v2.1.0

Release Notes

Highlights

📊 New Evaluator Components

Haystack introduces new components for both with model-based, and statistical evaluation: AnswerExactMatchEvaluator, ContextRelevanceEvaluator, DocumentMAPEvaluator, DocumentMRREvaluator, DocumentRecallEvaluator, FaithfulnessEvaluator, LLMEvaluator, SASEvaluator

Here's an example of how to use DocumentMAPEvaluator to evaluate retrieved documents and calculate mean average precision score:

from haystack import Document
from haystack.components.evaluators import DocumentMAPEvaluator

evaluator = DocumentMAPEvaluator()
result = evaluator.run(
    ground_truth_documents=[
        [Document(content="France")],
        [Document(content="9th century"), Document(content="9th")],
    ],
    retrieved_documents=[
        [Document(content="France")],
        [Document(content="9th century"), Document(content="10th century"), Document(content="9th")],
    ],
)

result["individual_scores"]
>> [1.0, 0.8333333333333333]
result["score"]
>> 0 .9166666666666666

To learn more about evaluating RAG pipelines both with model-based, and statistical metrics available in the Haystack, check out Tutorial: Evaluating RAG Pipelines.

🕸️ Support For Sparse Embeddings

Haystack offers robust support for Sparse Embedding Retrieval techniques, including SPLADE. Here's how to create a simple retrieval Pipeline with sparse embeddings:

from haystack import Pipeline
from haystack_integrations.components.retrievers.qdrant import QdrantSparseEmbeddingRetriever
from haystack_integrations.components.embedders.fastembed import FastembedSparseTextEmbedder

sparse_text_embedder = FastembedSparseTextEmbedder(model="prithvida/Splade_PP_en_v1")
sparse_retriever = QdrantSparseEmbeddingRetriever(document_store=document_store)

query_pipeline = Pipeline()
query_pipeline.add_component("sparse_text_embedder", sparse_text_embedder)
query_pipeline.add_component("sparse_retriever", sparse_retriever)

query_pipeline.connect("sparse_text_embedder.sparse_embedding", "sparse_retriever.query_sparse_embedding")

Learn more about this topic in our documentation on Sparse Embedding-based Retrievers
Start building with our new cookbook: 🧑‍🍳 Sparse Embedding Retrieval using Qdrant and FastEmbed.

🧐 Inspect Component Outputs

As of 2.1.0, you can now inspect each component output after running a pipeline. Provide component names with include_outputs_from key to pipeline.run:

pipe.run(data, include_outputs_from={"prompt_builder", "llm", "retriever"})

And the pipeline output should look like this:

{'llm': {'replies': ['The Rhodes Statue was described as being built with iron tie bars to which brass plates were fixed to form the skin. It stood on a 15-meter-high white marble pedestal near the Rhodes harbor entrance. The statue itself was about 70 cubits, or 32 meters, tall.'],
  'meta': [{'model': 'gpt-3.5-turbo-0125',
    ...
    'usage': {'completion_tokens': 57,
     'prompt_tokens': 446,
     'total_tokens': 503}}]},
 'retriever': {'documents': [Document(id=a3ee3a9a55b47ff651ae11dc56d84d2b6f8d931b795bd866c14eacfa56000965, content: 'Within it, too, are to be seen large masses of rock, by the weight of which the artist steadied it w...', meta: {'url': 'https://en.wikipedia.org/wiki/Colossus_of_Rhodes', '_split_id': 9}, score: 0.648961685430463),...]},
 'prompt_builder': {'prompt': "\nGiven the following information, answer the question.\n\nContext:\n\n    Within it, too, are to be seen large masses of rock, by the weight of which the artist steadied it while...
 ... levels during construction.\n\n\n\nQuestion: What does Rhodes Statue look like?\nAnswer:"}}

🚀 New Features

• Add several new Evaluation components, i.e:

  • AnswerExactMatchEvaluator
  • ContextRelevanceEvaluator
  • DocumentMAPEvaluator
  • DocumentMRREvaluator
  • DocumentRecallEvaluator
  • FaithfulnessEvaluator
  • LLMEvaluator
  • SASEvaluator

• Introduce a new SparseEmbedding class that can store a sparse vector representation of a document. It will be instrumental in supporting sparse embedding retrieval with the subsequent introduction of sparse embedders and sparse embedding retrievers.

• Added a SentenceTransformersDiversityRanker. The diversity ranker orders documents to maximize their overall diversity. The ranker leverages sentence-transformer models to calculate semantic embeddings for each document and the query.

