# Add ToolException that a tool can throw
This is an optional exception that tool throws when execution error
occurs.
When this exception is thrown, the agent will not stop working,but will
handle the exception according to the handle_tool_error variable of the
tool,and the processing result will be returned to the agent as
observation,and printed in pink on the console.It can be used like this:
```python
from langchain.schema import ToolException
from langchain import LLMMathChain, SerpAPIWrapper, OpenAI
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
class Error_tool:
def run(self, s: str):
raise ToolException('The current search tool is not available.')
def handle_tool_error(error) -> str:
return "The following errors occurred during tool execution:"+str(error)
search_tool1 = Error_tool()
search_tool2 = SerpAPIWrapper()
tools = [
Tool.from_function(
func=search_tool1.run,
name="Search_tool1",
description="useful for when you need to answer questions about current events.You should give priority to using it.",
handle_tool_error=handle_tool_error,
),
Tool.from_function(
func=search_tool2.run,
name="Search_tool2",
description="useful for when you need to answer questions about current events",
return_direct=True,
)
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
handle_tool_errors=handle_tool_error)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
## Who can review?
- @vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# docs: ecosystem/integrations update
It is the first in a series of `ecosystem/integrations` updates.
The ecosystem/integrations list is missing many integrations.
I'm adding the missing integrations in a consistent format:
1. description of the integrated system
2. `Installation and Setup` section with 'pip install ...`, Key setup,
and other necessary settings
3. Sections like `LLM`, `Text Embedding Models`, `Chat Models`... with
links to correspondent examples and imports of the used classes.
This PR keeps new docs, that are presented in the
`docs/modules/models/text_embedding/examples` but missed in the
`ecosystem/integrations`. The next PRs will cover the next example
sections.
Also updated `integrations.rst`: added the `Dependencies` section with a
link to the packages used in LangChain.
## Who can review?
@hwchase17
@eyurtsev
@dev2049
# docs: ecosystem/integrations update 2
#5219 - part 1
The second part of this update (parts are independent of each other! no
overlap):
- added diffbot.md
- updated confluence.ipynb; added confluence.md
- updated college_confidential.md
- updated openai.md
- added blackboard.md
- added bilibili.md
- added azure_blob_storage.md
- added azlyrics.md
- added aws_s3.md
## Who can review?
@hwchase17@agola11
@agola11
@vowelparrot
@dev2049
# Implemented appending arbitrary messages to the base chat message
history, the in-memory and cosmos ones.
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As discussed this is the alternative way instead of #4480, with a
add_message method added that takes a BaseMessage as input, so that the
user can control what is in the base message like kwargs.
<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
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@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Removed deprecated llm attribute for load_chain
Currently `load_chain` for some chain types expect `llm` attribute to be
present but `llm` is deprecated attribute for those chains and might not
be persisted during their `chain.save`.
Fixes#5224
[(issue)](https://github.com/hwchase17/langchain/issues/5224)
## Who can review?
@hwchase17
@dev2049
---------
Co-authored-by: imeckr <chandanroutray2012@gmail.com>
# Update llamacpp demonstration notebook
Add instructions to install with BLAS backend, and update the example of
model usage.
Fixes#5071. However, it is more like a prevention of similar issues in
the future, not a fix, since there was no problem in the framework
functionality
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @agola11
# Fix for `update_document` Function in Chroma
## Summary
This pull request addresses an issue with the `update_document` function
in the Chroma class, as described in
[#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947).
The issue was identified as an `AttributeError` raised when calling
`update_document` due to a missing corresponding method in the
`Collection` object. This fix refactors the `update_document` method in
`Chroma` to correctly interact with the `Collection` object.
## Changes
1. Fixed the `update_document` method in the `Chroma` class to correctly
call methods on the `Collection` object.
2. Added the corresponding test `test_chroma_update_document` in
`tests/integration_tests/vectorstores/test_chroma.py` to reflect the
updated method call.
3. Added an example and explanation of how to use the `update_document`
function in the Jupyter notebook tutorial for Chroma.
## Test Plan
All existing tests pass after this change. In addition, the
`test_chroma_update_document` test case now correctly checks the
functionality of `update_document`, ensuring that the function works as
expected and updates the content of documents correctly.
