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:

```python
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
query = "What did the president say about Justice Breyer"
docs = docsearch.similarity_search(query)
```

```python
from langchain.chains.qa_with_sources import load_qa_with_sources_chain

chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
```

<CodeOutputBlock lang="python">

```
    {'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
```

</CodeOutputBlock>