Backfill issue -420 — The AI Toolchain

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The daily firehose — everything the toolchain shipped today, already filtered.

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We read every release from the 174 tools on our watchlist at the source — GitHub and GitLab release notes, vendor release pages and changelogs, project blogs and feeds, vendor press releases, and the source code behind the tag. Bug-fix-only releases and non-product newsroom noise are dropped; what's left is summarized down to the new capability, how to try it, and any screenshots or videos the release itself published. Every entry links to the sources it was built from.

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$ tct list   # 3 tools matched

AI & LLM Tooling

◆ AI Agent Frameworks

deepset Haystack

v0.5.0 NOTES

Haystack v0.5.0 adds RAG-based generative QA, DPR training, MySQL support, and an Elasticsearch Query DSL-compliant REST API.

└──▷ GET THIS VERSION

$ git clone --branch v0.5.0 https://github.com/deepset-ai/haystack.git
# already have the repo? check out this version:
$ git checkout v0.5.0

└──▷ USE IT

Generate an answer from retrieved documents using RAG instead of extracting a span — useful when no single passage contains a clean answer.

python

retrieved_docs = retriever.retrieve(query="who got the first nobel prize in physics?")
predicted_result = generator.predict(
    question="who got the first nobel prize in physics?",
    documents=retrieved_docs,
    top_k=1
)

Fine-tune a DPR retriever on domain-specific query/passage pairs to improve retrieval accuracy before production deployment.

python

dense_passage_retriever.train(
    data_dir="/data/dpr",
    train_filename="train.json",
    dev_filename="dev.json",
    batch_size=16,
    embed_title=True,
    num_hard_negatives=1,
    n_epochs=3
)

›Adds generator.predict(question=..., documents=..., top_k=...) for Retrieval Augmented Generation (RAG), enabling generative QA where answers are generated from retrieved documents rather than extracted.
›Adds dense_passage_retriever.train(data_dir, train_filename, dev_filename, test_filename, batch_size, embed_title, num_hard_negatives, n_epochs) to train or fine-tune DPR models on custom domain data.
›Adds save and load methods to DensePassageRetriever for persisting and reloading trained DPR models.
›Adds use_fast_tokenizers and similarity_function parameters to DensePassageRetriever, and splits max_seq_len into independent max_seq_len_query and max_seq_len_passage parameters.
›Adds faiss_index_factory_str and return_embedding parameters to FAISSDocumentStore, with new default index type 'Flat'.

+11 moreshow less

›Adds support for MySQL databases in DocumentStore.
›Allows configuration of the Elasticsearch Analyzer in ElasticsearchDocumentStore (e.g. for non-English languages).
›Adds filter support to get_document_count() in DocumentStore.
›Adds Elasticsearch Query DSL-compliant Query API to the REST API.
›Adds create_index and similarity metric configuration to the REST API config.
›Allows configuration of log level in the REST API.
›Makes filter values optional in the REST API.
›Adds automatic mixed precision (AMP) support for FARMReader training.
›Adds a preprocessing pipeline via PreProcessor.
›Enables returning predictions in Finder and Retriever eval() calls.
›Makes creation of the label index optional in DocumentStore.

└──▷ BREAKING ON UPGRADE

!TransformersReader parameter model is replaced by model_name_or_path.
!FAISSDocumentStore parameter vector_size is renamed to vector_dim; faiss_index type changes from Optional[IndexHNSWFlat] to Optional[faiss.swigfaiss.Index]; default index type changes from HNSW to 'Flat'.
!DensePassageRetriever parameter max_seq_len is split into max_seq_len_query (default 64) and max_seq_len_passage (default 256); remove_sep_tok_from_untitled_passages parameter is removed.

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◆ AI Model & Data Infrastructure

NVIDIA Triton Inference Server

v2.5.0 NOTES

Triton v2.5.0 releases ModelAnalyzer, drops Caffe2, and moves PyTorch to a dedicated backend repo.

└──▷ GET THIS VERSION

$ git clone --branch v2.5.0 https://github.com/triton-inference-server/server.git
# already have the repo? check out this version:
$ git checkout v2.5.0

└──▷ TRY IT

Run Triton on Jetson with an explicit backend directory and TensorFlow 2.x selected instead of the default TF 1.x.

$ tritonserver --model-repository=/path/to/model_repo --backend-directory=/path/to/tritonserver/backends --backend-config=tensorflow,version=2

›Adds --backend-directory flag to explicitly set the backend directory path, required on Jetson deployments.
›Adds --backend-config=tensorflow,version=2 to select TensorFlow 2.x when Triton defaults to TensorFlow 1.x.
›Releases Triton ModelAnalyzer in the Triton SDK container and as a PIP package available in NVIDIA PyIndex.
›Moves the PyTorch backend to a dedicated repository at triton-inference-server/pytorch_backend.
›Updates ONNX Runtime backend to use ONNX Runtime 1.5.3.

+1 moreshow less

›Adds Jetson JetPack 4.4 support with TensorFlow 2.3.1, TensorFlow 1.15.4, TensorRT 7.1, custom backends, and ensembles (GPU metrics, GCS, and S3 storage not supported on Jetson).

└──▷ BREAKING ON UPGRADE

!The Caffe2 backend is removed; Caffe2 models are no longer supported.

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Other / Uncategorized

◆ VECTOR DB RAG

Weaviate

0.22.20 NOTES

Weaviate 0.22.20 adds kNN classification distance fields and brings standalone mode to feature parity with ES-based mode.

└──▷ GET THIS VERSION

$ git clone --branch 0.22.20 https://github.com/weaviate/weaviate.git
# already have the repo? check out this version:
$ git checkout 0.22.20

›Adds eight new distance fields to the _classification underscore prop's ref meta for kNN-classified objects: overallCount, winningCount, losingCount, meanWinningDistance, meanLosingDistance, closestOverallDistance, closestWinningDistance, and closestLosingDistance.
›Standalone mode reaches feature parity with the Elasticsearch-based mode, with a production-ready release (removing all ES features) targeted for v0.23.0.

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// END OF TRANSMISSION

The AI Toolchain · curated daily · November 27, 2020
Releases & features from the open-source and commercial AI tools that practitioners actually run.

The AI Toolchain — issue -420, November 27, 2020

deepset Haystack

NVIDIA Triton Inference Server

Weaviate