- **Description:** In order to use index and aindex in
libs/langchain/langchain/indexes/_api.py, I implemented delete method
and all async methods in opensearch_vector_search
- **Dependencies:** No changes
- **Description:** Improvement for #19599: fixing missing return of
graph.draw_mermaid_png and improve it to make the saving of the rendered
image optional
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
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**: "community: deprecating integrations moved to
langchain_google_community"
- [ ] **PR message**: deprecating integrations moved to
langchain_google_community
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Removes required usage of `requests` from `langchain-core`, all of which
has been deprecated.
- removes Tracer V1 implementations
- removes old `try_load_from_hub` github-based hub implementations
Removal done in a way where imports will still succeed, and usage will
fail with a `RuntimeError`.
**Description**: Improves the stability of all Cohere partner package
integration tests. Fixes a bug with document parsing (both dicts and
Documents are handled).
**Description**: This PR simplifies an integration test within the
Cohere partner package:
* It no longer relies on exact model answers
* It no longer relies on a third party tool
cohere: update imports and installs to langchain_cohere
---------
Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**: Adds an agent that uses Cohere with multiple hops and
multiple tools.
This PR is a continuation of
https://github.com/langchain-ai/langchain/pull/19650 - which was
previously approved. Conceptually nothing has changed, but this PR has
extra fixes, documentation and testing.
---------
Co-authored-by: BeatrixCohere <128378696+BeatrixCohere@users.noreply.github.com>
Co-authored-by: Erick Friis <erickfriis@gmail.com>
This PR completes work for PR #18798 to expose raw tool output in
on_tool_end.
Affected APIs:
* astream_log
* astream_events
* callbacks sent to langsmith via langsmith-sdk
* Any other code that relies on BaseTracer!
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- This ensures ids are stable across streamed chunks
- Multiple messages in batch call get separate ids
- Also fix ids being dropped when combining message chunks
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:** add `remove_comments` option (default: True): do not
extract html _comments_,
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** @nfcampos ,
- **Twitter handle:** peter_v
I ran `make format`, `make lint` and `make test`.
Discussion: I my use case, I prefer to not have the comments in the
extracted text:
* e.g. from a Google tag that is added in the html as comment
* e.g. content that the authors have temporarily hidden to make it non
visible to the regular reader
Removing the comments makes the extracted text more alike the intended
text to be seen by the reader.
**Choice to make:** do we prefer to make the default for this
`remove_comments` option to be True or False?
I have changed it to True in a second commit, since that is how I would
prefer to use it by default. Have the
cleaned text (without technical Google tags etc.) and also closer to the
actually visible and intended content.
I am not sure what is best aligned with the conventions of langchain in
general ...
INITIAL VERSION (new version above):
~**Choice to make:** do we prefer to make the default for this
`ignore_comments` option to be True or False?
I have set it to False now to be backwards compatible. On the other
hand, I would use it mostly with True.
I am not sure what is best aligned with the conventions of langchain in
general ...~
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** adds integration with [Layerup
Security](https://uselayerup.com). Docs can be found
[here](https://docs.uselayerup.com). Integrates directly with our Python
SDK.
**Dependencies:**
[LayerupSecurity](https://pypi.org/project/LayerupSecurity/)
**Note**: all methods for our product require a paid API key, so I only
included 1 test which checks for an invalid API key response. I have
tested extensively locally.
**Twitter handle**: [@layerup_](https://twitter.com/layerup_)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
As in #19346, this PR exposes `request_timeout` in `BaseCohere`, while
`max_retires` is no longer a parameter of the beneath client
(`cohere.Client`) and it is already configured in
`langchain_cohere.llms.Cohere`.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** the layout of html pages can be variant based on the
bootstrap framework or the styles of the pages. So we need to have a
splitter to transform the html tags to a proper layout and then split
the html content based on the provided list of tags to determine its
html sections. We are using BS4 library along with xslt structure to
split the html content using an section aware approach.
- **Dependencies:** No new dependencies
- **Twitter handle:** @m_setayesh
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.
-->
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
[Dria](https://dria.co/) is a hub of public RAG models for developers to
both contribute and utilize a shared embedding lake. This PR adds a
retriever that can retrieve documents from Dria.
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"
- [x] **PR message**:
- **Description:** Fix argument translation from OpenAPI spec to OpenAI
function call (and similar)
- **Issue:** OpenGPTs failures with calling Action Server based actions.
- **Dependencies:** None
- **Twitter handle:** mikkorpela
- [x] **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.~
- [x] **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: Update `ChatZhipuAI` to support the latest `glm-4` model.
Issue: N/A
Dependencies: httpx, httpx-sse, PyJWT
The previous `ChatZhipuAI` implementation requires the `zhipuai`
package, and cannot call the latest GLM model. This is because
- The old version `zhipuai==1.*` doesn't support the latest model.
