**Description:** Fix `metadata_extractor` type for `RecursiveUrlLoader`,
the default `_metadata_extractor` returns `dict` instead of `str`.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
Signed-off-by: Hemslo Wang <hemslo.wang@gmail.com>
- **Description:** Removing this line
```python
response = index.query(query, response_mode="no_text", **self.query_kwargs)
```
to
```python
response = index.query(query, **self.query_kwargs)
```
Since llama index query does not support response_mode anymore : ``` |
TypeError: BaseQueryEngine.query() got an unexpected keyword argument
'response_mode'````
- **Twitter handle:** @maximeperrin_
---------
Co-authored-by: Maxime Perrin <mperrin@doing.fr>
If the document loader recieves Pathlib path instead of str, it reads
the file correctly, but the problem begins when the document is added to
Deeplake.
This problem arises from casting the path to str in the metadata.
```python
deeplake = True
fname = Path('./lorem_ipsum.txt')
loader = TextLoader(fname, encoding="utf-8")
docs = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks= text_splitter.split_documents(docs)
if deeplake:
db = DeepLake(dataset_path=ds_path, embedding=embeddings, token=activeloop_token)
db.add_documents(chunks)
else:
db = Chroma.from_documents(docs, embeddings)
```
So using this snippet of code the error message for deeplake looks like
this:
```
[part of error message omitted]
Traceback (most recent call last):
File "/home/mwm/repositories/sources/fixing_langchain/main.py", line 53, in <module>
db.add_documents(chunks)
File "/home/mwm/repositories/sources/langchain/libs/core/langchain_core/vectorstores.py", line 139, in add_documents
return self.add_texts(texts, metadatas, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/deeplake.py", line 258, in add_texts
return self.vectorstore.add(
^^^^^^^^^^^^^^^^^^^^^
File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/deeplake_vectorstore.py", line 226, in add
return self.dataset_handler.add(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py", line 139, in add
dataset_utils.extend_or_ingest_dataset(
File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/vector_search/dataset/dataset.py", line 544, in extend_or_ingest_dataset
extend(
File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/vectorstore/vector_search/dataset/dataset.py", line 505, in extend
dataset.extend(batched_processed_tensors, progressbar=False)
File "/home/mwm/anaconda3/envs/langchain/lib/python3.11/site-packages/deeplake/core/dataset/dataset.py", line 3247, in extend
raise SampleExtendError(str(e)) from e.__cause__
deeplake.util.exceptions.SampleExtendError: Failed to append a sample to the tensor 'metadata'. See more details in the traceback. If you wish to skip the samples that cause errors, please specify `ignore_errors=True`.
```
Which is does not explain the error well enough.
The same error for chroma looks like this
```
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/mwm/repositories/sources/fixing_langchain/main.py", line 56, in <module>
db = Chroma.from_documents(docs, embeddings)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/chroma.py", line 778, in from_documents
return cls.from_texts(
^^^^^^^^^^^^^^^
File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/chroma.py", line 736, in from_texts
chroma_collection.add_texts(
File "/home/mwm/repositories/sources/langchain/libs/community/langchain_community/vectorstores/chroma.py", line 309, in add_texts
raise ValueError(e.args[0] + "\n\n" + msg)
ValueError: Expected metadata value to be a str, int, float or bool, got lorem_ipsum.txt which is a <class 'pathlib.PosixPath'>
Try filtering complex metadata from the document using langchain_community.vectorstores.utils.filter_complex_metadata.
```
Which is way more user friendly, so I just added information about
possible mismatch of the type in the error message, the same way it is
covered in chroma
https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/chroma.py#L224
- **Description**:
[`bigdl-llm`](https://github.com/intel-analytics/BigDL) is a library for
running LLM on Intel XPU (from Laptop to GPU to Cloud) using
INT4/FP4/INT8/FP8 with very low latency (for any PyTorch model). This PR
adds bigdl-llm integrations to langchain.
- **Issue**: NA
- **Dependencies**: `bigdl-llm` library
- **Contribution maintainer**: @shane-huang
Examples added:
- docs/docs/integrations/llms/bigdl.ipynb
Description-
- Changed the GitHub endpoint as existing was not working and giving 404
not found error
- Also the existing function was failing if file_filter is not passed as
the tree api return all paths including directory as well, and when
get_file_content was iterating over these path, the function was failing
for directory as the api was returning list of files inside the
directory, so added a condition to ignore the paths if it a directory
- Fixes this issue -
https://github.com/langchain-ai/langchain/issues/17453
Co-authored-by: Radhika Bansal <Radhika.Bansal@veritas.com>
## Description
Updates the `langchain_community.embeddings.fastembed` provider as per
the recent updates to [`FastEmbed`](https://github.com/qdrant/fastembed)
library.
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.
This PR migrates the existing MongoDBAtlasVectorSearch abstraction from
the `langchain_community` section to the partners package section of the
codebase.
- [x] Run the partner package script as advised in the partner-packages
documentation.
- [x] Add Unit Tests
- [x] Migrate Integration Tests
- [x] Refactor `MongoDBAtlasVectorStore` (autogenerated) to
`MongoDBAtlasVectorSearch`
- [x] ~Remove~ deprecate the old `langchain_community` VectorStore
references.
## Additional Callouts
- Implemented the `delete` method
- Included any missing async function implementations
- `amax_marginal_relevance_search_by_vector`
- `adelete`
- Added new Unit Tests that test for functionality of
`MongoDBVectorSearch` methods
- Removed [`del
res[self._embedding_key]`](e0c81e1cb0/libs/community/langchain_community/vectorstores/mongodb_atlas.py (L218))
in `_similarity_search_with_score` function as it would make the
`maximal_marginal_relevance` function fail otherwise. The `Document`
needs to store the embedding key in metadata to work.
Checklist:
- [x] PR title: Please title your PR "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"
- [x] PR message
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [x] Add tests and docs: If you're adding a new integration, please
include
1. Existing tests supplied in docs/docs do not change. Updated
docstrings for new functions like `delete`
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory. (This already exists)
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Steven Silvester <steven.silvester@ieee.org>
Co-authored-by: Erick Friis <erick@langchain.dev>
## PR title
partners: changed the README file for the Fireworks integration in the
libs/partners/fireworks folder
## PR message
Description: Changed the README file of partners/fireworks following the
docs on https://python.langchain.com/docs/integrations/llms/Fireworks
The README includes:
- Brief description
- Installation
- Setting-up instructions (API key, model id, ...)
