azureml-rag 0.2.36

Contains Retrieval Augmented Generation related utilities for Azure Machine Learning and OSS interoperability.

AzureML Retrieval Augmented Generation Utilities

This package is in alpha stage at the moment, use at risk of breaking changes and unstable behavior.

It contains utilities for:

• Processing text documents into chunks appropriate for use in LLM prompts, with metadata such is source url.
• Embedding chunks with OpenAI or HuggingFace embeddings models, including the ability to update a set of embeddings over time.
• Create MLIndex artifacts from embeddings, a yaml file capturing metadata needed to deserialize different kinds of Vector Indexes for use in langchain. Supported Index types:
  • FAISS index (via langchain)
  • Azure Cognitive Search index
  • Pinecone index
  • Milvus index
  • Azure Cosmos Mongo vCore index
  • MongoDB

Getting started

You can install AzureMLs RAG package using pip.

pip install azureml-rag

There are various extra installs you probably want to include based on intended use:

• faiss: When using FAISS based Vector Indexes
• cognitive_search: When using Azure Cognitive Search Indexes
• pinecone: When using Pinecone Indexes
• azure_cosmos_mongo_vcore: When using Azure Cosmos Mongo vCore Indexes
• hugging_face: When using Sentence Transformer embedding models from HuggingFace (local inference)
• document_parsing: When cracking and chunking documents locally to put in an Index
• mongodb: When using native mongo db indexes

MLIndex

MLIndex files describe an index of data + embeddings and the embeddings model used in yaml.

Azure Cognitive Search Index:

embeddings:
  dimension: 768
  kind: hugging_face
  model: sentence-transformers/all-mpnet-base-v2
  schema_version: '2'
index:
  api_version: 2021-04-30-Preview
  connection:
    id: /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/workspaces/<workspace>/connections/<acs_connection_name>
  connection_type: workspace_connection
  endpoint: https://<acs_name>.search.windows.net
  engine: azure-sdk
  field_mapping:
    content: content
    filename: filepath
    metadata: meta_json_string
    title: title
    url: url
    embedding: contentVector
  index: azureml-rag-test-206e03b6-3880-407b-9bc4-c0a1162d6c70
  kind: acs

Pinecone Index:

embeddings:
  dimension: 768
  kind: hugging_face
  model: sentence-transformers/all-mpnet-base-v2
  schema_version: '2'
index:
  connection:
    id: /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/workspaces/<workspace>/connections/<pinecone_connection_name>
  connection_type: workspace_connection
  engine: pinecone-sdk
  field_mapping:
    content: content
    filename: filepath
    metadata: metadata_json_string
    title: title
    url: url
  index: azureml-rag-test-206e03b6-3880-407b-9bc4-c0a1162d6c70
  kind: pinecone

Azure Cosmos Mongo vCore Index:

embeddings:
  dimension: 768
  kind: hugging_face
  model: sentence-transformers/all-mpnet-base-v2
  schema_version: '2'
index:
  connection:
    id: /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/workspaces/<workspace>/connections/<cosmos_connection_name>
  connection_type: workspace_connection
  engine: pymongo-sdk
  field_mapping:
    content: content
    filename: filepath
    metadata: metadata_json_string
    title: title
    url: url
    embedding: contentVector
  database: azureml-rag-test-db
  collection: azureml-rag-test-collection
  index: azureml-rag-test-206e03b6-3880-407b-9bc4-c0a1162d6c70
  kind: azure_cosmos_mongo_vcore

Create MLIndex

Examples using MLIndex remotely with AzureML and locally with langchain live here: https://github.com/Azure/azureml-examples/tree/main/sdk/python/generative-ai/rag

Consume MLIndex

from azureml.rag.mlindex import MLIndex

retriever = MLIndex(uri_to_folder_with_mlindex).as_langchain_retriever()
retriever.get_relevant_documents('What is an AzureML Compute Instance?')

Changelog

Please insert change log into "Next Release" ONLY.