- `zhipuai==2.*` requires `pydantic V2`, which is incompatible with
'langchain-community'.
This re-implementation invokes the GLM model by sending HTTP requests to
[open.bigmodel.cn](https://open.bigmodel.cn/dev/api) via the `httpx`
package, and uses the `httpx-sse` package to handle stream events.
---------
Co-authored-by: zR <2448370773@qq.com>
- **Description:** Add functionality to generate Mermaid syntax and
render flowcharts from graph data. This includes support for custom node
colors and edge curve styles, as well as the ability to export the
generated graphs to PNG images using either the Mermaid.INK API or
Pyppeteer for local rendering.
- **Dependencies:** Optional dependencies are `pyppeteer` if rendering
wants to be done using Pypeteer and Javascript code.
---------
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
* Replace `source_documents` with `documents`
* Pass `documents` as a named arg vs keyword
* Make `parsed_docs` more robust
* Fix edge case of doc page_content being `None`
- **Updating Together.ai Endpoint**: "langchain_together: Updated
Deprecated endpoint for partner package"
- Description: The inference API of together is deprecates, do replaced
with completions and made corresponding changes.
- Twitter handle: @dev_yashmathur
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Add attribution_token within
GoogleVertexAISearchRetriever so user can provide this information to
Google support team or product team during debug session.
Reference:
https://cloud.google.com/generative-ai-app-builder/docs/view-analytics#user-events
Attribution tokens. Attribution tokens are unique IDs generated by
Vertex AI Search and returned with each search request. Make sure to
include that attribution token as UserEvent.attributionToken with any
user events resulting from a search. This is needed to identify if a
search is served by the API. Only user events with a Google-generated
attribution token are used to compute metrics.
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** abehsu1992626
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Support reranking based on cross encoder models
available from HuggingFace.
- Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and
`SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality
to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it.
Please let me know if anything else needs to be done to make it visible
on the table-of-contents navigation bar on the left, or on the card list
on [retrievers documentation
page](https://python.langchain.com/docs/integrations/retrievers).
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
---------
Co-authored-by: Kenny Choe <kchoe@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Video imagery to text (Closed Captioning)
This pull request introduces the VideoCaptioningChain, a tool for
automated video captioning. It processes audio and video to generate
subtitles and closed captions, merging them into a single SRT output.
Issue: https://github.com/langchain-ai/langchain/issues/11770
Dependencies: opencv-python, ffmpeg-python, assemblyai, transformers,
pillow, torch, openai
Tag maintainer:
@baskaryan
@hwchase17
Hello! We are a group of students from the University of Toronto
(@LunarECL, @TomSadan, @nicoledroi1, @A2113S) that want to make a
contribution to the LangChain community! We have ran make format, make
lint and make test locally before submitting the PR. To our knowledge,
our changes do not introduce any new errors.
Thank you for taking the time to review our PR!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
This implementation adds functionality from the AlphaVantage API,
renowned for its comprehensive financial data. The class encapsulates
various methods, each dedicated to fetching specific types of financial
information from the API.
### Implemented Functions
- **`search_symbols`**:
- Searches the AlphaVantage API for financial symbols using the provided
keywords.
- **`_get_market_news_sentiment`**:
- Retrieves market news sentiment for a specified stock symbol from the
AlphaVantage API.
- **`_get_time_series_daily`**:
- Fetches daily time series data for a specific symbol from the
AlphaVantage API.
- **`_get_quote_endpoint`**:
- Obtains the latest price and volume information for a given symbol
from the AlphaVantage API.
- **`_get_time_series_weekly`**:
- Gathers weekly time series data for a particular symbol from the
AlphaVantage API.
- **`_get_top_gainers_losers`**:
- Provides details on top gainers, losers, and most actively traded
tickers in the US market from the AlphaVantage API.
### Issue:
- #11994
### Dependencies:
- 'requests' library for HTTP requests. (import requests)
- 'pytest' library for testing. (import pytest)
---------
Co-authored-by: Adam Badar <94140103+adam-badar@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Langchain-Predibase integration was failing, because
it was not current with the Predibase SDK; in addition, Predibase
integration tests were instantiating the Langchain Community `Predibase`
class with one required argument (`model`) missing. This change updates
the Predibase SDK usage and fixes the integration tests.
- **Twitter handle:** `@alexsherstinsky`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: added support for llmsherpa library"
- [x] **Add tests and docs**:
1. Integration test:
'docs/docs/integrations/document_loaders/test_llmsherpa.py'.
2. an example notebook:
`docs/docs/integrations/document_loaders/llmsherpa.ipynb`.
