- **Description:** Instantiating `GPT4AllEmbeddings` with no
`gpt4all_kwargs` argument raised a `ValidationError`. Root cause: #21238
added the capability to pass `gpt4all_kwargs` through to the `GPT4All`
instance via `Embed4All`, but broke code that did not specify a
`gpt4all_kwargs` argument.
- **Issue:** #25119
- **Dependencies:** None
- **Twitter handle:** [`@metadaddy`](https://twitter.com/metadaddy)
This PR does an aesthetic sort of the config object attributes. This
will make it a bit easier to go back and forth between pydantic v1 and
pydantic v2 on the 0.3.x branch
Among integration packages in libs/partners, Groq is an exception in
that it errors on warnings.
Following https://github.com/langchain-ai/langchain/pull/25084, Groq
fails with
> pydantic.warnings.PydanticDeprecatedSince20: The `__fields__`
attribute is deprecated, use `model_fields` instead. Deprecated in
Pydantic V2.0 to be removed in V3.0.
Here we update the behavior to no longer fail on warning, which is
consistent with the rest of the packages in libs/partners.
**Description:**
In this PR, I am adding three stock market tools from
financialdatasets.ai (my API!):
- get balance sheets
- get cash flow statements
- get income statements
Twitter handle: [@virattt](https://twitter.com/virattt)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Example: "community: Added bedrock 3-5 sonnet cost detials for
BedrockAnthropicTokenUsageCallbackHandler"
- [ ] **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.
Co-authored-by: Naval Chand <navalchand@192.168.1.36>
- description: I remove the limitation of mandatory existence of
`QIANFAN_AK` and default model name which langchain uses cause there is
already a default model nama underlying `qianfan` SDK powering langchain
component.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- community: Allow authorization to Confluence with bearer token
- **Description:** Allow authorization to Confluence with [Personal
Access
Token](https://confluence.atlassian.com/enterprise/using-personal-access-tokens-1026032365.html)
by checking for the keys `['client_id', token: ['access_token',
'token_type']]`
- **Issue:**
Currently the following error occurs when using an personal access token
for authorization.
```python
loader = ConfluenceLoader(
url=os.getenv('CONFLUENCE_URL'),
oauth2={
'token': {"access_token": os.getenv("CONFLUENCE_ACCESS_TOKEN"), "token_type": "bearer"},
'client_id': 'client_id',
},
page_ids=['12345678'],
)
```
```
ValueError: Error(s) while validating input: ["You have either omitted require keys or added extra keys to the oauth2 dictionary. key values should be `['access_token', 'access_token_secret', 'consumer_key', 'key_cert']`"]
```
With this PR the loader runs as expected.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** This includes Pydantic field metadata in
`_create_subset_model_v2` so that it gets included in the final
serialized form that get sent out.
- **Issue:** #25031
- **Dependencies:** n/a
- **Twitter handle:** @gramliu
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Fixes Neo4JVector.from_existing_graph integration with huggingface
Previously threw an error with existing databases, because
from_existing_graph query returns empty list of new nodes, which are
then passed to embedding function, and huggingface errors with empty
list.
Fixes [24401](https://github.com/langchain-ai/langchain/issues/24401)
---------
Co-authored-by: Jeff Katzy <jeffreyerickatz@gmail.com>
You can use this with:
```
from langchain_experimental.graph_transformers import GlinerGraphTransformer
gliner = GlinerGraphTransformer(allowed_nodes=["Person", "Organization", "Nobel"], allowed_relationships=["EMPLOYEE", "WON"])
from langchain_core.documents import Document
text = """
Marie Curie, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.
She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.
Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.
She was, in 1906, the first woman to become a professor at the University of Paris.
"""
documents = [Document(page_content=text)]
gliner.convert_to_graph_documents(documents)
```
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR adds a minimal document indexer abstraction.