- Basic usage
Issue: https://github.com/langchain-ai/langchain/issues/17545
Dependencies: None
Twitter handle: None
- **Description:** The current embedchain implementation seems to handle
document metadata differently than done in the current implementation of
langchain and a KeyError is thrown. I would love for someone else to
test this...
---------
Co-authored-by: KKUGLER <kai.kugler@mercedes-benz.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>
## PR title
partners: changed the README file for the IBM Watson AI integration in
the libs/partners/ibm folder.
## PR message
Description: Changed the README file of partners/ibm following the docs
on https://python.langchain.com/docs/integrations/llms/ibm_watsonx
The README includes:
- Brief description
- Installation
- Setting-up instructions (API key, project id, ...)
- Basic usage:
- Loading the model
- Direct inference
- Chain invoking
- Streaming the model output
Issue: https://github.com/langchain-ai/langchain/issues/17545
Dependencies: None
Twitter handle: None
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Sometimes, you want to use various parameters in the retrieval query of
Neo4j Vector to personalize/customize results. Before, when there were
only predefined chains, it didn't really make sense. Now that it's all
about custom chains and LCEL, it is worth adding since users can inject
any params they wish at query time. Isn't prone to SQL injection-type
attacks since we use parameters and not concatenating strings.
**Description:** Add facility to pass the optional output parser to
customize the parsing logic
---------
Co-authored-by: hasan <hasan@m2sys.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
In this PR, I am adding a `PolygonFinancials` tool, which can be used to
get financials data for a given ticker. The financials data is the
fundamental data that is found in income statements, balance sheets, and
cash flow statements of public US companies.
**Twitter**:
[@virattt](https://twitter.com/virattt)
**Description**
This PR sets the "caller identity" of the Astra DB clients used by the
integration plugins (`AstraDBChatMessageHistory`, `AstraDBStore`,
`AstraDBByteStore` and, pending #17767 , `AstraDBVectorStore`). In this
way, the requests to the Astra DB Data API coming from within LangChain
are identified as such (the purpose is anonymous usage stats to best
improve the Astra DB service).
- **Description:** A generic document loader adapter for SQLAlchemy on
top of LangChain's `SQLDatabaseLoader`.
- **Needed by:** https://github.com/crate-workbench/langchain/pull/1
- **Depends on:** GH-16655
- **Addressed to:** @baskaryan, @cbornet, @eyurtsev
Hi from CrateDB again,
in the same spirit like GH-16243 and GH-16244, this patch breaks out
another commit from https://github.com/crate-workbench/langchain/pull/1,
in order to reduce the size of this patch before submitting it, and to
separate concerns.
To accompany the SQLAlchemy adapter implementation, the patch includes
integration tests for both SQLite and PostgreSQL. Let me know if
corresponding utility resources should be added at different spots.
With kind regards,
Andreas.
### Software Tests
```console
docker compose --file libs/community/tests/integration_tests/document_loaders/docker-compose/postgresql.yml up
```
```console
cd libs/community
pip install psycopg2-binary
pytest -vvv tests/integration_tests -k sqldatabase
```
```
14 passed
```
---------
Co-authored-by: Andreas Motl <andreas.motl@crate.io>
some mails from flipkart , amazon are encoded with other plain text
format so to handle UnicodeDecode error , added exception and latin
decoder
Thank you for contributing to LangChain!
@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.
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.
## PR title
langchain_nvidia_ai_endpoints[patch]: Invoke callback prior to yielding
## PR message
**Description:** Invoke callback prior to yielding token in _stream and
_astream methods for nvidia_ai_endpoints.
**Issue:** https://github.com/langchain-ai/langchain/issues/16913
**Dependencies:** None
- **Description:** Add possibility to pass ModelInference or Model
object to WatsonxLLM class
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** :
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally. ✅
**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.
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`.
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:** 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>
- **Description:**
- Add DocumentManager class, which is a nosql record manager.
- In order to use index and aindex in
libs/langchain/langchain/indexes/_api.py, DocumentManager inherits
RecordManager.
- Also I added the MongoDB implementation of Document Manager too.
- **Dependencies:** pymongo, motor
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** Add DocumentManager class, which is a no sql record
manager. To use index method and aindex method in indexes._api.py,
Document Manager inherits RecordManager.Add the MongoDB implementation
of Document Manager.
- **Dependencies:** pymongo, motor
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---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR updates RunnableWithMessageHistory to use add_messages which
will save on round-trips for any chat
history abstractions that implement the optimization. If the
optimization isn't
implemented, add_messages automatically invokes add_message serially.
- make schema Optional with default val None, since in json_mode you
don't need it if not parsing to pydantic
- change return_type -> include_raw
- expand docstring examples
After upgrading langchain-community to 0.0.22, it's not possible to use
openai from the community package with streaming=True
```
File "/home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/.tox/langchain/lib/python3.11/site-packages/langchain_community/chat_models/openai.py", line 434, in _generate
return generate_from_stream(stream_iter)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/.tox/langchain/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 65, in generate_from_stream
for chunk in stream:
File "/home/runner/work/ragstack-ai/ragstack-ai/ragstack-e2e-tests/.tox/langchain/lib/python3.11/site-packages/langchain_community/chat_models/openai.py", line 418, in _stream
run_manager.on_llm_new_token(chunk.text, chunk=cg_chunk)
^^^^^^^^^^
AttributeError: 'AIMessageChunk' object has no attribute 'text'
```
Fix regression of https://github.com/langchain-ai/langchain/pull/17907
**Twitter handle:** @nicoloboschi
Thank you for contributing to LangChain!
- [x] **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"
- **Description:** fix SparkLLM error
- **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!
- **Description:** Added the `return_sparql_query` feature to the
`GraphSparqlQAChain` class, allowing users to get the formatted SPARQL
query along with the chain's result.
- **Issue:** NA
- **Dependencies:** None
Note: I've ensured that the PR passes linting and testing by running
make format, make lint, and make test locally.
I have added a test for the integration (which relies on network access)
and I have added an example to the notebook showing its use.
**Description**
This PR addresses a rare issue in `OpenAIWhisperParser` that causes it
to crash when processing an audio file with a duration very close to the
class's chunk size threshold of 20 minutes.