- [x] **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.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description
Implementing `_combine_llm_outputs` to `ChatMistralAI` to override the
default implementation in `BaseChatModel` returning `{}`. The
implementation is inspired by the one in `ChatOpenAI` from package
`langchain-openai`.
# Issue
None
# Dependencies
None
# Twitter handle
None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** We'd like to support passing additional kwargs in
`with_structured_output`. I believe this is the accepted approach to
enable additional arguments on API calls.
- **Description:** Haskell language support added in text_splitter
module
- **Dependencies:** No
- **Twitter handle:** @nisargtr
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** PR adds support for limiting number of messages
preserved in a session history for DynamoDBChatMessageHistory
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Subject: Fix Type Misdeclaration for index_schema in redis/base.py
I noticed a type misdeclaration for the index_schema column in the
redis/base.py file.
When following the instructions outlined in [Redis Custom Metadata
Indexing](https://python.langchain.com/docs/integrations/vectorstores/redis)
to create our own index_schema, it leads to a Pylance type error. <br/>
**The error message indicates that Dict[str, list[Dict[str, str]]] is
incompatible with the type Optional[Union[Dict[str, str], str,
os.PathLike]].**
```
index_schema = {
"tag": [{"name": "credit_score"}],
"text": [{"name": "user"}, {"name": "job"}],
"numeric": [{"name": "age"}],
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users_modified",
index_schema=index_schema,
)
```
Therefore, I have created this pull request to rectify the type
declaration problem.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Feature
- Set additional headers in constructor
- Headers will be sent in post request
This feature is useful if deploying Ollama on a cloud service such as
hugging face, which requires authentication tokens to be passed in the
request header.
## Tests
- Test if header is passed
- Test if header is not passed
Similar to https://github.com/langchain-ai/langchain/pull/15881
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
If `prompt` is passed into `create_sql_agent()`, then
`toolkit.get_context()` shouldn't be executed against the database
unless relevant prompt variables (`table_info` or `table_names`) are
present .
Description: I implemented a tool to use Hugging Face text-to-speech
inference API.
Issue: n/a
Dependencies: n/a
Twitter handle: No Twitter, but do have
[LinkedIn](https://www.linkedin.com/in/robby-horvath/) lol.
---------
Co-authored-by: Robby <h0rv@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: Implement DirectoryLoader lazy_load
function"
- [x] **Description**: The `lazy_load` function of the `DirectoryLoader`
yields each document separately. If the given `loader_cls` of the
`DirectoryLoader` also implemented `lazy_load`, it will be used to yield
subdocuments of the file.
- [x] **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:
`libs/community/tests/unit_tests/document_loaders/test_directory_loader.py`
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory:
`docs/docs/integrations/document_loaders/directory.ipynb`
- [x] **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.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
When using the SQLDatabaseChain with Llama2-70b LLM and, SQLite
database. I was getting `Warning: You can only execute one statement at
a time.`.
```
from langchain.sql_database import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
sql_database_path = '/dccstor/mmdataretrieval/mm_dataset/swimming_record/rag_data/swimmingdataset.db'
sql_db = get_database(sql_database_path)
db_chain = SQLDatabaseChain.from_llm(mistral, sql_db, verbose=True, callbacks = [callback_obj])
db_chain.invoke({
"query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?"
})
```
Error:
```
Warning Traceback (most recent call last)
Cell In[31], line 3
1 import langchain
2 langchain.debug=False
----> 3 db_chain.invoke({
4 "query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?"
5 })
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:162, in Chain.invoke(self, input, config, **kwargs)
160 except BaseException as e:
161 run_manager.on_chain_error(e)
--> 162 raise e
163 run_manager.on_chain_end(outputs)
164 final_outputs: Dict[str, Any] = self.prep_outputs(
165 inputs, outputs, return_only_outputs
166 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs)
149 run_manager = callback_manager.on_chain_start(
150 dumpd(self),
151 inputs,
152 name=run_name,
153 )
154 try:
155 outputs = (
--> 156 self._call(inputs, run_manager=run_manager)
157 if new_arg_supported
158 else self._call(inputs)
159 )
160 except BaseException as e:
161 run_manager.on_chain_error(e)
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:198, in SQLDatabaseChain._call(self, inputs, run_manager)
194 except Exception as exc:
195 # Append intermediate steps to exception, to aid in logging and later
196 # improvement of few shot prompt seeds
197 exc.intermediate_steps = intermediate_steps # type: ignore
--> 198 raise exc
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:143, in SQLDatabaseChain._call(self, inputs, run_manager)
139 intermediate_steps.append(
140 sql_cmd
141 ) # output: sql generation (no checker)
142 intermediate_steps.append({"sql_cmd": sql_cmd}) # input: sql exec
--> 143 result = self.database.run(sql_cmd)
144 intermediate_steps.append(str(result)) # output: sql exec
145 else:
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:436, in SQLDatabase.run(self, command, fetch, include_columns)
425 def run(
426 self,
427 command: str,
428 fetch: Literal["all", "one"] = "all",
429 include_columns: bool = False,
430 ) -> str:
431 """Execute a SQL command and return a string representing the results.
