Index Lookup tool for Azure AI Studio

Article
08/28/2024

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

If you have a flow that contains one of the deprecated legacy index tools ( the Vector Index Lookup tool, Vector DB Lookup tool, and Faiss Index Lookup tool) you will first need to upgrade your flow.
Create or open a flow in Azure AI Studio. For more information, see Create a flow.
Select + More tools > Index Lookup to add the Index Lookup tool to your flow.
Enter values for the Index Lookup tool input parameters. The large language model (LLM) tool can generate the vector input.
Add more tools to your flow, as needed. Or select Run to run the flow.
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
  },
    
]

Migrate from legacy tools to the Index Lookup tool

The Index Lookup tool looks to replace the three deprecated legacy index tools: the Vector Index Lookup tool, the Vector DB Lookup tool, and the Faiss Index Lookup tool. If you have a flow that contains one of these tools, follow the next steps to upgrade your flow.

Upgrade your tools

To update your runtime, go to the project Settings tab on the left pane in AI Studio. In the list of prompt flow runtimes that appears, select the name of the runtime you want to update. Then select Update. Wait for the runtime to update itself.
To go to your flow, select the Prompt flow tab on the left pane in AI Studio. Select the Flows tab, and then select the name of your flow.
Inside the flow, select + More tools. In the dropdown list, select Index Lookup [Preview] to add an instance of the Index Lookup tool.
Name the new node and select Add.
In the new node, select the mlindex_content textbox. It should be the first textbox in the list.
In Generate, follow these steps to upgrade from the three legacy tools:
- Vector Index Lookup: Select Registered Index in the index_type dropdown. Select your vector index asset from the mlindex_asset_id dropdown list.
- Faiss Index Lookup: Select Faiss in the index_type dropdown list. Specify the same path as in the legacy tool.
- Vector DB Lookup: Select AI Search or Pinecone depending on the DB type in the index_type dropdown list. Fill in the information, as necessary.
Select Save.
Back in the node, information is now populated in the mlindex_content textbox. Select the queries textbox and select the search terms you want to query. Select the same value as the input to the embed_the_question node. This value is typically either \${inputs.question} or ${modify_query_with_history.output}. Use \${inputs.question} if you're in a standard flow. Use ${modify_query_with_history.output} if you're in a chat flow.
Select a query type by selecting the dropdown next to query_type. Vector produces identical results as the legacy flow. Depending on your index configuration, other options such as Hybrid and Semantic might be available.
Edit downstream components to consume the output of your newly added node, instead of the output of the legacy Vector Index Lookup node.
Delete the Vector Index Lookup node and its parent embedding node.