promptflow-vectordb 0.2.17
Prompt flow tools for accessing popular vector databases

# Introduction

To store and search over unstructured data, a widely adopted approach is embedding data into vectors, stored and indexed in vector databases. The promptflow-vectordb SDK is designed for PromptFlow, provides essential tools for vector similarity search within popular vector databases, including  FAISS, Qdrant, Azure Congnitive Search, and more.

## 0.2.17
- Get the resource management URL from environment variable `AZURE_RESOURCE_MANAGER`.

## 0.2.16
- Un-pin azureml-rag to support langchain 0.3.x

## 0.2.15
- Pin azureml-rag to 0.2.36
- Install azureml-rag extras explicitly

## 0.2.14
- Upgrade azureml-rag to 0.2.37
- Move langchain dependency to azureml-rag

## 0.2.13
- Introduced new tool - `Rerank`, to serve as a single tool to perfom semantic ranking on given documents and query
- Marked `Rerank` as preview.

## 0.2.12
- Add azureml-telemetry as extra install option enabling further logging. Added fields to custom environment to get logged.

## 0.2.11
- Exlude azureml-rag 0.2.31 from vectordb package
- Add support for bring-your-own `Azure CosmosDB for PostgreSQL` index.

## 0.2.10
- Add support for bring-your-own `Elasticsearch` index.
- Serverless Deployments can now be used directly for embedding, without requiring the creation of a Serverless Connection.
- Rename `Serverless Endpoints` to `Serverless Deployments`.
- Remove preview tag from `Index Lookup`.

## 0.2.9
- Fix compatibility issue with langchain 0.1 that broke Azure AI Search semantic searches.
- Refactor metadata retrieval in `Index Lookup`. Metadata fields that are specifically requested are now present in the `metadata` property of a retrieval result, and all other retrieved fields have been moved to `additional_fields`, instead of being discarded.
- Add support for bring-your-own `Azure CosmosDB for MongoDB vCore` index.

## 0.2.8
- Add support for langchain 0.1
- Replace `FAISS Index Lookup`, `Vector Index Lookup` and `Vector DB Lookup` internals with `Index Lookup` internals.
- Use azureml.rag logger and promptflow.tool logger in `Index Lookup`.

## 0.2.7
- Add support for Serverless Deployment connections for embeddings in `Index Lookup`.
- Add support for multiple instances of `Index Lookup` running in the same process without conflicts.
- Auto-detect embedding vector length for supported embedding models.

## 0.2.6
- Emit granular trace information from `Index Lookup` for use by Action Analyzer.

## 0.2.5
- Introduce improved error messaging when input queries are of an unexpected type.
- Mark `FAISS Index Lookup`, `Vector Index Lookup` and `Vector DB Lookup` as archived.
- Add support for `text-embedding-3-small` and `text-embedding-3-large` embedding models.

## 0.2.4
- Mark `FAISS Index Lookup`, `Vector Index Lookup` and `Vector DB Lookup` as deprecated.
- Introduced a `self` section in the mlindex_content YAML, to carry information about the asset ID and path from which the MLIndex was retrieved.
- Index Lookup now caches vectorstore build steps for better runtime performance.
- Use `functools.lru_cache` instead of `functools.cache` for compatibility with python < 3.9
- Use `ruamel.yaml` instead of `pyyaml`, so that yaml 1.2 is supported.

## 0.2.3
- Implement HTTP caching to improve callback performance.
- Not specifying a value for `embedding_type` produces the same behavior as selecting `None`.
- Index Lookup honors log levels set via the `PF_LOGGING_LEVEL` environment variable.

## 0.2.2
- Introduced new tool - `Index Lookup`, to serve as a single tool to perform lookups against supported index types.
- Marked `Index Lookup` as preview.