**Description:**
I've added a new use-case to the Web scraping docs. I also fixed some
typos in the existing text.
---------
Co-authored-by: davidjohnbarton <41335923+davidjohnbarton@users.noreply.github.com>
- Description: Added support for Ollama embeddings
- Issue: the issue # it fixes (if applicable),
- Dependencies: N/A
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: @herrjemand
cc https://github.com/jmorganca/ollama/issues/436
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Hello,
this PR improves coverage for caching by the two Cassandra-related
caches (i.e. exact-match and semantic alike) by switching to the more
general `dumps`/`loads` serdes utilities.
This enables cache usage within e.g. `ChatOpenAI` contexts (which need
to store lists of `ChatGeneration` instead of `Generation`s), which was
not possible as long as the cache classes were relying on the legacy
`_dump_generations_to_json` and `_load_generations_from_json`).
Additionally, a slightly different init signature is introduced for the
cache objects:
- named parameters required for init, to pave the way for easier changes
in the future connect-to-db flow (and tests adjusted accordingly)
- added a `skip_provisioning` optional passthrough parameter for use
cases where the user knows the underlying DB table, etc already exist.
Thank you for a review!
Adding support for Neo4j vector index hybrid search option. In Neo4j,
you can achieve hybrid search by using a combination of vector and
fulltext indexes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description：
* Baidu AI Cloud's [Qianfan
Platform](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) is an
all-in-one platform for large model development and service deployment,
catering to enterprise developers in China. Qianfan Platform offers a
wide range of resources, including the Wenxin Yiyan model (ERNIE-Bot)
and various third-party open-source models.
- Issue: none
- Dependencies:
* qianfan
- Tag maintainer: @baskaryan
- Twitter handle:
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
`langchain.agents.openai_functions[_multi]_agent._parse_ai_message()`
incorrectly extracts AI message content, thus LLM response ("thoughts")
is lost and can't be logged or processed by callbacks.
This PR fixes function call message content retrieving.
- Description: Set up 'file_headers' params for accessing pdf file url
- Tag maintainer: @hwchase17
✅ make format, make lint, make test
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
The `self-que[ring`
navbar](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/)
has repeated `self-quering` repeated in each menu item. I've simplified
it to be more readable
- removed `self-quering` from a title of each page;
- added description to the vector stores
- added description and link to the Integration Card
(`integrations/providers`) of the vector stores when they are missed.
This PR addresses a few minor issues with the Cassandra vector store
implementation and extends the store to support Metadata search.
Thanks to the latest cassIO library (>=0.1.0), metadata filtering is
available in the store.
Further,
- the "relevance" score is prevented from being flipped in the [0,1]
interval, thus ensuring that 1 corresponds to the closest vector (this
is related to how the underlying cassIO class returns the cosine
difference);
- bumped the cassIO package version both in the notebooks and the
pyproject.toml;
- adjusted the textfile location for the vector-store example after the
reshuffling of the Langchain repo dir structure;
- added demonstration of metadata filtering in the Cassandra vector
store notebook;
- better docstring for the Cassandra vector store class;
- fixed test flakiness and removed offending out-of-place escape chars
from a test module docstring;
To my knowledge all relevant tests pass and mypy+black+ruff don't
complain. (mypy gives unrelated errors in other modules, which clearly
don't depend on the content of this PR).
Thank you!
Stefano
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
* More clarity around how geometry is handled. Not returned by default;
when returned, stored in metadata. This is because it's usually a waste
of tokens, but it should be accessible if needed.
* User can supply layer description to avoid errors when layer
properties are inaccessible due to passthrough access.
* Enhanced testing
* Updated notebook
---------
Co-authored-by: Connor Sutton <connor.sutton@swca.com>
Co-authored-by: connorsutton <135151649+connorsutton@users.noreply.github.com>
update newer generation format from OpenLLm where it returns a
dictionary for one shot generation
cc @baskaryan
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
---------
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
I have revamped the code to ensure uniform error handling for
ImportError. Instead of the previous reliance on ValueError, I have
adopted the conventional practice of raising ImportError and providing
informative error messages. This change enhances code clarity and
clearly signifies that any problems are associated with module imports.
After the refactoring #6570, the DistanceStrategy class was moved to
another module and this introduced a bug into the SingleStoreDB vector
store, as the `DistanceStrategy.EUCLEDIAN_DISTANCE` started to convert
into the 'DistanceStrategy.EUCLEDIAN_DISTANCE' string, instead of just
'EUCLEDIAN_DISTANCE' (same for 'DOT_PRODUCT').
In this change, I check the type of the parameter and use `.name`
attribute to get the correct object's name.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Replace this entire comment with:
- Description: fixed Google Enterprise Search Retriever where it was
consistently returning empty results,
- Issue: related to [issue
8219](https://github.com/langchain-ai/langchain/issues/8219),
- Dependencies: no dependencies,
- Tag maintainer: @hwchase17 ,
- Twitter handle: [Tomas Piaggio](https://twitter.com/TomasPiaggio)!
2a4b32dee2/langchain/vectorstores/chroma.py (L355-L375)
Currently, the defined update_document function only takes a single
document and its ID for updating. However, Chroma can update multiple
documents by taking a list of IDs and documents for batch updates. If we
update 'update_document' function both document_id and document can be
`Union[str, List[str]]` but we need to do type check. Because
embed_documents and update functions takes List for text and
document_ids variables. I believe that, writing a new function is the
best option.
I update the Chroma vectorstore with refreshed information from my
website every 20 minutes. Updating the update_document function to
perform simultaneous updates for each changed piece of information would
significantly reduce the update time in such use cases.
For my case I update a total of 8810 chunks. Updating these 8810
individual chunks using the current function takes a total of 8.5
minutes. However, if we process the inputs in batches and update them
collectively, all 8810 separate chunks can be updated in just 1 minute.
This significantly reduces the time it takes for users of actively used
chatbots to access up-to-date information.
I can add an integration test and an example for the documentation for
the new update_document_batch function.
@hwchase17
[berkedilekoglu](https://twitter.com/berkedilekoglu)
