Fix: Change _call_eas to return bytes instead of str #4
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) Thank you for contributing to LangChain! **Description** The current code snippet for `Fireworks` had incorrect parameters. This PR fixes those parameters. --------- Co-authored-by: Chester Curme <[email protected]> Co-authored-by: Bagatur <[email protected]>
) - **Description:** Add optional max_messages to MessagePlaceholder - **Issue:** [16096](langchain-ai#16096) - **Dependencies:** None - **Twitter handle:** @DaveDeCaprio Sometimes it's better to limit the history in the prompt itself rather than the memory. This is needed if you want different prompts in the chain to have different history lengths. --------- Co-authored-by: Harrison Chase <[email protected]>
fix bug The ZhipuAIEmbeddings class is not working. Co-authored-by: xu yandong <[email protected]>
Added missed docstrings. Format docstrings to the consistent format (used in the API Reference) Co-authored-by: ccurme <[email protected]>
…with pip installs of required langchain_chroma and langchain_community. (langchain-ai#23204) Description: Update Rag tutorial notebook so it includes an additional notebook cell with pip installs of required langchain_chroma and langchain_community. This fixes the issue with the rag tutorial gives you a 'missing modules' error if you run code in the notebook as is. --------- Co-authored-by: Chester Curme <[email protected]>
- [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/
…angchain-ai#22877) Currently, calling `with_structured_output()` with an invalid method argument raises `Unrecognized method argument. Expected one of 'function_calling' or 'json_format'`, but the JSON mode option [is now referred to](https://python.langchain.com/v0.2/docs/how_to/structured_output/#the-with_structured_output-method) by `'json_mode'`. This fixes that. Co-authored-by: Harrison Chase <[email protected]>
Added missed docstrings. Format docstrings to the consistent format (used in the API Reference)
- Fix bug with TypedDicts rendering inherited methods if inherting from typing_extensions.TypedDict rather than typing.TypedDict - Do not surface inherited pydantic methods for subclasses of BaseModel - Subclasses of RunnableSerializable will not how methods inherited from Runnable or from BaseModel - Subclasses of Runnable that not pydantic models will include a link to RunnableInterface (they still show inherited methods, we can fix this later)
These do not work anyway. Let's remove them for now for simplicity.
…hain-ai#13200) **Description:** langchain offers three technologies to save data: - [vectorstore](https://python.langchain.com/docs/modules/data_connection/vectorstores/) - [docstore](https://js.langchain.com/docs/api/schema/classes/Docstore) - [record manager](https://python.langchain.com/docs/modules/data_connection/indexing) If you want to combine these technologies in a sample persistence stategy you need a common implementation for each. `DocStore` propose `InMemoryDocstore`. We propose the class `MemoryRecordManager` to complete the system. This is the prelude to another full-request, which needs a consistent combination of persistence components. **Tag maintainer:** @baskaryan **Twitter handle:** @pprados --------- Co-authored-by: Eugene Yurtsev <[email protected]>
…#21559) - description: Add encoding parameters. - @baskaryan, @efriis, @eyurtsev, @hwchase17. 
The return type of `json.loads` is `Any`. In fact, the return type of `dumpd` must be based on `json.loads`, so the correction here is understandable. Co-authored-by: Harrison Chase <[email protected]>
…anfanChatEndpoint, MiniMaxChat, ChatSparkLLM, ChatZhipuAI (langchain-ai#22853) This PR updates root validators for: - ChatModels: ChatBaichuan, QianfanChatEndpoint, MiniMaxChat, ChatSparkLLM, ChatZhipuAI Issues langchain-ai#22819 --------- Co-authored-by: Eugene Yurtsev <[email protected]>
…gchain-ai#23227) Add streaming clarification to runnable lambda docstring.