432
433 If the statement returns rows, a string of the results is returned.
434 If the statement returns no rows, an empty string is returned.
435 """
--> 436 result = self._execute(command, fetch)
438 res = [
439 {
440 column: truncate_word(value, length=self._max_string_length)
(...)
443 for r in result
444 ]
446 if not include_columns:
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:413, in SQLDatabase._execute(self, command, fetch)
410 elif self.dialect == "postgresql": # postgresql
411 connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
--> 413 cursor = connection.execute(text(command))
414 if cursor.returns_rows:
415 if fetch == "all":
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1416, in Connection.execute(self, statement, parameters, execution_options)
1414 raise exc.ObjectNotExecutableError(statement) from err
1415 else:
-> 1416 return meth(
1417 self,
1418 distilled_parameters,
1419 execution_options or NO_OPTIONS,
1420 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/sql/elements.py:516, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options)
514 if TYPE_CHECKING:
515 assert isinstance(self, Executable)
--> 516 return connection._execute_clauseelement(
517 self, distilled_params, execution_options
518 )
519 else:
520 raise exc.ObjectNotExecutableError(self)
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1639, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options)
1627 compiled_cache: Optional[CompiledCacheType] = execution_options.get(
1628 "compiled_cache", self.engine._compiled_cache
1629 )
1631 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1632 dialect=dialect,
1633 compiled_cache=compiled_cache,
(...)
1637 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1638 )
-> 1639 ret = self._execute_context(
1640 dialect,
1641 dialect.execution_ctx_cls._init_compiled,
1642 compiled_sql,
1643 distilled_parameters,
1644 execution_options,
1645 compiled_sql,
1646 distilled_parameters,
1647 elem,
1648 extracted_params,
1649 cache_hit=cache_hit,
1650 )
1651 if has_events:
1652 self.dispatch.after_execute(
1653 self,
1654 elem,
(...)
1658 ret,
1659 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1848, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1843 return self._exec_insertmany_context(
1844 dialect,
1845 context,
1846 )
1847 else:
-> 1848 return self._exec_single_context(
1849 dialect, context, statement, parameters
1850 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1988, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1985 result = context._setup_result_proxy()
1987 except BaseException as e:
-> 1988 self._handle_dbapi_exception(
1989 e, str_statement, effective_parameters, cursor, context
1990 )
1992 return result
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:2346, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context, is_sub_exec)
2344 else:
2345 assert exc_info[1] is not None
-> 2346 raise exc_info[1].with_traceback(exc_info[2])
2347 finally:
2348 del self._reentrant_error
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1969, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1967 break
1968 if not evt_handled:
-> 1969 self.dialect.do_execute(
1970 cursor, str_statement, effective_parameters, context
1971 )
1973 if self._has_events or self.engine._has_events:
1974 self.dispatch.after_cursor_execute(
1975 self,
1976 cursor,
(...)
1980 context.executemany,
1981 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/default.py:922, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
921 def do_execute(self, cursor, statement, parameters, context=None):
--> 922 cursor.execute(statement, parameters)
Warning: You can only execute one statement at a time.
```
**Issue:**
The Error occurs because when generating the SQLQuery, the llm_input
includes the stop character of "\nSQLResult:", so for this user query
the LLM generated response is **SELECT Time FROM men_butterfly_100m
WHERE Swimmer = 'Lance Larson';\nSQLResult:** it is required to remove
the SQLResult suffix on the llm response before executing it on the
database.
```
llm_inputs = {
"input": input_text,
"top_k": str(self.top_k),
"dialect": self.database.dialect,
"table_info": table_info,
"stop": ["\nSQLResult:"],
}
sql_cmd = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
if SQL_RESULT in sql_cmd:
sql_cmd = sql_cmd.split(SQL_RESULT)[0].strip()
result = self.database.run(sql_cmd)
```
<!-- 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
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Description: Fix xml parser to handle strings that only contain the root
tag
Issue: N/A
Dependencies: None
Twitter handle: N/A
A valid xml text can contain only the root level tag. Example: <body>
Some text here
</body>
The example above is a valid xml string. If parsed with the current
implementation the result is {"body": []}. This fix checks if the root
level text contains any non-whitespace character and if that's the case
it returns {root.tag: root.text}. The result is that the above text is
correctly parsed as {"body": "Some text here"}
@ale-delfino
Thank you for contributing to LangChain!
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: **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!
