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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes #3983 Mimicing what we do for saving and loading VectorDBQA chain, I added the logic for RetrievalQA chain. Also added a unit test. I did not find how we test other chains for their saving and loading functionality, so I just added a file with one test case. Let me know if there are recommended ways to test it. #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: @dev2049 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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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