Get all nodes

[d3buggerdan]

@dosu how can i get all nodes from a AzureAiSearchVectorStore?

[dosubot[bot]]

To get all nodes from an AzureAISearchVectorStore, you can use the search method from the AzureAISearchVectorStore class. Here's an example:

# Assuming you have already created an instance of AzureAISearchVectorStore
vector_store = AzureAISearchVectorStore(
    search_or_index_client=your_search_client,
    id_field_key="id",
    chunk_field_key="chunk",
    embedding_field_key="embedding",
    metadata_string_field_key="metadata",
    doc_id_field_key="doc_id",
    filterable_metadata_field_keys=None,
    index_name="your_index_name",
    index_management=IndexManagement.NO_VALIDATION,
    embedding_dimensionality=1536
)

# Create a query object
query = VectorStoreQuery(
    similarity_top_k=1000  # Adjust the number as needed
)

# Perform the search to get all nodes
result = vector_store.search(query)

# Access the nodes
nodes = result.nodes

This code will retrieve all nodes from the Azure AI Search index and store them in the nodes variable. Adjust the similarity_top_k parameter as needed to control the number of nodes retrieved ^[1][2].

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

To retrieve all nodes from an AzureAISearchVectorStore that match the filter user_id = "1" without performing a specific query, you can use the MetadataFilters and ExactMatchFilter classes to set up the filter and then use the retriever to retrieve the nodes. Here is how you can do it:

from llama_index.legacy.vector_stores.types import MetadataFilters, ExactMatchFilter

# Define the filter for user_id = "1"
filters = MetadataFilters(
    filters=[ExactMatchFilter(key="user_id", value="1")]
)

# Create a retriever with the specified filters
retriever = index.as_retriever(filters=filters)

# Retrieve the nodes that match the filter
nodes = retriever.retrieve("")

# Print the retrieved nodes
for node_with_score in nodes:
    node = node_with_score.node  # The TextNode object
    print(node.text)
    print(node.metadata)

This code sets up a filter to match nodes where user_id is "1" and retrieves those nodes without performing a specific query. The AzureQueryResultSearchBase class in the AzureAISearchVectorStore handles the search operation, ensuring that the filter is applied correctly to retrieve the desired nodes. The search query is set to "*" to match all documents, and the filter is applied to narrow down the results to those with user_id = "1" ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[d3buggerdan]

@dosu
{
"name": "TypeError",
"message": "'NoneType' object is not subscriptable",
"stack": "---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in
10
11 # Retrieve the nodes that match the filter
---> 12 nodes = retriever.retrieve("")
13
14 # Print the retrieved nodes

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\core\instrumentation\dispatcher.py in wrapper(func, instance, args, kwargs)
233 )
234 try:
--> 235 result = func(*args, **kwargs)
236 except BaseException as e:
237 self.event(SpanDropEvent(span_id=id_, err_str=str(e)))

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\core\base\base_retriever.py in retrieve(self, str_or_query_bundle)
241 payload={EventPayload.QUERY_STR: query_bundle.query_str},
242 ) as retrieve_event:
--> 243 nodes = self._retrieve(query_bundle)
244 nodes = self._handle_recursive_retrieval(query_bundle, nodes)
245 retrieve_event.on_end(

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\core\instrumentation\dispatcher.py in wrapper(func, instance, args, kwargs)
233 )
234 try:
--> 235 result = func(*args, **kwargs)
236 except BaseException as e:
237 self.event(SpanDropEvent(span_id=id_, err_str=str(e)))

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\core\indices\vector_store\retrievers\retriever.py in _retrieve(self, query_bundle)
99 )
100 )
--> 101 return self._get_nodes_with_embeddings(query_bundle)
102
103 @dispatcher.span

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\core\indices\vector_store\retrievers\retriever.py in _get_nodes_with_embeddings(self, query_bundle_with_embeddings)
175 ) -> List[NodeWithScore]:
176 query = self._build_vector_store_query(query_bundle_with_embeddings)
--> 177 query_result = self._vector_store.query(query, **self._kwargs)
178 return self._build_node_list_from_query_result(query_result)
179

