Added to `docs/how_to/tools_runtime` as a proof of concept, will apply
everywhere if we like.
A bit more compact than the default callouts, will help standardize the
layout of our pages since we frequently use these boxes.
<img width="1088" alt="Screenshot 2024-07-23 at 4 49 02 PM"
src="https://github.com/user-attachments/assets/7380801c-e092-4d31-bcd8-3652ee05f29e">
- **Description:** The UnstructuredClient will have a breaking change in
the near future. Add a note in the docs that the examples here may not
use the latest version and users should refer to the SDK docs for the
latest info.
Thank you for contributing to LangChain!
- [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"
- **Description:**
Support ChatMlflow.bind_tools method
Tested in Databricks:
<img width="836" alt="image"
src="https://github.com/user-attachments/assets/fa28ef50-0110-4698-8eda-4faf6f0b9ef8">
- [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, ccurme, vbarda, hwchase17.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
- **Description:** When adding docs for constructing ChatHuggingFace
using a HuggingFacePipeline, I forgot to add `return_full_text=False` as
an argument. In this setup, the chat response would incorrectly contain
all the input text. I am fixing that here by adding that line to the
offending notebook.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
instead of hardcoding a linter for each, iterate through the lines of
the template notebook and find lines that start with `##` (includes
lower headings), and enforce that those headings are found in new docs
that are contributed
supports following UX
```python
class SubTool(TypedDict):
"""Subtool docstring"""
args: Annotated[Dict[str, Any], {}, "this does bar"]
class Tool(TypedDict):
"""Docstring
Args:
arg1: foo
"""
arg1: str
arg2: Union[int, str]
arg3: Optional[List[SubTool]]
arg4: Annotated[Literal["bar", "baz"], ..., "this does foo"]
arg5: Annotated[Optional[float], None]
```
- can parse google style docstring
- can use Annotated to specify default value (second arg)
- can use Annotated to specify arg description (third arg)
- can have nested complex types
**Description:** Updated the Langgraph migration docs to use
`state_modifier` rather than `messages_modifier`
**Issue:** N/A
**Dependencies:** N/A
- [ 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: Chester Curme <chester.curme@gmail.com>
## Description
This PR:
- Fixes the validation error in `FastEmbedEmbeddings`.
- Adds support for `batch_size`, `parallel` params.
- Removes support for very old FastEmbed versions.
- Updates the FastEmbed doc with the new params.
Associated Issues:
- Resolves#24039
- Resolves #https://github.com/qdrant/fastembed/issues/296
Thank you for contributing to LangChain!
- [x] **PR title**: "Add documentaiton on InMemoryVectorStore driver for
MemoryDB to langchain-aws"
- Langchain-aws repo :Add MemoryDB documentation
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Added documentation on InMemoryVectorStore driver to
aws.mdx and usage example on MemoryDB clusuter
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: If you're adding a new integration, please
include
Add memorydb notebook to docs/docs/integrations/ folde
- [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, ccurme, vbarda, hwchase17.
Thank you for contributing to LangChain!
- [x] **PR title**: "community:add Yi LLM", "docs:add Yi Documentation"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** This PR adds support for the Yi model to LangChain.
- **Dependencies:**
[langchain_core,requests,contextlib,typing,logging,json,langchain_community]
- **Twitter handle:** 01.AI
- [x] **Add tests and docs**: I've added the corresponding documentation
to the relevant paths
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Description:
- This PR adds a self query retriever implementation for SAP HANA Cloud
Vector Engine. The retriever supports all operators except for contains.