- [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. 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.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
When testing Nomic embeddings --
```
from langchain_community.embeddings import LlamaCppEmbeddings
embd_model_path = "/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.Q4_K_S.gguf"
embd_lc = LlamaCppEmbeddings(model_path=embd_model_path)
embedding_lc = embd_lc.embed_query(query)
```
We were seeing this error for strings > a certain size --
```
File ~/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/llama.py:827, in Llama.embed(self, input, normalize, truncate, return_count)
824 s_sizes = []
826 # add to batch
--> 827 self._batch.add_sequence(tokens, len(s_sizes), False)
828 t_batch += n_tokens
829 s_sizes.append(n_tokens)
File ~/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/_internals.py:542, in _LlamaBatch.add_sequence(self, batch, seq_id, logits_all)
540 self.batch.token[j] = batch[i]
541 self.batch.pos[j] = i
--> 542 self.batch.seq_id[j][0] = seq_id
543 self.batch.n_seq_id[j] = 1
544 self.batch.logits[j] = logits_all
ValueError: NULL pointer access
```
The default `n_batch` of llama-cpp-python's Llama is `512` but we were
explicitly setting it to `8`.
These need to be set to equal for embedding models.
* The embedding.cpp example has an assertion to make sure these are
always equal.
* Apparently this is not being done properly in llama-cpp-python.
With `n_batch` set to 8, if more than 8 tokens are passed the batch runs
out of space and it crashes.
This also explains why the CPU compute buffer size was small:
raw client with default `n_batch=512`
```
llama_new_context_with_model: CPU input buffer size = 3.51 MiB
llama_new_context_with_model: CPU compute buffer size = 21.00 MiB
```
langchain with `n_batch=8`
```
llama_new_context_with_model: CPU input buffer size = 0.04 MiB
llama_new_context_with_model: CPU compute buffer size = 0.33 MiB
```
We can work around this by passing `n_batch=512`, but this will not be
obvious to some users:
```
embedding = LlamaCppEmbeddings(model_path=embd_model_path,
n_batch=512)
```
From discussion w/ @cebtenzzre. Related:
https://github.com/abetlen/llama-cpp-python/issues/1189
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** The base URL for OpenAI is retrieved from the
environment variable "OPENAI_BASE_URL", whereas for langchain it is
obtained from "OPENAI_API_BASE". By adding `base_url =
os.environ.get("OPENAI_API_BASE")`, the OpenAI proxy can execute
correctly.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- **Description:** added unit tests for NotebookLoader. Linked PR:
https://github.com/langchain-ai/langchain/pull/17614
- **Issue:**
[#17614](https://github.com/langchain-ai/langchain/pull/17614)
- **Twitter handle:** @paulodoestech
- [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. 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, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: lachiewalker <lachiewalker1@hotmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Created a Langchain Tool for OpenAI DALLE Image
Generation.
**Issue:**
[#15901](https://github.com/langchain-ai/langchain/issues/15901)
**Dependencies:** n/a
**Twitter handle:** @paulodoestech
- [x] **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.
- [x] **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.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**: adding checking codes for calling AI model get error
in chat_models/base.py and llms/base.py
**Issue**: Sometimes the AI Model calling will get error, we should
raise it.
Otherwise, the next code 'choices.extend(response["choices"])' will
throw a "TypeError: 'NoneType' object is not iterable" error to mask the
true error.
Because 'response["choices"]' is None.
**Dependencies**: None
---------
Co-authored-by: yangkx <yangkx@asiainfo-int.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## PR message
**Description:** This PR adds a README file for the Together API in the
`libs/partners` folder of this repository. The README includes:
- A brief description of the package
- Installation instructions and class introductions
- Simple usage examples
**Issue:** #17545
This PR only contains document changes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
1. Fix the BiliBiliLoader that can receive cookie parameters, it
requires 3 other parameters to run. The change is backward compatible.
2. Add test;
3. Add example in docs
- **Issue:** [#14213]
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- [x] **PR title**: "community: Support streaming in Azure ML and few
naming changes"
- [x] **PR message**:
- **Description:** Added support for streaming for azureml_endpoint.
Also, renamed and AzureMLEndpointApiType.realtime to
AzureMLEndpointApiType.dedicated. Also, added new classes
CustomOpenAIChatContentFormatter and CustomOpenAIContentFormatter and
updated the classes LlamaChatContentFormatter and LlamaContentFormatter
to now show a deprecated warning message when instantiated.
---------
Co-authored-by: Sachin Paryani <saparan@microsoft.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** At times, BaseChatMemory._get_input_output may acquire
some extra keys such as 'intermediate_steps' (agent_executor with
return_intermediate_steps set to True) and 'messages'
(agent_executor.iter with memory). In these instances, _get_input_output
can raise an error due to the presence of multiple keys. The 'output'
field should be used as the default field in these cases.
