github deepset-ai/haystack v1.12.1
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latest releases: v1.26.4, v2.7.0, v2.7.0-rc1...
23 months ago

⭐ Highlights

Large Language Models with PromptNode

Introducing PromptNode, a new feature that brings the power of large language models (LLMs) to various NLP tasks. PromptNode is an easy-to-use, customizable node you can run on its own or in a pipeline. We've designed the API to be user-friendly and suitable for everyday experimentation, but also fully compatible with production-grade Haystack deployments.

By setting a prompt template for a PromptNode you define what task you want it to do. This way, you can have multiple PromptNodes in your pipeline, each performing a different task. But that's not all. You can also inject the output of one PromptNode into the input of another one.

Out of the box, we support both Google T5 Flan and OpenAI GPT-3 models, and you can even mix and match these models in your pipelines. 

from haystack.nodes.prompt import PromptNode

# Initialize the node:
prompt_node = PromptNode("google/flan-t5-base")  # try also 'text-davinci-003' if you have an OpenAI key

prompt_node("What is the capital of Germany?")

This node can do a lot more than simply querying LLMs: they can manage prompt templates, run batches, share models among instances, be chained together in pipelines, and more. Check its documentation for details!

Support for BM25Retriever in InMemoryDocumentStore

InMemoryDocumentStore has always been the go-to document store for small prototypes. The addition of BM25 support makes it officially one of the document stores to support all Retrievers available to Haystack, just like FAISS and Elasticsearch-like stores, but without the external dependencies. Don't use it in your million-documents-throughput deployments to production, though. It's not the fastest document store out there.

🏆 Honorable mention to @anakin87 for this outstanding contribution, among many many others! 🏆

Haystack is always open to external contributions, and every little bit is appreciated. Don't know where to start? Have a look at the Contributors Guidelines.

Extended support for Cohere and OpenAI embeddings

We enabled EmbeddingRetriever to use the latest Cohere multilingual embedding models and OpenAI embedding models.

Simply use the model's full name (along with your API key) in EmbeddingRetriever to get them: 

# Cohere
retriever = EmbeddingRetriever(embedding_model="multilingual-22-12", batch_size=16, api_key=api_key)
# OpenAI
retriever = EmbeddingRetriever(embedding_model="text-embedding-ada-002", batch_size=32, api_key=api_key, max_seq_len=8191)

Speeding up dense searches in batch mode (Elasticsearch and OpenSearch)

Whenever you need to execute multiple dense searches at once, ElasticsearchDocumentStore and OpenSearchDocumentStore can now do it in parallel. This not only speeds up run_batch and eval_batch for dense pipelines when used with those document stores but also significantly speeds up multi-embedding retrieval pipelines like, for example, MostSimilarDocumentsPipeline.

For this, we measured a speed up of up to 49% on a realistic dataset.

Under the hood, our newly introduced query_by_embedding_batch document store function uses msearch to unchain the full power of your Elasticsearch/OpenSearch cluster.

⚠️ Deprecated Docker images discontinued

1.12 is the last release we're shipping with the old Docker images deepset/haystack-cpu, deepset/haystack-gpu, and their relative tags. We'll remove the corresponding, deprecated Docker files /Dockerfile, /Dockerfile-GPU, and /Dockerfile-GPU-minimal from the codebase after the release.

What's Changed

Pipeline

  • fix: ParsrConverter fails on pages without text by @anakin87 in #3605
  • fix: Convert eval metrics to python float by @tstadel in #3612
  • feat: add support for BM25Retriever in InMemoryDocumentStore by @anakin87 in #3561
  • chore: fix return type of aggregate_labels by @tstadel in #3617
  • refactor: change MultiModal retriever to be of type DenseRetriever by @mayankjobanputra in #3598
  • fix: Move entire forward pass of TableQA within torch.no_grad() by @sjrl in #3636
  • feat: add offsets_in_context to evaluation result by @julian-risch in #3640
  • bug: Use tqdm auto instead of plain tqdm by @vblagoje in #3672
  • fix: monkey patch for SklearnQueryClassifier by @anakin87 in #3678
  • feat: Update table reader tests to check the answer scores by @sjrl in #3641
  • feat: Adds all_terms_must_match parameter to BM25Retriever at runtime by @ugm2 in #3627
  • fix: fix PreProcessor split_by schema by @ZanSara in #3680
  • refactor: Generate JSON schema when missing by @masci in #3533
  • refactor: replace torch.no_grad with torch.inference_mode (where possible) by @anakin87 in #3601
  • Adjust get_type() method for pipelines by @vblagoje in #3657
  • refactor: improve Multilabel design by @anakin87 in #3658
  • feat: Update cohere embedding models #3704 by @vblagoje #3704
  • feat: Enable text-embedding-ada-002 for EmbeddingRetriever #3721 by @vblagoje #3721
  • feat: Expand LLM support with PromptModel, PromptNode, and PromptTemplate by @vblagoje in #3667

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Documentation

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Full Changelog: v1.11.1...v1.12.1

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