I've added a bilibili loader, bilibili is a very active video site in
China and I think we need this loader.
Example:
```python
from langchain.document_loaders.bilibili import BiliBiliLoader
loader = BiliBiliLoader(
["https://www.bilibili.com/video/BV1xt411o7Xu/",
"https://www.bilibili.com/video/av330407025/"]
)
docs = loader.load()
```
Co-authored-by: 了空 <568250549@qq.com>
**Description**
Add custom vector field name and text field name while indexing and
querying for OpenSearch
**Issues**
https://github.com/hwchase17/langchain/issues/2500
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
Adds a new pdf loader using the existing dependency on PDFMiner.
The new loader can be helpful for chunking texts semantically into
sections as the output html content can be parsed via `BeautifulSoup` to
get more structured and rich information about font size, page numbers,
pdf headers/footers, etc. which may not be available otherwise with
other pdf loaders
Using `pytest-vcr` in integration tests has several benefits. Firstly,
it removes the need to mock external services, as VCR records and
replays HTTP interactions on the fly. Secondly, it simplifies the
integration test setup by eliminating the need to set up and tear down
external services in some cases. Finally, it allows for more reliable
and deterministic integration tests by ensuring that HTTP interactions
are always replayed with the same response.
Overall, `pytest-vcr` is a valuable tool for simplifying integration
test setup and improving their reliability
This commit adds the `pytest-vcr` package as a dependency for
integration tests in the `pyproject.toml` file. It also introduces two
new fixtures in `tests/integration_tests/conftest.py` files for managing
cassette directories and VCR configurations.
In addition, the
`tests/integration_tests/vectorstores/test_elasticsearch.py` file has
been updated to use the `@pytest.mark.vcr` decorator for recording and
replaying HTTP interactions.
Finally, this commit removes the `documents` fixture from the
`test_elasticsearch.py` file and replaces it with a new fixture defined
in `tests/integration_tests/vectorstores/conftest.py` that yields a list
of documents to use in any other tests.
This also includes my second attempt to fix issue :
https://github.com/hwchase17/langchain/issues/2386
Maybe related https://github.com/hwchase17/langchain/issues/2484
### Features include
- Metadata based embedding search
- Choice of distance metric function (`L2` for Euclidean, `L1` for
Nuclear, `max` L-infinity distance, `cos` for cosine similarity, 'dot'
for dot product. Defaults to `L2`
- Returning scores
- Max Marginal Relevance Search
- Deleting samples from the dataset
### Notes
- Added numerous tests, let me know if you would like to shorten them or
make smarter
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
- Create a new docker-compose file to start an Elasticsearch instance
for integration tests.
- Add new tests to `test_elasticsearch.py` to verify Elasticsearch
functionality.
- Include an optional group `test_integration` in the `pyproject.toml`
file. This group should contain dependencies for integration tests and
can be installed using the command `poetry install --with
test_integration`. Any new dependencies should be added by running
`poetry add some_new_deps --group "test_integration" `
Note:
New tests running in live mode, which involve end-to-end testing of the
OpenAI API. In the future, adding `pytest-vcr` to record and replay all
API requests would be a nice feature for testing process.More info:
https://pytest-vcr.readthedocs.io/en/latest/
Fixes https://github.com/hwchase17/langchain/issues/2386
This PR updates Qdrant to 1.1.1 and introduces local mode, so there is
no need to spin up the Qdrant server. By that occasion, the Qdrant
example notebooks also got updated, covering more cases and answering
some commonly asked questions. All the Qdrant's integration tests were
switched to local mode, so no Docker container is required to launch
them.
## Description
Thanks for the quick maintenance for great repository!!
I modified wikipedia api wrapper
## Details
- Add output for missing search results
- Add tests
Solves #2247. Noted that the only test I added checks for the
BeautifulSoup behaviour change. Happy to add a test for
`DirectoryLoader` if deemed necessary.
@3coins + @zoltan-fedor.... heres the pr + some minor changes i made.
thoguhts? can try to get it into tmrws release
---------
Co-authored-by: Zoltan Fedor <zoltan.0.fedor@gmail.com>
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Technically a duplicate fix to #1619 but with unit tests and a small
documentation update
- Propagate `filter` arg in Chroma `similarity_search` to delegated call
to `similarity_search_with_score`
- Add `filter` arg to `similarity_search_by_vector`
- Clarify doc strings on FakeEmbeddings
The `CollectionStore` for `PGVector` has a `cmetadata` field but it's
never used. This PR add the ability to save metadata information to the
collection.
Fix#1756
Use the `namespace` argument of `Pinecone.from_exisiting_index` to set
the default value of `namespace` for other methods. Leads to more
expected behavior and easier integration in chains.
For the test, I've added a line to delete and rebuild the
`langchain-demo` index at the beginning of the test. I'm not 100% sure
if it's a good idea but it makes the test reproducible.
Given that different models have very different latencies and pricings,
it's benefitial to pass the information about the model that generated
the response. Such information allows implementing custom callback
managers and track usage and price per model.
Addresses https://github.com/hwchase17/langchain/issues/1557.
This `BSHTMLLoader` document_loader loads an HTML document, extracts
text and adds the page title to the returned Document's metadata. The
loader uses the already installed bs4 package to extract both text
content and the page title.
Included in this PR is an example HTML file and an integration test that
tests against this file.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
This PR implements a basic metadata filtering mechanism similar to the
ones in Chroma and Pinecone. It still cannot express complex conditions,
as there are no operators, but some users requested to have that feature
available.
# Description
Add `RediSearch` vectorstore for LangChain
RediSearch: [RediSearch quick
start](https://redis.io/docs/stack/search/quick_start/)
# How to use
```
from langchain.vectorstores.redisearch import RediSearch
rds = RediSearch.from_documents(docs, embeddings,redisearch_url="redis://localhost:6379")
```
`OnlinePDFLoader` and `PagedPDFSplitter` lived separate from the rest of
the pdf loaders.
Because they're all similar, I propose moving all to `pdy.py` and the
same docs/examples page.
Additionally, `PagedPDFSplitter` naming doesn't match the pattern the
rest of the loaders follow, so I renamed to `PyPDFLoader` and had it
inherit from `BasePDFLoader` so it can now load from remote file
sources.
for https://github.com/hwchase17/langchain/issues/1582
I simply added the `return_intermediate_steps` and changed the
`output_keys` function.
I added 2 simple tests, 1 for SQLDatabaseSequentialChain without the
intermediate steps and 1 with
Co-authored-by: brad-nemetski <115185478+brad-nemetski@users.noreply.github.com>
Different PDF libraries have different strengths and weaknesses. PyMuPDF
does a good job at extracting the most amount of content from the doc,
regardless of the source quality, extremely fast (especially compared to
Unstructured).
https://pymupdf.readthedocs.io/en/latest/index.html
This PR:
- Increases `qdrant-client` version to 1.0.4
- Introduces custom content and metadata keys (as requested in #1087)
- Moves all the `QdrantClient` parameters into the method parameters to
simplify code completion
iFixit is a wikipedia-like site that has a huge amount of open content
on how to fix things, questions/answers for common troubleshooting and
"things" related content that is more technical in nature. All content
is licensed under CC-BY-SA-NC 3.0
Adding docs from iFixit as context for user questions like "I dropped my
phone in water, what do I do?" or "My macbook pro is making a whining
noise, what's wrong with it?" can yield significantly better responses
than context free response from LLMs.