## Reviewers
@dev2049
This fix will ensure that users are able to use the `update_document`
function as expected, without encountering the previous
`AttributeError`. This will enhance the usability and reliability of the
Chroma class for all users.
Thank you for considering this pull request. I look forward to your
feedback and suggestions.
# Add async support for (LLM) routing chains
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<!-- Remove if not applicable -->
Add asynchronous LLM calls support for the routing chains. More
specifically:
- Add async `aroute` function (i.e. async version of `route`) to the
`RouterChain` which calls the routing LLM asynchronously
- Implement the async `_acall` for the `LLMRouterChain`
- Implement the async `_acall` function for `MultiRouteChain` which
first calls asynchronously the routing chain with its new `aroute`
function, and then calls asynchronously the relevant destination chain.
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2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
## Who can review?
- @agola11
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Async
- @agola11
-->
# Fix lost mimetype when using Blob.from_data method
The mimetype is lost due to a typo in the class attribue name
Fixes # - (no issue opened but I can open one if needed)
## Changes
* Fixed typo in name
* Added unit-tests to validate the output Blob
## Review
@eyurtsev
We shouldn't be calling a constructor for a default value - should use
default_factory instead. This is especially ad in this case since it
requires an optional dependency and an API key to be set.
Resolves#5361
# Fix: Handle empty documents in ContextualCompressionRetriever (Issue
#5304)
Fixes#5304
Prevent cohere.error.CohereAPIError caused by an empty list of documents
by adding a condition to check if the input documents list is empty in
the compress_documents method. If the list is empty, return an empty
list immediately, avoiding the error and unnecessary processing.
@dev2049
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add path validation to DirectoryLoader
This PR introduces a minor adjustment to the DirectoryLoader by adding
validation for the path argument. Previously, if the provided path
didn't exist or wasn't a directory, DirectoryLoader would return an
empty document list due to the behavior of the `glob` method. This could
potentially cause confusion for users, as they might expect a
file-loading error instead.
So, I've added two validations to the load method of the
DirectoryLoader:
- Raise a FileNotFoundError if the provided path does not exist
- Raise a ValueError if the provided path is not a directory
Due to the relatively small scope of these changes, a new issue was not
created.
## Before submitting
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1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@eyurtsev
# Remove re-use of iter within add_embeddings causing error
As reported in https://github.com/hwchase17/langchain/issues/5336 there
is an issue currently involving the atempted re-use of an iterator
within the FAISS vectorstore adapter
Fixes # https://github.com/hwchase17/langchain/issues/5336
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
# Add SKLearnVectorStore
This PR adds SKLearnVectorStore, a simply vector store based on
NearestNeighbors implementations in the scikit-learn package. This
provides a simple drop-in vector store implementation with minimal
dependencies (scikit-learn is typically installed in a data scientist /
ml engineer environment). The vector store can be persisted and loaded
from json, bson and parquet format.
SKLearnVectorStore has soft (dynamic) dependency on the scikit-learn,
numpy and pandas packages. Persisting to bson requires the bson package,
persisting to parquet requires the pyarrow package.
## Before submitting
Integration tests are provided under
`tests/integration_tests/vectorstores/test_sklearn.py`
Sample usage notebook is provided under
`docs/modules/indexes/vectorstores/examples/sklear.ipynb`
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Added the ability to pass kwargs to cosmos client constructor
The cosmos client has a ton of options that can be set, so allowing
those to be passed to the constructor from the chat memory constructor
with this PR.
# Sample Notebook for DynamoDB Chat Message History
@dev2049
Adding a sample notebook for the DynamoDB Chat Message History class.
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# remove empty lines in GenerativeAgentMemory that cause
InvalidRequestError in OpenAIEmbeddings
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<!-- Remove if not applicable -->
Let's say the text given to `GenerativeAgent._parse_list` is
```
text = """
Insight 1: <insight 1>
Insight 2: <insight 2>
"""
```
This creates an `openai.error.InvalidRequestError: [''] is not valid
under any of the given schemas - 'input'` because
`GenerativeAgent.add_memory()` tries to add an empty string to the
vectorstore.