Next release

0.2.36

• Implement mongodb vector store and ml index supports
• Detect OBO credential with AZUREML_OBO_ENABLED environment variable
• ACS update on changed (new or deleted) documents
• Drop azure-search-documents 11.4.0 beta version support

0.2.35

• Implement cosmosdb for nosql vector store and ml index supports
• Relax langchain version constraint
• Upgraded langchain-pinecone version to 0.1.1 and pinecone-client version

0.2.34

• Update azure-ai-ml version to 1.16.1 by introducing noneCredentialConfigure and add authType for AadCredentialConfigure
• Use set of exceptions as retry_exceptions in backoff_retry_on_exceptions

0.2.33

• Support existing qdrant indices
• Mitigate PF failure while more than 3 lookup tools used in a flow
• Add the retry for the embedder if there was a successfully embedding

0.2.32

• Implement langchain weaviate vectorstore in mlindex
• Get connection in get_connection_by_id_v2 with caller specified credential
• Set upper bound for azure-ai-ml to 1.15.0

0.2.31.1

• Update search index with azure-search-documents 11.4.0
• Add azureml-core in the dependency list

0.2.31

• Categorize user error and system error, and update RH accordingly to show in logs
• Bugfix using obo credential for AAD connections.
• Prevention fix to support AadCredentialConfig in Connection object
• Update Pinecone legacy API
• Creating image embedding index with azure-search-documents 11.4.0

0.2.30.2

• Bugfix remove azure_ad_token_provider from EmbeddingContainer metadata
• Set embeddings_model as optional argument

0.2.30.1

• Introduce elasticsearch extra to declare transitive dependency on the elasticsearch package when using Elasticsearch indices.

0.2.30

• Bugfix in models.py to handle empty deployment name.
• Supporting existing elasticsearch indices
• Bug fix in crack_and_chunk_and_embed_and_index
• Fixing bug in using AAD auth type ACS connections.

0.2.29.2

• Fixing ACS index creation failure with azure-search-documents 11.4.0

0.2.29.1

• Fixing FAISS, dependable_faiss_import import failure with Langchain 0.1.x

0.2.29

• Support AAD and MSI auth type in AOAI, ACS connection

0.2.28

• Ensure compatibility with newer versions of azure-ai-ml.
• Upgrade langchain to support up to 0.1

0.2.27

• Support Cohere serverless endpoint
• Support multiple ACS lookups in the same process, eliminating field mapping conflicts
• Support pass-in credential in get_connection_by_name_v2 to unblock managed vNet setup
• Update validate_deployments in crack_chunk_embed_index_and_register.py

0.2.26

• Support for .csv and .json file extensions in pipeline
• Ignore mlflow.exceptions.RestException in safe_mlflow_log_metric
• validate_deployments supports openai v1.0+
• Removing unexpected keyword argument 'engine'
• Checking ACS account has enough index quota
• infer_deployment supports openai v1.0+
• Create missing fields for existing index

0.2.25

• Using local cached encodings.
• Adding convert_to_dict() for openai v1.0+
• Check index_config before passing in validate_deployments.py
• Limit size of documents upload to ACS in one batch to solve RequestEntityTooLargeError

0.2.24.2

• Supporting *.cognitiveservices.* endpoint
• Adding azureml-rag specific user_agent when using DocumentIntelligence
• Refactored update index tasks
• Supporting uppercase file extensions name in crack_and_chunk
• Fixing Deployment importing bug in utils
• Adding the playgroundType tag in MLIndex Asset used for Azure AI studio playground
• Remove mandatory module-level imports of optional extra packages

0.2.24.1

• Fixing is_florence key detection
• Using 'embedding_connection_id' instead of 'florence_connection_id' as parameter name

0.2.24

• Introducing image ingestion with florence embedding API
• Adding dummy output to validate_deployments for holding the right order
• Fixing DeploymentNotFound bug

0.2.23.5

• Deprecate pkg_resources in logging.py (https://setuptools.pypa.io/en/latest/pkg_resources.html)

0.2.23.4

• Make the api_type parameter non-case sensitive in OpenAIEmbedder
• Bug fix in embeddings container path

0.2.23.3

• Set upper bound for langchain to 0.0.348

0.2.23.2

• Make tiktoken pull from a cache instead of making the outgoing network call to get encodings files
• Add support for Azure Cosmos Mongo vCore

0.2.23.1

• Fixing exception handling in validate_deployments to support OpenAI v1.0+

0.2.23

• Support OpenAI v1.0 +
• Handle FAISS.load_local() change since Langchain 0.0.318
• Handle mailto links in url crawling component.
• Add support for Milvus vector store

0.2.22

• update pypdf's version to 3.17.1 in document-parsing.

