Azure AI Search extension not working about azurechat HOT 6 OPEN

@vlad-tsoy - I'm creating my index with these fields:

fields = [
    SearchField(name="id", type=SearchFieldDataType.String, key=True, filterable=True, sortable=False, facetable=False, analyzer_name="keyword"),
    #SearchField(name="user", type=SearchFieldDataType.String, sortable=False, filterable=True, facetable=False), #used for filtering
    #SearchField(name="chatThreadId", type=SearchFieldDataType.String, sortable=False, filterable=True, facetable=False), #used for filtering
    SearchField(name="pageContent", type=SearchFieldDataType.String, sortable=False, filterable=False, facetable=False),
    SearchField(name="metadata", type=SearchFieldDataType.String, sortable=False, filterable=False, facetable=False),
    SearchField(name="embedding", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), vector_search_dimensions=1536, vector_search_profile_name="myHnswProfile"),
    SearchField(name="parent_id", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True)
]

And configured my skillset output mappings to match:

          mappings=[  
               InputFieldMappingEntry(name="pageContent", source="/document/pages/*"),  
               InputFieldMappingEntry(name="embedding", source="/document/pages/*/vector"),  
               InputFieldMappingEntry(name="metadata", source="/document/metadata_storage_name")  
           ],  

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i had the same problem and solved it with a quick fix. but i think someone should have a closer look at it.
the problem is, that my search had the content in a different field as pageContent so my content got lost in the FormatCitations Function.

when i update the FormatCitations Function it works for me:
(citation-service.ts)

export const FormatCitations = (citation: any[]) => {
  const withoutEmbedding: DocumentSearchResponse[] = [];
  citation.forEach((d) => {
    withoutEmbedding.push({
      score: d.score,
      document: {
        metadata: d.document.metadata,
        pageContent: d.document.pageContent || d.document.content || d.document.chunk,
        chatThreadId: d.document.chatThreadId,
        id: "",
        user: "",
      },
    });
  });

  return withoutEmbedding;
};

pageContent: d.document.pageContent || d.document.content || d.document.chunk
this should include your possible fields with the content.

or just make sure that the content in your Azure Search is in the Field pageContent.

from azurechat.

I'm having the same issue trying to connect to the index I've created using integrated vectorization. The wizard and python examples create the same index format, and when I connect the extension I get exactly the results in OP's screenshot.

I'm looking for an approach to use integrated vectorization via Indexer Skillsets to maintain indexes for this solution.

from azurechat.

Solved - modified the index and the output mappings of my skillset

from azurechat.

Solved - modified the index and the output mappings of my skillset

I'm also trying to use integrated vectorization, how did you modify the index?

from azurechat.

@vlad-tsoy - I'm creating my index with these fields:

fields = [
    SearchField(name="id", type=SearchFieldDataType.String, key=True, filterable=True, sortable=False, facetable=False, analyzer_name="keyword"),
    #SearchField(name="user", type=SearchFieldDataType.String, sortable=False, filterable=True, facetable=False), #used for filtering
    #SearchField(name="chatThreadId", type=SearchFieldDataType.String, sortable=False, filterable=True, facetable=False), #used for filtering
    SearchField(name="pageContent", type=SearchFieldDataType.String, sortable=False, filterable=False, facetable=False),
    SearchField(name="metadata", type=SearchFieldDataType.String, sortable=False, filterable=False, facetable=False),
    SearchField(name="embedding", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), vector_search_dimensions=1536, vector_search_profile_name="myHnswProfile"),
    SearchField(name="parent_id", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True)
]

And configured my skillset output mappings to match:

          mappings=[  
               InputFieldMappingEntry(name="pageContent", source="/document/pages/*"),  
               InputFieldMappingEntry(name="embedding", source="/document/pages/*/vector"),  
               InputFieldMappingEntry(name="metadata", source="/document/metadata_storage_name")  
           ],  

Thank you, Michael!

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