From `langchain_community 0.0.30`, there's a bug that cannot send a
file-like object via `file` parameter instead of `file path` due to
casting the `file_path` to str type even if `file_path` is None.
which means that when I call the `partition_via_api()`, exactly one of
`filename` and `file` must be specified by the following error message.
however, from `langchain_community 0.0.30`, `file_path` is casted into
`str` type even `file_path` is None in `get_elements_from_api()` and got
an error at `exactly_one(filename=filename, file=file)`.
here's an error message
```
---> 51 exactly_one(filename=filename, file=file)
53 if metadata_filename and file_filename:
54 raise ValueError(
55 "Only one of metadata_filename and file_filename is specified. "
56 "metadata_filename is preferred. file_filename is marked for deprecation.",
57 )
File /opt/homebrew/lib/python3.11/site-packages/unstructured/partition/common.py:441, in exactly_one(**kwargs)
439 else:
440 message = f"{names[0]} must be specified."
--> 441 raise ValueError(message)
ValueError: Exactly one of filename and file must be specified.
```
So, I simply made a change that casting to str type when `file_path` is
not None.
I use `UnstructuredAPIFileLoader` like below.
```
from langchain_community.document_loaders.unstructured import UnstructuredAPIFileLoader
documents: list = UnstructuredAPIFileLoader(
file_path=None,
file=file, # file-like object, io.BytesIO type
mode='elements',
url='http://127.0.0.1:8000/general/v0/general',
content_type='application/pdf',
metadata_filename='asdf.pdf',
).load_and_split()
```
- [x] **PR title**: "community: improve kuzu cypher generation prompt"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Improves the Kùzu Cypher generation prompt to be more
robust to open source LLM outputs
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** @kuzudb
- [x] **Add tests and docs**: If you're adding a new integration, please
include
No new tests (non-breaking. change)
- [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/
## Description:
The PR introduces 3 changes:
1. added `recursive` property to `O365BaseLoader`. (To keep the behavior
unchanged, by default is set to `False`). When `recursive=True`,
`_load_from_folder()` also recursively loads all nested folders.
2. added `folder_id` to SharePointLoader.(similar to (this
PR)[https://github.com/langchain-ai/langchain/pull/10780] ) This
provides an alternative to `folder_path` that doesn't seem to reliably
work.
3. when none of `document_ids`, `folder_id`, `folder_path` is provided,
the loader fetches documets from root folder. Combined with
`recursive=True` this provides an easy way of loading all compatible
documents from SharePoint.
The PR contains the same logic as [this stale
PR](https://github.com/langchain-ai/langchain/pull/10780) by
@WaleedAlfaris. I'd like to ask his blessing for moving forward with
this one.
## Issue:
- As described in https://github.com/langchain-ai/langchain/issues/19938
and https://github.com/langchain-ai/langchain/pull/10780 the sharepoint
loader often does not seem to work with folder_path.
- Recursive loading of subfolders is a missing functionality
## Dependecies: None
Twitter handle:
@martintriska1 @WRhetoric
This is my first PR here, please be gentle :-)
Please review @baskaryan
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR updates OctoAIEndpoint LLM to subclass BaseOpenAI as OctoAI is
an OpenAI-compatible service. The documentation and tests have also been
updated.
**Description:** Adds ThirdAI NeuralDB retriever integration. NeuralDB
is a CPU-friendly and fine-tunable text retrieval engine. We previously
added a vector store integration but we think that it will be easier for
our customers if they can also find us under under
langchain-community/retrievers.
---------
Co-authored-by: kartikTAI <129414343+kartikTAI@users.noreply.github.com>
Co-authored-by: Kartik Sarangmath <kartik@thirdai.com>
**Description:** Make ChatDatabricks model supports stream
**Issue:** N/A
**Dependencies:** MLflow nightly build version (we will release next
MLflow version soon)
**Twitter handle:** N/A
Manually test:
(Before testing, please install `pip install
git+https://github.com/mlflow/mlflow.git`)
```python
# Test Databricks Foundation LLM model
from langchain.chat_models import ChatDatabricks
chat_model = ChatDatabricks(
endpoint="databricks-llama-2-70b-chat",
max_tokens=500
)
from langchain_core.messages import AIMessageChunk
for chunk in chat_model.stream("What is mlflow?"):
print(chunk.content, end="|")
```
- [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.
---------
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Add conditional: bool property to json representation of the graphs
- Add option to generate mermaid graph stripped of styles (useful as a
text representation of graph)
…s arg too
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:**
This PR adds a callback handler for UpTrain. It performs evaluations in
the RAG pipeline to check the quality of retrieved documents, generated
queries and responses.
