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Index Lookup tool for Azure AI Foundry

Important

Items marked (preview) in this article are currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

The prompt flow Index Lookup tool enables the use of common vector indices (such as Azure AI Search, Faiss, and Pinecone) for retrieval augmented generation in prompt flow. The tool automatically detects the indices in the workspace and allows the selection of the index to be used in the flow.

Build with the Index Lookup tool

  1. Create or open a flow in Azure AI Foundry. For more information, see Create a flow.

  2. Select + More tools > Index Lookup to add the Index Lookup tool to your flow.

    Screenshot that shows the Index Lookup tool added to a flow in Azure AI Foundry portal.

  3. Enter values for the Index Lookup tool input parameters. The large language model (LLM) tool can generate the vector input.

  4. Add more tools to your flow, as needed. Or select Run to run the flow.

  5. To learn more about the returned output, see the Outputs table.

Inputs

The following input parameters are available.

Name Type Description Required
mlindex_content string The type of index to be used. Input depends on the index type. An example of an Azure AI Search index JSON can be seen underneath the table. Yes
queries string, Union[string, List[String]] The text to be queried. Yes
query_type string The type of query to be performed. Options include Keyword, Semantic, Hybrid, and others. Yes
top_k integer The count of top-scored entities to return. Default value is 3. No

Here's an example of an Azure AI Search index input:

embeddings:
  api_base: <api_base>
  api_type: azure
  api_version: 2023-07-01-preview
  batch_size: '1'
  connection:
    id: /subscriptions/<subscription>/resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/workspaces/<workspace> /connections/<AOAI_connection>
  connection_type: workspace_connection
  deployment: <embedding_deployment>
  dimension: <embedding_model_dimension>
  kind: open_ai
  model: <embedding_model>
  schema_version: <version>
index:
  api_version: 2023-07-01-Preview
  connection:
    id: /subscriptions/<subscription>/resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/workspaces/<workspace> /connections/<cogsearch_connection>
  connection_type: workspace_connection
  endpoint: <cogsearch_endpoint>
  engine: azure-sdk
  field_mapping:
    content: id
    embedding: content_vector_open_ai
    metadata: id
  index: <index_name>
  kind: acs
  semantic_configuration_name: azureml-default

Outputs

The following JSON format response is an example returned by the tool that includes the top-k scored entities. The entity follows a generic schema of vector search results provided by the promptflow-vectordb SDK. For the Vector Index Search, the following fields are populated:

Field name Type Description
metadata dict The customized key-value pairs provided by the user when creating the index.
page_content string The content of the vector chunk being used in the lookup.
score float Depends on the index type defined in the Vector Index. If the index type is Faiss, the score is L2 distance. If the index type is Azure AI Search, the score is cosine similarity. 
[
  {
    "metadata":{
      "answers":{},
      "captions":{
        "highlights":"sample_highlight1",
        "text":"sample_text1"
      },
      "page_number":44,
      "source":{
        "filename":"sample_file1.pdf",
        "mtime":1686329994,
        "stats":{
          "chars":4385,
          "lines":41,
          "tiktokens":891
        },
        "url":"sample_url1.pdf"
      },
      "stats":{
        "chars":4385,"lines":41,"tiktokens":891
      }
    },
    "page_content":"vector chunk",
    "score":0.021349556744098663
  },

  {
    "metadata":{
      "answers":{},
      "captions":{
        "highlights":"sample_highlight2",
        "text":"sample_text2"
      },
      "page_number":44,
      "source":{
        "filename":"sample_file2.pdf",
        "mtime":1686329994,
        "stats":{
          "chars":4385,
          "lines":41,
          "tiktokens":891
        },
        "url":"sample_url2.pdf"
      },
      "stats":{
        "chars":4385,"lines":41,"tiktokens":891
      }
    },
    "page_content":"vector chunk",
    "score":0.021349556744098663
  },
    
]

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