector store with legal documents
# 2. Create retriever from vector store
# 3. Create ConversationalRetrievalChain
template = """
You are a legal assistant specializing in answering legal questions.
Use your knowledge of laws and regulations to provide an accurate and helpful answer to the question.
Question: {question}
Provide a clear, concise answer citing relevant laws or precedents when appropriate.
Include a disclaimer that your answer is not legal advice.
"""
prompt = PromptTemplate(
template=template,
input_variables=["question"]
)
qa_chain = LLMChain(
llm=LLM,
prompt=prompt
)
response = qa_chain.run(question=query)
# Update conversation memory
self.memory.chat_memory.add_user_message(query)
self.memory.chat_memory.add_ai_message(response)
return response |