**Issue**
#11449
**Dependencies**
None
**Tag maintainer**
@agola11 @eyurtsev
**Twitter handle**
leonardodiegues
---------
Co-authored-by: Leonardo Diegues <leonardo.diegues@grupofolha.com.br>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# PR Message
- **Description:** This PR adds a README file for the Anthropic API in
the `libs/partners` folder of this repository. The README includes:
- A brief description of the Anthropic package
- Installation & API instructions
- Usage examples
- **Issue:**
[17545](https://github.com/langchain-ai/langchain/issues/17545)
- **Dependencies:** None
Additional notes:
This change only affects the docs package and does not introduce any new
dependencies.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: callback on_llm_new_token before yield chunk for
_stream/_astream for some chat models, make all chat models in a
consistent behaviour.
- Issue: N/A
- Dependencies: N/A
**Description:** Initial pull request for Kinetica LLM wrapper
**Issue:** N/A
**Dependencies:** No new dependencies for unit tests. Integration tests
require gpudb, typeguard, and faker
**Twitter handle:** @chad_juliano
Note: There is another pull request for Kinetica vectorstore. Ultimately
we would like to make a partner package but we are starting with a
community contribution.
**Description:**
Change type hint on `QuerySQLDataBaseTool` to be compatible with
SQLAlchemy v1.4.x.
**Issue:**
Users locked to `SQLAlchemy < 2.x` are unable to import
`QuerySQLDataBaseTool`.
closes https://github.com/langchain-ai/langchain/issues/17819
**Dependencies:**
None
**Description:** This PR adds an `__init__` method to the
NeuralDBVectorStore class, which takes in a NeuralDB object to
instantiate the state of NeuralDBVectorStore.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
**Description:**
Updated documentation for DeepLake init method.
Especially the exec_option docs needed improvement, but did a general
cleanup while I was looking at it.
**Issue:** n/a
**Dependencies:** None
---------
Co-authored-by: Nathan Voxland <nathan@voxland.net>
- **Description:** In order to override the bool value of
"fetch_schema_from_transport" in the GraphQLAPIWrapper, a
"fetch_schema_from_transport" value needed to be added to the
"_EXTRA_OPTIONAL_TOOLS" dictionary in load_tools in the "graphql" key.
The parameter "fetch_schema_from_transport" must also be passed in to
the GraphQLAPIWrapper to allow reading of the value when creating the
client. Passing as an optional parameter is probably best to avoid
breaking changes. This change is necessary to support GraphQL instances
that do not support fetching schema, such as TigerGraph. More info here:
[TigerGraph GraphQL Schema
Docs](https://docs.tigergraph.com/graphql/current/schema)
- **Threads handle:** @zacharytoliver
---------
Co-authored-by: Zachary Toliver <zt10191991@hotmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Add missing chunk parameter for _stream/_astream for some
chat models, make all chat models in a consistent behaviour.
- Issue: N/A
- Dependencies: N/A
**Description:** Here is a minimal example to illustrate behavior:
```python
from langchain_core.runnables import RunnableLambda
def my_function(*args, **kwargs):
return 3 + kwargs.get("n", 0)
runnable = RunnableLambda(my_function).bind(n=1)
assert 4 == runnable.invoke({})
assert [4] == list(runnable.stream({}))
assert 4 == await runnable.ainvoke({})
assert [4] == [item async for item in runnable.astream({})]
```
Here, `runnable.invoke({})` and `runnable.stream({})` work fine, but
`runnable.ainvoke({})` raises
```
TypeError: RunnableLambda._ainvoke.<locals>.func() got an unexpected keyword argument 'n'
```
and similarly for `runnable.astream({})`:
```
TypeError: RunnableLambda._atransform.<locals>.func() got an unexpected keyword argument 'n'
```
Here we assume that this behavior is undesired and attempt to fix it.
**Issue:** https://github.com/langchain-ai/langchain/issues/17241,
https://github.com/langchain-ai/langchain/discussions/16446
In this pull request, we introduce the add_images method to the
SingleStoreDB vector store class, expanding its capabilities to handle
multi-modal embeddings seamlessly. This method facilitates the
incorporation of image data into the vector store by associating each
image's URI with corresponding document content, metadata, and either
pre-generated embeddings or embeddings computed using the embed_image
method of the provided embedding object.
the change includes integration tests, validating the behavior of the
add_images. Additionally, we provide a notebook showcasing the usage of
this new method.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
- **Description:**
The existing `RedisCache` implementation lacks proper handling for redis
client failures, such as `ConnectionRefusedError`, leading to subsequent
failures in pipeline components like LLM calls. This pull request aims
to improve error handling for redis client issues, ensuring a more
robust and graceful handling of such errors.
- **Issue:** Fixes#16866
- **Dependencies:** No new dependency
- **Twitter handle:** N/A
Co-authored-by: snsten <>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Sent to LangSmith
Thank you for contributing to LangChain!
Checklist:
- [ ] PR title: Please title your PR "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 template message** and replace it
with the following bulleted list
- **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!
- [ ] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [ ] 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.
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.
Description:
In this PR, I am adding a PolygonTickerNews Tool, which can be used to
get the latest news for a given ticker / stock.
Twitter handle: [@virattt](https://twitter.com/virattt)
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.
**Description**: CogniSwitch focusses on making GenAI usage more
reliable. It abstracts out the complexity & decision making required for
tuning processing, storage & retrieval. Using simple APIs documents /
URLs can be processed into a Knowledge Graph that can then be used to
answer questions.
**Dependencies**: No dependencies. Just network calls & API key required
**Tag maintainer**: @hwchase17
**Twitter handle**: https://github.com/CogniSwitch
**Documentation**: Please check
`docs/docs/integrations/toolkits/cogniswitch.ipynb`
**Tests**: The usual tool & toolkits tests using `test_imports.py`
PR has passed linting and testing before this submission.
---------
Co-authored-by: Saicharan Sridhara <145636106+saiCogniswitch@users.noreply.github.com>
## Amazon Personalize support on Langchain
This PR is a successor to this PR -
https://github.com/langchain-ai/langchain/pull/13216
This PR introduces an integration with [Amazon
Personalize](https://aws.amazon.com/personalize/) to help you to
retrieve recommendations and use them in your natural language
applications. This integration provides two new components:
1. An `AmazonPersonalize` client, that provides a wrapper around the
Amazon Personalize API.
2. An `AmazonPersonalizeChain`, that provides a chain to pull in
recommendations using the client, and then generating the response in
natural language.
We have added this to langchain_experimental since there was feedback
from the previous PR about having this support in experimental rather
than the core or community extensions.
