core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]
```python
class ToolCall(TypedDict):
name: str
args: Dict[str, Any]
id: Optional[str]
class InvalidToolCall(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
error: Optional[str]
class ToolCallChunk(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
index: Optional[int]
class AIMessage(BaseMessage):
...
tool_calls: List[ToolCall] = []
invalid_tool_calls: List[InvalidToolCall] = []
...
class AIMessageChunk(AIMessage, BaseMessageChunk):
...
tool_call_chunks: Optional[List[ToolCallChunk]] = None
...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
- additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).
Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: Add semantic caching and memory using
MongoDB"
- [ ] **PR message**:
- **Description:** This PR introduces functionality for adding semantic
caching and chat message history using MongoDB in RAG applications. By
leveraging the MongoDBCache and MongoDBChatMessageHistory classes,
developers can now enhance their retrieval-augmented generation
applications with efficient semantic caching mechanisms and persistent
conversation histories, improving response times and consistency across
chat sessions.
- **Issue:** N/A
- **Dependencies:** Requires `datasets`, `langchain`,
`langchain-mongodb`, `langchain-openai`, `pymongo`, and `pandas` for
implementation. MongoDB Atlas is used for database services, and the
OpenAI API for model access.
- **Twitter handle:** @richmondalake
Co-authored-by: Erick Friis <erick@langchain.dev>
Last year Microsoft [changed the
name](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
of Azure Cognitive Search to Azure AI Search. This PR updates the
Langchain Azure Retriever API and it's associated docs to reflect this
change. It may be confusing for users to see the name Cognitive here and
AI in the Microsoft documentation which is why this is needed. I've also
added a more detailed example to the Azure retriever doc page.
There are more places that need a similar update but I'm breaking it up
so the PRs are not too big 😄 Fixing my errors from the previous PR.
Twitter: @marlene_zw
Two new tests added to test backward compatibility in
`libs/community/tests/integration_tests/retrievers/test_azure_cognitive_search.py`
---------
Co-authored-by: Chester Curme <chester.curme@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>
This patch updates multiple function "run" to "invoke" in
llm_symbolic_math.ipynb.
Without this patch, you see following message.
The function `run` was deprecated in LangChain 0.1.0
and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
- **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>
Thank you for contributing to LangChain!
- [ ] **cookbook** - update example for SalesGPT - include Stripe
Payment Link Generation
- **Description:** We updated the Jupyter notebook example with the
ability of the AI Agent to negotiate with customers and then close the
deal by generating a custom Stripe payment link.
- **Issue:** N/A
- **Dependencies:** N/a
- **Twitter handle:** @FilipMichalsky @0xtotaylor
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Filip Michalsky <filip_michalsky@g.harvard.edu>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Update Azure Document Intelligence implementation by
Microsoft team and RAG cookbook with Azure AI Search
---------
Co-authored-by: Lu Zhang (AI) <luzhan@microsoft.com>
Co-authored-by: Yateng Hong <yatengh@microsoft.com>
Co-authored-by: teethache <hongyateng2006@126.com>
Co-authored-by: Lu Zhang <44625949+luzhang06@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This patch updates function "run" to "invoke" in fake_llm.ipynb. Without
this patch, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
**Description:** Update stales link in Together AI documentation
**Issue:** Some links pointed to legacy webpages on the Together AI
website
**Dependencies:** None
**Lint and test**: `make format`, `make lint` were run
This PR is a successor to this PR -
https://github.com/langchain-ai/langchain/pull/17436
This PR updates the cookbook README with the notebook so that it is
available on langchain docs for discoverability.
cc: @baskaryan, @3coins
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This path updates function "run" to "invoke" in llm_bash.ipynb.
Without this path, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0
and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Follow up on https://github.com/langchain-ai/langchain/pull/17467.
- Update all references to the Elasticsearch classes to use the partners
package.
- Deprecate community classes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This patch updates function "run" to "invoke" in smart_llm.ipynb.
Without this patch, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Currently llm_checker.ipynb uses a function "run".
Update to "invoke" to avoid following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0
and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
This patch updates function "run" to "invoke".
Without this patch you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
- [ ] **PR title**: "cookbook: using Gemma on LangChain"
- [ ] **PR message**:
- **Description:** added a tutorial how to use Gemma with LangChain
(from VertexAI or locally from Kaggle or HF)
- **Dependencies:** langchain-google-vertexai==0.0.7
- **Twitter handle:** lkuligin
### Description
This PR moves the Elasticsearch classes to a partners package.
Note that we will not move (and later remove) `ElasticKnnSearch`. It
were previously deprecated.
`ElasticVectorSearch` is going to stay in the community package since it
is used quite a lot still.
Also note that I left the `ElasticsearchTranslator` for self query
untouched because it resides in main `langchain` package.
### Dependencies
There will be another PR that updates the notebooks (potentially pulling
them into the partners package) and templates and removes the classes
from the community package, see
https://github.com/langchain-ai/langchain/pull/17468
#### Open question
How to make the transition smooth for users? Do we move the import
aliases and require people to install `langchain-elasticsearch`? Or do
we remove the import aliases from the `langchain` package all together?
What has worked well for other partner packages?
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
## Amazon Personalize support on Langchain
This PR is a successor to this PR -
https://github.com/langchain-ai/langchain/pull/13216
This PR introduces an integration with [Amazon
Personalize](https://aws.amazon.com/personalize/) to help you to
retrieve recommendations and use them in your natural language
applications. This integration provides two new components:
1. An `AmazonPersonalize` client, that provides a wrapper around the
Amazon Personalize API.
2. An `AmazonPersonalizeChain`, that provides a chain to pull in
recommendations using the client, and then generating the response in
natural language.
We have added this to langchain_experimental since there was feedback
from the previous PR about having this support in experimental rather
than the core or community extensions.
Here is some sample code to explain the usage.
```python
from langchain_experimental.recommenders import AmazonPersonalize
from langchain_experimental.recommenders import AmazonPersonalizeChain
from langchain.llms.bedrock import Bedrock
recommender_arn = "<insert_arn>"
client=AmazonPersonalize(
credentials_profile_name="default",
region_name="us-west-2",
recommender_arn=recommender_arn
)
bedrock_llm = Bedrock(
model_id="anthropic.claude-v2",
region_name="us-west-2"
)
chain = AmazonPersonalizeChain.from_llm(
llm=bedrock_llm,
client=client
)
response = chain({'user_id': '1'})
```
Reviewer: @3coins
Adding a new notebook that demonstrates how to use LangChain's standard
chat features while passing the chat messages back and forth via Apache
Kafka.
This goal is to simulate an architecture where the chat front end and
the LLM are running as separate services that need to communicate with
one another over an internal nework.
It's an alternative to typical pattern of requesting a reponse from the
model via a REST API (there's more info on why you would want to do this
at the end of the notebook).
NOTE: Assuming "uses cases" is the right place for this but feel free to
propose another location.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
<!-- Thank you for contributing to LangChain!
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whichever of langchain, community, core, experimental, etc. is being
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
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Please make sure your PR is passing linting and testing before
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See contribution guidelines for more information on how to write/run
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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
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Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI
Embeddings from `langchain_openai`
There are likely more
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