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\vector_stores\azureaisearch\base.py in query(self, query, **kwargs)
1052 odata_filter = None
1053 if query.filters is not None:
-> 1054 odata_filter = self._create_odata_filter(query.filters)
1055 azure_query_result_search: AzureQueryResultSearchBase = (
1056 AzureQueryResultSearchDefault(

~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\llama_index\vector_stores\azureaisearch\base.py in _create_odata_filter(self, metadata_filters)
1006 metadata_mapping = self._metadata_to_index_field_map.get(subfilter.key)
1007
-> 1008 index_field = metadata_mapping[0]
1009
1010 if not metadata_mapping:

TypeError: 'NoneType' object is not subscriptable"
}

[dosubot[bot]]

To resolve the error and successfully retrieve all nodes that match the filter user_id = "1" using AzureAISearchVectorStore without performing a specific query, you can use the _odata_filter parameter in the AzureQueryResultSearchBase class. Here is how you can do it:

1. Create an instance of VectorStoreQuery with the necessary parameters.
2. Set the _odata_filter to user_id eq '1'.
3. Use the AzureQueryResultSearchBase class to perform the search.

Here is a code example:

from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore, AzureQueryResultSearchBase
from llama_index.vector_stores.types import VectorStoreQuery

# Assuming you have already created an instance of AzureAISearchVectorStore
vector_store = AzureAISearchVectorStore(
    search_or_index_client=search_client,  # Your Azure SearchClient or SearchIndexClient
    id_field_key="id",
    chunk_field_key="chunk",
    embedding_field_key="embedding",
    metadata_string_field_key="metadata",
    doc_id_field_key="doc_id",
    filterable_metadata_field_keys=["user_id"]
)

# Create a VectorStoreQuery instance
query = VectorStoreQuery(similarity_top_k=10)

# Set the OData filter
odata_filter = "user_id eq '1'"

# Create an instance of AzureQueryResultSearchBase
search_base = AzureQueryResultSearchBase(
    query=query,
    field_mapping=vector_store._field_mapping,
    odata_filter=odata_filter,
    search_client=vector_store._search_client
)

# Perform the search
search_query = search_base._create_search_query()
vectors = search_base._create_query_vector()
result = search_base._create_query_result(search_query, vectors)

# The result contains the nodes that match the filter
print(result.nodes)

This code sets up the filter to retrieve nodes where user_id is equal to "1" and performs the search using the AzureQueryResultSearchBase class ^[1][2].

To continue talking to Dosu, mention @dosu.

[d3buggerdan]

@dosu
from llama_index.legacy.vector_stores.types import MetadataFilters, ExactMatchFilter

Define the filter for user_id = "1"

filters = MetadataFilters(
filters=[ExactMatchFilter(key="userid", value="1")]
)

Create a retriever with the specified filters

retriever = index.as_retriever(filters=filters)

Retrieve the nodes that match the filter

nodes = retriever.retrieve("1")

Print the retrieved nodes

for node_with_score in nodes:
node = node_with_score.node # The TextNode object
print(node.text)
print(node.metadata)

its only giving me 2 nodes how to change topk

[dosubot[bot]]

To change the top_k parameter and retrieve more nodes that match the filter user_id = "1", you need to adjust the similarity_top_k attribute in the VectorStoreQuery object. Here's how you can do it:

from llama_index.legacy.vector_stores.types import MetadataFilters, ExactMatchFilter, VectorStoreQuery

# Define the filter for user_id = "1"
filters = MetadataFilters(
    filters=[ExactMatchFilter(key="userid", value="1")]
)

# Create a query object with the desired top_k value
query = VectorStoreQuery(
    filters=filters,
    similarity_top_k=10  # Adjust this value to retrieve more nodes
)

# Create a retriever with the specified filters
retriever = index.as_retriever(filters=filters)

# Retrieve the nodes that match the filter
nodes = retriever.retrieve(query)

# Print the retrieved nodes
for node_with_score in nodes:
    node = node_with_score.node  # The TextNode object
    print(node.text)
    print(node.metadata)

In this example, the similarity_top_k parameter is set to 10, which means the query will retrieve the top 10 most similar nodes that match the filter user_id = "1". Adjust the similarity_top_k value as needed to retrieve more or fewer nodes ^[1].

To continue talking to Dosu, mention @dosu.