From c59572e8996f278c2c2a4b64003df1a9b64702d1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= Date: Thu, 30 Nov 2023 16:44:47 +0100 Subject: [PATCH] Add huggingface integrations page (#71) --- integrations/huggingface.md | 117 ++++++++++++++++++++++++++++++++++++ logos/huggingface.png | Bin 0 -> 185450 bytes 2 files changed, 117 insertions(+) create mode 100644 integrations/huggingface.md create mode 100644 logos/huggingface.png diff --git a/integrations/huggingface.md b/integrations/huggingface.md new file mode 100644 index 00000000..1b8e409a --- /dev/null +++ b/integrations/huggingface.md @@ -0,0 +1,117 @@ +--- +layout: integration +name: Hugging Face +description: Use Models on Hugging Face with Haystack +authors: + - name: deepset + socials: + github: deepset-ai + twitter: deepset_ai + linkedin: deepset-ai +pypi: https://pypi.org/project/farm-haystack +repo: https://github.com/deepset-ai/haystack +type: Model Provider +report_issue: https://github.com/deepset-ai/haystack/issues +logo: /logos/huggingface.png +--- + +You can use models on [Hugging Face](https://huggingface.co/) in your Haystack pipelines with the [PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node), [EmbeddingRetriever](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended), [Ranker](https://docs.haystack.deepset.ai/docs/ranker), [Reader](https://docs.haystack.deepset.ai/docs/reader) and more! + +## Installation + +```bash +pip install farm-haystack +``` + +## Usage + +You can use models on Hugging Face in various ways: + +### Embedding Models + +To use embedding models on Hugging Face, initialize an `EmbeddingRetriever` with the model name. You can then use this `EmbeddingRetriever` in an indexing pipeline to create semantic embeddings for documents and index them to a document store. + +Below is the example indexing pipeline with `PreProcessor`, `InMemoryDocumentStore` and `EmbeddingRetriever`: + +```python +from haystack.nodes import EmbeddingRetriever +from haystack.document_stores import InMemoryDocumentStore +from haystack.pipelines import Pipeline +from haystack.schema import Document + +document_store = InMemoryDocumentStore(embedding_dim=384) +preprocessor = PreProcessor() +retriever = EmbeddingRetriever( + embedding_model="sentence-transformers/all-MiniLM-L6-v2", document_store=document_store +) + +indexing_pipeline = Pipeline() +indexing_pipeline.add_node(component=preprocessor, name="Preprocessor", inputs=["File"]) +indexing_pipeline.add_node(component=retriever, name="Retriever", inputs=["Preprocessor"]) +indexing_pipeline.add_node(component=document_store, name="document_store", inputs=["Retriever"]) +indexing_pipeline.run(documents=[Document("This is my document")]) +``` + +### Generative Models (LLMs) + +To use text generation models on Hugging Face, initialize a `PromptNode` with the model name and the prompt template. You can then use this `PromptNode` to generate questions from the given context. + +Below is the example of question generation pipeline using RAG with `EmbeddingRetriever` and `PromptNode`: + +```python +from haystack import Pipeline +from haystack.nodes import BM25Retriever, PromptNode + +retriever = EmbeddingRetriever( + embedding_model="sentence-transformers/all-MiniLM-L6-v2", document_store=document_store +) +prompt_node = PromptNode(model_name_or_path = "mistralai/Mistral-7B-Instruct-v0.1", + api_key = "HF_API_KEY", + default_prompt_template = "deepset/question-generation") +query_pipeline = Pipeline() +query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"]) +query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"]) + +query_pipeline.run(query = "Berlin") +``` + +> If you would like to use the [Inference API](https://huggingface.co/inference-api), you need pass your Hugging Face token to PromptNode. + + +### Ranker Models + +To use cross encoder models on Hugging Face, initialize a `SentenceTransformersRanker` with the model name. You can then use this `SentenceTransformersRanker` to sort documents based on their relevancy to the query. + +Below is the example of document retrieval pipeline with `BM25Retriever` and `SentenceTransformersRanker`: + +```python +from haystack.nodes import SentenceTransformersRanker, BM25Retriever +from haystack.pipelines import Pipeline + +retriever = BM25Retriever(document_store=document_store) +ranker = SentenceTransformersRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2") + +document_retrieval_pipeline = Pipeline() +document_retrieval_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"]) +document_retrieval_pipeline.add_node(component=ranker, name="Ranker", inputs=["Retriever"]) +document_retrieval_pipeline.run("YOUR_QUERY") +``` + +### Reader Models + +To use question answering models on Hugging Face, initialize a `FarmReader` with the model name. You can then use this `FarmReader` to extract answers from the relevant context. + +Below is the example of extractive question answering pipeline with `BM25Retriever` and `FARMReader`: + +```python +from haystack.nodes import BM25Retriever, FARMReader +from haystack.pipelines import Pipeline + +retriever = BM25Retriever(document_store=document_store) +reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True) + +querying_pipeline = Pipeline() +querying_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"]) +querying_pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"]) +querying_pipeline.run("YOUR_QUERY") +``` \ No newline at end of file diff --git a/logos/huggingface.png b/logos/huggingface.png new file mode 100644 index 0000000000000000000000000000000000000000..49e2841dd5b28c471cf89ea484cf496256d61c1b GIT binary patch literal 185450 zcmeFY_cxn=_&=VYwxVcJt5i{YwRY&BMr~DFOKqk0PEd+kRjZ2Fd(>7nOHowpP3+jQ 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