Here is some sample code to explain the usage.
```python
from langchain_experimental.recommenders import AmazonPersonalize
from langchain_experimental.recommenders import AmazonPersonalizeChain
from langchain.llms.bedrock import Bedrock
recommender_arn = "<insert_arn>"
client=AmazonPersonalize(
credentials_profile_name="default",
region_name="us-west-2",
recommender_arn=recommender_arn
)
bedrock_llm = Bedrock(
model_id="anthropic.claude-v2",
region_name="us-west-2"
)
chain = AmazonPersonalizeChain.from_llm(
llm=bedrock_llm,
client=client
)
response = chain({'user_id': '1'})
```
Reviewer: @3coins
Hi, I'm from the LanceDB team.
Improves LanceDB integration by making it easier to use - now you aren't
required to create tables manually and pass them in the constructor,
although that is still backward compatible.
Bug fix - pandas was being used even though it's not a dependency for
LanceDB or langchain
PS - this issue was raised a few months ago but lost traction. It is a
feature improvement for our users kindly review this , Thanks !
- OpenLLM was using outdated method to get the final text output from
openllm client invocation which was raising the error. Therefore
corrected that.
- OpenLLM `_identifying_params` was getting the openllm's client
configuration using outdated attributes which was raising error.
- Updated the docstring for OpenLLM.
- Added timeout parameter to be passed to underlying openllm client.
Another PR will be done for the langchain-astradb package.
Note: for future PRs, devs will be done in the partner package only. This one is just to align with the rest of the components in the community package and it fixes a bunch of issues.
- **Description:** adds an `exclude` parameter to the DirectoryLoader
class, based on similar behavior in GenericLoader
- **Issue:** discussed in
https://github.com/langchain-ai/langchain/discussions/9059 and I think
in some other issues that I cannot find at the moment 🙇
- **Dependencies:** None
- **Twitter handle:** don't have one sorry! Just https://github/nejch
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Addresses the bugs described in linked issue where an
import was erroneously removed and the rename of a keyword argument was
missed when migrating from beta --> stable of the azure-search-documents
package
- **Issue:** https://github.com/langchain-ai/langchain/issues/17598
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** This fixes an issue with working with RecordManager.
RecordManager was generating new hashes on documents because `add_texts`
was modifying the metadata directly. Additionally moved some tests to
unit tests since that was a more appropriate home.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
The current issue:
Most of the deprecation descriptions are duplicated. For example:
`[Deprecated] Chat Agent.[Deprecated] Chat Agent.` for the [ChatAgent
class](https://api.python.langchain.com/en/latest/langchain_api_reference.html#classes)
description.
NOTE: I've tested it only with new ut! I cannot build API Reference
locally :(
**Description:** This PR introduces a new "Astra DB" Partner Package.
So far only the vector store class is _duplicated_ there, all others
following once this is validated and established.
Along with the move to separate package, incidentally, the class name
will change `AstraDB` => `AstraDBVectorStore`.
The strategy has been to duplicate the module (with prospected removal
from community at LangChain 0.2). Until then, the code will be kept in
sync with minimal, known differences (there is a makefile target to
automate drift control. Out of convenience with this check, the
community package has a class `AstraDBVectorStore` aliased to `AstraDB`
at the end of the module).
With this PR several bugfixes and improvement come to the vector store,
as well as a reshuffling of the doc pages/notebooks (Astra and
Cassandra) to align with the move to a separate package.
**Dependencies:** A brand new pyproject.toml in the new package, no
changes otherwise.
**Twitter handle:** `@rsprrs`
---------
Co-authored-by: Christophe Bornet <cbornet@hotmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Updates to the Kuzu API had broken this
functionality. These updates resolve those issues and add a new test to
demonstrate the updates.
- **Issue:** #11874
- **Dependencies:** No new dependencies
- **Twitter handle:** @amirk08
Test results:
```
tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_no_params PASSED [ 33%]
tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_params PASSED [ 66%]
tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_refresh_schema PASSED [100%]
=================================================== slowest 5 durations ===================================================
0.53s call tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_refresh_schema
0.34s call tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_no_params
0.28s call tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_params
0.03s teardown tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_refresh_schema
0.02s teardown tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_params
==================================================== 3 passed in 1.27s ====================================================
```
- **Description:** Allow a bool value to be passed to
fetch_schema_from_transport since not all GraphQL instances support this
feature, such as TigerGraph.
- **Threads:** @zacharytoliver
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Resolving problem in
`langchain_community\document_loaders\pebblo.py` with `import pwd`.
`pwd` is not available on windows. import moved to try catch block
- **Issue:** #17514
This PR is adding support for NVIDIA NeMo embeddings issue #16095.
---------
Co-authored-by: Praveen Nakshatrala <pnakshatrala@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Noticed and fixed a few typos in the SmartLLMChain default ideation and
critique prompts
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Adds an optional name param to our base message to support passing names
into LLMs.
OpenAI supports having a name on anything except tool message now
(system, ai, user/human).
https://github.com/langchain-ai/langchain/issues/17525
### Example Code
```python
from langchain_community.document_loaders.athena import AthenaLoader
database_name = "database"
s3_output_path = "s3://bucket-no-prefix"
query="""SELECT
CAST(extract(hour FROM current_timestamp) AS INTEGER) AS current_hour,
CAST(extract(minute FROM current_timestamp) AS INTEGER) AS current_minute,
CAST(extract(second FROM current_timestamp) AS INTEGER) AS current_second;
"""
profile_name = "AdministratorAccess"
loader = AthenaLoader(
query=query,
database=database_name,
s3_output_uri=s3_output_path,
profile_name=profile_name,
)
documents = loader.load()
print(documents)
```
### Error Message and Stack Trace (if applicable)
NoSuchKey: An error occurred (NoSuchKey) when calling the GetObject
operation: The specified key does not exist
### Description
Athena Loader errors when result s3 bucket uri has no prefix. The Loader
instance call results in a "NoSuchKey: An error occurred (NoSuchKey)
when calling the GetObject operation: The specified key does not exist."
error.
If s3_output_path contains a prefix like:
```python
s3_output_path = "s3://bucket-with-prefix/prefix"
```
Execution works without an error.
