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import logging
import torch
from dotenv import load_dotenv
load_dotenv()
from langchain_classic.chains import RetrievalQA
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_pinecone import PineconeVectorStore as LangchainPinecone
from pinecone import Pinecone
logger = logging.getLogger(__name__)
LLM_BACKEND = os.getenv("LLM_BACKEND", "groq")
LOCAL_MODEL = os.getenv("MODEL_PATH", "./models/merged/exp2_lr2e-4_r16")
EMBED_MODEL = os.getenv("EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
PINECONE_INDEX = os.getenv("PINECONE_INDEX", "llmops-rag")
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
GROQ_MODEL = "llama-3.1-8b-instant"
PROMPT_TEMPLATE = """You are a knowledgeable Genshin Impact assistant. \
Answer using ONLY the context provided below. If the context does not \
contain enough information to answer confidently, say so — do not invent \
weapon names, artifact sets, or lore details.
Context:
{context}
Question: {question}
Answer:"""
def _build_groq_llm():
from langchain_groq import ChatGroq
if not GROQ_API_KEY:
raise EnvironmentError("GROQ_API_KEY not set in environment.")
logger.info(f"Using Groq backend — model: {GROQ_MODEL}")
return ChatGroq(
api_key=GROQ_API_KEY,
model_name=GROQ_MODEL,
temperature=0.2,
max_tokens=512,
)
def _build_local_llm():
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from langchain_community.llms import HuggingFacePipeline
logger.info(f"Loading local model from {LOCAL_MODEL}")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
LOCAL_MODEL,
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.bfloat16,
max_memory={0: "5.5GiB", "cpu": "24GiB"},
)
model.eval()
hf_pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=256,
do_sample=False,
temperature=None,
top_p=None,
repetition_penalty=1.3,
return_full_text=False,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
)
logger.info("Local model loaded.")
return HuggingFacePipeline(pipeline=hf_pipe)
class RAGChain:
def __init__(self):
self.ready = False
self.chain = None
self.vectorstore = None
def load(self):
llm = _build_groq_llm() if LLM_BACKEND == "groq" else _build_local_llm()
logger.info("Connecting to Pinecone...")
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
pc = Pinecone(api_key=PINECONE_API_KEY)
index = pc.Index(PINECONE_INDEX)
self.vectorstore = LangchainPinecone(index, embeddings, "text")
logger.info("Pinecone connected.")
prompt = PromptTemplate(
template=PROMPT_TEMPLATE,
input_variables=["context", "question"],
)
self.chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=self.vectorstore.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True,
chain_type_kwargs={"prompt": prompt},
)
self.ready = True
logger.info(f"RAG chain ready — backend: {LLM_BACKEND}")
def query(self, question: str, top_k: int = 3) -> tuple[str, list[str]]:
if not self.ready:
raise RuntimeError("RAG chain is not loaded.")
self.chain.retriever.search_kwargs["k"] = top_k
result = self.chain.invoke({"query": question})
answer = result["result"].strip().replace("</s>", "").strip()
sources = [
doc.metadata.get("source", "unknown")
for doc in result.get("source_documents", [])
]
return answer, list(dict.fromkeys(sources)) |