- **Dependencies:**
- The UpTrainCallbackHandler requires the uptrain package
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
enviroment variable ANTHROPIC_API_URL will not work if anthropic_api_url
has default value
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
**Description**: Support filter by OR and AND for deprecated PGVector
version
**Issue**: #20445
**Dependencies**: N/A
**Twitter** handle: @martinferenaz
- **Description:**Add Google Firestore Vector store docs
- **Issue:** NA
- **Dependencies:** NA
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **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!
Description: fixes LangChainDeprecationWarning: The class
`langchain_community.embeddings.cohere.CohereEmbeddings` was deprecated
in langchain-community 0.0.30 and will be removed in 0.2.0. An updated
version of the class exists in the langchain-cohere package and should
be used instead. To use it run `pip install -U langchain-cohere` and
import as `from langchain_cohere import CohereEmbeddings`.
Dependencies : langchain_cohere
Twitter handle: @Mo_Noumaan
Description of features on mermaid graph renderer:
- Fixing CDN to use official Mermaid JS CDN:
https://www.jsdelivr.com/package/npm/mermaid?tab=files
- Add device_scale_factor to allow increasing quality of resulting PNG.
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for [DeepInfra]
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in [DeepInfra]
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Description: This update refines the documentation for
`RunnablePassthrough` by removing an unnecessary import and correcting a
minor syntactical error in the example provided. This change enhances
the clarity and correctness of the documentation, ensuring that users
have a more accurate guide to follow.
Issue: N/A
Dependencies: None
This PR focuses solely on documentation improvements, specifically
targeting the `RunnablePassthrough` class within the `langchain_core`
module. By clarifying the example provided in the docstring, users are
offered a more straightforward and error-free guide to utilizing the
`RunnablePassthrough` class effectively.
As this is a documentation update, it does not include changes that
require new integrations, tests, or modifications to dependencies. It
adheres to the guidelines of minimal package interference and backward
compatibility, ensuring that the overall integrity and functionality of
the LangChain package remain unaffected.
Thank you for considering this documentation refinement for inclusion in
the LangChain project.
Fix of YandexGPT embeddings.
The current version uses a single `model_name` for queries and
documents, essentially making the `embed_documents` and `embed_query`
methods the same. Yandex has a different endpoint (`model_uri`) for
encoding documents, see
[this](https://yandex.cloud/en/docs/yandexgpt/concepts/embeddings). The
bug may impact retrievers built with `YandexGPTEmbeddings` (for instance
FAISS database as retriever) since they use both `embed_documents` and
`embed_query`.
A simple snippet to test the behaviour:
```python
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings()
q_emb = embeddings.embed_query('hello world')
doc_emb = embeddings.embed_documents(['hello world', 'hello world'])
q_emb == doc_emb[0]
```
The response is `True` with the current version and `False` with the
changes I made.
Twitter: @egor_krash
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Updates the documentation for Portkey and Langchain.
Also updates the notebook. The current documentation is fairly old and
is non-functional.
**Twitter handle:** @portkeyai
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
`_ListSQLDatabaseToolInput` raise error if model returns `{}`.
For example, gpt-4-turbo returns `{}` with SQL Agent initialized by
`create_sql_agent`.
So, I set default value `""` for `_ListSQLDatabaseToolInput` tool_input.
This is actually a gpt-4-turbo issue, not a LangChain issue, but I
thought it would be helpful to set a default value `""`.
This problem is discussed in detail in the following Issue.
**Issue:** https://github.com/langchain-ai/langchain/issues/20405
**Dependencies:** none
Sorry, I did not add or change the test code, as tests for this
components was not exist .
However, I have tested the following code based on the [SQL Agent
Document](https://python.langchain.com/docs/use_cases/sql/agents/), to
make sure it works.
```
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
agent_executor = create_sql_agent(llm, db=db, agent_type="openai-tools", verbose=True)
result = agent_executor.invoke("List the total sales per country. Which country's customers spent the most?")
print(result["output"])
```
- **Description:** Complete the support for Lua code in
langchain.text_splitter module.
- **Dependencies:** No
- **Twitter handle:** @saberuster
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>
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_groq import ChatGroq
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatGroq(model_name="mixtral-8x7b-32768", temperature=0)
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
```
> Entering new AgentExecutor chain...
Invoking: `magic_function` with `{'input': 3}`
5The value of magic\_function(3) is 5.
> Finished chain.
{'input': 'what is the value of magic_function(3)?',
'output': 'The value of magic\\_function(3) is 5.'}
```
