| Name | promptflow-vectordb JSON |
| Version |
0.2.13
JSON |
| download |
| home_page | None |
| Summary | Prompt flow tools for accessing popular vector databases |
| upload_time | 2024-08-08 22:01:18 |
| maintainer | None |
| docs_url | None |
| author | Microsoft Corporation |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
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# 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.
## 0.2.13
- 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.
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"description": "# Introduction\r\n\r\nTo 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.\r\n\r\n## 0.2.13\r\n- Introduced new tool - `Rerank`, to serve as a single tool to perfom semantic ranking on given documents and query\r\n- Marked `Rerank` as preview.\r\n\r\n## 0.2.12\r\n- Add azureml-telemetry as extra install option enabling further logging. Added fields to custom environment to get logged.\r\n\r\n## 0.2.11\r\n- Exlude azureml-rag 0.2.31 from vectordb package\r\n- Add support for bring-your-own `Azure CosmosDB for PostgreSQL` index.\r\n\r\n## 0.2.10\r\n- Add support for bring-your-own `Elasticsearch` index.\r\n- Serverless Deployments can now be used directly for embedding, without requiring the creation of a Serverless Connection.\r\n- Rename `Serverless Endpoints` to `Serverless Deployments`.\r\n- Remove preview tag from `Index Lookup`.\r\n\r\n## 0.2.9\r\n- Fix compatibility issue with langchain 0.1 that broke Azure AI Search semantic searches.\r\n- 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.\r\n- Add support for bring-your-own `Azure CosmosDB for MongoDB vCore` index.\r\n\r\n## 0.2.8\r\n- Add support for langchain 0.1\r\n- Replace `FAISS Index Lookup`, `Vector Index Lookup` and `Vector DB Lookup` internals with `Index Lookup` internals.\r\n- Use azureml.rag logger and promptflow.tool logger in `Index Lookup`.\r\n\r\n## 0.2.7\r\n- Add support for Serverless Deployment connections for embeddings in `Index Lookup`.\r\n- Add support for multiple instances of `Index Lookup` running in the same process without conflicts.\r\n- Auto-detect embedding vector length for supported embedding models.\r\n\r\n## 0.2.6\r\n- Emit granular trace information from `Index Lookup` for use by Action Analyzer.\r\n\r\n## 0.2.5\r\n- Introduce improved error messaging when input queries are of an unexpected type.\r\n- Mark `FAISS Index Lookup`, `Vector Index Lookup` and `Vector DB Lookup` as archived.\r\n- Add support for `text-embedding-3-small` and `text-embedding-3-large` embedding models.\r\n\r\n## 0.2.4\r\n- Mark `FAISS Index Lookup`, `Vector Index Lookup` and `Vector DB Lookup` as deprecated.\r\n- Introduced a `self` section in the mlindex_content YAML, to carry information about the asset ID and path from which the MLIndex was retrieved.\r\n- Index Lookup now caches vectorstore build steps for better runtime performance.\r\n- Use `functools.lru_cache` instead of `functools.cache` for compatibility with python < 3.9\r\n- Use `ruamel.yaml` instead of `pyyaml`, so that yaml 1.2 is supported.\r\n\r\n## 0.2.3\r\n- Implement HTTP caching to improve callback performance.\r\n- Not specifying a value for `embedding_type` produces the same behavior as selecting `None`.\r\n- Index Lookup honors log levels set via the `PF_LOGGING_LEVEL` environment variable.\r\n\r\n## 0.2.2\r\n- Introduced new tool - `Index Lookup`, to serve as a single tool to perform lookups against supported index types.\r\n- Marked `Index Lookup` as preview.\r\n",
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