## Suggested solution
Modify:
```python
key = "/".join(tokens[1:]) + "/" + query_execution_id + ".csv"
```
to
```python
key = "/".join(tokens[1:]) + ("/" if tokens[1:] else "") + query_execution_id + ".csv"
```
9e8a3fc4ff/libs/community/langchain_community/document_loaders/athena.py (L128)
### System Info
System Information
------------------
> OS: Darwin
> OS Version: Darwin Kernel Version 22.6.0: Fri Sep 15 13:41:30 PDT
2023; root:xnu-8796.141.3.700.8~1/RELEASE_ARM64_T8103
> Python Version: 3.9.9 (main, Jan 9 2023, 11:42:03)
[Clang 14.0.0 (clang-1400.0.29.102)]
Package Information
-------------------
> langchain_core: 0.1.23
> langchain: 0.1.7
> langchain_community: 0.0.20
> langsmith: 0.0.87
> langchain_openai: 0.0.6
> langchainhub: 0.1.14
Packages not installed (Not Necessarily a Problem)
--------------------------------------------------
The following packages were not found:
> langgraph
> langserve
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
1. integrate with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. update `langchain.llms`
3. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
If the SQLAlchemyMd5Cache is shared among multiple processes, it is
possible to encounter a race condition during the cache update.
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Support filtering databases in the use case where
devs do not want to query ALL entries within a DB,
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Twitter handle:** I don't have Twitter but feel free to tag my
Github!
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This pull request introduces support for various Approximate Nearest
Neighbor (ANN) vector index algorithms in the VectorStore class,
starting from version 8.5 of SingleStore DB. Leveraging this enhancement
enables users to harness the power of vector indexing, significantly
boosting search speed, particularly when handling large sets of vectors.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
1. Added _clear_edges()_ and _get_number_of_nodes()_ functions in
NetworkxEntityGraph class.
2. Added the above two function in graph_networkx_qa.ipynb
documentation.
- **Description:** Callback manager can't catch chain input or output
validation errors because `prepare_input` and `prepare_output` are not
part of the try/raise logic, this PR fixes that logic.
- **Issue:** #15954
- **Description:** Fixes a type annotation issue in the definition of
BedrockBase. This issue was that the annotation for the `config`
attribute includes a ForwardRef to `botocore.client.Config` which is
only imported when `TYPE_CHECKING`. This can cause pydantic to raise an
error like `pydantic.errors.ConfigError: field "config" not yet prepared
so type is still a ForwardRef, ...`.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@__nat_n__`
- **Description :**
Fix: Use shallow copy for schema manipulation in get_format_instructions
Prevents side effects on the original schema object by using a
dictionary comprehension for a safer and more controlled manipulation of
schema key-value pairs, enhancing code reliability.
- **Issue:** #17161
- **Dependencies:** None
- **Twitter handle:** None
Users can provide an Elasticsearch connection with custom headers. This
PR makes sure these headers are preserved when adding the langchain user
agent header.
- **Description:** Depending on `token_max` used in
`load_summarize_chain`, it could cause an infinite loop when documents
cannot collapse under `token_max`. This change would not affect the
existing feature, but it also gives an option to users to avoid the
situation.
- **Issue:** https://github.com/langchain-ai/langchain/issues/16251
- **Dependencies:** None
- **Twitter handle:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
1. integrate chat models with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)
Yuan2.0 is a new generation Fundamental Large Language Model developed
by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan
2.0-51B, and Yuan 2.0-2B.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
Addresses a problem where the Date type within an Elasticsearch
SelfQueryRetriever would encounter difficulties in generating a valid
query.
Issue: #17042
---------
Co-authored-by: Max Jakob <max.jakob@elastic.co>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
I am submitting this for a school project as part of a team of 5. Other
team members are @LeilaChr, @maazh10, @Megabear137, @jelalalamy. This PR
also has contributions from community members @Harrolee and @Mario928.
Initial context is in the issue we opened (#11229).
This pull request adds:
- Generic framework for expanding the languages that `LanguageParser`
can handle, using the
[tree-sitter](https://github.com/tree-sitter/py-tree-sitter#py-tree-sitter)
parsing library and existing language-specific parsers written for it
- Support for the following additional languages in `LanguageParser`:
- C
- C++
- C#
- Go
- Java (contributed by @Mario928
https://github.com/ThatsJustCheesy/langchain/pull/2)
- Kotlin
- Lua
- Perl
- Ruby
- Rust
- Scala
- TypeScript (contributed by @Harrolee
https://github.com/ThatsJustCheesy/langchain/pull/1)
Here is the [design
document](https://docs.google.com/document/d/17dB14cKCWAaiTeSeBtxHpoVPGKrsPye8W0o_WClz2kk)
if curious, but no need to read it.
## Issues
- Closes#11229
- Closes#10996
- Closes#8405
## Dependencies
`tree_sitter` and `tree_sitter_languages` on PyPI. We have tried to add
these as optional dependencies.
## Documentation
We have updated the list of supported languages, and also added a
section to `source_code.ipynb` detailing how to add support for
additional languages using our framework.
## Maintainer
- @hwchase17 (previously reviewed
https://github.com/langchain-ai/langchain/pull/6486)
Thanks!!
## Git commits
We will gladly squash any/all of our commits (esp merge commits) if
necessary. Let us know if this is desirable, or if you will be
squash-merging anyway.
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---------
Co-authored-by: Maaz Hashmi <mhashmi373@gmail.com>
Co-authored-by: LeilaChr <87657694+LeilaChr@users.noreply.github.com>
Co-authored-by: Jeremy La <jeremylai511@gmail.com>
Co-authored-by: Megabear137 <zubair.alnoor27@gmail.com>
Co-authored-by: Lee Harrold <lhharrold@sep.com>
Co-authored-by: Mario928 <88029051+Mario928@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
- The existing code was trying to find a `.embeddings` property on the
`Coroutine` returned by calling `cohere.async_client.embed`.
- Instead, the `.embeddings` property is present on the value returned
by the `Coroutine`.
- Also, it seems that the original cohere client expects a value of
`max_retries` to not be `None`. Hence, setting the default value of
`max_retries` to `3`.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Pebblo opensource project enables developers to
safely load data to their Gen AI apps. It identifies semantic topics and
entities found in the loaded data and summarizes them in a
developer-friendly report.
- **Dependencies:** none
- **Twitter handle:** srics
@hwchase17
**Description**: This PR adds a chain for Amazon Neptune graph database
RDF format. It complements the existing Neptune Cypher chain. The PR
also includes a Neptune RDF graph class to connect to, introspect, and
query a Neptune RDF graph database from the chain. A sample notebook is
provided under docs that demonstrates the overall effect: invoking the
chain to make natural language queries against Neptune using an LLM.