• Introduced new HuggingFace API components, namely:

  • HuggingFaceAPIChatGenerator, which will replace the HuggingFaceTGIChatGenerator in the future.
  • HuggingFaceAPIDocumentEmbedder, which will replace the HuggingFaceTEIDocumentEmbedder in the future.
  • HuggingFaceAPIGenerator, which will replace the HuggingFaceTGIGenerator in the future.
  • HuggingFaceAPITextEmbedder, which will replace the HuggingFaceTEITextEmbedder in the future.
  • These components support different Hugging Face APIs:
    • free Serverless Inference API
    • paid Inference Endpoints
    • self-hosted Text Generation Inference

⚡️ Enhancement Notes

• Compatibility with huggingface_hub>=0.22.0 for HuggingFaceTGIGenerator and HuggingFaceTGIChatGenerator components.

• Adds truncate and normalize parameters to HuggingFaceTEITextEmbedder and HuggingFaceTEITextEmbedder to allow truncation and normalization of embeddings.

• Adds trust_remote_code parameter to SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder for allowing custom models and scripts.

• Adds streaming_callback parameter to HuggingFaceLocalGenerator, allowing users to handle streaming responses.

• Adds a ZeroShotTextRouter that uses an NLI model from HuggingFace to classify texts based on a set of provided labels and routes them based on the label they were classified with.

• Adds dimensions parameter to Azure OpenAI Embedders (AzureOpenAITextEmbedder and AzureOpenAIDocumentEmbedder) to fully support new embedding models like text-embedding-3-small, text-embedding-3-large and upcoming ones

• Now the DocumentSplitter adds the page_number field to the metadata of all output documents to keep track of the page of the original document it belongs to.

• Allows users to customise text extraction from PDF files. This is particularly useful for PDFs with unusual layouts, such as multiple text columns. For instance, users can configure the object to retain the reading order.

• Enhanced PromptBuilder to specify and enforce required variables in prompt templates.

• Set max_new_tokens default to 512 in HuggingFace generators.

• Enhanced the AzureOCRDocumentConverter to include advanced handling of tables and text. Features such as extracting preceding and following context for tables, merging multiple column headers, and enabling single-column page layout for text have been introduced. This update furthers the flexibility and accuracy of document conversion within complex layouts.

• Enhanced DynamicChatPromptBuilder's capabilities by allowing all user and system messages to be templated with provided variables. This update ensures a more versatile and dynamic templating process, making chat prompt generation more efficient and customised to user needs.

• Improved HTML content extraction by attempting to use multiple extractors in order of priority until successful. An additional try_others parameter in HTMLToDocument, True by default, determines whether subsequent extractors are used after a failure. This enhancement decreases extraction failures, ensuring more dependable content retrieval.

• Enhanced FileTypeRouter with regex pattern support for MIME types. This powerful addition allows for more granular control and flexibility in routing files based on their MIME types, enabling the handling of broad categories or specific MIME type patterns with ease. This feature particularly benefits applications requiring sophisticated file classification and routing logic.

• In Jupyter notebooks, the image of the Pipeline will no longer be displayed automatically. Instead, the textual representation of the Pipeline will be displayed. To display the Pipeline image, use the show method of the Pipeline object.

• Add support for callbacks during pipeline deserialization. Currently supports a pre-init hook for components that can be used to inspect and modify the initialization parameters before the invocation of the component's __init__ method.

• pipeline.run() accepts a set of component names whose intermediate outputs are returned in the final pipeline output dictionary.

• Refactor PyPDFToDocument to simplify support for custom PDF converters. PDF converters are classes that implement the PyPDFConverter protocol and have 3 methods: convert, to_dict and from_dict.

⚠️ Deprecation Notes

• Deprecate HuggingFaceTGIChatGenerator, will be removed in Haystack 2.3.0. Use HuggingFaceAPIChatGenerator instead.
• Deprecate HuggingFaceTEIDocumentEmbedder, will be removed in...

v2.1.0-rc1

Release Notes

v2.1.0-rc1

Highlights

Add the "page_number" field to the metadata of all output documents.

⬆️ Upgrade Notes

• The HuggingFaceTGIGenerator and HuggingFaceTGIChatGenerator components have been modified to be compatible with huggingface_hub>=0.22.0.

  If you use these components, you may need to upgrade the huggingface_hub library. To do this, run the following command in your environment: `bash pip install "huggingface_hub>=0.22.0"`

🚀 New Features

• Add SentenceTransformersDiversityRanker. The Diversity Ranker orders documents in such a way as to maximize the overall diversity of the given documents. The ranker leverages sentence-transformer models to calculate semantic embeddings for each document and the query.

