If Session and/or keyspace are not provided, they are resolved from
cassio's context. So they are not required.
This change is fully backward compatible.
Issue: the `langkit` package is not presented in the `pyproject.toml`
but it is a requirement for the `WhyLabsCallbackHandler`
Change: added `langkit`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "langchain-ibm: Add support for ibm-watsonx-ai new
major version"
- [x] **PR message**:
- **Description:** Add support for ibm-watsonx-ai new major version
- **Dependencies:** `ibm_watsonx_ai`
- [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/
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
The `LocalFileStore` class can be used to create an on-disk
`CacheBackedEmbeddings` cache. The number of files in these embeddings
caches can grow to be quite large over time (hundreds of thousands) as
embeddings are computed for new versions of content, but the embeddings
for old/deprecated content are not removed.
A *least-recently-used* (LRU) cache policy could be applied to the
`LocalFileStore` directory to delete cache entries that have not been
referenced for some time:
```bash
# delete files that have not been accessed in the last 90 days
find embeddings_cache_dir/ -atime 90 -print0 | xargs -0 rm
```
However, most filesystems in enterprise environments disable access time
modification on read to improve performance. As a result, the access
times of these cache entry files are not updated when their values are
read.
To resolve this, this pull request updates the `LocalFileStore`
constructor to offer an `update_atime` parameter that causes access
times to be updated when a cache entry is read.
For example,
```python
file_store = LocalFileStore(temp_dir, update_atime=True)
```
The default is `False`, which retains the original behavior.
**Testing:**
I updated the LocalFileStore unit tests to test the access time update.
Before you could only extract triples (diffbot calls it facts) from
diffbot to avoid isolated nodes. However, sometimes isolated nodes can
still be useful like for prefiltering, so we want to allow users to
extract them if they want. Default behaviour is unchanged.
**Description:** Update unit test for ChatAnthropic
**Issue:** Test for key passed in from the environment should not have
the key initialized in the constructor
**Dependencies:** None
Thank you for contributing to LangChain!
- Oracle AI Vector Search
Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
- Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
This Pull Requests Adds the following functionalities
Oracle AI Vector Search : Vector Store
Oracle AI Vector Search : Document Loader
Oracle AI Vector Search : Document Splitter
Oracle AI Vector Search : Summary
Oracle AI Vector Search : Oracle Embeddings
- We have added unit tests and have our own local unit test suite which
verifies all the code is correct. We have made sure to add guides for
each of the components and one end to end guide that shows how the
entire thing runs.
- We have made sure that make format and make lint run clean.
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: skmishraoracle <shailendra.mishra@oracle.com>
Co-authored-by: hroyofc <harichandan.roy@oracle.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Memory return could be set as `str` or `message` by `return_messages`
flag as mentioned in
https://python.langchain.com/docs/modules/memory/#whether-memory-is-a-string-or-a-list-of-messages,
where
`langchain.chains.conversation.memory.ConversationSummaryBufferMemory`
did not implement that.
This commit added `buffer_as_str` and `buffer_as_messages` function, and
`buffer` now affected by `return_messages` flag.
## Example Test Code and Output
```python
# Fix: ConversationSummaryBufferMemory with return_messages flag function
# Test code
from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
from langchain_community.llms.ollama import Ollama
llm = Ollama()
# Create an instance of ConversationSummaryBufferMemory with return_messages set to True
memory = ConversationSummaryBufferMemory(return_messages=True, llm=llm)
# Add user and AI messages to the chat memory
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("what's up?")
# Print the buffer
print("Buffer:")
print(*map(type, memory.buffer), sep="\n")
print(memory.buffer, "\n")
# Print the buffer as a string
print("Buffer as String:")
print(type(memory.buffer_as_str))
print(memory.buffer_as_str, "\n")
# Print the buffer as messages
print("Buffer as Messages:")
print(*map(type, memory.buffer_as_messages), sep="\n")
print(memory.buffer_as_messages, "\n")
# Print the buffer after setting return_messages to False
memory.return_messages = False
print("Buffer after setting return_messages to False:")
print(type(memory.buffer))
print(memory.buffer, "\n")
```
```plaintext
Buffer:
<class 'langchain_core.messages.human.HumanMessage'>
<class 'langchain_core.messages.ai.AIMessage'>
[HumanMessage(content='hi!'), AIMessage(content="what's up?")]
