|
|
|
from langchain import PromptTemplate, LLMChain |
|
from langchain.embeddings import HuggingFaceEmbeddings |
|
from langchain.chains import RetrievalQAWithSourcesChain |
|
from langchain.chains.qa_with_sources import load_qa_with_sources_chain |
|
|
|
|
|
|
|
from climateqa.retriever import ClimateQARetriever |
|
from climateqa.vectorstore import get_pinecone_vectorstore |
|
from climateqa.chains import load_climateqa_chain |
|
|
|
|
|
class ClimateQA: |
|
def __init__(self,hf_embedding_model = "sentence-transformers/multi-qa-mpnet-base-dot-v1", |
|
show_progress_bar = False,batch_size = 1,max_tokens = 1024,**kwargs): |
|
|
|
self.llm = self.get_llm(max_tokens = max_tokens,**kwargs) |
|
self.embeddings_function = HuggingFaceEmbeddings( |
|
model_name=hf_embedding_model, |
|
encode_kwargs={"show_progress_bar":show_progress_bar,"batch_size":batch_size} |
|
) |
|
|
|
|
|
|
|
def get_vectorstore(self): |
|
pass |
|
|
|
|
|
def reformulate(self): |
|
pass |
|
|
|
|
|
def retrieve(self): |
|
pass |
|
|
|
|
|
def ask(self): |
|
pass |