**Issue**: This is a new feature
**Dependencies**: The RDF graph class depends on the AWS boto3 library
if using IAM authentication to connect to the Neptune database.
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This PR adds support for
[flashrank](https://github.com/PrithivirajDamodaran/FlashRank) for
reranking as alternative to Cohere.
I'm not sure `libs/langchain` is the right place for this change. At
first, I wanted to put it under `libs/community`. All the compressors
were under `libs/langchain/retrievers/document_compressors` though. Hope
this makes sense!
- **Description:** Improve test cases for `SQLDatabase` adapter
component, see
[suggestion](https://github.com/langchain-ai/langchain/pull/16655#pullrequestreview-1846749474).
- **Depends on:** GH-16655
- **Addressed to:** @baskaryan, @cbornet, @eyurtsev
_Remark: This PR is stacked upon GH-16655, so that one will need to go
in first._
Edit: Thank you for bringing in GH-17191, @eyurtsev. This is a little
aftermath, improving/streamlining the corresponding test cases.
- **Description:**
[AS-IS] When dealing with a yaml file, the extension must be .yaml.
[TO-BE] In the absence of extension length constraints in the OS, the
extension of the YAML file is yaml, but control over the yml extension
must still be made.
It's as if it's an error because it's a .jpg extension in jpeg support.
- **Issue:** -
- **Dependencies:**
no dependencies required for this change,
- **Description:** The from__xx methods of FAISS class have hardcoded
InMemoryStore implementation and thereby not let users pass a custom
DocStore implementation,
- **Issue:** no referenced issue,
- **Dependencies:** none,
- **Twitter handle:** ksachdeva
**Description:**
Bugfix: Langchain_community's GitHub Api wrapper throws a TypeError when
searching for issues and/or PRs (the `search_issues_and_prs` method).
This is because PyGithub's PageinatedList type does not support the
len() method. See https://github.com/PyGithub/PyGithub/issues/1476
**Dependencies:** None
**Twitter handle**: @ChrisKeoghNZ
I haven't registered an issue as it would take me longer to fill the
template out than to make the fix, but I'm happy to if that's deemed
essential.
I've added a simple integration test to cover this as there were no
existing unit tests and it was going to be tricky to set them up.
Co-authored-by: Chris Keogh <chris.keogh@xero.com>
- **Description:** This adds a delete method so that rocksetdb can be
used with `RecordManager`.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
---------
Co-authored-by: Rockset API Bot <admin@rockset.io>
**Description:** Invoke callback prior to yielding token in stream
method for Ollama.
**Issue:** [Callback for on_llm_new_token should be invoked before the
token is yielded by the model
#16913](https://github.com/langchain-ai/langchain/issues/16913)
Co-authored-by: Robby <h0rv@users.noreply.github.com>
This PR replaces the memory stream implementation used by the
LogStreamCallbackHandler.
This implementation resolves an issue in which streamed logs and
streamed events originating from sync code would arrive only after the
entire sync code would finish execution (rather than arriving in real
time as they're generated).
One example is if trying to stream tokens from an llm within a tool. If
the tool was an async tool, but the llm was invoked via stream (sync
variant) rather than astream (async variant), then the tokens would fail
to stream in real time and would all arrived bunched up after the tool
invocation completed.
**Description:** Invoke callback prior to yielding token in stream
method for watsonx.
**Issue:** [Callback for on_llm_new_token should be invoked before the
token is yielded by the model
#16913](https://github.com/langchain-ai/langchain/issues/16913)
Co-authored-by: Robby <h0rv@users.noreply.github.com>
**Description:** changed filtering so that failed filter doesn't add
document to results. Currently filtering is entirely broken and all
documents are returned whether or not they pass the filter.
fixes issue introduced in
https://github.com/langchain-ai/langchain/pull/16190
- **Description:** Adds the document loader for [AWS
Athena](https://aws.amazon.com/athena/), a serverless and interactive
analytics service.
- **Dependencies:** Added boto3 as a dependency
- **Description:** This PR adds support for `search_types="mmr"` and
`search_type="similarity_score_threshold"` to retrievers using
`DatabricksVectorSearch`,
- **Issue:**
- **Dependencies:**
- **Twitter handle:**
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Ref: https://openai.com/pricing
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Unlike vector results, the LLM has to completely trust the context of a
graph database result, even if it doesn't provide whole context. We
tried with instructions, but it seems that adding a single example is
the way to go to solve this issue.
### This pull request makes the following changes:
* Fixed issue #16913
Fixed the google gen ai chat_models.py code to make sure that the
callback is called before the token is yielded
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@baskaryan, @eyurtsev, @hwchase17.
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
<!-- Thank you for contributing to LangChain!
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Pydantic's `dict()` function raises an error here if you pass in a
generator. We have a more robust serialization function in lagnsmith
that we will use instead.
**Description**
Make some functions work with Milvus:
1. get_ids: Get primary keys by field in the metadata
2. delete: Delete one or more entities by ids
3. upsert: Update/Insert one or more entities
**Issue**
None
**Dependencies**
None
**Tag maintainer:**
@hwchase17
**Twitter handle:**
None
---------
Co-authored-by: HoaNQ9 <hoanq.1811@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
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@baskaryan, @eyurtsev, @hwchase17.
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- **Description:**
1. Modify LLMs/Anyscale to work with OAI v1
2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale
3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name
convention (reverted)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
## Summary
This PR upgrades LangChain's Ruff configuration in preparation for
Ruff's v0.2.0 release. (The changes are compatible with Ruff v0.1.5,
which LangChain uses today.) Specifically, we're now warning when
linter-only options are specified under `[tool.ruff]` instead of
`[tool.ruff.lint]`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Issue:** Issue with model argument support (been there for a while
actually):
- Non-specially-handled arguments like temperature don't work when
passed through constructor.
- Such arguments DO work quite well with `bind`, but also do not abide
by field requirements.
- Since initial push, server-side error messages have gotten better and
v0.0.2 raises better exceptions. So maybe it's better to let server-side
handle such issues?
- **Description:**
- Removed ChatNVIDIA's argument fields in favor of
`model_kwargs`/`model_kws` arguments which aggregates constructor kwargs
(from constructor pathway) and merges them with call kwargs (bind
pathway).