- Issue: N/A
- Dependencies: no new dependencies added
**Add tests and docs:**
Added integration tests to:
libs/community/tests/unit_tests/query_constructors/test_hanavector.py
**Documentation for self query retriever:**
/docs/integrations/retrievers/self_query/hanavector_self_query.ipynb
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- [x] **PR title**:
community: Add OCI Generative AI tool and structured output support
- [x] **PR message**:
- **Description:** adding tool calling and structured output support for
chat models offered by OCI Generative AI services. This is an update to
our last PR 22880 with changes in
/langchain_community/chat_models/oci_generative_ai.py
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
- [x] **Add tests and docs**:
1. we have updated our unit tests
2. we have updated our documentation under
/docs/docs/integrations/chat/oci_generative_ai.ipynb
- [x] **Lint and test**: `make format`, `make lint` and `make test` we
run successfully
---------
Co-authored-by: RHARPAZ <RHARPAZ@RHARPAZ-5750.us.oracle.com>
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
**Description:**
- This PR exposes some functions in VDMS vectorstore, updates VDMS
related notebooks, updates tests, and upgrade version of VDMS (>=0.0.20)
**Issue:** N/A
**Dependencies:**
- Update vdms>=0.0.20
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, ccurme, vbarda, hwchase17.
Lots of duplicated content from concepts, missing pointers to the second
half of the tool calling loop
Simpler + more focused + a more prominent link to the second half of the
loop was what I was aiming for, but down to be more conservative and
just more prominently link the "passing tools back to the model" guide.
I have also moved the tool calling conceptual guide out from under
`Structured Output` (while leaving a small section for structured
output-specific information) and added more content. The existing
`#functiontool-calling` link will go to this new section.
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, ccurme, vbarda, hwchase17.
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, ccurme, vbarda, hwchase17.
Added [ScrapingAnt](https://scrapingant.com/) Web Loader integration.
ScrapingAnt is a web scraping API that allows extracting web page data
into accessible and well-formatted markdown.
Description: Added ScrapingAnt web loader for retrieving web page data
as markdown
Dependencies: scrapingant-client
Twitter: @WeRunTheWorld3
---------
Co-authored-by: Oleg Kulyk <oleg@scrapingant.com>
#### Update (2):
A single `UnstructuredLoader` is added to handle both local and api
partitioning. This loader also handles single or multiple documents.
#### Changes in `community`:
Changes here do not affect users. In the initial process of using the
SDK for the API Loaders, the Loaders in community were refactored.
Other changes include:
The `UnstructuredBaseLoader` has a new check to see if both
`mode="paged"` and `chunking_strategy="by_page"`. It also now has
`Element.element_id` added to the `Document.metadata`.
`UnstructuredAPIFileLoader` and `UnstructuredAPIFileIOLoader`. As such,
now both directly inherit from `UnstructuredBaseLoader` and initialize
their `file_path`/`file` attributes respectively and implement their own
`_post_process_elements` methods.
--------
#### Update:
New SDK Loaders in a [partner
package](https://python.langchain.com/v0.1/docs/contributing/integrations/#partner-package-in-langchain-repo)
are introduced to prevent breaking changes for users (see discussion
below).
##### TODO:
- [x] Test docstring examples
--------
- **Description:** UnstructuredAPIFileIOLoader and
UnstructuredAPIFileLoader calls to the unstructured api are now made
using the unstructured-client sdk.
- **New Dependencies:** unstructured-client
- [x] **Add tests and docs**: If you're adding a new integration, please
include
- [x] a test for the integration, preferably unit tests that do not rely
on network access,
- [x] update the description in
`docs/docs/integrations/providers/unstructured.mdx`
- [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.
TODO:
- [x] Update
https://python.langchain.com/v0.1/docs/integrations/document_loaders/unstructured_file/#unstructured-api
-
`langchain/docs/docs/integrations/document_loaders/unstructured_file.ipynb`
- The description here needs to indicate that users should install
`unstructured-client` instead of `unstructured`. Read over closely to
look for any other changes that need to be made.
- [x] Update the `lazy_load` method in `UnstructuredBaseLoader` to
handle json responses from the API instead of just lists of elements.
- This method may need to be overwritten by the API loaders instead of
changing it in the `UnstructuredBaseLoader`.
