Release Notes

v1.26.0-rc1

Prelude

The utility functions fetch_archive_from_http, build_pipeline and add_example_data were removed from Haystack.

This release changes the PDFToTextConverter so that it doesn't support PyMuPDF anymore. The converter will always assume xpdf is used by default.

⬆️ Upgrade Notes

• We recommend replacing calls to the fetch_archive_from_http function with other tools available in Python or in the operating system of use.
• To keep using PyMuPDF you must create a custom node, you can use the previous Haystack version for inspiration.

⚡️ Enhancement Notes

• Support for Llama3 models on AWS Bedrock.
• Support for MistralAI and new Claude 3 models on AWS Bedrock.
• Upgrade transformers to version 4.39.3 so that Haystack can support the new Cohere Command R models.
• Review and update context windows for OpenAI GPT models.
• Support gated repos for Huggingface inference.
• Add a check to verify that the embedding dimension set in the FAISS Document Store and retriever are equal before running embedding calculations.

🐛 Bug Fixes

• Pipeline run error when using the FileTypeClassifier with the raise_on_error: True option. Instead of returning an unexpected NoneType, we route the file to a dead-end edge.

• Ensure that the crawled files are downloaded to the output_dir directory, as specified in the Crawler constructor. Previously, some files were incorrectly downloaded to the current working directory.

• Fixes SearchEngineDocumentStore.get_metadata_values_by_key method to make use of self.index if no index is provided.

• Fixes OutputParser usage in PromptTemplate after making invocation context immutable in https://github.com/deepset-ai/haystack/pull/7510.

• When using a Pipeline with a JoinNode (e.g. JoinDocuments) all information from the previous nodes was lost other than a few select fields (e.g. documents). This was due to the JoinNode not properly passing on the information from the previous nodes. This has been fixed and now all information from the previous nodes is passed on to the next node in the pipeline.

  For example, this is a pipeline that rewrites the query during pipeline execution combined with a hybrid retrieval setup that requires a JoinDocuments node. Specifically the first prompt node rewrites the query to fix all spelling errors, and this new query is used for retrieval. And now the JoinDocuments node will now pass on the rewritten query so it can be used by the QAPromptNode node whereas before it would pass on the original query. `python from haystack import Pipeline from haystack.nodes import BM25Retriever, EmbeddingRetriever, PromptNode, Shaper, JoinDocuments, PromptTemplate from haystack.document_stores import InMemoryDocumentStore document_store = InMemoryDocumentStore(use_bm25=True) dicts = [{"content": "The capital of Germany is Berlin."}, {"content": "The capital of France is Paris."}] document_store.write_documents(dicts) query_prompt_node = PromptNode( model_name_or_path="gpt-3.5-turbo", api_key="", default_prompt_template=PromptTemplate("You are a spell checker. Given a user query return the same query with all spelling errors fixed.\nUser Query: {query}\nSpell Checked Query:") ) shaper = Shaper( func="join_strings", inputs={"strings": "results"}, outputs=["query"], ) qa_prompt_node = PromptNode( model_name_or_path="gpt-3.5-turbo", api_key="", default_prompt_template=PromptTemplate("Answer the user query. Query: {query}") ) sparse_retriever = BM25Retriever( document_store=document_store, top_k=2 ) dense_retriever = EmbeddingRetriever( document_store=document_store, embedding_model="intfloat/e5-base-v2", model_format="sentence_transformers", top_k=2 ) document_store.update_embeddings(dense_retriever) pipeline = Pipeline() pipeline.add_node(component=query_prompt_node, name="QueryPromptNode", inputs=["Query"]) pipeline.add_node(component=shaper, name="ListToString", inputs=["QueryPromptNode"]) pipeline.add_node(component=sparse_retriever, name="BM25", inputs=["ListToString"]) pipeline.add_node(component=dense_retriever, name="Embedding", inputs=["ListToString"]) pipeline.add_node( component=JoinDocuments(join_mode="concatenate"), name="Join", inputs=["BM25", "Embedding"] ) pipeline.add_node(component=qa_prompt_node, name="QAPromptNode", inputs=["Join"]) out = pipeline.run(query="What is the captial of Grmny?", debug=True) print(out["invocation_context"]) # Before Fix # {'query': 'What is the captial of Grmny?', <-- Original Query!! # 'results': ['The capital of Germany is Berlin.'], # 'prompts': ['Answer the user query. Query: What is the captial of Grmny?'], <-- Original Query!! # After Fix # {'query': 'What is the capital of Germany?', <-- Rewritten Query!! # 'results': ['The capital of Germany is Berlin.'], # 'prompts': ['Answer the user query. Query: What is the capital of Germany?'], <-- Rewritten Query!!`

• When passing empty inputs (such as query="") to PromptNode, the node would raise an error. This has been fixed.

v1.26.0-rc0

⚡️ Enhancement Notes

• Add raise_on_failure flag to BaseConverter class so that big processes can optionally continue without breaking from exceptions.

• Upgrade Transformers to the latest version 4.37.2. This version adds support for the Phi-2 and Qwen2 models and improves support for quantization.

• Add support for latest OpenAI embedding models text-embedding-3-large and text-embedding-3-small.

• API_BASE can now be passed as an optional parameter in the getting_started sample. Only openai provider is supported in this set of changes. PromptNode and PromptModel were enhanced to allow passing of this parameter. This allows RAG against a local endpoint (e.g, http://localhost:1234/v1), so long as it is OpenAI compatible (such as LM Studio)

  Logging in the getting started sample was made more verbose, to make it easier for people to see what was happening under the covers.

• Added new option split_by="page" to the preprocessor so we can chunk documents by page break.

🐛 Bug Fixes

• Change the dummy vector used internally in the Pinecone Document Store. A recent change to the Pinecone API does not allow to use vectors filled with zeros as was the previous dummy vector.
• The types of meta data values accepted by RouteDocuments was unnecessarily restricted to string types. This causes validation errors (for example when loading from a yaml file) if a user tries to use a boolean type for example. We add boolean and int types as valid types for metadata_values.
• Fixed a bug that made it impossible to write Documents to Weaviate when some of the fields were empty lists (e.g. split_overlap for preprocessed documents).