Buffer as String:
<class 'str'>
Human: hi!
AI: what's up?
Buffer as Messages:
<class 'langchain_core.messages.human.HumanMessage'>
<class 'langchain_core.messages.ai.AIMessage'>
[HumanMessage(content='hi!'), AIMessage(content="what's up?")]
Buffer after setting return_messages to False:
<class 'str'>
Human: hi!
AI: what's up?
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Issue: we have several helper functions to import third-party libraries
like tools.gmail.utils.import_google in
[community.tools](https://api.python.langchain.com/en/latest/community_api_reference.html#id37).
And we have core.utils.utils.guard_import that works exactly for this
purpose.
The import_<package> functions work inconsistently and rather be private
functions.
Change: replaced these functions with the guard_import function.
Related to #21133
Issues (nit):
1. `utils.guard_import` prints wrong error message when there is an
import `error.` It prints the whole `module_name` but should be only the
first part as the pip package name. E.i. `langchain_core.utils` -> print
not `langchain-core` but `langchain_core.utils`. Also replace '_' with
'-' in the pip package name.
2. it does not handle the `ModuleNotFoundError` which raised if
`guard_import("wrong_module")`
Fixed issues; added ut-s. Controversial: I've reraised
`ModuleNotFoundError` as `ImportError`, since in case of the error, the
proposed action is the same - we need to install a missed package.
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.
Issue: `load_summarize_chain` is placed in the __init__.py file. As a
result, it doesn't listed in the API Reference docs.
Change: moved code from __init__.py into a new file.
# Newline Characters breaking formatting
**Description**:
As you can see in the image below, the formatting in the documentation
is broken. As far as I can see the two added `\n` characters are
breaking the documentation. Therefore I would propose to remove those
**Dependencies**:
None
**Twitter Handle**
- epu9byj
---------
Co-authored-by: gere <gere@kapo.zh.ch>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**PR message**:
- **Description:** Corrected a syntax error in the code comments within
the `create_tool_calling_agent` function in the langchain package.
- **Issue:** N/A
- **Dependencies:** No additional dependencies required.
- **Twitter handle:** N/A
This PR fixes#21196.
The error was occurring when calling chat completion API with a chat
history. Indeed, the Mistral API does not accept both `content` and
`tool_calls` in the same body.
This PR removes one of theses variables depending on the necessity.
---------
Co-authored-by: Maxime Perrin <mperrin@doing.fr>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
* Introduce individual `fetch_` methods for easier typing.
* Rework some docstrings to google style
* Move some logic to the tool
* Merge the 2 cassandra utility files
- support two-tuples of any sequence type (eg. json.loads never produces
tuples)
- support type alias for role key
- if id is passed in in dict form use it
- if tool_calls passed in in dict form use them
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
https://twitter.com/Rememberizer
**Description:** Add tests to check API keys and Active Directory tokens
are masked
**Issue:** Resolves#12165 for OpenAI and Azure OpenAI models
**Dependencies:** None
Also resolves#12473 which may be closed.
Additional contributors @alex4321 (#12473) and @onesolpark (#12542)
- [ ] **PR message**:
- **Description:** Refactored the lazy_load method to use asynchronous
execution for improved performance. The method now initiates scraping of
all URLs simultaneously using asyncio.gather, enhancing data fetching
efficiency. Each Document object is yielded immediately once its content
becomes available, streamlining the entire process.
- **Issue:** N/A
- **Dependencies:** Requires the asyncio library for handling
asynchronous tasks, which should already be part of standard Python
libraries in Python 3.7 and above.
- **Email:** [r73327118@gmail.com](mailto:r73327118@gmail.com)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Update python.py（experimental：Added code for PythonREPL）
Added code for PythonREPL, defining a static method 'sanitize_input'
that takes the string 'query' as input and returns a sanitizing string.
The purpose of this method is to remove unwanted characters from the
input string, Specifically:
1. Delete the whitespace at the beginning and end of the string (' \s').
2. Remove the quotation marks (`` ` ``) at the beginning and end of the
string.