- Shuffled a few functions from `_NVIDIAClient` to `ChatNVIDIA` to
streamline construction for future integrations.
- Minor/Optional: Old services didn't have stop support, so client-side
stopping was implemented. Now do both.
- **Any Breaking Changes:** Minor breaking changes if you strongly rely
on chat_model.temperature, etc. This is captured by
chat_model.model_kwargs.
PR passes tests and example notebooks and example testing. Still gonna
chat with some people, so leaving as draft for now.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Description: Missing _identifying_params create issues when dealing with
callbacks to get current run model parameters.
All other model partners implementation provide this property and also
provide _default_params. I'm not sure about the default values to
include or if we can re-use the same as for _VertexAICommon(), this
change allows you to access the model parameters correctly.
Issue: Not exactly this issue but could be related
https://github.com/langchain-ai/langchain/issues/14711
Twitter handle:@musicaoriginal2
The streaming API doesn't separate safety_settings from the
generation_config payload. As the result the following error is observed
when using `stream` API. The functionality is correct with `invoke` API.
The fix separates the `safety_settings` from params and sets it as
argument to the `send_message` method.
```
ERROR: Unknown field for GenerationConfig: safety_settings
Traceback (most recent call last):
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 250, in stream
raise e
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 234, in stream
for chunk in self._stream(
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/langchain_google_vertexai/chat_models.py", line 501, in _stream
for response in responses:
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/vertexai/generative_models/_generative_models.py", line 921, in _send_message_streaming
for chunk in stream:
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/vertexai/generative_models/_generative_models.py", line 514, in _generate_content_streaming
request = self._prepare_request(
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/vertexai/generative_models/_generative_models.py", line 256, in _prepare_request
gapic_generation_config = gapic_content_types.GenerationConfig(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/proto/message.py", line 576, in __init__
raise ValueError(
ValueError: Unknown field for GenerationConfig: safety_settings
```
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
I noticed that RunnableConfigurableAlternatives which is an important
composition in LCEL has no Docstring. Therefore I added the detailed
Docstring for it.
@baskaryan, @eyurtsev, @hwchase17 please have a look and let me if the
docstring is looking good.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR enables changing the behaviour of huggingface pipeline between
different calls. For example, before this PR there's no way of changing
maximum generation length between different invocations of the chain.
This is desirable in cases, such as when we want to scale the maximum
output size depending on a dynamic prompt size.
Usage example:
```python
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
hf = HuggingFacePipeline(pipeline=pipe)
hf("Say foo:", pipeline_kwargs={"max_new_tokens": 42})
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- **Description: changes to you.com files**
- general cleanup
- adds community/utilities/you.py, moving bulk of code from retriever ->
utility
- removes `snippet` as endpoint
- adds `news` as endpoint
- adds more tests
<s>**Description: update community MAKE file**
- adds `integration_tests`
- adds `coverage`</s>
- **Issue:** the issue # it fixes if applicable,
- [For New Contributors: Update Integration
Documentation](https://github.com/langchain-ai/langchain/issues/15664#issuecomment-1920099868)
- **Dependencies:** n/a
- **Twitter handle:** @scottnath
- **Mastodon handle:** scottnath@mastodon.social
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This adds a recursive json splitter class to the
existing text_splitters as well as unit tests
- **Issue:** splitting text from structured data can cause issues if you
have a large nested json object and you split it as regular text you may
end up losing the structure of the json. To mitigate against this you
can split the nested json into large chunks and overlap them, but this
causes unnecessary text processing and there will still be times where
the nested json is so big that the chunks get separated from the parent
keys.
As an example you wouldn't want the following to be split in half:
```shell
{'val0': 'DFWeNdWhapbR',
'val1': {'val10': 'QdJo',
'val11': 'FWSDVFHClW',
'val12': 'bkVnXMMlTiQh',
'val13': 'tdDMKRrOY',
'val14': 'zybPALvL',
'val15': 'JMzGMNH',
'val16': {'val160': 'qLuLKusFw',
'val161': 'DGuotLh',
'val162': 'KztlcSBropT',
-----------------------------------------------------------------------split-----
'val163': 'YlHHDrN',
'val164': 'CtzsxlGBZKf',
'val165': 'bXzhcrWLmBFp',
'val166': 'zZAqC',
'val167': 'ZtyWno',
'val168': 'nQQZRsLnaBhb',
'val169': 'gSpMbJwA'},
'val17': 'JhgiyF',
'val18': 'aJaqjUSFFrI',
'val19': 'glqNSvoyxdg'}}
```
Any llm processing the second chunk of text may not have the context of
val1, and val16 reducing accuracy. Embeddings will also lack this
context and this makes retrieval less accurate.
Instead you want it to be split into chunks that retain the json
structure.
```shell
{'val0': 'DFWeNdWhapbR',
'val1': {'val10': 'QdJo',
'val11': 'FWSDVFHClW',
'val12': 'bkVnXMMlTiQh',
'val13': 'tdDMKRrOY',
'val14': 'zybPALvL',
'val15': 'JMzGMNH',
'val16': {'val160': 'qLuLKusFw',
'val161': 'DGuotLh',
'val162': 'KztlcSBropT',
'val163': 'YlHHDrN',
'val164': 'CtzsxlGBZKf'}}}
```
and
```shell
{'val1':{'val16':{
'val165': 'bXzhcrWLmBFp',
'val166': 'zZAqC',
'val167': 'ZtyWno',
'val168': 'nQQZRsLnaBhb',
'val169': 'gSpMbJwA'},
'val17': 'JhgiyF',
'val18': 'aJaqjUSFFrI',
'val19': 'glqNSvoyxdg'}}
```
This recursive json text splitter does this. Values that contain a list
can be converted to dict first by using split(... convert_lists=True)
otherwise long lists will not be split and you may end up with chunks
larger than the max chunk.
In my testing large json objects could be split into small chunks with
✅ Increased question answering accuracy
✅ The ability to split into smaller chunks meant retrieval queries can
use fewer tokens
- **Dependencies:** json import added to text_splitter.py, and random
added to the unit test
- **Twitter handle:** @joelsprunger
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** Databricks LLM does not support SerDe the
transform_input_fn and transform_output_fn. After saving and loading,
the LLM will be broken. This PR serialize these functions into a hex
string using pickle, and saving the hex string in the yaml file. Using
pickle to serialize a function can be flaky, but this is a simple
workaround that unblocks many use cases. If more sophisticated SerDe is
needed, we can improve it later.
