How to use Metadata Replacement + Node Sentence Window, without generating an answer to query

[burberg92]

My goal is to retrieve nodes based on a query and then replace them with the window, as in this guide https://docs.llamaindex.ai/en/stable/examples/node_postprocessor/MetadataReplacementDemo.html. However I do not want the query engine to generate a response to the query. How can I achieve this?

[dosubot[bot]]

🤖

Hello @burberg92! Good to see you again. I hope your work with LlamaIndex is going well. Let's dive into your new issue.

To achieve your goal of retrieving nodes based on a query and replacing them with the window, without having the query engine generate a response, you can follow these steps:

1. Create a SentenceWindowNodeParser instance with your desired window size and metadata keys. This will parse documents into single sentences per node and each node will contain a "window" with the sentences on either side of the node sentence.

from llama_index.node_parser import SentenceWindowNodeParser

node_parser = SentenceWindowNodeParser.from_defaults(
    window_size=3,
    window_metadata_key="window",
    original_text_metadata_key="original_text",
)

2. Extract the nodes from your documents using the get_nodes_from_documents method of the node_parser.

nodes = node_parser.get_nodes_from_documents(documents)

3. Build the vector index using the VectorStoreIndex class and the nodes you extracted.

from llama_index import VectorStoreIndex

sentence_index = VectorStoreIndex(nodes, service_context=ctx)

4. Use the MetadataReplacementPostProcessor to replace the sentence in each node with its surrounding context. You can do this by creating a query engine with the as_query_engine method of the sentence_index and passing the MetadataReplacementPostProcessor as a node postprocessor.

from llama_index.postprocessor import MetadataReplacementPostProcessor

query_engine = sentence_index.as_query_engine(
    similarity_top_k=2,
    node_postprocessors=[
        MetadataReplacementPostProcessor(target_metadata_key="window")
    ],
)

5. Retrieve nodes based on a query using the query method of the query_engine. This will return a response object that contains the retrieved nodes.

window_response = query_engine.query(
    "What are the concerns surrounding the AMOC?"
)

6. If you want to get the original sentence that was retrieved for each node, as well as the actual window of sentences that was sent to the LLM, you can do this:

window = window_response.source_nodes[0].node.metadata["window"]
sentence = window_response.source_nodes[0].node.metadata["original_text"]

print(f"Window: {window}")
print("------------------")
print(f"Original Sentence: {sentence}")

This process does not generate a response from the LLM. It only retrieves the nodes based on the query and replaces them with the window. You can find more details in the MetadataReplacementDemo.ipynb notebook.

The SentenceWindowNodeParser class in the LlamaIndex codebase is used to split a document into Nodes, with each node being a sentence. Each node contains a window from the surrounding sentences in the metadata. This class is particularly useful for tasks that require understanding the context of a sentence within a document, as it provides a window of surrounding sentences for each sentence node. You can find more details in the sentence_window.py file.

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• docs/examples/node_postprocessor/MetadataReplacementDemo.ipynb
• llama_index/node_parser/text/sentence_window.py

---

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[burberg92]

@dosu-bot thank you, I have two follow up questions.

1. I have already upserted my nodes to pinecone vector store, how do I implement this with the already uploaded nodes from pinecone?
2. Am I able to retrieve documents from multiple queries simultaneously? My goal is to provide several related queries to broaden the scope of the retrieved documents. I already have a process that generates the queries, I would like to pass in multiple queries as a list, is this possible?

[dosubot[bot]]

🤖

To implement the Metadata Replacement + Node Sentence Window process with nodes already upserted to a Pinecone vector store, you can use the add method of the PineconeVectorStore class. This method takes a list of nodes as input and upserts them to the Pinecone index. Here's a simplified example:

from llama_index.schema import TextNode

# Assuming `pinecone_store` is an instance of PineconeVectorStore
nodes = [TextNode(node_id="node1", text="This is a test."), TextNode(node_id="node2", text="Another test.")]
pinecone_store.add(nodes)

In this example, TextNode is a type of BaseNode that contains text data. You can replace TextNode with any other node type that fits your needs.

As for passing in multiple queries as a list to the LlamaIndex to retrieve documents from several related queries simultaneously, the current implementation of the query method in the PineconeVectorStore class does not support this. The query method takes a single VectorStoreQuery object as input, which represents a single query. If you want to perform multiple queries, you would need to call the query method multiple times, once for each query. Here's an example:

from llama_index.vector_stores.types import VectorStoreQuery

# Assuming `pinecone_store` is an instance of PineconeVectorStore
queries = [VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3]), VectorStoreQuery(query_embedding=[0.4, 0.5, 0.6])]
results = [pinecone_store.query(query) for query in queries]

In this example, VectorStoreQuery is a dataclass that represents a query to the vector store. It contains a query_embedding attribute, which is a list of floats representing the query vector. The query method returns a VectorStoreQueryResult object, which contains the results of the query.

