[Question]: MetadataReplacementPostProcessor - giving irrelavant output

[Sridevi17j]

Question Validation

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Question

Here is my program

from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader( input_files=["data13/SrideviP_Profile 1.pdf"] ).load_data()

nodes = node_parser.get_nodes_from_documents(documents) base_nodes = text_splitter.get_nodes_from_documents(documents)

from llama_index.core import VectorStoreIndex

sentence_index = VectorStoreIndex(nodes) base_index = VectorStoreIndex(base_nodes)

from llama_index.core.postprocessor import MetadataReplacementPostProcessor

query_engine = base_index.as_query_engine( similarity_top_k=1,

the target key defaults to window to match the node_parser's default

node_postprocessors=[
    MetadataReplacementPostProcessor(target_metadata_key="window")
],

) window_response = query_engine.query( "What is this document name" ) print(window_response)

i use gemma:2b using ollama, as llm, and all-minilm-v6 as embedding model. below is my output

The document name is not provided in the context, so I cannot answer this question from the provided context.

It gives the same answer for any questions that i ask....

[dosubot[bot]]

The issue with the MetadataReplacementPostProcessor giving irrelevant output for any question you ask could be due to several factors. First, ensure that the target_metadata_key you've specified matches the key in your nodes' metadata. If there's a mismatch or if the metadata values are not set correctly, the processor won't replace the content as expected.

Additionally, consider the integration with your embedding models (gemma:2b, ollama, llm, and all-minilm-v6). The output from these models must align with the expected structure for the postprocessor to work correctly. Misconfiguration here could lead to the irrelevant output you're experiencing.

Review the structure of your nodes and the metadata associated with them. Ensure that the metadata is relevant and correctly set for the questions being asked. For a practical example of how the MetadataReplacementPostProcessor should function, you might want to look at the test cases in the LlamaIndex repository, which demonstrate its intended functionality in a controlled environment.

If after checking these points you still face issues, providing more details about the nodes' structure, the metadata, and how you're using the embedding models could help in identifying the root cause and suggesting a more specific solution.

#### Details

**Sources** - [llama-index-core/tests/postprocessor/test_metadata_replacement.py](https://github.com/run-llama/llama_index/blob/main/llama-index-core/tests/postprocessor/test_metadata_replacement.py) - [llama-index-core/llama_index/core/postprocessor/metadata_replacement.py](https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/postprocessor/metadata_replacement.py)
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[Sridevi17j]

@dosu

Here is my answer:

1. target_metadata_key I've specified matches the key in my nodes' metadata. Its the same "window"
2. I dont know how to check below one Additionally, consider the integration with your embedding models (gemma:2b, ollama, llm, and all-minilm-v6). The output from these models must align with the expected structure for the postprocessor to work correctly. Misconfiguration here could lead to the irrelevant output you're experiencing.

could you give steps how to check this one?

[dosubot[bot]]

To check if the output from your embedding models (gemma:2b, ollama, llm, and all-minilm-v6) aligns with the expected structure for the MetadataReplacementPostProcessor, follow these steps:

1. Ensure Model Output Structure: Your models should output data that can be encapsulated within a TextNode structure, which includes a metadata dictionary. This dictionary must contain a key that matches your target_metadata_key ("window" in your case). The value associated with this key in the metadata is what the MetadataReplacementPostProcessor will use to replace the node content.

2. Implement a Test Case: Create a simple test case similar to the example provided, where you manually create a TextNode with the expected metadata and then apply the MetadataReplacementPostProcessor to see if it correctly replaces the content. This will help you understand the expected input and output format.

3. Wrap Model Outputs: If your models output embeddings or text directly, you'll need to wrap these outputs in the TextNode structure, ensuring that each node has a metadata dictionary with the correct target_metadata_key. This is crucial for the postprocessor to function as intended.

4. Review and Adjust: After wrapping your models' outputs, review the metadata content for each node to ensure it contains the expected replacement text. If the output remains irrelevant, there might be an issue with how the metadata is being set or with the models' integration.

By following these steps, you can diagnose and potentially resolve the issue with the MetadataReplacementPostProcessor giving irrelevant output. If you encounter specific challenges or need further clarification on any of these steps, please provide more details about your implementation for targeted assistance.

#### Details

**Sources** - [llama-index-core/tests/postprocessor/test_metadata_replacement.py](https://github.com/run-llama/llama_index/blob/main/llama-index-core/tests/postprocessor/test_metadata_replacement.py) - [llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py) - [llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py) - [llama-index-legacy/llama_index/legacy/embeddings/llm_rails.py](https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/embeddings/llm_rails.py)
**About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.