**Issue:** #16791
- Description: Added missing `from_documents` method to `KNNRetriever`,
providing the ability to supply metadata to LangChain `Document`s, and
to give it parity to the other retrievers, which do have
`from_documents`.
- Issue: None
- Dependencies: None
- Twitter handle: None
Co-authored-by: Victor Adan <vadan@netroadshow.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Relates to #17048
Description : Applied fix to dynamodb and elasticsearch file.
Error was : `Cannot override writeable attribute with read-only
property`
Suggestion:
instead of adding
```
@messages.setter
def messages(self, messages: List[BaseMessage]) -> None:
raise NotImplementedError("Use add_messages instead")
```
we can change base class property
`messages: List[BaseMessage]`
to
```
@property
def messages(self) -> List[BaseMessage]:...
```
then we don't need to add `@messages.setter` in all child classes.
**Description:**
While not technically incorrect, the TypeVar used for the `@beta`
decorator prevented pyright (and thus most vscode users) from correctly
seeing the types of functions/classes decorated with `@beta`.
This is in part due to a small bug in pyright
(https://github.com/microsoft/pyright/issues/7448 ) - however, the
`Type` bound in the typevar `C = TypeVar("C", Type, Callable)` is not
doing anything - classes are `Callables` by default, so by my
understanding binding to `Type` does not actually provide any more
safety - the modified annotation still works correctly for both
functions, properties, and classes.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Update to the docstring for class RunnableSerializable,
method configurable_fields
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Update to the docstring for class RunnableSerializable,
method configurable_alternatives
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
In this small PR I added the `template_tool_response` arg to the
`create_json_chat` function, so that users can customize this prompt in
case of need.
Thanks for your reviews!
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Add our solar chat models, available model choices:
* solar-1-mini-chat
* solar-1-mini-translate-enko
* solar-1-mini-translate-koen
More documents and pricing can be found at
https://console.upstage.ai/services/solar.
The references to our solar model can be found at
* https://arxiv.org/abs/2402.17032
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Adds support for `with_structured_output` to Cohere,
which supports single function calling.
---------
Co-authored-by: BeatrixCohere <128378696+BeatrixCohere@users.noreply.github.com>
This PR allows to calculate token usage for prompts and completion
directly in the generation method of BedrockChat. The token usage
details are then returned together with the generations, so that other
downstream tasks can access them easily.
This allows to define a callback for tokens tracking and cost
calculation, similarly to what happens with OpenAI (see
[OpenAICallbackHandler](https://api.python.langchain.com/en/latest/_modules/langchain_community/callbacks/openai_info.html#OpenAICallbackHandler).
I plan on adding a BedrockCallbackHandler later.
Right now keeping track of tokens in the callback is already possible,
but it requires passing the llm, as done here:
https://how.wtf/how-to-count-amazon-bedrock-anthropic-tokens-with-langchain.html.
However, I find the approach of this PR cleaner.
Thanks for your reviews. FYI @baskaryan, @hwchase17
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **PR title**: "community: fix baidu qianfan missing stop
parameter"
- [x] **PR message**:
- **Description: Baidu Qianfan lost the stop parameter when requesting
service due to extracting it from kwargs. This bug can cause the agent
to receive incorrect results
---------
Co-authored-by: ligang33 <ligang33@baidu.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Bug fixes in this PR:
* allows for other params such as "message" not just the input param to
the prompt for the cohere tools agent
* fixes to documents kwarg from messages
* fixes to tool_calls API call
---------
Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
- **Issue:** When passing an empty list to MergerRetriever it fails with
error: ValueError: max() arg is an empty sequence
- **Description:** We have a use case where we dynamically select
retrievers and use MergerRetriever for merging the output of the
retrievers. We faced this issue when the retriever_docs list is empty.
Adding a default 0 for cases when retriever_docs is an empty list to
avoid "ValueError: max() arg is an empty sequence". Also, changed to use
map() which is more than twice as fast compared to the current
implementation.
