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from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains.retrieval import create_retrieval_chain
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
import os
from langchain_core.prompts import PromptTemplate
import logging
from embeddings.embedder import get_vectorstore
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelName)s - %(message)s'
)
logger = logging.getLogger(__name__)
rag_prompt_template = '''
You are a legal assistant trained to answer questions using legal documents.
If the answer cannot be determined from the available legal text, you must answer without include this phrase:
> According to Article <article>, Chapter <chapter>, <title>
For example:
> The answer cannot be determined from the available legal text.
Otherwise, you must STRICTLY follow this 3-step structure:
1. **Begin your response ONLY with this format** (fill in the values from metadata):
> According to Article <article>, Chapter <chapter>, <title>:
2. **Then, extract and summarize the most relevant point** from the provided context.
3. **Finally, answer the user’s question clearly and formally**, referring only to the point above.
---
💥 IMPORTANT RULES:
- Do NOT repeat or paraphrase the law reference later.
- Do NOT invent any legal information — use ONLY the provided context and metadata.
- Do NOT add phrase like 'The most relevant point is that' when mentioning the context.
- If the context is insufficient, nrever include the legal citation and respond with:
> "The answer cannot be determined from the available legal text."
---
**User Question:**
{input}
**Retrieved Legal Context:**
{context}
'''
document_prompt = PromptTemplate.from_template(
'''
Article: {article}
Chapter: {chapter}
Title: {title}
Content: {page_content}
'''
)
prompt = ChatPromptTemplate.from_messages([
('system', rag_prompt_template),
('user', "Context:\n{context}\n\nQuestion:\n{input}\n\nAnswer:")
])
def get_rag_chain(model_name='llama-3.3-70b-versatile', k=3):
db = get_vectorstore()
groq_api_key = os.getenv('GROQ_API_KEY')
llm = ChatGroq(
model_name=model_name,
temperature=0,
max_tokens=10000,
api_key=groq_api_key,
)
retriever = db.as_retriever(
search_type = 'similarity',
search_kwargs = {'k': k}
)
combine_doc_chain = create_stuff_documents_chain(
prompt=prompt,
llm=llm,
document_prompt=document_prompt,
)
return create_retrieval_chain(retriever, combine_doc_chain)
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