Have noticed transient ref example misalignment. I believe this is
caused by the logic of assigning an example within the thread executor
rather than before.
Current problems:
1. Evaluating LLMs or Chat models isn't smooth. Even specifying
'generations' as the output inserts a redundant list into the eval
template
2. Configuring input / prediction / reference keys in the
`get_qa_evaluator` function is confusing. Unless you are using a chain
with the default keys, you have to specify all the variables and need to
reason about whether the key corresponds to the traced run's inputs,
outputs or the examples inputs or outputs.
Proposal:
- Configure the run evaluator according to a model. Use the model type
and input/output keys to assert compatibility where possible. Only need
to specify a reference_key for certain evaluators (which is less
confusing than specifying input keys)
When does this work:
- If you have your langchain model available (assumed always for
run_on_dataset flow)
- If you are evaluating an LLM, Chat model, or chain
- If the LLM or chat models are traced by langchain (wouldn't work if
you add an incompatible schema via the REST API)
When would this fail:
- Currently if you directly create an example from an LLM run, the
outputs are generations with all the extra metadata present. A simple
`example_key` and dumping all to the template could make the evaluations
unreliable
- Doesn't help if you're not using the low level API
- If you want to instantiate the evaluator without instantiating your
chain or LLM (maybe common for monitoring, for instance) -> could also
load from run or run type though
What's ugly:
- Personally think it's better to load evaluators one by one since
passing a config down is pretty confusing.
- Lots of testing needs to be added
- Inconsistent in that it makes a separate run and example input mapper
instead of the original `RunEvaluatorInputMapper`, which maps a run and
example to a single input.
Example usage running the for an LLM, Chat Model, and Agent.
```
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
model = ChatOpenAI()
configured_evaluators = load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer")
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators)
```
<details>
<summary>Full code with dataset upload</summary>
```
## Create dataset
from langchain.evaluation.run_evaluators.loading import load_run_evaluators_for_model
from langchain.evaluation import load_dataset
import pandas as pd
lcds = load_dataset("llm-math")
df = pd.DataFrame(lcds)
from uuid import uuid4
from langsmith import Client
client = Client()
ds_name = "llm-math - " + str(uuid4())[0:8]
ds = client.upload_dataframe(df, name=ds_name, input_keys=["question"], output_keys=["answer"])
## Define the models we'll test over
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import tool
llm = OpenAI(temperature=0)
chat_model = ChatOpenAI(temperature=0)
@tool
def sum(a: float, b: float) -> float:
"""Add two numbers"""
return a + b
def construct_agent():
return initialize_agent(
llm=chat_model,
tools=[sum],
agent=AgentType.OPENAI_MULTI_FUNCTIONS,
)
agent = construct_agent()
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
models = [llm, chat_model, agent]
run_evaluators = []
for model in models:
run_evaluators.append(load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer"))
# Run on LLM, Chat Model, and Agent
from langchain.client.runner_utils import run_on_dataset
to_test = [llm, chat_model, construct_agent]
for model, configured_evaluators in zip(to_test, run_evaluators):
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators, verbose=True)
```
</details>
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Continuing with Tolkien inspired series of langchain tools. I bring to
you:
**The Fellowship of the Vectors**, AKA EmbeddingsClusteringFilter.
This document filter uses embeddings to group vectors together into
clusters, then allows you to pick an arbitrary number of documents
vector based on proximity to the cluster centers. That's a
representative sample of the cluster.
The original idea is from [Greg Kamradt](https://github.com/gkamradt)
from this video (Level4):
https://www.youtube.com/watch?v=qaPMdcCqtWk&t=365s
I added few tricks to make it a bit more versatile, so you can
parametrize what to do with duplicate documents in case of cluster
overlap: replace the duplicates with the next closest document or remove
it. This allow you to use it as an special kind of redundant filter too.
Additionally you can choose 2 diff orders: grouped by cluster or
respecting the original retriever scores.
In my use case I was using the docs grouped by cluster to run refine
chains per cluster to generate summarization over a large corpus of
documents.
Let me know if you want to change anything!
@rlancemartin, @eyurtsev, @hwchase17,
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
- Description: pydantic's `ModelField.type_` only exposes the native
data type but not complex type hints like `List`. Thus, generating a
Tool with `from_function` through function signature produces incorrect
argument schemas (e.g., `str` instead of `List[str]`)
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @hinthornw
- Twitter handle: `mapped`
All the unittest (with an additional one in this PR) passed, though I
didn't try integration tests...
- Description: Adding to Chroma integration the option to run a
similarity search by a vector with relevance scores. Fixing two minor
typos.
