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Remove stray requirements.txt files and update README.md (#2075)
* Remove stray requirements.txt files and update README.md * Remove requirement files * Add details about pip bug and link to setup.cfg Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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Sara Zan
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README.md
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README.md
@ -93,6 +93,26 @@ You can also clone it from GitHub — in case you'd like to work with the master
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To update your installation, do a ``git pull``. The ``--editable`` flag will update changes immediately.
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To update your installation, do a ``git pull``. The ``--editable`` flag will update changes immediately.
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Note that this command will install the **base** version of the package, which includes only the
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Elasticsearch document store and the most commonly used components.
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For a complete installation that includes all optional components, please run instead:
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```
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git clone https://github.com/deepset-ai/haystack.git
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cd haystack
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pip install --upgrade pip
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pip install --editable .[all] # or 'all-gpu' to get the GPU-enabled dependencies
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```
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Do not forget to upgrade pip before performing the installation: pip version below 21.3.1 might
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enter infinite loops due to a bug. If you encounter such loop, either upgrade pip or replace
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`[all]` with `[docstores,crawler,preprocessing,ocr,ray,rest,ui,dev,onnx]`.
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For an complete list of the dependency groups available, have a look at the
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[setup.cfg file](https://github.com/deepset-ai/haystack/blob/488c3e9e52b9286afc3ad9a5f2e3161772be2e2f/setup.cfg#L103).
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**3. Installing on Windows**
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**3. Installing on Windows**
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On Windows, you might need:
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On Windows, you might need:
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@ -170,27 +170,6 @@ These are used to condition the generator as it generates the answer.
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What it should return then are novel text spans that form and answer to your question!
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What it should return then are novel text spans that form and answer to your question!
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```python
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# Now generate an answer for each question
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for question in QUESTIONS:
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# Retrieve related documents from retriever
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retriever_results = retriever.retrieve(
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query=question
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)
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# Now generate answer from question and retrieved documents
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predicted_result = generator.predict(
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query=question,
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documents=retriever_results,
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top_k=1
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)
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# Print you answer
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answers = predicted_result["answers"]
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print(f'Generated answer is \'{answers[0].answer}\' for the question = \'{question}\'')
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```
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```python
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```python
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# Or alternatively use the Pipeline class
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# Or alternatively use the Pipeline class
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from haystack.pipelines import GenerativeQAPipeline
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from haystack.pipelines import GenerativeQAPipeline
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@ -1,8 +0,0 @@
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# Add extra dependencies only required for tests and local dev setup
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mypy
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pytest
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selenium
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webdriver-manager
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beautifulsoup4
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markdown
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responses
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@ -1,66 +0,0 @@
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# basics
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setuptools
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wheel
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# PyTorch
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# Temp. disabled the next line as it gets currently resolved to https://download.pytorch.org/whl/rocm3.8/torch-1.7.1%2Brocm3.8-cp38-cp38-linux_x86_64.whl
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# --find-links=https://download.pytorch.org/whl/torch_stable.html
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torch>1.9,<1.11
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# progress bars in model download and training scripts
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tqdm
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# Used for downloading models over HTTP
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requests
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# Scipy & sklearn for stats in run_classifier
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scipy>=1.3.2
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scikit-learn>=1.0.0
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# Metrics or logging related
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seqeval
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mlflow<=1.13.1
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# huggingface repository
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transformers==4.13.0
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# pickle extension for (de-)serialization
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dill
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# Inference with ONNX models. Install onnxruntime-gpu for Inference on GPUs
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# onnxruntime
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# onnxruntime_tools
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psutil
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# haystack
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fastapi
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uvicorn
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gunicorn
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pandas
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psycopg2-binary; sys_platform != 'win32' and sys_platform != 'cygwin'
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elasticsearch>=7.7,<=7.10
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elastic-apm
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tox
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coverage
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langdetect # for PDF conversions
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# for PDF conversions using OCR
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pytesseract==0.3.7
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pillow==9.0.0
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pdf2image==1.14.0
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sentence-transformers>=0.4.0
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python-multipart
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python-docx
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sqlalchemy>=1.4.2
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sqlalchemy_utils
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# for using FAISS with GPUs, install faiss-gpu
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faiss-cpu>=1.6.3
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tika
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uvloop==0.14; sys_platform != 'win32' and sys_platform != 'cygwin'
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httptools
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nltk
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more_itertools
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networkx
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# Refer milvus version support matrix at https://github.com/milvus-io/pymilvus#install-pymilvus
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# For milvus 2.x version use this library `pymilvus===2.0.0rc6`
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pymilvus<2.0.0
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# Optional: For crawling
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#selenium
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#webdriver-manager
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SPARQLWrapper
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mmh3
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weaviate-client==2.5.0
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ray>=1.9.1
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dataclasses-json
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quantulum3
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azure-ai-formrecognizer==3.2.0b2
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