The goal of this abstraction is to allow developers to create custom
retrievers that also have a standard indexing API and allow updating the
document content in them.
The abstraction comes with a test suite that can verify that the indexer
implements the correct semantics.
This is an iteration over a previous PRs
(https://github.com/langchain-ai/langchain/pull/24364). The main
difference is that we're sub-classing from BaseRetriever in this
iteration and as so have consolidated the sync and async interfaces.
The main problem with the current design is that runt time search
configuration has to be specified at init rather than provided at run
time.
We will likely resolve this issue in one of the two ways:
(1) Define a method (`get_retriever`) that will allow creating a
retriever at run time with a specific configuration.. If we do this, we
will likely break the subclass on BaseRetriever
(2) Generalize base retriever so it can support structured queries
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
This PR fixes a bug where if `enable_dynamic_field` and
`partition_key_field` are enabled at the same time, a pymilvus error
occurs.
Milvus requires the partition key field to be a full schema defined
field, and not a dynamic one, so it will throw the error "the specified
partition key field {field} not exist" when creating the collection.
When `enabled_dynamic_field` is set to `True`, all schema field creation
based on `metadatas` is skipped. This code now checks if
`partition_key_field` is set, and creates the field.
Integration test added.
**Twitter handle:** StuartMarshUK
---------
Co-authored-by: Stuart Marsh <stuart.marsh@qumata.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** This PR makes the AthenaLoader profile_name optional
and fixes the type hint which says the type is `str` but it should be
`str` or `None` as None is handled in the loader init. This is a minor
problem but it just confused me when I was using the Athena Loader to
why we had to use a Profile, as I want that for local but not
production.
- **Issue:** #24957
- **Dependencies:** None.
Description: RetryWithErrorOutputParser.from_llm() creates a retry chain
that returns a Generation instance, when it should actually just return
a string.
This class was forgotten when fixing the issue in PR #24687
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"
- [ ] **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!
- [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.
- [ ] **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.
Hardens index commands with try/except for free clusters and optional
waits for syncing and tests.
[efriis](https://github.com/efriis) These are the upgrades to the search
index commands (CRUD) that I mentioned.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR introduces a module with some helper utilities for the pydantic
1 -> 2 migration.
They're meant to be used in the following way:
1) Use the utility code to get unit tests pass without requiring
modification to the unit tests
2) (If desired) upgrade the unit tests to match pydantic 2 output
3) (If desired) stop using the utility code
Currently, this module contains a way to map `schema()` generated by
pydantic 2 to (mostly) match the output from pydantic v1.
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>
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"
- [x] **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!
- [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.
**Description:** This PR fixes a KeyError in NotionDBLoader when the
"name" key is missing in the "people" property.
**Issue:** Fixes#24223
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add compatibility for pydantic 2 for a utility function.
This will help push some small changes to master, so they don't have to
be kept track of on a separate branch.
The @pre_init validator is a temporary solution for base models. It has
similar (but not identical) semantics to @root_validator(), but it works
strictly as a pre-init validator.
It'll work as expected as long as the pydantic model type hints were
correct.
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
This PR adds annotations in comunity package.
Annotations are only strictly needed in subclasses of BaseModel for
pydantic 2 compatibility.
This PR adds some unnecessary annotations, but they're not bad to have
regardless for documentation pages.
Title: [pebblo_retrieval] Identifying entities in prompts given in
PebbloRetrievalQA leading to prompt governance
Description: Implemented identification of entities in the prompt using
Pebblo prompt governance API.
Issue: NA
Dependencies: NA
Add tests and docs: NA
- **Title:** [PebbloSafeLoader] Implement content-size-based batching in
the classification flow(loader/doc API)
- **Description:**
- Implemented content-size-based batching in the loader/doc API, set to
100KB with no external configuration option, intentionally hard-coded to
prevent timeouts.