```
import timeit
# Sample retriever_docs with varying lengths of sublists
retriever_docs = [[i for i in range(j)] for j in range(1, 1000)]
# First code snippet
code1 = '''
max_docs = max(len(docs) for docs in retriever_docs)
'''
# Second code snippet
code2 = '''
max_docs = max(map(len, retriever_docs), default=0)
'''
# Benchmarking
time1 = timeit.timeit(stmt=code1, globals=globals(), number=10000)
time2 = timeit.timeit(stmt=code2, globals=globals(), number=10000)
# Output
print(f"Execution time for code snippet 1: {time1} seconds")
print(f"Execution time for code snippet 2: {time2} seconds")
```
- **Dependencies:** none
The previous version didn't had Voyage rerank in the init file
- [ ] **PR title**: langchain_voyageai reranker is not working
- [ ] **PR message**:
- **Description:** This fix let you run reranker from voyage
- **Issue:** Was not able to run reranker from voyage
@efriis
#### Description
Fixed the following error with `rerank` method from `CohereRerank`:
```
---> [79](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:79) results = self.client.rerank(
[80](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:80) query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
[81](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:81) )
[82](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:82) result_dicts = []
[83](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:83) for res in results.results:
TypeError: BaseCohere.rerank() takes 1 positional argument but 4 positional arguments (and 2 keyword-only arguments) were given
```
This was easily fixed going from this:
```
def rerank(
self,
documents: Sequence[Union[str, Document, dict]],
query: str,
*,
model: Optional[str] = None,
top_n: Optional[int] = -1,
max_chunks_per_doc: Optional[int] = None,
) -> List[Dict[str, Any]]:
...
if len(documents) == 0: # to avoid empty api call
return []
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
model = model or self.model
top_n = top_n if (top_n is None or top_n > 0) else self.top_n
results = self.client.rerank(
query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
)
result_dicts = []
for res in results:
result_dicts.append(
{"index": res.index, "relevance_score": res.relevance_score}
)
return result_dicts
```
to this:
```
def rerank(
self,
documents: Sequence[Union[str, Document, dict]],
query: str,
*,
model: Optional[str] = None,
top_n: Optional[int] = -1,
max_chunks_per_doc: Optional[int] = None,
) -> List[Dict[str, Any]]:
...
if len(documents) == 0: # to avoid empty api call
return []
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
model = model or self.model
top_n = top_n if (top_n is None or top_n > 0) else self.top_n
results = self.client.rerank(
query=query, documents=docs, model=model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc <-------------
)
result_dicts = []
for res in results.results: <-------------
result_dicts.append(
{"index": res.index, "relevance_score": res.relevance_score}
)
return result_dicts
```
#### Unit & Integration tests
I added a unit test to check the behaviour of `rerank`. Also fixed the
original integration test which was failing.
#### Format & Linting
Everything worked properly with `make lint_diff`, `make format_diff` and
`make format`. However I noticed an error coming from other part of the
library when doing `make lint`:
```
(langchain-py3.9) ➜ langchain git:(master) make format
[ "." = "" ] || poetry run ruff format .
1636 files left unchanged
[ "." = "" ] || poetry run ruff --select I --fix .
(langchain-py3.9) ➜ langchain git:(master) make lint
./scripts/check_pydantic.sh .
./scripts/lint_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run ruff format . --diff
1636 files already formatted
[ "." = "" ] || poetry run ruff --select I .
[ "." = "" ] || mkdir -p .mypy_cache && poetry run mypy . --cache-dir .mypy_cache
langchain/agents/openai_assistant/base.py:252: error: Argument "file_ids" to "create" of "Assistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]" [arg-type]
langchain/agents/openai_assistant/base.py:374: error: Argument "file_ids" to "create" of "AsyncAssistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]" [arg-type]
Found 2 errors in 1 file (checked 1634 source files)
make: *** [Makefile:65: lint] Error 1
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Due to changes in the OpenAI SDK, the previous method of setting the
OpenAI proxy in ChatOpenAI no longer works. This PR fixes this issue,
making the previous way of setting the OpenAI proxy in ChatOpenAI
effective again.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This is a follow up to #18371. These are the changes:
- New **Azure AI Services** toolkit and tools to replace those of
**Azure Cognitive Services**.
- Updated documentation for Microsoft platform.
- The image analysis tool has been rewritten to use the new package
`azure-ai-vision-imageanalysis`, doing a proper replacement of
`azure-ai-vision`.
These changes:
- Update outdated naming from "Azure Cognitive Services" to "Azure AI
Services".
- Update documentation to use non-deprecated methods to create and use
agents.
- Removes need to depend on yanked python package (`azure-ai-vision`)
There is one new dependency that is needed as a replacement to
`azure-ai-vision`:
- `azure-ai-vision-imageanalysis`. This is optional and declared within
a function.
There is a new `azure_ai_services.ipynb` notebook showing usage; Changes
have been linted and formatted.
I am leaving the actions of adding deprecation notices and future
removal of Azure Cognitive Services up to the LangChain team, as I am
not sure what the current practice around this is.
---
If this PR makes it, my handle is @galo@mastodon.social
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description**: `bigdl-llm` library has been renamed to
[`ipex-llm`](https://github.com/intel-analytics/ipex-llm). This PR
migrates the `bigdl-llm` integration to `ipex-llm` .
