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28 lines
1.1 KiB
Python
28 lines
1.1 KiB
Python
"""Test RetrievalQA functionality."""
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from pathlib import Path
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from langchain.chains import RetrievalQA
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from langchain.chains.loading import load_chain
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from langchain.document_loaders import TextLoader
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.llms import OpenAI
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.vectorstores import Chroma
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def test_retrieval_qa_saving_loading(tmp_path: Path) -> None:
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"""Test saving and loading."""
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loader = TextLoader("docs/modules/state_of_the_union.txt")
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documents = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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texts = text_splitter.split_documents(documents)
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embeddings = OpenAIEmbeddings()
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docsearch = Chroma.from_documents(texts, embeddings)
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qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=docsearch.as_retriever())
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file_path = tmp_path / "RetrievalQA_chain.yaml"
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qa.save(file_path=file_path)
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qa_loaded = load_chain(file_path, retriever=docsearch.as_retriever())
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assert qa_loaded == qa
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