### 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>
Add API Headers support for Amazon API Gateway to enable Authentication
using DynamoDB.
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Was preparing for a demo project of NebulaGraphQAChain to find out the
prompt needed to be optimized a little bit.
Please @hwchase17 kindly help review.
Thanks!
### Overview
This PR aims at building on #4378, expanding the capabilities and
building on top of the `cassIO` library to interface with the database
(as opposed to using the core drivers directly).
Usage of `cassIO` (a library abstracting Cassandra access for
ML/GenAI-specific purposes) is already established since #6426 was
merged, so no new dependencies are introduced.
In the same spirit, we try to uniform the interface for using Cassandra
instances throughout LangChain: all our appreciation of the work by
@jj701 notwithstanding, who paved the way for this incremental work
(thank you!), we identified a few reasons for changing the way a
`CassandraChatMessageHistory` is instantiated. Advocating a syntax
change is something we don't take lighthearted way, so we add some
explanations about this below.
Additionally, this PR expands on integration testing, enables use of
Cassandra's native Time-to-Live (TTL) features and improves the phrasing
around the notebook example and the short "integrations" documentation
paragraph.
We would kindly request @hwchase to review (since this is an elaboration
and proposed improvement of #4378 who had the same reviewer).
### About the __init__ breaking changes
There are
[many](https://docs.datastax.com/en/developer/python-driver/3.28/api/cassandra/cluster/)
options when creating the `Cluster` object, and new ones might be added
at any time. Choosing some of them and exposing them as `__init__`
parameters `CassandraChatMessageHistory` will prove to be insufficient
for at least some users.
On the other hand, working through `kwargs` or adding a long, long list
of arguments to `__init__` is not a desirable option either. For this
reason, (as done in #6426), we propose that whoever instantiates the
Chat Message History class provide a Cassandra `Session` object, ready
to use. This also enables easier injection of mocks and usage of
Cassandra-compatible connections (such as those to the cloud database
DataStax Astra DB, obtained with a different set of init parameters than
`contact_points` and `port`).
We feel that a breaking change might still be acceptable since LangChain
is at `0.*`. However, while maintaining that the approach we propose
will be more flexible in the future, room could be made for a
"compatibility layer" that respects the current init method. Honestly,
we would to that only if there are strong reasons for it, as that would
entail an additional maintenance burden.
### Other changes
We propose to remove the keyspace creation from the class code for two
reasons: first, production Cassandra instances often employ RBAC so that
the database user reading/writing from tables does not necessarily (and
generally shouldn't) have permission to create keyspaces, and second
that programmatic keyspace creation is not a best practice (it should be
done more or less manually, with extra care about schema mismatched
among nodes, etc). Removing this (usually unnecessary) operation from
the `__init__` path would also improve initialization performance
(shorter time).
We suggest, likewise, to remove the `__del__` method (which would close
the database connection), for the following reason: it is the
recommended best practice to create a single Cassandra `Session` object
throughout an application (it is a resource-heavy object capable to
handle concurrency internally), so in case Cassandra is used in other
ways by the app there is the risk of truncating the connection for all
usages when the history instance is destroyed. Moreover, the `Session`
object, in typical applications, is best left to garbage-collect itself
automatically.
As mentioned above, we defer the actual database I/O to the `cassIO`
library, which is designed to encode practices optimized for LLM
applications (among other) without the need to expose LangChain
developers to the internals of CQL (Cassandra Query Language). CassIO is
already employed by the LangChain's Vector Store support for Cassandra.
We added a few more connection options in the companion notebook example
(most notably, Astra DB) to encourage usage by anyone who cannot run
their own Cassandra cluster.
We surface the `ttl_seconds` option for automatic handling of an
expiration time to chat history messages, a likely useful feature given
that very old messages generally may lose their importance.
We elaborated a bit more on the integration testing (Time-to-live,
separation of "session ids", ...).
### Remarks from linter & co.
We reinstated `cassio` as a dependency both in the "optional" group and
in the "integration testing" group of `pyproject.toml`. This might not
be the right thing do to, in which case the author of this PR offer his
apologies (lack of confidence with Poetry - happy to be pointed in the
right direction, though!).
During linter tests, we were hit by some errors which appear unrelated
to the code in the PR. We left them here and report on them here for
awareness:
```
langchain/vectorstores/mongodb_atlas.py:137: error: Argument 1 to "insert_many" of "Collection" has incompatible type "List[Dict[str, Sequence[object]]]"; expected "Iterable[Union[MongoDBDocumentType, RawBSONDocument]]" [arg-type]
langchain/vectorstores/mongodb_atlas.py:186: error: Argument 1 to "aggregate" of "Collection" has incompatible type "List[object]"; expected "Sequence[Mapping[str, Any]]" [arg-type]
langchain/vectorstores/qdrant.py:16: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:19: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:20: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:22: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:23: error: Name "grpc" is not defined [name-defined]
```
In the same spirit, we observe that to even get `import langchain` run,
it seems that a `pip install bs4` is missing from the minimal package
installation path.