….2 (langchain-ai#22094) This change updates the requirements in `libs/partners/pinecone/pyproject.toml` to allow all versions of `pinecone-client` greater than or equal to 3.2.2. This change resolves issue [21955](langchain-ai#21955). --------- Co-authored-by: Erick Friis <[email protected]> Co-authored-by: Bagatur <[email protected]>
… Job (langchain-ai#23224) **Desscription**: When the ``sql_database.from_databricks`` is executed from a Workflow Job, the ``context`` object does not have a "browserHostName" property, resulting in an error. This change manages the error so the "DATABRICKS_HOST" env variable value is used instead of stoping the flow Co-authored-by: lmorosdb <lmorosdb>
Code blocks need extra space around them to be rendered properly by sphinx
…import error quick fix. (langchain-ai#22725) minor changes to module import error handling and minor issues in tutorial documents. --------- Co-authored-by: Bagatur <[email protected]> Co-authored-by: Eugene Yurtsev <[email protected]> Co-authored-by: Eugene Yurtsev <[email protected]>
rst table not md 
Including streaming
If we're moving documenting inherited params think these kinds of tables become more important 
…n-ai#23214) - **Description:** AsyncRootListenersTracer support on_chat_model_start, it's schema_format should be "original+chat". - **Issue:** N/A - **Dependencies:**
Added missed docstrings. Formatted docstrings to the consistent form.
Added missed docstrings. Formatted docstrings to the consistent form.
- **Description:** Add interface to `ChatBaichuan` to support
asynchronous requests
- `_agenerate` method
- `_astream` method
---------
Co-authored-by: ccurme <[email protected]>
This PR includes fix for DeepInfra tool call parsing.
Use pydantic to infer nested schemas and all that fun. Include bagatur's convenient docstring parser Include annotation support Previously we didn't adequately support many typehints in the bind_tools() method on raw functions (like optionals/unions, nested types, etc.)
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
…23819) enviroment -> environment - [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"
…angchain-ai#23818) The mongdb have some errors. - `add_texts() -> List` returns a list of `ObjectId`, and not a list of string - `delete()` with `id` never remove chunks. --------- Co-authored-by: Eugene Yurtsev <[email protected]>
Added missed docstrings. Formatted docstrings to the consistent form.
…order to format an `ImagePromptTemplate` (langchain-ai#22613) - Description: Add support for `path` and `detail` keys in `ImagePromptTemplate`. Previously, only variables associated with the `url` key were considered. This PR allows for the inclusion of a local image path and a detail parameter as input to the format method. - Issues: - fixes langchain-ai#20820 - related to langchain-ai#22024 - Dependencies: None - Twitter handle: @DeschampsTho5 --------- Co-authored-by: tdeschamps <[email protected]> Co-authored-by: Eugene Yurtsev <[email protected]> Co-authored-by: Eugene Yurtsev <[email protected]>
to correctly display the link Co-authored-by: Mu Xianming <[email protected]>
**Description**: The ``declarative_base()`` function is now available as sqlalchemy.orm.declarative_base(). (depreca ted since: 2.0) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9) --------- Co-authored-by: Isaac Francisco <[email protected]> Co-authored-by: isaac hershenson <[email protected]>
Speed up unit tests for imports
… that is used as a key for caching chat models responses (langchain-ai#23842) This PR should fix the following issue: langchain-ai#23824 Introduced as part of this PR: langchain-ai#23416 I am unable to reproduce the issue locally though it's clear that we're getting a `serialized` object which is not a dictionary somehow. The test below passes for me prior to the PR as well ```python def test_cache_with_sqllite() -> None: from langchain_community.cache import SQLiteCache from langchain_core.globals import set_llm_cache cache = SQLiteCache(database_path=".langchain.db") set_llm_cache(cache) chat_model = FakeListChatModel(responses=["hello", "goodbye"], cache=True) assert chat_model.invoke("How are you?").content == "hello" assert chat_model.invoke("How are you?").content == "hello" ```
Update former pull request: langchain-ai#22595. Modified `libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py`, where the API call function does not match current [Text Embeddings Inference API](https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/embed). One example is: ```json { "inputs": "string", "normalize": true, "truncate": false } ``` Parameters in `_model_kwargs` are not passed properly in the latest version. By the way, the issue *[why cause 413? langchain-ai#50](huggingface/text-embeddings-inference#50 might be solved.
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This pull request addresses issue langchain-ai#20453 by changing the return type of the
_call_easmethod in thePaiEasChatEndpointclass to returnbytesinstead ofstr.