I use a sentence-window retriever in this method : ...

ttatanfort

@kapa.ai I use a sentence-window retriever in this method :
def retrieve(self, query):
return self.query_engine.retrieve(query)
It is using a qdrant vector store. I would like apply some filters based on the metadatas. How can I do that?

11 comments

kkapa.ai

Please stand by @tatanfort as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

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To apply metadata filters in a Qdrant vector store, you can use the MetadataFilters and MetadataFilter classes from the llama_index.core.vector_stores module. Here is an example of how you can do it:

Plain Text

from llama_index.core.vector_stores import MetadataFilters, MetadataFilter, FilterOperator

# Define your filters
filters = MetadataFilters(
    filters=[
        MetadataFilter(key="your_key", operator=FilterOperator.EQ, value="your_value"),
        # Add more filters as needed
    ]
)

# Use the filters when creating the retriever
retriever = index.as_retriever(filters=filters)

# Now you can retrieve with the applied filters
result = retriever.retrieve("Your query")

In the MetadataFilter, you specify the key which is the metadata field you want to filter on, the operator which is the comparison operator (e.g., equal, greater than, less than, etc.), and the value which is the value you want to compare the metadata field to.

Please replace "your_key", "your_value", and "Your query" with your actual key, value, and query.

You can find more examples in the Qdrant Vector Store - Metadata Filter () and Qdrant Vector Store - Default Qdrant Filters () notebooks.
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ttatanfort

@kapa.ai How can I use those filters only when required while querying with the retriever ?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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You can conditionally apply filters while querying with the retriever by creating the retriever without any filters initially and then setting the filters only when needed. Here's an example of how you can do it:

Plain Text

# Create the retriever without any filters
retriever = index.as_retriever()

# Define your query
query = "Your query"

# Conditionally apply filters
if condition:  # Replace with your actual condition
    from llama_index.core.vector_stores import MetadataFilters, MetadataFilter, FilterOperator

    # Define your filters
    filters = MetadataFilters(
        filters=[
            MetadataFilter(key="your_key", operator=FilterOperator.EQ, value="your_value"),
            # Add more filters as needed
        ]
    )

    # Set the filters in the retriever
    retriever.filters = filters

# Now you can retrieve with the applied filters
result = retriever.retrieve(query)

In this example, replace "Your query", "your_key", "your_value", and condition with your actual query, key, value, and condition.

Please note that this is a general approach and the actual implementation may vary depending on the specifics of your use case and the retriever you are using.
----------------------
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ttatanfort

@kapa.ai ModuleNotFoundError: No module named 'llama_index.core.vector_stores'

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
It seems like you're having trouble importing the llama_index.core.vector_stores module. This could be due to a few reasons:

LlamaIndex is not installed: Ensure that you have installed LlamaIndex in your environment. You can do this by running the following command in your Python environment:

Plain Text

    !pip install llama-index

Incorrect LlamaIndex version: The module might not exist in the version of LlamaIndex you have installed. Make sure you have the latest version of LlamaIndex installed. You can upgrade LlamaIndex using the following command:

Plain Text

    !pip install --upgrade llama-index

Virtual Environment: If you're using a virtual environment, make sure that you've activated the correct environment where LlamaIndex is installed.

If you're still facing issues, it would be helpful to know more about your setup. Are you using a Jupyter notebook, or running a script directly? Which version of Python are you using? This information can help in diagnosing the issue.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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ttatanfort

@kapa.ai ImportError: cannot import name 'LLM' from 'llama_index.core.llms' (/Users/nathanredin/Library/Caches/pypoetry/virtualenvs/retrieval-KPpKWBg9-py3.12/lib/python3.12/site-packages/llama_index/core/llms/init.py). Did you mean: 'llm'?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
It seems like you're trying to import the LLM class from llama_index.core.llms, but you're getting an ImportError. This could be due to a few reasons:

Incorrect Class Name: The error message suggests that you might want to import llm instead of LLM. Python is case-sensitive, so make sure you're using the correct case.

Incorrect Module: The LLM class might not be in the llama_index.core.llms module. Make sure you're trying to import from the correct module.

LlamaIndex Version: The LLM class might not exist in the version of LlamaIndex you have installed. Make sure you have the latest version of LlamaIndex installed. You can upgrade LlamaIndex using the following command:

Plain Text

    !pip install --upgrade llama-index

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I use a sentence-window retriever in this method : def retrieve(self, query):