• Adds truncate and normalize parameters to HuggingFaceTEITextEmbedder and HuggingFaceTEITextEmbedder for allowing truncation and normalization of embeddings.

• Add trust_remote_code parameter to SentenceTransformersDocumentEmbedder and SentenceTransformersTextEmbedder for allowing custom models and scripts.

• Add a new ContextRelevanceEvaluator component that can be used to evaluate whether retrieved documents are relevant to answer a question with a RAG pipeline. Given a question and a list of retrieved document contents (contexts), an LLM is used to score to what extent the provided context is relevant. The score ranges from 0 to 1.

• Add DocumentMAPEvaluator, it can be used to calculate mean average precision of retrieved documents.

• Add DocumentMRREvaluator, it can be used to calculate mean reciprocal rank of retrieved documents.

• Add a new FaithfulnessEvaluator component that can be used to evaluate faithfulness / groundedness / hallucinations of LLMs in a RAG pipeline. Given a question, a list of retrieved document contents (contexts), and a predicted answer, FaithfulnessEvaluator returns a score ranging from 0 (poor faithfulness) to 1 (perfect faithfulness). The score is the proportion of statements in the predicted answer that could by inferred from the documents.

• Introduce HuggingFaceAPIChatGenerator. This text-generation component uses the ChatMessage format and supports different Hugging Face APIs: - free Serverless Inference API - paid Inference Endpoints - self-hosted Text Generation Inference.

  This generator will replace the HuggingFaceTGIChatGenerator in the future.

• Introduce HuggingFaceAPIDocumentEmbedder. This component can be used to compute Document embeddings using different Hugging Face APIs: - free Serverless Inference API - paid Inference Endpoints - self-hosted Text Embeddings Inference. This embedder will replace the HuggingFaceTEIDocumentEmbedder in the future.

• Introduce HuggingFaceAPIGenerator. This text-generation component supports different Hugging Face APIs:

  • free Serverless Inference API
  • paid Inference Endpoints
  • self-hosted Text Generation Inference.

  This generator will replace the HuggingFaceTGIGenerator in the future.

• Introduce HuggingFaceAPITextEmbedder. This component can be used to embed strings using different Hugging Face APIs: - free Serverless Inference API - paid Inference Endpoints - self-hosted Text Embeddings Inference. This embedder will replace the HuggingFaceTEITextEmbedder in the future.

• Adds 'streaming_callback' parameter to 'HuggingFaceLocalGenerator', allowing users to handle streaming responses.

• Added a new EvaluationRunResult dataclass that wraps the results of an evaluation pipeline, allowing for its transformation and visualization.

• Add a new LLMEvaluator component that leverages LLMs through the OpenAI api to evaluate pipelines.

• Add DocumentRecallEvaluator, a Component that can be used to calculate the Recall single-hit or multi-hit metric given a list of questions, a list of expected documents for each question and the list of predicted documents for each question.

• Add SASEvaluator, it can be used to calculate Semantic Answer Similarity of generated answers from an LLM

• Introduce a new SparseEmbedding class which can be used to store a sparse vector representation of a Document. It will be instrumental to support Sparse Embedding Retrieval with the subsequent introduction of Sparse Embedders and Sparse Embedding Retrievers.

• Add a Zero Shot Text Router that uses an NLI model from HF to classify texts based on a set of provided labels and routes them based on the label they were classified with.

⚡️ Enhancement Notes

• add dimensions parameter to Azure OpenAI Embedders (AzureOpenAITextEmbedder and AzureOpenAIDocumentEmbedder) to fully support new embedding models like text-embedding-3-small, text-embedding-3-large and upcoming ones

• Now the DocumentSplitter adds the "page_number" field to the metadata of all output documents to keep track of the page of the original document it belongs to.

• Provides users the ability to customize text extraction from PDF files. It is particularly useful for PDFs with unusual layouts, such as those containing multiple text columns. For instance, users can configure the object to retain the reading order.

• Enhanced PromptBuilder to specify and enforce required variables in prompt templates.

• Set max_new_tokens default to 512 in Hugging Face generators.

• Enhanced the AzureOCRDocumentConverter to include advanced handling of tables and text. Features such as extracting preceding and following context for tables, merging multiple column headers, and enabling single column page layout for text have been introduced. This update furthers the flexibility and accuracy of document conversion within complex layouts.

