20c6ade2fc
### Scientific Article PDF Parsing via Grobid
`Description:`
This change adds the GrobidParser class, which uses the Grobid library
to parse scientific articles into a universal XML format containing the
article title, references, sections, section text etc. The GrobidParser
uses a local Grobid server to return PDFs document as XML and parses the
XML to optionally produce documents of individual sentences or of whole
paragraphs. Metadata includes the text, paragraph number, pdf relative
bboxes, pages (text may overlap over two pages), section title
(Introduction, Methodology etc), section_number (i.e 1.1, 2.3), the
title of the paper and finally the file path.
Grobid parsing is useful beyond standard pdf parsing as it accurately
outputs sections and paragraphs within them. This allows for
post-fitering of results for specific sections i.e. limiting results to
the methodology section or results. While sections are split via
headings, ideally they could be classified specifically into
introduction, methodology, results, discussion, conclusion. I'm
currently experimenting with chatgpt-3.5 for this function, which could
later be implemented as a textsplitter.
`Dependencies:`
For use, the grobid repo must be cloned and Java must be installed, for
colab this is:
```
!apt-get install -y openjdk-11-jdk -q
!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
!git clone https://github.com/kermitt2/grobid.git
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
os.chdir('grobid')
!./gradlew clean install
```
Once installed the server is ran on localhost:8070 via
```
get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
```
@rlancemartin, @eyurtsev
Twitter Handle: @Corranmac
Grobid Demo Notebook is
[here](https://colab.research.google.com/drive/1X-St_mQRmmm8YWtct_tcJNtoktbdGBmd?usp=sharing).
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Readme tests(draft)
Integrations Tests
Prepare
This repository contains functional tests for several search engines and databases. The tests aim to verify the correct behavior of the engines and databases according to their specifications and requirements.
To run some integration tests, such as tests located in
tests/integration_tests/vectorstores/, you will need to install the following
software:
- Docker
- Python 3.8.1 or later
We have optional group test_integration in the pyproject.toml file. This group
should contain dependencies for the integration tests and can be installed using the
command:
poetry install --with test_integration
Any new dependencies should be added by running:
# add package and install it after adding:
poetry add tiktoken@latest --group "test_integration" && poetry install --with test_integration
Before running any tests, you should start a specific Docker container that has all the
necessary dependencies installed. For instance, we use the elasticsearch.yml container
for test_elasticsearch.py:
cd tests/integration_tests/vectorstores/docker-compose
docker-compose -f elasticsearch.yml up
Prepare environment variables for local testing:
- copy
tests/.env.exampletotests/.env - set variables in
tests/.envfile, e.gOPENAI_API_KEY
Additionally, it's important to note that some integration tests may require certain
environment variables to be set, such as OPENAI_API_KEY. Be sure to set any required
environment variables before running the tests to ensure they run correctly.
Recording HTTP interactions with pytest-vcr
Some of the integration tests in this repository involve making HTTP requests to external services. To prevent these requests from being made every time the tests are run, we use pytest-vcr to record and replay HTTP interactions.
When running tests in a CI/CD pipeline, you may not want to modify the existing cassettes. You can use the --vcr-record=none command-line option to disable recording new cassettes. Here's an example:
pytest --log-cli-level=10 tests/integration_tests/vectorstores/test_pinecone.py --vcr-record=none
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --vcr-record=none
Run some tests with coverage:
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --cov=langchain --cov-report=html
start "" htmlcov/index.html || open htmlcov/index.html