- [x] Update the documentation links in the class docstrings (the
Unstructured documents have moved)
- [x] Update Document.metadata to include `element_id` (see thread
[here](https://unstructuredw-kbe4326.slack.com/archives/C044N0YV08G/p1718187499818419))
---------
Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: ChengZi <chen.zhang@zilliz.com>
This PR is under WIP and adds the following functionalities:
- [X] Supports tool calling across the langchain ecosystem. (However
streaming is not supported)
- [X] Update documentation
- [ ] **Community**: "Retrievers: Product Quantization"
- [X] This PR adds Product Quantization feature to the retrievers to the
Langchain Community. PQ is one of the fastest retrieval methods if the
embeddings are rich enough in context due to the concepts of
quantization and representation through centroids
- **Description:** Adding PQ as one of the retrievers
- **Dependencies:** using the package nanopq for this PR
- **Twitter handle:** vishnunkumar_
- [X] **Add tests and docs**: If you're adding a new integration, please
include
- [X] Added unit tests for the same in the retrievers.
- [] Will add an example notebook subsequently
- [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/ -
done the same
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: Update IBM docs about information to pass client
into WatsonxLLM and WatsonxEmbeddings object.
- [x] **PR message**:
- **Description:** Update IBM docs about information to pass client into
WatsonxLLM and WatsonxEmbeddings object.
- [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**
Add support for Pinecone hosted embedding models as
`PineconeEmbeddings`. Replacement for #22890
**Dependencies**
Add `aiohttp` to support async embeddings call against REST directly
- [x] **Add tests and docs**: If you're adding a new integration, please
include
Added `docs/docs/integrations/text_embedding/pinecone.ipynb`
- [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/
Twitter: `gdjdg17`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
### Description
This pull request added new document loaders to load documents of
various formats using [Dedoc](https://github.com/ispras/dedoc):
- `DedocFileLoader` (determine file types automatically and parse)
- `DedocPDFLoader` (for `PDF` and images parsing)
- `DedocAPIFileLoader` (determine file types automatically and parse
using Dedoc API without library installation)
[Dedoc](https://dedoc.readthedocs.io) is an open-source library/service
that extracts texts, tables, attached files and document structure
(e.g., titles, list items, etc.) from files of various formats. The
library is actively developed and maintained by a group of developers.
`Dedoc` supports `DOCX`, `XLSX`, `PPTX`, `EML`, `HTML`, `PDF`, images
and more.
Full list of supported formats can be found
[here](https://dedoc.readthedocs.io/en/latest/#id1).
For `PDF` documents, `Dedoc` allows to determine textual layer
correctness and split the document into paragraphs.
### Issue
This pull request extends variety of document loaders supported by
`langchain_community` allowing users to choose the most suitable option
for raw documents parsing.
### Dependencies
The PR added a new (optional) dependency `dedoc>=2.2.5` ([library
documentation](https://dedoc.readthedocs.io)) to the
`extended_testing_deps.txt`
### Twitter handle
None
### Add tests and docs
1. Test for the integration:
`libs/community/tests/integration_tests/document_loaders/test_dedoc.py`
2. Example notebook:
`docs/docs/integrations/document_loaders/dedoc.ipynb`
3. Information about the library:
`docs/docs/integrations/providers/dedoc.mdx`
### Lint and test
Done locally:
- `make format`
- `make lint`
- `make integration_tests`
- `make docs_build` (from the project root)
---------
Co-authored-by: Nasty <bogatenkova.anastasiya@mail.ru>
https://www.youtube.com/watch?v=ZIyB9e_7a4c
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, ccurme, vbarda, hwchase17.
**Description:** Fixes typo `Le'ts` -> `Let's`.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:**
**TextEmbed** is a high-performance embedding inference server designed
to provide a high-throughput, low-latency solution for serving
embeddings. It supports various sentence-transformer models and includes
the ability to deploy image and text embedding models. TextEmbed offers
flexibility and scalability for diverse applications.