Thank you!
If I upload a dataset with a single input and output column, we should
be able to let the chain prepare the input without having to maintain a
strict dataset format.
# Adding support for async (_acall) for VertexAICommon LLM
This PR implements the `_acall` method under `_VertexAICommon`. Because
VertexAI itself does not provide an async interface, I implemented it
via a ThreadPoolExecutor that can delegate execution of VertexAI calls
to other threads.
Twitter handle: @polecitoem : )
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
fyi - @agola11 for async functionality
fyi - @Ark-kun from VertexAI
## Description
Tag maintainer: @rlancemartin, @eyurtsev
### log_and_data_dir
`AwaDB.__init__()` accepts a parameter named `log_and_data_dir`. But
`AwaDB.from_texts()` and `AwaDB.from_documents()` accept a parameter
named `logging_and_data_dir`. This inconsistency in this parameter name
can lead to confusion on the part of the caller.
This PR renames `logging_and_data_dir` to `log_and_data_dir` to make all
functions consistent with the constructor.
### embedding
`AwaDB.__init__()` accepts a parameter named `embedding_model`. But
`AwaDB.from_texts()` and `AwaDB.from_documents()` accept a parameter
named `embeddings`. This inconsistency in this parameter name can lead
to confusion on the part of the caller.
This PR renames `embedding_model` to `embeddings` to make AwaDB's
constructor consistent with the classmethod "constructors" as specified
by `VectorStore` abstract base class.
A user has been testing the Apify integration inside langchain and he
was not able to run saved Actor tasks.
This PR adds support for calling saved Actor tasks on the Apify platform
to the existing integration. The structure of very similar to the one of
calling Actors.
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### Adding the functionality to return the scores with retrieved
documents when using the max marginal relevance
- Description: Add the method
`max_marginal_relevance_search_with_score_by_vector` to the FAISS
wrapper. Functionality operates the same as
`similarity_search_with_score_by_vector` except for using the max
marginal relevance retrieval framework like is used in the
`max_marginal_relevance_search_by_vector` method.
- Dependencies: None
- Tag maintainer: @rlancemartin @eyurtsev
- Twitter handle: @RianDolphin
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Description:
- The current code uses `PydanticSchema.schema()` and
`_get_extraction_function` at the same time. As a result, a response
from OpenAI has two nested `info`, and
`PydanticAttrOutputFunctionsParser` fails to parse it. This PR will use
the pydantic class given as an arg instead.
- Issue: no related issue yet
- Dependencies: no dependency change
- Tag maintainer: @dev2049
- Twitter handle: @shotarok28
Description: Adds a brief example of using an OAuth access token with
the Zapier wrapper. Also links to the Zapier documentation to learn more
about OAuth flows.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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<!-- Remove if not applicable -->
### Summary
This PR adds a LarkSuite (FeiShu) document loader.
> [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration
platform developed by ByteDance.
### Tests
- an integration test case is added
- an example notebook showing usage is added. [Notebook
preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb)
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### Who can review?
- PTAL @eyurtsev @hwchase17
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@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
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- @agola11
Agents / Tools / Toolkits
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---------
Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
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<!-- Remove if not applicable -->
- add tencent cos directory and file support for document-loader
#### Before submitting
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#### Who can review?
@eyurtsev
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<!-- Remove if not applicable -->
#### Add streaming only final async iterator of agent
This callback returns an async iterator and only streams the final
output of an agent.
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#### Who can review?
Tag maintainers/contributors who might be interested: @agola11
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- @agola11
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- @dev2049
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Distance-based vector database retrieval embeds (represents) queries in
high-dimensional space and finds similar embedded documents based on
"distance". But, retrieval may produce difference results with subtle
changes in query wording or if the embeddings do not capture the
semantics of the data well. Prompt engineering / tuning is sometimes
done to manually address these problems, but can be tedious.
The `MultiQueryRetriever` automates the process of prompt tuning by
using an LLM to generate multiple queries from different perspectives
for a given user input query. For each query, it retrieves a set of
relevant documents and takes the unique union across all queries to get
a larger set of potentially relevant documents. By generating multiple
perspectives on the same question, the `MultiQueryRetriever` might be
able to overcome some of the limitations of the distance-based retrieval
and get a richer set of results.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Proxies are helpful, especially when you start querying against more
anti-bot websites.
[Proxy
services](https://developers.oxylabs.io/advanced-proxy-solutions/web-unblocker/making-requests)
(of which there are many) and `requests` make it easy to rotate IPs to
prevent banning by just passing along a simple dict to `requests`.