3. Remove the keyword "python" at the beginning of the string (case
insensitive) because the user may have typed it.
This method uses regular expressions (regex) to implement sanitizing.
It all started with this code：
from langchain.agents import Tool
from langchain_experimental.utilities import PythonREPL
python_repl = PythonREPL()
repl_tool = Tool(
name="python_repl",
description="Remove redundant formatting marks at the beginning and end
of source code from input.Use a Python shell to execute python commands.
If you want to see the output of a value, you should print it out with
`print(...)`.",
func=python_repl.run,
)
When I call the agent to write a piece of code for me and execute it
with the defined code, I must get an error: SyntaxError('invalid
syntax', ('<string>', 1, 1,'In', 1, 2))
After checking, I found that pythonREPL has less formatting of input
code than the soon-to-be deprecated pythonREPL tool, so I added this
step to it, so that no matter what code I ask the agent to write for me,
it can be executed smoothly and get the output result.
I have tried modifying the prompt words to solve this problem before,
but it did not work, and by adding a simple format check, the problem is
well resolved.
<img width="1271" alt="image"
src="https://github.com/langchain-ai/langchain/assets/164149097/c49a685f-d246-4b11-b655-fd952fc2f04c">
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
This pull request updates the Bagel Network package name from
"betabageldb" to "bagelML" to align with the latest changes made by the
Bagel Network team.
The following modifications have been made:
- Updated all references to the old package name ("betabageldb") with
the new package name ("bagelML") throughout the codebase.
- Modified the documentation, and any relevant scripts to reflect the
package name change.
- Tested the changes to ensure that the functionality remains intact and
no breaking changes were introduced.
By merging this pull request, our project will stay up to date with the
latest Bagel Network package naming convention, ensuring compatibility
and smooth integration with their updated library.
Please review the changes and provide any feedback or suggestions. Thank
you!
**Description:** Update UpstageLayoutAnalysisParser and Loader and add
upstage loader example in pdf section
**Dependencies:** langchain_community
**Twitter handle:** [@upstageai](https://twitter.com/upstageai)
- [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.
**Issue:**
Currently `AzureSearch` vector store does not implement `delete` method.
This PR implements it. This also makes it compatible with LangChain
indexer.
**Dependencies:**
None
**Twitter handle:**
@martintriska1
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Upgrades prompts module to use optional imports.
This code was generated with a migration script, but had to be adjusted
manually a bit.
Testing in preparation for applying this code modification across the
rest of the modules in langchain package to reverse the dependency
between langchain community and langchain.
## Summary
No new diagnostics (given that the set of enabled rules hasn't changed),
but gains access to our new parser (much faster) and reduced false
positives all around.
As shown in #13749 , `RecursiveUrlLoader` has encoding issue. This PR is
to solve this.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description:
When attempting to download PDF files from arXiv, an unexpected 404
error frequently occurs. This error halts the operation, regardless of
whether there are additional documents to process. As a solution, I
suggest implementing a mechanism to ignore and communicate this error
and continue processing the next document from the list.
Proposed Solution: To address the issue of unexpected 404 errors during
PDF downloads from arXiv, I propose implementing the following solution:
- Error Handling: Implement error handling mechanisms to catch and
handle 404 errors gracefully.
- Communication: Inform the user or logging system about the occurrence
of the 404 error.
- Continued Processing: After encountering a 404 error, continue
processing the remaining documents from the list without interruption.
This solution ensures that the application can handle unexpected errors
without terminating the entire operation. It promotes resilience and
robustness in the face of intermittent issues encountered during PDF
downloads from arXiv.
### Issue:
#20909
### Dependencies:
none
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## Summary
I ran `ruff check --extend-select RUF100 -n` to identify `# noqa`
comments that weren't having any effect in Ruff, and then `ruff check
--extend-select RUF100 -n --fix` on select files to remove all of the
unnecessary `# noqa: F401` violations. It's possible that these were
needed at some point in the past, but they're not necessary in Ruff
v0.1.15 (used by LangChain) or in the latest release.
Co-authored-by: Erick Friis <erick@langchain.dev>