- Remove unused field(pb_id) from doc_metadata
- **Issue:** NA
- **Dependencies:** NA
- **Add tests and docs:** Updated
Description: The old method will be discontinued; use the official SDK
for more model options.
Issue: None
Dependencies: None
Twitter handle: None
Co-authored-by: trumanyan <trumanyan@tencent.com>
PR title: Experimental: Add config to convert_to_graph_documents
Description: In order to use langfuse, i need to pass the langfuse
configuration when invoking the chain. langchain_experimental does not
allow to add any parameters (beside the documents) to the
convert_to_graph_documents method. This way, I cannot monitor the chain
in langfuse.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Catarina Franco <catarina.franco@criticalsoftware.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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
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
**Description:**
This update significantly improves the Brave Search Tool's utility
within the LangChain library by enriching the search results it returns.
The tool previously returned title, link, and snippet, with the snippet
being a truncated 140-character description from the search engine. To
make the search results more informative, this update enables
extra_snippets by default and introduces additional result fields:
title, link, description (enhancing and renaming the former snippet
field), age, and snippets. The snippets field provides a list of strings
summarizing the webpage, utilizing Brave's capability for more detailed
search insights. This enhancement aims to make the search tool far more
informative and beneficial for users.
**Issue:** N/A
**Dependencies:** No additional dependencies introduced.
**Twitter handle:** @davidalexr987
**Code Changes Summary:**
- Changed the default setting to include extra_snippets in search
results.
- Renamed the snippet field to description to accurately reflect its
content and included an age field for search results.
- Introduced a snippets field that lists webpage summaries, providing
users with comprehensive search result insights.
**Backward Compatibility Note:**
The renaming of snippet to description improves the accuracy of the
returned data field but may impact existing users who have developed
integration's or analyses based on the snippet field. I believe this
change is essential for clarity and utility, and it aligns better with
the data provided by Brave Search.
**Additional Notes:**
This proposal focuses exclusively on the Brave Search package, without
affecting other LangChain packages or introducing new dependencies.
Description: Since moving away from `langchain-community` is
recommended, `init_chat_models()` should import ChatOllama from
`langchain-ollama` instead.
Anthropic models (including via Bedrock and other cloud platforms)
accept a status/is_error attribute on tool messages/results
(specifically in `tool_result` content blocks for Anthropic API). Adding
a ToolMessage.status attribute so that users can set this attribute when
using those models
**Description:** Add empty string default for api_key and change
`server_url` to `url` to match existing loaders.
- [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.
**Description**
Fixes DocumentDBVectorSearch similarity_search when no filter is used;
it defaults to None but $match does not accept None, so changed default
to empty {} before pipeline is created.
**Issue**
AWS DocumentDB similarity search does not work when no filter is used.
Error msg: "the match filter must be an expression in an object" #24775
**Dependencies**
No dependencies
**Twitter handle**
https://x.com/perepasamonte
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Mixtral with Groq has started consistently failing tool calling tests.
Here we restrict testing to llama 3.1.
- `.schema` is deprecated in pydantic proper in favor of
`.model_json_schema`.
There is an issue with the prompt format in `GenerativeAgentMemory` ,
try to fix it.
The prompt is same as the one in method `_score_memory_importance`.
issue: #24615
descriptions: The _Graph pydantic model generated from
create_simple_model (which LLMGraphTransformer uses when allowed nodes
and relationships are provided) does not constrain the relationships
(source and target types, relationship type), and the node and
relationship properties with enums when using ChatOpenAI.
The issue is that when calling optional_enum_field throughout
create_simple_model the llm_type parameter is not passed in except for
when creating node type. Passing it into each call fixes the issue.
Co-authored-by: Lifu Wu <lifu@nextbillion.ai>
- [ ] **PR title**: "langchain-openai: openai proxy added to base
embeddings"
- [ ] **PR message**:
- **Description:**
Dear langchain developers,
You've already supported proxy for ChatOpenAI implementation in your
package. At the same time, if somebody needed to use proxy for chat, it
also could be necessary to be able to use it for OpenAIEmbeddings.