Regarding your question about updating the metadata of nodes already upserted to a Pinecone vector store, I wasn't able to find a specific method for this in the LlamaIndex repository. However, you might be able to achieve this by re-upserting the nodes with the updated metadata using the add method, as shown in the first example. Please note that this is just a suggestion and might not work as expected depending on the specifics of your use case and the implementation of the Pinecone vector store.

You can find more information about these methods in the llama_index/vector_stores/pinecone.py file in the LlamaIndex repository.

Sources

• llama_index/vector_stores/pinecone.py

---

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.

If you want to continue the conversation, start your reply with @dosu-bot.

[burberg92]

@dosu-bot , the original response you gave me does in fact generate a response from the llm. I found this:

query_engine = index.as_query_engine(response_mode='no_text')
response = query_engine.query("my query")
print(response.source_nodes)

from this post: #3255

My question regarding, pinecone was as follows:

How do I initialize a query engine from an existing pinecone vector store, without passing in the nodes?

[dosubot[bot]]

🤖

To initialize a query engine from an existing Pinecone vector store without passing in the nodes and ensuring the query engine does not generate a response from the LLM, you can use the from_params class method of the PineconeVectorStore class. This method initializes a Pinecone vector store without requiring nodes to be passed in.

Here is a code snippet on how to do this:

from llama_index.vector_stores.pinecone import PineconeVectorStore

# Initialize PineconeVectorStore
pinecone_vector_store = PineconeVectorStore.from_params(
    api_key="<your_api_key>",
    index_name="<your_index_name>",
    environment="<your_environment>",
    namespace="<your_namespace>",
    insert_kwargs={"upsert_timeout": 30},
    add_sparse_vector=False,
    text_key="text",
    batch_size=100,
    remove_text_from_metadata=False
)

In this code snippet, replace <your_api_key>, <your_index_name>, <your_environment>, and <your_namespace> with your actual Pinecone API key, index name, environment, and namespace respectively.

Please note that the add_sparse_vector parameter is set to False to ensure that the query engine does not generate a response from the LLM.

This is based on the following code from the context:

@classmethod
def from_params(
    cls,
    api_key: Optional[str] = None,
    index_name: Optional[str] = None,
    environment: Optional[str] = None,
    namespace: Optional[str] = None,
    insert_kwargs: Optional[Dict] = None,
    add_sparse_vector: bool = False,
    tokenizer: Optional[Callable] = None,
    text_key: str = DEFAULT_TEXT_KEY,
    batch_size: int = DEFAULT_BATCH_SIZE,
    remove_text_from_metadata: bool = False,
    default_empty_query_vector: Optional[List[float]] = None,
    **kwargs: Any,
) -> "PineconeVectorStore":
    try:
        import pinecone
    except ImportError:
        raise ImportError(import_err_msg)

    pinecone.init(api_key=api_key, environment=environment)
    pinecone_index = pinecone.Index(index_name)
return cls(
        pinecone_index=pinecone_index,
        api_key=api_key,
        index_name=index_name,
        environment=environment,
        namespace=namespace,
        insert_kwargs=insert_kwargs,
        add_sparse_vector=add_sparse_vector,
        tokenizer=tokenizer,
        text_key=text_key,
        batch_size=batch_size,
        remove_text_from_metadata=remove_text_from_metadata,
        default_empty_query_vector=default_empty_query_vector,
        **kwargs,
    )

You can find this code in the llama_index/vector_stores/pinecone.py file.

Regarding your question about the response_mode parameter in the as_query_engine method, it determines the type of query engine that will be used to retrieve data from the SQL Struct Store Index. If response_mode is set to SQLQueryMode.NL, the method returns an instance of NLStructStoreQueryEngine which is designed to handle natural language queries. If response_mode is set to SQLQueryMode.SQL, the method returns an instance of SQLStructStoreQueryEngine which is designed to handle raw SQL queries. If a value other than SQLQueryMode.NL or SQLQueryMode.SQL is provided, the method raises a ValueError. You can find more details in the llama_index/indices/struct_store/sql.py file.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• llama_index/vector_stores/pinecone.py
• llama_index/indices/struct_store/sql.py

---

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.

If you want to continue the conversation, start your reply with @dosu-bot.

[logan-markewich]

@burberg92 if you want to retrieve and apply a node postprocessor, you can do so (I think dosu has mislead you a bit)

retriever = index.as_retriever(similarity_top_k=2)
postprocessor = MetadataReplacementPostProcessor(target_metadata_key="window")

nodes = retriever.retrieve(query_str)
nodes = postprocessor.postprocess_nodes(nodes)

If you need to run many at the same time, I would use multiprocessing. (sadly, pinecone does not have an async query method, otherwise I would suggest using aretrieve())

[Gamebred94]

On using nodes = postprocessor(nodes), it gives me the error:
TypeError: 'MetadataReplacementPostProcessor' object is not callable

[logan-markewich]

my bad, nodes = postprocessor.postprocess_nodes(nodes)