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from dotenv import load_dotenv | |
load_dotenv() | |
class StuffChain: | |
def __init__(self, model_kwargs): | |
self.llm = self.make_llm(**model_kwargs) | |
self.chain = self.make_chain(self.llm) | |
def make_llm(self, model_name="text-davinci-003", temperature=0.): | |
from langchain.llms import OpenAI | |
llm = OpenAI(temperature=temperature, model_name=model_name) | |
return llm | |
def make_chain(self, llm): | |
from langchain.chains.qa_with_sources import load_qa_with_sources_chain | |
chain = load_qa_with_sources_chain(llm, chain_type="stuff") | |
return chain | |
def __call__(self, *args, **kwargs): | |
return self.chain(*args, **kwargs) | |
class Runner: | |
def __init__(self, chain, docstore): | |
self.chain = chain | |
self.docstore = docstore | |
def __call__(self, query): | |
raise NotImplementedError | |
class StuffChainRunner(Runner): | |
def __call__(self, query): | |
proposed_docs = self.docstore[query] | |
output = self.chain({"input_documents": proposed_docs, "question": query}, return_only_outputs=True) | |
return output["output_text"] | |