Test:
Added a simple unit test.
I did manual test on Databricks and it works well.
The saved yaml looks like:
```
llm:
_type: databricks
cluster_driver_port: null
cluster_id: null
databricks_uri: databricks
endpoint_name: databricks-mixtral-8x7b-instruct
extra_params: {}
host: e2-dogfood.staging.cloud.databricks.com
max_tokens: null
model_kwargs: null
n: 1
stop: null
task: null
temperature: 0.0
transform_input_fn: 80049520000000000000008c085f5f6d61696e5f5f948c0f7472616e73666f726d5f696e7075749493942e
transform_output_fn: null
```
@baskaryan
```python
from langchain_community.embeddings import DatabricksEmbeddings
from langchain_community.llms import Databricks
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
import mlflow
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
def transform_input(**request):
request["messages"] = [
{
"role": "user",
"content": request["prompt"]
}
]
del request["prompt"]
return request
llm = Databricks(endpoint_name="databricks-mixtral-8x7b-instruct", transform_input_fn=transform_input)
persist_dir = "faiss_databricks_embedding"
# Create the vector db, persist the db to a local fs folder
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
db = FAISS.from_documents(docs, embeddings)
db.save_local(persist_dir)
def load_retriever(persist_directory):
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
vectorstore = FAISS.load_local(persist_directory, embeddings)
return vectorstore.as_retriever()
retriever = load_retriever(persist_dir)
retrievalQA = RetrievalQA.from_llm(llm=llm, retriever=retriever)
with mlflow.start_run() as run:
logged_model = mlflow.langchain.log_model(
retrievalQA,
artifact_path="retrieval_qa",
loader_fn=load_retriever,
persist_dir=persist_dir,
)
# Load the retrievalQA chain
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}]))
```
- **Description:**
Embedding field name was hard-coded named "embedding".
So I suggest that change `res["embedding"]` into
`res[self._embedding_key]`.
- **Issue:** #17177,
- **Twitter handle:**
[@bagcheoljun17](https://twitter.com/bagcheoljun17)
- **Description:** Fixes in the Ontotext GraphDB Graph and QA Chain
related to the error handling in case of invalid SPARQL queries, for
which `prepareQuery` doesn't throw an exception, but the server returns
400 and the query is indeed invalid
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** @OntotextGraphDB
**Description:**
Implemented unique ID validation in the FAISS component to ensure all
document IDs are distinct. This update resolves issues related to
non-unique IDs, such as inconsistent behavior during deletion processes.
**Description:** enable _parse_response_candidate to support complex
structure format.
**Issue:**
currently, if Gemini response complex args format, people will get
"TypeError: Object of type RepeatedComposite is not JSON serializable"
error from _parse_response_candidate.
response candidate example
```
content {
role: "model"
parts {
function_call {
name: "Information"
args {
fields {
key: "people"
value {
list_value {
values {
string_value: "Joe is 30, his mom is Martha"
}
}
}
}
}
}
}
}
finish_reason: STOP
safety_ratings {
category: HARM_CATEGORY_HARASSMENT
probability: NEGLIGIBLE
}
safety_ratings {
category: HARM_CATEGORY_HATE_SPEECH
probability: NEGLIGIBLE
}
safety_ratings {
category: HARM_CATEGORY_SEXUALLY_EXPLICIT
probability: NEGLIGIBLE
}
safety_ratings {
category: HARM_CATEGORY_DANGEROUS_CONTENT
probability: NEGLIGIBLE
}
```
error msg:
```
Traceback (most recent call last):
File "/home/jupyter/user/abehsu/gemini_langchain_tools/example2.py", line 36, in <module>
print(tagging_chain.invoke({"input": "Joe is 30, his mom is Martha"}))
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2053, in invoke
input = step.invoke(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 3887, in invoke
return self.bound.invoke(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 165, in invoke
self.generate_prompt(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 543, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 407, in generate
raise e
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 397, in generate
self._generate_with_cache(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 576, in _generate_with_cache
return self._generate(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_google_vertexai/chat_models.py", line 406, in _generate
generations = [
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_google_vertexai/chat_models.py", line 408, in <listcomp>
message=_parse_response_candidate(c),
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_google_vertexai/chat_models.py", line 280, in _parse_response_candidate
function_call["arguments"] = json.dumps(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type RepeatedComposite is not JSON serializable
```
**Twitter handle:** @abehsu1992626
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description: added logic to override get_num_tokens_from_messages()
for ChatVertexAI. Currently ChatVertexAI was inheriting
get_num_tokens_from_messages() from BaseChatModel which in-turn was
calling GPT-2 tokenizer
- **Issue: NA
- **Dependencies: NA
- **Twitter handle:@aditya_rane
@lkuligin for review
---------
Co-authored-by: adityarane@google.com <adityarane@google.com>
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
- **Description:**
Actually the test named `test_openai_apredict` isn't testing the
apredict method from ChatOpenAI.
- **Twitter handle:**
https://twitter.com/OAlmofadas
* This PR adds async methods to the LLM cache.
* Adds an implementation using Redis called AsyncRedisCache.
* Adds a docker compose file at the /docker to help spin up docker
* Updates redis tests to use a context manager so flushing always happens by default
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **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!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:**
This PR standardizes the `output_parser.py` file across all agent types
to ensure a uniform parsing mechanism is implemented. It introduces a
cohesive structure and common interface for output parsing, facilitating
easier modifications and extensions by users. The standardized approach
enhances maintainability and scalability of the codebase by providing a
consistent pattern for output parsing, which can be easily understood
and utilized across different agent types.
This PR builds upon the foundation set by a previously merged PR, which
focused exclusively on standardizing the `output_parser.py` for the
`conversational_agent` ([PR
#16945](https://github.com/langchain-ai/langchain/pull/16945)). With
this new update, I extend the standardization efforts to encompass
`output_parser.py` files across all agent types. This enhancement not
only unifies the parsing mechanism across the board but also introduces
the flexibility for users to incorporate custom `FORMAT_INSTRUCTIONS`.
- **Issue:**
https://github.com/langchain-ai/langchain/issues/10721https://github.com/langchain-ai/langchain/issues/4044
- **Dependencies:**
No new dependencies required for this change
- **Twitter handle:**
With my github user is enough. Thanks
I hope you accept my PR.
Based on my experiments, the newline isn't always there, so we can make
the regex slightly more robust by allowing an optional newline after the
bacticks