CC @rlancemartin, @eyurtsev
### Summary
The Unstructured API will soon begin requiring API keys. This PR updates
the Unstructured integrations docs with instructions on how to generate
Unstructured API keys.
### Reviewers
@rlancemartin
@eyurtsev
@hwchase17
Replace this comment with:
- Description: Add Async functionality to Zapier NLA Tools
- Issue: n/a
- Dependencies: n/a
- Tag maintainer:
Maintainer responsibilities:
- Agents / Tools / Toolkits: @vowelparrot
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
Added parentheses to ensure the division operation is performed before
multiplication. This now correctly calculates the cost by dividing the
number of tokens by 1000 first (to get the cost per token), and then
multiplies it with the model's cost per 1k tokens @agola11
- **Description**: this PR adds the possibility to raise an exception in
the case the http request did not return a 2xx status code. This is
particularly useful in the situation when the url points to a
non-existent web page, the server returns a http status of 404 NOT
FOUND, but WebBaseLoader anyway parses and returns the http body of the
error message.
- **Dependencies**: none,
- **Tag maintainer**: @rlancemartin, @eyurtsev,
- **Twitter handle**: jtolgyesi
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- Memory: @hwchase17
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- Async: @agola11
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Adds a way to create the guardrails output parser from a pydantic model.
Description: When a 401 response is given back by Zapier, hint to the
end user why that may have occurred
- If an API Key was initialized with the wrapper, ask them to check
their API Key value
- if an access token was initialized with the wrapper, ask them to check
their access token or verify that it doesn't need to be refreshed.
Tag maintainer: @dev2049
#### Summary
A new approach to loading source code is implemented:
Each top-level function and class in the code is loaded into separate
documents. Then, an additional document is created with the top-level
code, but without the already loaded functions and classes.
This could improve the accuracy of QA chains over source code.
For instance, having this script:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
if __name__ == '__main__':
main()
```
The loader will create three documents with this content:
First document:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
```
Second document:
```
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
```
Third document:
```
# Code for: class MyClass:
# Code for: def main():
if __name__ == '__main__':
main()
```
A threshold parameter is added to control whether small scripts are
split in this way or not.
At this moment, only Python and JavaScript are supported. The
appropriate parser is determined by examining the file extension.
#### Tests
This PR adds:
- Unit tests
- Integration tests
#### Dependencies
Only one dependency was added as optional (needed for the JavaScript
parser).
#### Documentation
A notebook is added showing how the loader can be used.
#### Who can review?
@eyurtsev @hwchase17
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Description: Update documentation to
1) point to updated documentation links at Zapier.com (we've revamped
our help docs and paths), and
2) To provide clarity how to use the wrapper with an access token for
OAuth support
Demo:
Initializing the Zapier Wrapper with an OAuth Access Token
`ZapierNLAWrapper(zapier_nla_oauth_access_token="<redacted>")`
Using LangChain to resolve the current weather in Vancouver BC
leveraging Zapier NLA to lookup weather by coords.
```
> Entering new chain...
I need to use a tool to get the current weather.
Action: The Weather: Get Current Weather
Action Input: Get the current weather for Vancouver BC
Observation: {"coord__lon": -123.1207, "coord__lat": 49.2827, "weather": [{"id": 802, "main": "Clouds", "description": "scattered clouds", "icon": "03d", "icon_url": "http://openweathermap.org/img/wn/03d@2x.png"}], "weather[]icon_url": ["http://openweathermap.org/img/wn/03d@2x.png"], "weather[]icon": ["03d"], "weather[]id": [802], "weather[]description": ["scattered clouds"], "weather[]main": ["Clouds"], "base": "stations", "main__temp": 71.69, "main__feels_like": 71.56, "main__temp_min": 67.64, "main__temp_max": 76.39, "main__pressure": 1015, "main__humidity": 64, "visibility": 10000, "wind__speed": 3, "wind__deg": 155, "wind__gust": 11.01, "clouds__all": 41, "dt": 1687806607, "sys__type": 2, "sys__id": 2011597, "sys__country": "CA", "sys__sunrise": 1687781297, "sys__sunset": 1687839730, "timezone": -25200, "id": 6173331, "name": "Vancouver", "cod": 200, "summary": "scattered clouds", "_zap_search_was_found_status": true}
Thought: I now know the current weather in Vancouver BC.
Final Answer: The current weather in Vancouver BC is scattered clouds with a temperature of 71.69 and wind speed of 3
```
**Description:** Add a documentation page for the Streamlit Callback
Handler integration (#6315)
Notes:
- Implemented as a markdown file instead of a notebook since example
code runs in a Streamlit app (happy to discuss / consider alternatives
now or later)
- Contains an embedded Streamlit app ->
https://mrkl-minimal.streamlit.app/ Currently this app is hosted out of
a Streamlit repo but we're working to migrate the code to a LangChain
owned repo
cc @dev2049 @tconkling