- Issue: The "lambda_mult" typo is related to #4861
- Maintainer: @rlancemartin, @eyurtsev
- Description:
- When `keep_separator` is `True` the `_split_text_with_regex()` method
in `text_splitter` uses regex to split, but when `keep_separator` is
`False` it uses `str.split()`. This causes problems when the separator
is a special regex character like `.` or `*`. This PR fixes that by
using `re.split()` in both cases.
- Issue: #7262
- Tag maintainer: @baskaryan
### Description
This pull request introduces the "Cube Semantic Layer" document loader,
which demonstrates the retrieval of Cube's data model metadata in a
format suitable for passing to LLMs as embeddings. This enhancement aims
to provide contextual information and improve the understanding of data.
Twitter handle:
@the_cube_dev
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
This PR brings in a vectorstore interface for
[Marqo](https://www.marqo.ai/).
The Marqo vectorstore exposes some of Marqo's functionality in addition
the the VectorStore base class. The Marqo vectorstore also makes the
embedding parameter optional because inference for embeddings is an
inherent part of Marqo.
Docs, notebook examples and integration tests included.
Related PR:
https://github.com/hwchase17/langchain/pull/2807
---------
Co-authored-by: Tom Hamer <tom@marqo.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: Allow `InMemoryDocstore` to be created without passing a
dict to the constructor; the constructor can create a dict at runtime if
one isn't provided.
- Tag maintainer: @dev2049
Hi, there
This pull request contains two commit:
**1. Implement delete interface with optional ids parameter on
AnalyticDB.**
**2. Allow customization of database connection behavior by exposing
engine_args parameter in interfaces.**
- This commit adds the `engine_args` parameter to the interfaces,
allowing users to customize the behavior of the database connection. The
`engine_args` parameter accepts a dictionary of additional arguments
that will be passed to the create_engine function. Users can now modify
various aspects of the database connection, such as connection pool size
and recycle time. This enhancement provides more flexibility and control
to users when interacting with the database through the exposed
interfaces.
This commit is related to VectorStores @rlancemartin @eyurtsev
Thank you for your attention and consideration.
This fixes#4833 and the critical vulnerability
https://nvd.nist.gov/vuln/detail/CVE-2023-34540
Previously, the JIRA API Wrapper had a mode that simply pipelined user
input into an `exec()` function.
[The intended use of the 'other' mode is to cover any of Atlassian's API
that don't have an existing
interface](cc33bde74f/langchain/tools/jira/prompt.py (L24))
Fortunately all of the [Atlassian JIRA API methods are subfunctions of
their `Jira`
class](https://atlassian-python-api.readthedocs.io/jira.html), so this
implementation calls these subfunctions directly.
As well as passing a string representation of the function to call, the
implementation flexibly allows for optionally passing args and/or
keyword-args. These are given as part of the dictionary input. Example:
```
{
"function": "update_issue_field", #function to execute
"args": [ #list of ordered args similar to other examples in this JiraAPIWrapper
"key",
{"summary": "New summary"}
],
"kwargs": {} #dict of key value keyword-args pairs
}
```
the above is equivalent to `self.jira.update_issue_field("key",
{"summary": "New summary"})`
Alternate query schema designs are welcome to make querying easier
without passing and evaluating arbitrary python code. I considered
parsing (without evaluating) input python code and extracting the
function, args, and kwargs from there and then pipelining them into the
callable function via `*f(args, **kwargs)` - but this seemed more
direct.
@vowelparrot @dev2049
---------
Co-authored-by: Jamal Rahman <jamal.rahman@builder.ai>
## Description
Added Office365 tool modules to `__init__.py` files
## Issue
As described in Issue
https://github.com/hwchase17/langchain/issues/6936, the Office365
toolkit can't be loaded easily because it is not included in the
`__init__.py` files.
## Reviewer
@dev2049
- [x] wire up tools
- [x] wire up retrievers
- [x] add integration test
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- 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: If their are missing or extra variables when validating
Jinja 2 template then a warning is issued rather than raising an
exception. This allows for better flexibility for the developer as
described in #7044. Also changed the relevant test so pytest is checking
for raised warnings rather than exceptions.
