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23 lines
937 B
Text
23 lines
937 B
Text
We can also perform document QA and return the sources that were used to answer the question. To do this we'll just need to make sure each document has a "source" key in the metadata, and we'll use the `load_qa_with_sources` helper to construct our chain:
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```python
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docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
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query = "What did the president say about Justice Breyer"
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docs = docsearch.similarity_search(query)
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```
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```python
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from langchain.chains.qa_with_sources import load_qa_with_sources_chain
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chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
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query = "What did the president say about Justice Breyer"
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chain({"input_documents": docs, "question": query}, return_only_outputs=True)
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```
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<CodeOutputBlock lang="python">
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```
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{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
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```
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</CodeOutputBlock>
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