- **PyPI Package:** [TextEmbed on
PyPI](https://pypi.org/project/textembed/)
- **Docker Image:** [TextEmbed on Docker
Hub](https://hub.docker.com/r/kevaldekivadiya/textembed)
- **GitHub Repository:** [TextEmbed on
GitHub](https://github.com/kevaldekivadiya2415/textembed)
**PR Description**
This PR adds functionality for embedding documents and queries using the
`TextEmbedEmbeddings` class. The implementation allows for both
synchronous and asynchronous embedding requests to a TextEmbed API
endpoint. The class handles batching and permuting of input texts to
optimize the embedding process.
**Example Usage:**
```python
from langchain_community.embeddings import TextEmbedEmbeddings
# Initialise the embeddings class
embeddings = TextEmbedEmbeddings(model="your-model-id", api_key="your-api-key", api_url="your_api_url")
# Define a list of documents
documents = [
"Data science involves extracting insights from data.",
"Artificial intelligence is transforming various industries.",
"Cloud computing provides scalable computing resources over the internet.",
"Big data analytics helps in understanding large datasets.",
"India has a diverse cultural heritage."
]
# Define a query
query = "What is the cultural heritage of India?"
# Embed all documents
document_embeddings = embeddings.embed_documents(documents)
# Embed the query
query_embedding = embeddings.embed_query(query)
# Print embeddings for each document
for i, embedding in enumerate(document_embeddings):
print(f"Document {i+1} Embedding:", embedding)
# Print the query embedding
print("Query Embedding:", query_embedding)
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Fix MultiQueryRetriever breaking Embeddings with empty lines
```
[chain/end] [1:chain:ConversationalRetrievalChain > 2:retriever:Retriever > 3:retriever:Retriever > 4:chain:LLMChain] [2.03s] Exiting Chain run with output:
[outputs]
> /workspaces/Sfeir/sncf/metabot-backend/.venv/lib/python3.11/site-packages/langchain/retrievers/multi_query.py(116)_aget_relevant_documents()
-> if self.include_original:
(Pdb) queries
['## Alternative questions for "Hello, tell me about phones?":', '', '1. **What are the latest trends in smartphone technology?** (Focuses on recent advancements)', '2. **How has the mobile phone industry evolved over the years?** (Historical perspective)', '3. **What are the different types of phones available in the market, and which one is best for me?** (Categorization and recommendation)']
```
Example of failure on VertexAIEmbeddings
```
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INVALID_ARGUMENT
details = "The text content is empty."
debug_error_string = "UNKNOWN:Error received from peer ipv4:142.250.184.234:443 {created_time:"2024-04-30T09:57:45.625698408+00:00", grpc_status:3, grpc_message:"The text content is empty."}"
```
Fixes: #15959
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Add Riza Python/JS code execution tool
- **Issue:** N/A
- **Dependencies:** an optional dependency on the `rizaio` pypi package
- **Twitter handle:** [@rizaio](https://x.com/rizaio)
[Riza](https://riza.io) is a safe code execution environment for
agent-generated Python and JavaScript that's easy to integrate into
langchain apps. This PR adds two new tool classes to the community
package.
This PR updates docs to mention correct version of the
`langchain-openai` package required to use the `stream_usage` parameter.
As it can be noticed in the details of this [merge
commit](722c8f50ea),
that functionality is available only in `langchain-openai >= 0.1.9`
while docs state it's available in `langchain-openai >= 0.1.8`.
Description: added support for LangChain v0.2 for nvidia ai endpoint.
Implremented inMemory storage for chains using
RunnableWithMessageHistory which is analogous to using
`ConversationChain` which was used in v0.1 with the default
`ConversationBufferMemory`. This class is deprecated in favor of
`RunnableWithMessageHistory` in LangChain v0.2
Issue: None
Dependencies: None.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Adds MongoDBAtlasVectorSearch to list of VectorStores compatible with
the Indexing API.
(One line change.)