• Enhanced DynamicChatPromptBuilder's capabilities by allowing all user and system messages to be templated with provided variables. This update ensures a more versatile and dynamic templating process, making chat prompt generation more efficient and customized to user needs.

• Improved HTML content extraction by attempting to use multiple extractors in order of priority until successful. An additional try_others parameter in HTMLToDocument, which is true by default, determines whether subsequent extractors are used after a failure. This enhancement decreases extraction failures, ensuring more dependable content retrieval.

• Enhanced FileTypeRouter with Regex Pattern Support for MIME Types: This introduces a significant enhancement to the FileTypeRouter, now featuring support for regex pattern matching for MIME types. This powerful addition allows for more granular control and flexibility in routing files based on their MIME types, enabling the handling of broad categories or specific MIME type patterns with ease. This feature is particularly beneficial for applications requiring sophisticated file classification and routing logic.

  Usage example: `python from haystack.components.routers import FileTypeRouter router = FileTypeRouter(mime_types=[r"text/.*", r"application/(pdf|json)"]) # Example files to classify file_paths = [ Path("document.pdf"), Path("report.json"), Path("notes.txt"), Path("image.png"), ] result = router.run(sources=file_paths) for mime_type, files in result.items(): print(f"MIME Type: {mime_type}, Files: {[str(file) for file in files]}")`

• Improved pipeline run tracing to include pipeline input/output data.

• In Jupyter notebooks, the image of the Pipeline will no longer be displayed automatically. The textual representation of the Pipeline will be displayed.

  To display the Pipeline image, use the show method of the Pipeline object.

• Add support for callbacks during pipeline deserialization. Currently supports a pre-init hook for components that can be used to inspect and modify the initialization parameters before the invocation of the component's __init__ method.

• pipeline.run accepts a set of component names whose intermediate outputs are returned in the final pipeline output dictionary.

• Pipeline.inputs and Pipeline.outputs can optionally include components input/output sockets that are connected.

• Refactor PyPDFToDocument to simplify support for custom PDF converters. PDF converters are classes that implement the PyPDFConverter protocol and have 3 methods: convert, to_dict and from_dict. The DefaultConverter class is provided as a default implementation.

• Add an __eq__ method to SparseEmbedding class to compare two SparseEmbedding objects.

⚠️ Deprecation Notes

• Deprecate HuggingFaceTGIChatGenerator. This component will be removed in Haystack 2.3.0. Use HuggingFaceAPIChatGenerator instead.
• Deprecate HuggingFaceTEIDocumentEmbedder. This component will be removed in Haystack 2.3.0. Use HuggingFaceAPIDocumentEmbedder instead.
• Deprecate <span class="t...

v1.25.5

Release Notes

v1.25.5

🐛 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.

v1.25.4

Release Notes

v1.25.4

🐛 Bug Fixes

• Fixes OutputParser usage in PromptTemplate after making invocation context immutable in #7510.

v1.25.3

Release Notes

v1.25.3

⚡️ 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.

🐛 Bug Fixes

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

• 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.

v2.0.1

Release Notes

v2.0.1

⬆️ Upgrade Notes

• The HuggingFaceTGIGenerator and HuggingFaceTGIChatGenerator components have been modified to be compatible with huggingface_hub>=0.22.0.

  If you use these components, you may need to upgrade the huggingface_hub library. To do this, run the following command in your environment: pip install "huggingface_hub>=0.22.0"

🚀 New Features

• Adds streaming_callback parameter to HuggingFaceLocalGenerator, allowing users to handle streaming responses.
• Introduce a new SparseEmbedding class which can be used to store a sparse vector representation of a Document. It will be instrumental to support Sparse Embedding Retrieval with the subsequent introduction of Sparse Embedders and Sparse Embedding Retrievers.

⚡️ Enhancement Notes

• Set max_new_tokens default to 512 in Hugging Face generators.

• In Jupyter notebooks, the image of the Pipeline will no longer be displayed automatically. The textual representation of the Pipeline will be displayed.

  To display the Pipeline image, use the show method of the Pipeline object.