0.2.21

• Use workspace connection tags instead of metadata since it's deprecated.
• Fix bug handling single files in files_to_document_sources

0.2.20

• Initial introduction of validate_deployments.
• Asset registration in *_and_register attempts to infer target workspace from asset_uri and handle multiple auth options
• activity_logger moved out as first arg, this is an intermediate step as logger also shouldn't be first arg and instead handled by get_logger, activity_logger should be truly optional.
• validate_deployments itself was modified to make its interface closer to what existing tasks expect as input, and callable from other tasks as a function.

0.2.19

• Introduce a new path parameter in the index section of MLIndex documents over FAISS indices, to allow the path to FAISS index files to be different from the MLIndex document path.
• Ensure MLIndex.base_uri is never undefined for a valid MLIndex object.

0.2.18.1

• Only save out metadata before embedding in crack_and_chunk_and_embed_and_index
• Update create_embeddings to return num_embedded value.
  • This enables crack_and_chunk_and_embed to skip loading EmbeddedDocument partitions when no documents were embedded (all reused).

0.2.18

• Add new task to crack, chunk, embed, index to ACS, and register MLIndex in one step.
• Handle openai.api_type being None

0.2.17

• Fix loading MLIndex failure. Don't need to get the endpoint from connection when it is already provided.
• Try use langchain VectorStore and fallback to vendor
• Support `azure-search-documents==11.4.0b11``
• Add support for Pinecone in DataIndex

0.2.16

• Use Retry-After when aoai embedding endpoint throws RateLimitError

0.2.15.1

• Fix vendored FAISS langchain VectorStore to only error when a doc is None (rather than when a Document isn't exactly the right class)

0.2.15

• Support PDF cracking with Azure Document Intelligence service
• crack_and_chunk_and_embed now pulls documents through to embedding (streaming) and embeds documents in parallel batches
• Update default field names.
• Fix long file name bug when writing to output during crack and chunk

0.2.14

• Fix git_clone to handle WorkspaceConnections, again.

0.2.13

• Fix git_clone to handle WorkspaceConnection objects and urls with usernames already in them.

0.2.12

• Only process .jsonl and .csv files when reading chunks for embedding.

0.2.11

• Check casing for model kind and api_type
• Ensure api_version not being set is supported and default make sense.
• Add support for Pinecone indexes

0.2.10

• Fix QA generator and connections check for ApiType metadata

0.2.9

• QA data generation accepts connection as input

0.2.8

• Remove allowed_special="all" from tiktoken usage as it encodes special tokens like <|endoftext|> as their special token rather then as plain text (which is the case when only disallowed_special=() is set on its own)
• Stop truncating texts to embed (to model ctx length) as new azureml.rag.embeddings.OpenAIEmbedder handles batching and splitting long texts pre-embed then averaging the results into a single final embedding.
• Loosen tiktoken version range from ~=0.3.0 to <1

0.2.7

• Don't try and use MLClient for connections if azure-ai-ml<1.10.0
• Handle Custom Conenctions which azure-ai-ml can't deserialize today.
• Allow passing faiss index engine to MLIndex local
• Pass chunks directly into write_chunks_to_jsonl

0.2.6

• Fix jsonl output mode of crack_and_chunk writing csv internally.

0.2.5

• Ensure EmbeddingsContainer.mount_and_load sets create_destination=True when mounting to create embeddings_cache location if it's not already created.
• Fix safe_mlflow_start_run to yield None when mlflow not available
• Handle custom field_mappings passed to update_acs task.

0.2.4

• Introduce crack_and_chunk_and_embed task which tracks deletions and reused source + documents to enable full sync with indexes, levering EmbeddingsContainer for storage of this information across Snapshots.
• Restore workspace_connection_to_credential function.

0.2.3

• Fix git clone url format bug

0.2.2

• Fix all langchain splitter to use tiktoken in an airgap friendly way.