- Issue: #7044
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- 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
-->
# [SPARQL](https://www.w3.org/TR/rdf-sparql-query/) for
[LangChain](https://github.com/hwchase17/langchain)
## Description
LangChain support for knowledge graphs relying on W3C standards using
RDFlib: SPARQL/ RDF(S)/ OWL with special focus on RDF \
* Works with local files, files from the web, and SPARQL endpoints
* Supports both SELECT and UPDATE queries
* Includes both a Jupyter notebook with an example and integration tests
## Contribution compared to related PRs and discussions
* [Wikibase agent](https://github.com/hwchase17/langchain/pull/2690) -
uses SPARQL, but specifically for wikibase querying
* [Cypher qa](https://github.com/hwchase17/langchain/pull/5078) - graph
DB question answering for Neo4J via Cypher
* [PR 6050](https://github.com/hwchase17/langchain/pull/6050) - tries
something similar, but does not cover UPDATE queries and supports only
RDF
* Discussions on [w3c mailing list](mailto:semantic-web@w3.org) related
to the combination of LLMs (specifically ChatGPT) and knowledge graphs
## Dependencies
* [RDFlib](https://github.com/RDFLib/rdflib)
## Tag maintainer
Graph database related to memory -> @hwchase17
[Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a
convenient, efficient, and adaptable graph database, compatible with the
Apache TinkerPop3 framework and the Gremlin query language.
In this PR, the HugeGraph and HugeGraphQAChain provide the same
functionality as the existing integration with Neo4j and enables query
generation and question answering over HugeGraph database. The
difference is that the graph query language supported by HugeGraph is
not cypher but another very popular graph query language
[Gremlin](https://tinkerpop.apache.org/gremlin.html).
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- 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
-->
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- 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 type hint for `FAISS.__init__()`'s `relevance_score_fn` parameter
allowed the parameter to be set to `None`. However, a default function
is provided by the constructor. This led to an unnecessary check in the
code, as well as a test to verify this check.
**ASSUMPTION**: There's no reason to ever set `relevance_score_fn` to
`None`.
This PR changes the type hint and removes the unnecessary code.
**Description**:
The JSON Lines format is used by some services such as OpenAI and
HuggingFace. It's also a convenient alternative to CSV.
This PR adds JSON Lines support to `JSONLoader` and also updates related
tests.
**Tag maintainer**: @rlancemartin, @eyurtsev.
PS I was not able to build docs locally so didn't update related
section.
Update to Vectara integration
- By user request added "add_files" to take advantage of Vectara
capabilities to process files on the backend, without the need for
separate loading of documents and chunking in the chain.
- Updated vectara.ipynb example notebook to be broader and added testing
of add_file()
@hwchase17 - project lead
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Retrying with the same improvements as in #6772, this time trying not to
mess up with branches.
@rlancemartin doing a fresh new PR from a branch with a new name. This
should do. Thank you for your help!
---------
Co-authored-by: Jonathan Ellis <jbellis@datastax.com>
Co-authored-by: rlm <pexpresss31@gmail.com>
should be no functional changes
also keep __init__ exposing a lot for backwards compat
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Summary
Updates `UnstructuredEmailLoader` so that it can process attachments in
addition to the e-mail content. The loader will process attachments if
the `process_attachments` kwarg is passed when the loader is
instantiated.
### Testing
```python
file_path = "fake-email-attachment.eml"
loader = UnstructuredEmailLoader(
file_path, mode="elements", process_attachments=True
)
docs = loader.load()
docs[-1]
```
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
Handle the new retriever events in a way that (I think) is entirely
backwards compatible? Needs more testing for some of the chain changes
and all.
This creates an entire new run type, however. We could also just treat
this as an event within a chain run presumably (same with memory)
Adds a subclass initializer that upgrades old retriever implementations
to the new schema, along with tests to ensure they work.
First commit doesn't upgrade any of our retriever implementations (to
show that we can pass the tests along with additional ones testing the
upgrade logic).
Second commit upgrades the known universe of retrievers in langchain.
- [X] Add callback handling methods for retriever start/end/error (open
to renaming to 'retrieval' if you want that)
- [X] Update BaseRetriever schema to support callbacks
- [X] Tests for upgrading old "v1" retrievers for backwards
compatibility
- [X] Update existing retriever implementations to implement the new
interface
- [X] Update calls within chains to .{a]get_relevant_documents to pass
the child callback manager
- [X] Update the notebooks/docs to reflect the new interface
- [X] Test notebooks thoroughly
Not handled:
- Memory pass throughs: retrieval memory doesn't have a parent callback
manager passed through the method
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
If you create a dataset from runs and run the same chain or llm on it
later, it usually works great.
If you have an agent dataset and want to run a different agent on it, or
have more complex schema, it's hard for us to automatically map these
values every time. This PR lets you pass in an input_mapper function
that converts the example inputs to whatever format your model expects
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
### 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>
### 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!
### 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>
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
<!-- 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)
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
### Who can review?
- PTAL @eyurtsev @hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
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