🐛 Bug Fixes

• The test_comparison_in test case in the base document store tests used to always pass, no matter how the in filtering logic was implemented in document stores. With the fix, the in logic is actually tested. Some tests might start to fail for document stores that don't implement the in filter correctly.
• Put HFTokenStreamingHandler in a lazy import block in HuggingFaceLocalGenerator. This fixed some breaking core-integrations.
• Fixes Pipeline.run() logic so Components that have all their inputs with a default are run in the correct order. This happened we gather a list of Components to run internally when running the Pipeline in the order they are added during creation of the Pipeline. This caused some Components to run before they received all their inputs.
• Fixes HuggingFaceTEITextEmbedder returning an embedding of incorrect shape when used with a Text-Embedding-Inference endpoint deployed using Docker.
• Add the @component decorator to HuggingFaceTGIChatGenerator. The lack of this decorator made it impossible to use the HuggingFaceTGIChatGenerator in a pipeline.

v1.25.2

Release Notes

v1.25.2

⚡️ Enhancement Notes

• Add support for response_format and seed in OpenAI and Azure OpenAI invocation layers #7422
• Add boolen to toggle prompt truncation #7431

Full Changelog: v1.25.1...v1.25.2

v1.25.1

Release Notes

v1.25.1

⚡️ Enhancement Notes

• Review and update context windows for OpenAI GPT models.

v2.0.0

Today, we’ve released the stable version of Haystack 2.0. This is ultimately a rewrite of the Haystack framework, so these release notes are not what you’d usually expect to see in regular release notes where we highlight specific changes to the codebase. Instead, we will highlight features of Haystack 2.0 and how it’s meant to be used.

To read more about our motivation for Haystack 2.0 and what makes up our design choices, you can read our release announcement article.

To get started with Haystack, follow our quick starting guide.

🕺 Highlights

• 📦 A New Package
• 💪 Powerful Pipelines
• 🔌 Customizable Components
• 🍱 Ready-Made Pipeline Templates
• 🗃️ Document Stores
• 🧩 Integrations
• 🕵️ Logging & Tracing
• 🏎️ Device Management
• 🔐 Secret Management
• 📜 Prompt Templating

📦 A New Package

Haystack 2.0 is distributed with haystack-ai, while Haystack 1.x will continue to be supported with farm-haystack with security updates and bug fixes.

NOTE: Installing haystack-ai and farm-haystack into the same Python environment will lead to conflicts - Please use separate virtual environments for each package.

Check out the installation guide for more information.

💪 Powerful Pipelines

In Haystack 2.0, pipelines are dynamic computation graphs that support:

• 🚦 Control flow: Need to run different components based on the output of another? Not a problem with 2.0.
• ➿ Loops: Implement complex behavior such as self-correcting flows by executing parts of the graph repeatedly.
• 🎛️ Data flow: Consume it only where you need it. Haystack 2.0 only exposes data to components which need it - benefiting speed and transparency.
• ✅ Validation and type-checking: Ensures all components in your pipeline are compatible even before running it.
• 💾 Serialization: Save and restore your pipelines from different formats.

Pipelines can be built with a few easy steps:

1. Create the Pipeline object.
2. Add components to the pipeline with the add_component() method.
3. Connect the components with the connect() method. Trying to connect components that are not compatible in type will raise an error.
4. Execute the pipeline with the run() method.

Example

The following pipeline does question-answering on a given URL:

import os

from haystack import Pipeline
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.utils import Secret

os.environ["OPENAI_API_KEY"] = "Your OpenAI API Key"

fetcher = LinkContentFetcher()
converter = HTMLToDocument()
prompt_template = """
According to the contents of this website:
{% for document in documents %}
  {{document.content}}
{% endfor %}
Answer the given question: {{query}}
Answer:
"""
prompt_builder = PromptBuilder(template=prompt_template)
llm = OpenAIGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY"))

pipeline = Pipeline()
pipeline.add_component("fetcher", fetcher)
pipeline.add_component("converter", converter)
pipeline.add_component("prompt", prompt_builder)
pipeline.add_component("llm", llm)

# pass the fetchers's `streams` output to the converter using the `sources` parameter
pipeline.connect("fetcher.streams", "converter.sources")
# pass the converted `documents to the prompt_builder using the `documents` parameter
pipeline.connect("converter.documents", "prompt.documents")
# pass the interpolated `prompt to the llm using the `prompt` parameter
pipeline.connect("prompt.prompt", "llm.prompt")

pipeline.run({"fetcher": {"urls": ["https://haystack.deepset.ai/overview/quick-start"]},
              "prompt": {"query": "How should I install Haystack?"}})

print(result["llm"]["replies"][0])

🔌 Customizable Components

Previously known as Nodes, components have been formalized with well-defined inputs and outputs that allow for easy extensibility and composability.