0.2.1

• Introduce DataIndex interface for scheduling Vector Index Pipeline in AzureML and creating MLIndex Assets
• Vendor various langchain components to avoid breaking changes to MLIndex internal logic

0.1.24.2

• Fix all langchain splitter to use tiktoken in an airgap friendly way.

0.1.24.1

• Fix subsplitter init bug in MarkdownHeaderSplitter
• Support getting langchain retriever for ACS based MLIndex with embeddings.kind: none.

0.1.24

• Don't mlflow log unless there's an active mlflow run.
• Support langchain.vectorstores.azuresearch after langchain>=0.0.273 upgraded to azure-search-documents==11.4.0b8
• Use tiktoken encodings from package for other splitter types

0.1.23.2

• Handle Path objects passed into MLIndex init.

0.1.23.1

• Handle .api.cognitive style aoai endpoints correctly

0.1.23

• Ensure tiktoken encodings are packaged in wheel

0.1.22

• Set environment variables to pull encodings files from directory with cache key to avoid tiktoken external network call
• Fix mlflow log error when there's no files input

0.1.21

• Fix top level imports in update_acs task failing without helpful reason when old azure-search-documents is installed.

0.1.20

• Fix Crack'n'Chunk race-condition where same named files would overwrite each other.

0.1.19

• Various bug fixes:
  • Handle some malformed git urls in git_clone task
  • Try fall back when parsing csv with pandas fails
  • Allow chunking special tokens
  • Ensure logging with mlflow can't fail a task
• Update to support latest azure-search-documents==11.4.0b8

0.1.18

• Add FaissAndDocStore and FileBasedDocStore which closely mirror langchains' FAISS and InMemoryDocStore without the langchain or pickle dependency. These are default not used until PromptFlow support has been added.
• Pin azure-documents-search==11.4.0b6 as there's breaking changes in 11.4.0b7 and 11.4.0b8

0.1.17

• Update interactions with Azure Cognitive Search to use latest azure-documents-search SDK

0.1.16

• Convert api_type from Workspace Connections to lower case to appease langchains case sensitive checking.

0.1.15

• Add support for custom loaders
• Added logging for MLIndex.init to understand usage of MLindex

0.1.14

• Add Support for CustomKeys connections
• Add OpenAI support for QA Gen and Embeddings

0.1.13 (2023-07-12)

• Implement single node non-PRS embed task to enable clearer logs for users.

0.1.12 (2023-06-29)

• Fix casing check of ApiVersion, ApiType in infer_deployment util

0.1.11 (2023-06-28)

• Update casing check for workspace connection ApiVersion, ApiType
• int casting for temperature, max_tokens

0.1.10 (2023-06-26)

• Update data asset registering to have adjustable output_type
• Remove asset registering from generate_qa.py

0.1.9 (2023-06-22)

• Add azureml.rag.data_generation module.
• Fixed bug that would cause crack_and_chunk to fail for documents that contain non-utf-8 characters. Currently these characters will be ignored.
• Improved heading extraction from Markdown files. When use_rcts=False Markdown files will be split on headings and each chunk with have the heading context up to the root as a prefix (e.g. # Heading 1\n## Heading 2\n# Heading 3\n{content})

0.1.8 (2023-06-21)

• Add deployment inferring util for use in azureml-insider notebooks.

0.1.7 (2023-06-08)

• Improved telemetry for tasks (used in RAG Pipeline Components)

0.1.6 (2023-05-31)

• Fail crack_and_chunk task when no files were processed (usually because of a malformed input_glob)
• Change update_acs.py to default push_embeddings=True instead of False.

0.1.5 (2023-05-19)

• Add api_base back to MLIndex embeddings config for back-compat (until all clients start getting it from Workspace Connection).
• Add telemetry for tasks used in pipeline components, not enabled by default for SDK usage.

0.1.4 (2023-05-17)

• Fix bug where enabling rcts option on split_documents used nltk splitter instead.

0.1.3 (2023-05-12)

• Support Workspace Connection based auth for Git, Azure OpenAI and Azure Cognitive Search usage.

0.1.2 (2023-05-05)

• Refactored document chunking to allow insertion of custom processing logic

0.0.1 (2023-04-25)

Features Added

• Introduced package
• langchain Retriever for Azure Cognitive Search