**Description**: This PR adds support for using the [LLMLingua project
](https://github.com/microsoft/LLMLingua) especially the LongLLMLingua
(Enhancing Large Language Model Inference via Prompt Compression) as a
document compressor / transformer.
The LLMLingua project is an interesting project that can greatly improve
RAG system by compressing prompts and contexts while keeping their
semantic relevance.
**Issue**: https://github.com/microsoft/LLMLingua/issues/31
**Dependencies**: [llmlingua](https://pypi.org/project/llmlingua/)
@baskaryan
---------
Co-authored-by: Ayodeji Ayibiowu <ayodeji.ayibiowu@getinge.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
### Description
This PR moves the Elasticsearch classes to a partners package.
Note that we will not move (and later remove) `ElasticKnnSearch`. It
were previously deprecated.
`ElasticVectorSearch` is going to stay in the community package since it
is used quite a lot still.
Also note that I left the `ElasticsearchTranslator` for self query
untouched because it resides in main `langchain` package.
### Dependencies
There will be another PR that updates the notebooks (potentially pulling
them into the partners package) and templates and removes the classes
from the community package, see
https://github.com/langchain-ai/langchain/pull/17468
#### Open question
How to make the transition smooth for users? Do we move the import
aliases and require people to install `langchain-elasticsearch`? Or do
we remove the import aliases from the `langchain` package all together?
What has worked well for other partner packages?
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**
Adding different threshold types to the semantic chunker. I’ve had much
better and predictable performance when using standard deviations
instead of percentiles.
For all the documents I’ve tried, the distribution of distances look
similar to the above: positively skewed normal distribution. All skews
I’ve seen are less than 1 so that explains why standard deviations
perform well, but I’ve included IQR if anyone wants something more
robust.
Also, using the percentile method backwards, you can declare the number
of clusters and use semantic chunking to get an ‘optimal’ splitting.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** By default it expects a list but that's not the case
in corner scenarios when there is no document ingested(use case:
Bootstrap application).
\
Hence added as check, if the instance is panda Dataframe instead of list
then it will procced with return immediately.
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** jaskiratsingh1
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
## Description & Issue
While following the official doc to use clickhouse as a vectorstore, I
found only the default `annoy` index is properly supported. But I want
to try another engine `usearch` for `annoy` is not properly supported on
ARM platforms.
Here is the settings I prefer:
``` python
settings = ClickhouseSettings(
table="wiki_Ethereum",
index_type="usearch", # annoy by default
index_param=[],
)
```
The above settings do not work for the command `set
allow_experimental_annoy_index=1` is hard-coded.
This PR will make sure the experimental feature follow the `index_type`
which is also consistent with Clickhouse's naming conventions.
**Description:** Update the example fiddler notebook to use community
path, instead of langchain.callback
**Dependencies:** None
**Twitter handle:** @bhalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
h/t @hinthornw
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Avoids deprecation warning that triggered at import time, e.g. with
`python -c 'import langchain.smith'`
/opt/venv/lib/python3.12/site-packages/langchain/callbacks/__init__.py:37:
LangChainDeprecationWarning: Importing this callback from langchain is
deprecated. Importing it from langchain will no longer be supported as
of langchain==0.2.0. Please import from langchain-community instead:
`from langchain_community.callbacks import base`.
To install langchain-community run `pip install -U langchain-community`.
I tried to configure MongoDBChatMessageHistory using the code from the
original documentation to store messages based on the passed session_id
in MongoDB. However, this configuration did not take effect, and the
session id in the database remained as 'test_session'. To resolve this
issue, I found that when configuring MongoDBChatMessageHistory, it is
necessary to set session_id=session_id instead of
session_id=test_session.
Issue: DOC: Ineffective Configuration of MongoDBChatMessageHistory for
Custom session_id Storage
previous code：
```python
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: MongoDBChatMessageHistory(
session_id="test_session",
connection_string="mongodb://root:Y181491117cLj@123.56.224.232:27017",
database_name="my_db",
collection_name="chat_histories",
),
input_messages_key="question",
history_messages_key="history",
)
config = {"configurable": {"session_id": "mmm"}}
chain_with_history.invoke({"question": "Hi! I'm bob"}, config)
```
Modified code:
```python
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: MongoDBChatMessageHistory(
session_id=session_id, # here is my modify code
connection_string="mongodb://root:Y181491117cLj@123.56.224.232:27017",
database_name="my_db",
collection_name="chat_histories",
),
input_messages_key="question",
history_messages_key="history",
)
config = {"configurable": {"session_id": "mmm"}}
chain_with_history.invoke({"question": "Hi! I'm bob"}, config)
```
Effect after modification (it works)：
These packages have moved to
https://github.com/langchain-ai/langchain-google
Left tombstone readmes incase anyone ends up at the "Source Code" link
from old pypi releases. Can keep these around for a few months.
- **Description:** Introduce a new parameter `graph_kwargs` to
`RdfGraph` - parameters used to initialize the `rdflib.Graph` if
`query_endpoint` is set. Also, do not set
`rdflib.graph.DATASET_DEFAULT_GRAPH_ID` as default value for the
`rdflib.Graph` `identifier` if `query_endpoint` is set.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** I encountered this error when I tried to use
LLMChainFilter. Even if the message slightly differs, like `Not relevant
(NO)` this results in an error. It has been reported already here:
https://github.com/langchain-ai/langchain/issues/. This change hopefully
makes it more robust.
- **Issue:** #11408
- **Dependencies:** No
- **Twitter handle:** dokatox
**Description:** Update the azure search notebook to have more
descriptive comments, and an option to choose between OpenAI and
AzureOpenAI Embeddings
---------
Co-authored-by: Matt Gotteiner <[email protected]>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Llama Guard is deprecated from Anyscale public
endpoint.
**Issue:** Change the default model. and remove the limitation of only
use Llama Guard with Anyscale LLMs
Anyscale LLM can also works with all other Chat model hosted on
Anyscale.
Also added `async_client` for Anyscale LLM
**Description:** Callback handler to integrate fiddler with langchain.
This PR adds the following -
1. `FiddlerCallbackHandler` implementation into langchain/community
2. Example notebook `fiddler.ipynb` for usage documentation
[Internal Tracker : FDL-14305]
**Issue:**
NA
**Dependencies:**
- Installation of langchain-community is unaffected.
- Usage of FiddlerCallbackHandler requires installation of latest
fiddler-client (2.5+)
**Twitter handle:** @fiddlerlabs @behalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
- **Description:** Fixing outdated imports after v0.10 llama index
update and updating metadata and source text access
- **Issue:** #17860
- **Twitter handle:** @maximeperrin_
---------
Co-authored-by: Maxime Perrin <mperrin@doing.fr>
