value='Testing') limit=None    [Document(page_content='![Untitled](Auto-Evaluation%20of%20Metadata%20Filtering%2018502448c85240828f33716740f9574b/Untitled%202.png)', metadata={'Section': 'Testing'}),     Document(page_content='`SelfQueryRetriever` works well in [many cases](https://twitter.com/hwchase17/status/1656791488569954304/photo/1). For example, given [this test case](https://twitter.com/hwchase17/status/1656791488569954304?s=20):  \n![Untitled](Auto-Evaluation%20of%20Metadata%20Filtering%2018502448c85240828f33716740f9574b/Untitled%201.png)  \nThe query can be nicely broken up into semantic query and metadata filter:  \n```python\nsemantic query: "prompt injection"', metadata={'Section': 'Testing'}),     Document(page_content='Below, we can see detailed results from the app:  \n- Kor extraction is above to perform the transformation between query and metadata format ‚úÖ\n- Self-querying attempts to filter using the episode ID (`252`) in the query and fails üö´\n- Baseline returns docs from 3 different episodes (one from `252`), confusing the answer üö´', metadata={'Section': 'Testing'}),     Document(page_content='will use in retrieval [here](https://github.com/langchain-ai/auto-evaluator/blob/main/streamlit/kor_retriever_lex.py).', metadata={'Section': 'Testing'})]Now, we can create chat or Q+A apps that are aware of the explicit document structure. The ability to retain document structure for metadata filtering can be helpful for complicated or longer documents.from langchain.chains import RetrievalQAfrom langchain.chat_models import ChatOpenAIllm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever)qa_chain.run("Summarize the Testing section of the document")    query='Testing' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Section', value='Testing') limit=None    'The Testing section of the document describes the evaluation of the `SelfQueryRetriever` component in
Text splitting for vector storage often uses sentences or other delimiters to keep related text together.
Text splitting for vector storage often uses sentences or other delimiters to keep related text together. ->: value='Testing') limit=None    [Document(page_content='![Untitled](Auto-Evaluation%20of%20Metadata%20Filtering%2018502448c85240828f33716740f9574b/Untitled%202.png)', metadata={'Section': 'Testing'}),     Document(page_content='`SelfQueryRetriever` works well in [many cases](https://twitter.com/hwchase17/status/1656791488569954304/photo/1). For example, given [this test case](https://twitter.com/hwchase17/status/1656791488569954304?s=20):  \n![Untitled](Auto-Evaluation%20of%20Metadata%20Filtering%2018502448c85240828f33716740f9574b/Untitled%201.png)  \nThe query can be nicely broken up into semantic query and metadata filter:  \n```python\nsemantic query: "prompt injection"', metadata={'Section': 'Testing'}),     Document(page_content='Below, we can see detailed results from the app:  \n- Kor extraction is above to perform the transformation between query and metadata format ‚úÖ\n- Self-querying attempts to filter using the episode ID (`252`) in the query and fails üö´\n- Baseline returns docs from 3 different episodes (one from `252`), confusing the answer üö´', metadata={'Section': 'Testing'}),     Document(page_content='will use in retrieval [here](https://github.com/langchain-ai/auto-evaluator/blob/main/streamlit/kor_retriever_lex.py).', metadata={'Section': 'Testing'})]Now, we can create chat or Q+A apps that are aware of the explicit document structure. The ability to retain document structure for metadata filtering can be helpful for complicated or longer documents.from langchain.chains import RetrievalQAfrom langchain.chat_models import ChatOpenAIllm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever)qa_chain.run("Summarize the Testing section of the document")    query='Testing' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Section', value='Testing') limit=None    'The Testing section of the document describes the evaluation of the `SelfQueryRetriever` component in