- **Issue**: N/A. The original PR of `bigdl-llm` is
https://github.com/langchain-ai/langchain/pull/17953
- **Dependencies**: `ipex-llm` library
- **Contribution maintainer**: @shane-huang
Updated doc: docs/docs/integrations/llms/ipex_llm.ipynb
Updated test:
libs/community/tests/integration_tests/llms/test_ipex_llm.py
- **Description:** Add support for Intel Lab's [Visual Data Management
System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store
- **Dependencies:** `vdms` library which requires protobuf = "4.24.2".
There is a conflict with dashvector in `langchain` package but conflict
is resolved in `community`.
- **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe)
- **Added tests:**
libs/community/tests/integration_tests/vectorstores/test_vdms.py
- **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb
- **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
If you use an embedding dist function in an eval loop, you get warned
every time. Would prefer to just check once and forget about it.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- .stream() and .astream() call on_llm_new_token, removing the need for
subclasses to do so. Backwards compatible because now we don't pass
run_manager into ._stream and ._astream
- .generate() and .agenerate() now handle `stream: bool` kwarg for
_generate and _agenerate. Subclasses handle this arg by delegating to
._stream(), now one less thing they need to do. Backwards compat because
this is an optional arg that we now never pass to the subclasses
- .generate() and .agenerate() now inspect callback handlers to decide
on a default value for stream:bool if not passed in. This auto enables
streaming when using astream_events and astream_log
- as a result of these three changes any usage of .astream_events and
.astream_log should now yield chat model stream events
- In future PRs we can update all subclasses to reflect these two things
now handled by base class, but in meantime all will continue to work
* **Description**: add `None` type for `file_path` along with `str` and
`List[str]` types.
* `file_path`/`filename` arguments in `get_elements_from_api()` and
`partition()` can be `None`, however, there's no `None` type hint for
`file_path` in `UnstructuredAPIFileLoader` and `UnstructuredFileLoader`
currently.
* calling the function with `file_path=None` is no problem, but my IDE
annoys me lol.
* **Issue**: N/A
* **Dependencies**: N/A
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Updates Meilisearch vectorstore for compatibility
with v1.6 and above. Adds embedders settings and embedder_name which are
now required.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
This PR adds a slightly more helpful message to a Tool Exception
```
# current state
langchain_core.tools.ToolException: Too many arguments to single-input tool
# proposed state
langchain_core.tools.ToolException: Too many arguments to single-input tool. Consider using a StructuredTool instead.
```
**Issue:** Somewhat discussed here 👉#6197
**Dependencies:** None
**Twitter handle:** N/A
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
As mentioned in #18322, the current PydanticOutputParser won't work for
anyone trying to parse to pydantic v2 models. This PR adds a separate
`PydanticV2OutputParser`, as well as a `langchain_core.pydantic_v2`
namespace that will fail on import to any projects using pydantic<2.
Happy to update the docs for output parsers if this is something we're
interesting in adding.
On a separate note, I also updated `check_pydantic.sh` to detect
pydantic imports with leading whitespace and excluded the internal
namespaces. That change can be separated into its own PR if needed.
---------
Co-authored-by: Jan Nissen <jan23@gmail.com>
Added example to the docstring of the "bind" method of Runnable. This
makes it easier to understand the purpose of the method when reviewing
in code editors. E.g. VS Code below.
<img width="833" alt="Screenshot 2024-03-27 at 16 24 18"
src="https://github.com/langchain-ai/langchain/assets/45722942/ad022d4e-7bc0-4f4b-aa7a-838f1816cc52">
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
PebbloSafeLoader: Add support for non-file-based Document Loaders
This pull request enhances PebbloSafeLoader by introducing support for
several non-file-based Document Loaders. With this update,
PebbloSafeLoader now seamlessly integrates with the following loaders:
- GoogleDriveLoader
- SlackDirectoryLoader
- Unstructured EmailLoader
**Issue:** NA
**Dependencies:** - None
**Twitter handle:** @Raj__725
---------
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Patch potential XML vulnerability CVE-2024-1455
This patches a potential XML vulnerability in the XMLOutputParser in
langchain-core. The vulnerability in some situations could lead to a
denial of service attack.
At risk are users that:
1) Running older distributions of python that have older version of
libexpat
2) Are using XMLOutputParser with an agent
3) Accept inputs from untrusted sources with this agent (e.g., endpoint
on the web that allows an untrusted user to interact wiith the parser)
Introduction
[Intel® Extension for
Transformers](https://github.com/intel/intel-extension-for-transformers)
is an innovative toolkit designed to accelerate GenAI/LLM everywhere
with the optimal performance of Transformer-based models on various
Intel platforms
Description
adding ITREX runtime embeddings using intel-extension-for-transformers.
added mdx documentation and example notebooks
added embedding import testing.
---------
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