As of `langchain-mongodb = "0.1.7"`, the requirements that the
VectorStore have both add_documents and delete methods with an ids kwarg
is satisfied. #23535 contains the implementation of that, and has been
merged.
**Description:** : Add support for chat message history using Couchbase
- [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: Nithish Raghunandanan <nithishr@users.noreply.github.com>
Description: added support for LangChain v0.2 for PipelineAI
integration. Removed deprecated classes and incorporated support for
LangChain v0.2 to integrate with PipelineAI. Removed LLMChain and
replaced it with Runnable interface. Also added StrOutputParser, that
parses LLMResult into the top likely string.
Issue: None
Dependencies: None.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: Added support for langchain v0.2 for shale protocol.
Replaced LLMChain with Runnable interface which allows any two Runnables
to be 'chained' together into sequences. Also added
StreamingStdOutCallbackHandler. Callback handler for streaming.
Issue: None
Dependencies: None.
Thank you for contributing to LangChain!
- [X] *ApertureDB as vectorstore**: "community: Add ApertureDB as a
vectorestore"
- **Description:** this change provides a new community integration that
uses ApertureData's ApertureDB as a vector store.
- **Issue:** none
- **Dependencies:** depends on ApertureDB Python SDK
- **Twitter handle:** ApertureData
- [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.
Integration tests rely on a local run of a public docker image.
Example notebook additionally relies on a local Ollama server.
- [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/
All lint tests pass.
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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Gautam <gautam@aperturedata.io>
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, ccurme, vbarda, hwchase17.
**Description:**
Databricks Vector Search recently added support for hybrid
keyword-similarity search.
See [usage
examples](https://docs.databricks.com/en/generative-ai/create-query-vector-search.html#query-a-vector-search-endpoint)
from their documentation.
This PR updates the Langchain vectorstore interface for Databricks to
enable the user to pass the *query_type* parameter to
*similarity_search* to make use of this functionality.
By default, there will not be any changes for existing users of this
interface. To use the new hybrid search feature, it is now possible to
do
```python
# ...
dvs = DatabricksVectorSearch(index)
dvs.similarity_search("my search query", query_type="HYBRID")
```
Or using the retriever:
```python
retriever = dvs.as_retriever(
search_kwargs={
"query_type": "HYBRID",
}
)
retriever.invoke("my search query")
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
The functions `convert_to_messages` has had an expansion of the
arguments it can take:
1. Previously, it only could take a `Sequence` in order to iterate over
it. This has been broadened slightly to an `Iterable` (which should have
no other impact).
2. Support for `PromptValue` and `BaseChatPromptTemplate` has been
added. These are generated when combining messages using the overloaded
`+` operator.
Functions which rely on `convert_to_messages` (namely `filter_messages`,
`merge_message_runs` and `trim_messages`) have had the type of their
arguments similarly expanded.
Resolves#23706.
<!--
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
-->
---------
Signed-off-by: JP-Ellis <josh@jpellis.me>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Spell check fixes for docs, comments, and a couple of
strings. No code change e.g. variable names.
**Issue:** none
**Dependencies:** none
**Twitter handle:** hmartin
Latest langchain-cohere sdk mandates passing in the model parameter into
the Embeddings and Reranker inits.
This PR is to update the docs to reflect these changes.
Thank you for contributing to LangChain!
**Description:** Add support for caching (standard + semantic) LLM
responses using Couchbase
- [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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Disabled by default.
```python
from langchain_core.tools import tool
@tool(parse_docstring=True)
def foo(bar: str, baz: int) -> str:
"""The foo.
Args:
bar: this is the bar
baz: this is the baz
"""
return bar
foo.args_schema.schema()
```
```json
{
"title": "fooSchema",
"description": "The foo.",
"type": "object",
"properties": {
"bar": {
"title": "Bar",
"description": "this is the bar",
"type": "string"
},
"baz": {
"title": "Baz",
"description": "this is the baz",
"type": "integer"
}
},
"required": [
"bar",
"baz"
]
}
```
