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87 lines
4.0 KiB
Markdown
87 lines
4.0 KiB
Markdown
# Faiss Index Lookup
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Faiss Index Lookup is a tool tailored for querying within a user-provided Faiss-based vector store. In combination with our Large Language Model (LLM) tool, it empowers users to extract contextually relevant information from a domain knowledge base.
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## Requirements
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- For AzureML users, the tool is installed in default image, you can use the tool without extra installation.
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- For local users, if your index is stored in local path,
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`pip install promptflow-vectordb`
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if your index is stored in Azure storage,
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`pip install promptflow-vectordb[azure]`
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## Prerequisites
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### For AzureML users,
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- step 1. Prepare an accessible path on Azure Blob Storage. Here's the guide if a new storage account needs to be created: [Azure Storage Account](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-create?tabs=azure-portal).
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- step 2. Create related Faiss-based index files on Azure Blob Storage. We support the LangChain format (index.faiss + index.pkl) for the index files, which can be prepared either by employing our promptflow-vectordb SDK or following the quick guide from [LangChain documentation](https://python.langchain.com/docs/integrations/vectorstores/faiss). Please refer to the instructions of <a href="https://aka.ms/pf-sample-build-faiss-index" target="_blank">An example code for creating Faiss index</a> for building index using promptflow-vectordb SDK.
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- step 3. Based on where you put your own index files, the identity used by the promptflow runtime should be granted with certain roles. Please refer to [Steps to assign an Azure role](https://learn.microsoft.com/en-us/azure/role-based-access-control/role-assignments-steps):
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| Location | Role |
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| ---- | ---- |
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| workspace datastores or workspace default blob | AzureML Data Scientist |
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| other blobs | Storage Blob Data Reader |
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### For local users,
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- Create Faiss-based index files in local path by only doing step 2 above.
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## Inputs
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The tool accepts the following inputs:
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| Name | Type | Description | Required |
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| ---- | ---- | ----------- | -------- |
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| path | string | URL or path for the vector store.<br><br>local path (for local users):<br>`<local_path_to_the_index_folder>`<br><br> Azure blob URL format (with [azure] extra installed):<br>https://`<account_name>`.blob.core.windows.net/`<container_name>`/`<path_and_folder_name>`.<br><br>AML datastore URL format (with [azure] extra installed):<br>azureml://subscriptions/`<your_subscription>`/resourcegroups/`<your_resource_group>`/workspaces/`<your_workspace>`/data/`<data_path>`<br><br>public http/https URL (for public demonstration):<br>http(s)://`<path_and_folder_name>` | Yes |
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| vector | list[float] | The target vector to be queried, which can be generated by the LLM tool. | Yes |
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| top_k | integer | The count of top-scored entities to return. Default value is 3. | No |
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## Outputs
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The following is an example for JSON format response returned by the tool, which includes the top-k scored entities. The entity follows a generic schema of vector search result provided by our promptflow-vectordb SDK. For the Faiss Index Search, the following fields are populated:
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| Field Name | Type | Description |
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| ---- | ---- | ----------- |
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| text | string | Text of the entity |
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| score | float | Distance between the entity and the query vector |
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| metadata | dict | Customized key-value pairs provided by user when create the index |
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<details>
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<summary>Output</summary>
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```json
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[
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{
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"metadata": {
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"link": "http://sample_link_0",
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"title": "title0"
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},
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"original_entity": null,
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"score": 0,
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"text": "sample text #0",
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"vector": null
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},
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{
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"metadata": {
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"link": "http://sample_link_1",
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"title": "title1"
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},
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"original_entity": null,
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"score": 0.05000000447034836,
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"text": "sample text #1",
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"vector": null
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},
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{
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"metadata": {
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"link": "http://sample_link_2",
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"title": "title2"
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},
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"original_entity": null,
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"score": 0.20000001788139343,
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"text": "sample text #2",
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"vector": null
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}
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]
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```
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</details> |