This PR fixes the issue by removing the empty line between `Insight 1`
and `Insight 2`
## Before submitting
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1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
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etc:
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-->
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Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
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@hwchase17
@vowelparrot
@dev2049
Fixed the issue of blank Thoughts being printed in verbose when
`handle_parsing_errors=True`, as below:
Before Fix:
```
Observation: There are 38175 accounts available in the dataframe.
Thought:
Observation: Invalid or incomplete response
Thought:
Observation: Invalid or incomplete response
Thought:
```
After Fix:
```
Observation: There are 38175 accounts available in the dataframe.
Thought:AI: {
"action": "Final Answer",
"action_input": "There are 38175 accounts available in the dataframe."
}
Observation: Invalid Action or Action Input format
Thought:AI: {
"action": "Final Answer",
"action_input": "The number of available accounts is 38175."
}
Observation: Invalid Action or Action Input format
```
@vowelparrot currently I have set the colour of thought to green (same
as the colour when `handle_parsing_errors=False`). If you want to change
the colour of this "_Exception" case to red or something else (when
`handle_parsing_errors=True`), feel free to change it in line 789.
# Add Chainlit to deployment options
Add [Chainlit](https://github.com/Chainlit/chainlit) as deployment
options
Used links to Github examples and Chainlit doc on the LangChain
integration
Co-authored-by: Dan Constantini <danconstantini@Dan-Constantini-MacBook.local>
# docs: improve flow of llm caching notebook
The notebook `llm_caching` demos various caching providers. In the
previous version, there was setup common to all examples but under the
`In Memory Caching` heading.
If a user comes and only wants to try a particular example, they will
run the common setup, then the cells for the specific provider they are
interested in. Then they will get import and variable reference errors.
This commit moves the common setup to the top to avoid this.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049
# Add instructions to pyproject.toml
* Add instructions to pyproject.toml about how to handle optional
dependencies.
## Before submitting
## Who can review?
---------
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
# Better docs for weaviate hybrid search
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Fixes: NA
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
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@dev2049
# Fixed passing creds to VertexAI LLM
Fixes #5279
It looks like we should drop a type annotation for Credentials.
Co-authored-by: Leonid Kuligin <kuligin@google.com>
# Update contribution guidelines and PR template
This PR updates the contribution guidelines to include more information
on how to handle optional dependencies.
The PR template is updated to include a link to the contribution guidelines document.
# Add example to LLMMath to help with power operator
Add example to LLMMath that helps the model to interpret `^` as the power operator rather than the python xor operator.
This PR adds LLM wrapper for Databricks. It supports two endpoint types:
* serving endpoint
* cluster driver proxy app
An integration notebook is included to show how it works.
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fixed typo: 'ouput' to 'output' in all documentation
In this instance, the typo 'ouput' was amended to 'output' in all
occurrences within the documentation. There are no dependencies required
for this change.
# Add Momento as a standard cache and chat message history provider
This PR adds Momento as a standard caching provider. Implements the
interface, adds integration tests, and documentation. We also add
Momento as a chat history message provider along with integration tests,
and documentation.
[Momento](https://www.gomomento.com/) is a fully serverless cache.
Similar to S3 or DynamoDB, it requires zero configuration,
infrastructure management, and is instantly available. Users sign up for
free and get 50GB of data in/out for free every month.
## Before submitting
✅ We have added documentation, notebooks, and integration tests
demonstrating usage.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Your PR Title (What it does)
Adding an if statement to deal with bigquery sql dialect. When I use
bigquery dialect before, it failed while using SET search_path TO. So
added a condition to set dataset as the schema parameter which is
equivalent to SET search_path TO . I have tested and it works.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@dev2049
The current `HuggingFacePipeline.from_model_id` does not allow passing
of pipeline arguments to the transformer pipeline.
This PR enables adding important pipeline parameters like setting
`max_new_tokens` for example.
Previous to this PR it would be necessary to manually create the
pipeline through huggingface transformers then handing it to langchain.
For example instead of this
```py
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
```
You can write this
```py
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}
)
```
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