Haystack 2.0 provides a diverse selection of built-in components. Here’s a non-exhaustive overview:

Category	Description	External Providers & Integrations
Audio Transcriber	Transcribe audio to text	OpenAI
Builders	Build prompts and answers from templates
Classifiers	Classify documents based on specific criteria
Connectors	Interface with external services	OpenAPI
Converters	Convert data between different formats	Azure, Tika, Unstructured, PyPDF, OpenAPI, Jinja
Embedders	Transform texts and documents to vector representations	Amazon Bedrock, Azure, Cohere, FastEmbed, Gradient, Hugging Face (Optimum, Sentence Transformers, Text Embedding Inference), Instructor, Jina, Mistral, Nvidia, Ollama, OpenAI
Extractors	Extract information from documents	Hugging Face, spaCy
Evaluators	Evaluate components using metrics	Ragas, DeepEval, UpTrain
Fetcher	Fetch data from remote URLs
Generators	Prompt and generate text using generative models	Amazon Bedrock, Amazon Sagemaker, Azure, Cohere, Google AI, Google Vertex, Gradient, Hugging Face, Llama.cpp, Mistral, Nvidia, Ollama, OpenAI
Joiners	Combine documents from different components
Preprocessors	Preprocess text and documents
Rankers	Sort documents based on specific criteria	Hugging Face
Readers	Find answers in documents
Retrievers	Fetch documents from a document store based on a query	Astra, Chroma, Elasticsearch, MongoDB Atlas, OpenSearch, Pgvector, Pinecone, Qdrant, Weaviate
Routers	Manipulate pipeline control flow
Validators	Validate data based on schemas
Web Search	Perform search queries	Search, SerperDev
Writers	Write data into data sources

---

Custom Components

If Haystack lacks a functionality that you need, you can easily create your own component and slot that into a pipeline. Broadly speaking, writing a custom component requires:

• Creating a class with the @component decorator.
• Providing a run() method. The parameters passed to this method double as the component’s inputs.
• Defining the outputs and the output types of the run() method with a @component.output_types() decorator.
• Returning a dictionary that includes the outputs of the component.

Below is an example of a toy Embedder component that receives a text input and returns a random vector representation as embedding.

import random
from typing import List  
from haystack import component, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

@component
class MyEmbedder:
	def __init__(self, dim: int = 128):
		self.dim = dim

  @component.output_types(embedding=List[float])
  def run(self, text: str):
		print(f"Random embedding for text : {text}")
		embedding = [random.uniform(1.0, -1.0) for _ in range(self.dim)]
    return {"embedding": embedding}

# Using the component directly
my_embedder = MyEmbedder()
my_embedder.run(text="Hi, my name is Tuana") 

# Using the component in a pipeline
document_store = InMemoryDocumentStore()
query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", MyEmbedder())
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query_pipeline.run({"text_embedder":{"text": "Who lives in Berlin?"}})

🍱 Ready-made Pipeline Templates

Haystack 2.0 offers ready-made pipeline templates for common use cases, which can be created with just a single line of code.

Example

from haystack import Pipeline, PredefinedPipeline

pipeline = Pipeline.from_template(PredefinedPipeline.CHAT_WITH_WEBSITE)

# and then you can run this pipeline 👇
# pipeline.run({
#    "fetcher": {"urls": ["https://haystack.deepset.ai/overview/quick-start"]},
#    "prompt": {"query": "How should I install Haystack?"}}
# )

🗃️ Document Stores

In Haystack 2.0, Document Stores provide a common interface through which pipeline components can read and manipulate data without any knowledge of the backend technology. Furthermore, Document Stores are paired with specialized retriever components that can be used to fetch documents from a particular data source based on specific queries.

This separation of interface and implementation lets us provide support for several third-party providers of vector databases such as Weaviate, Chroma, Pinecone, Astra DB, MongoDB, Qdrant, Pgvector, Elasticsearch, OpenSearch, Neo4j and Marqo.

Example

#pip install chroma-haystack

from haystack_integrations.document_stores.chroma import ChromaDocumentStore
from haystack_integrations.components.retrievers.chroma import ChromaEmbeddingRetriever

document_store = ChromaDocumentStore()
retriever = ChromaEmbeddingRetriever(document_store)

🧩 Integrations

Thanks to Haystack 2.0’s flexible infrastructure, pipelines can be easily extended with external technologies and libraries in the form of new components, document stores, etc, all the while keeping dependencies cleanly separated.

Starting with 2.0, [integrations](https://docs.haystack.dee...

v1.25.0

Release Notes

v1.25.0

⚡️ 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).
• Correct page meta field for pdfs that contain pages without any text content