That's why I think it's important to add proxy support for OpenAI
embeddings. That's what I've done in this PR.
@baskaryan
---------
Co-authored-by: karpov <karpov@dohod.ru>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
In the `ChatFireworks` class definition, the Field() call for the "stop"
("stop_sequences") parameter is missing the "default" keyword.
**Issue:**
Type checker reports "stop_sequences" as a missing arg (not recognizing
the default value is None)
**Dependencies:**
None
**Twitter handle:**
None
community:Add support for specifying document_loaders.firecrawl api url.
Add support for specifying document_loaders.firecrawl api url.
This is mainly to support the
[self-hosting](https://github.com/mendableai/firecrawl/blob/main/SELF_HOST.md)
option firecrawl provides. Eg. now I can specify localhost:....
The corresponding firecrawl class already provides functionality to pass
the argument. See here:
4c9d62f6d3/apps/python-sdk/firecrawl/firecrawl.py (L29)
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: OutputFixingParser.from_llm() creates a retry chain that
returns a Generation instance, when it should actually just return a
string.
Issue: https://github.com/langchain-ai/langchain/issues/24600
Twitter handle: scribu
---------
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
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>
Raise `LangChainException` instead of `Exception`. This alleviates the
need for library users to use bare try/except to handle exceptions
raised by `AzureSearch`.
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description:
add a optional score relevance threshold for select only coherent
document, it's in complement of top_n
Discussion:
add relevance score threshold in flashrank_rerank document compressors
#24013
Dependencies:
no dependencies
---------
Co-authored-by: Benjamin BERNARD <benjamin.bernard@openpathview.fr>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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>
**Description:** Expanded the chat model functionality to support tools
in the 'baichuan.py' file. Updated module imports and added tool object
handling in message conversions. Additional changes include the
implementation of tool binding and related unit tests. The alterations
offer enhanced model capabilities by enabling interaction with tool-like
objects.
---------
Co-authored-by: ccurme <chester.curme@gmail.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>
This PR proposes to create a rate limiter in the chat model directly,
and would replace: https://github.com/langchain-ai/langchain/pull/21992
It resolves most of the constraints that the Runnable rate limiter
introduced:
1. It's not annoying to apply the rate limiter to existing code; i.e.,
possible to roll out the change at the location where the model is
instantiated,
rather than at every location where the model is used! (Which is
necessary
if the model is used in different ways in a given application.)
2. batch rate limiting is enforced properly
3. the rate limiter works correctly with streaming
4. the rate limiter is aware of the cache
5. The rate limiter can take into account information about the inputs
into the
model (we can add optional inputs to it down-the road together with
outputs!)
The only downside is that information will not be properly reflected in
tracing
as we don't have any metadata evens about a rate limiter. So the total
time
spent on a model invocation will be:
* time spent waiting for the rate limiter
* time spend on the actual model request
## Example
```python
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_groq import ChatGroq
groq = ChatGroq(rate_limiter=InMemoryRateLimiter(check_every_n_seconds=1))
groq.invoke('hello')
```
**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.
Fixes for Eden AI Custom tools and ChatEdenAI:
- add missing import in __init__ of chat_models
- add `args_schema` to custom tools. otherwise '__arg1' would sometimes
be passed to the `run` method
- fix IndexError when no human msg is added in ChatEdenAI
Mistral appears to have added validation for the format of its tool call
IDs:
`{"object":"error","message":"Tool call id was abc123 but must be a-z,
A-Z, 0-9, with a length of
9.","type":"invalid_request_error","param":null,"code":null}`
This breaks compatibility of messages from other providers. Here we add
a function that converts any string to a Mistral-valid tool call ID, and
apply it to incoming messages.
Thank you for contributing to LangChain!
**Description:**
This PR allows users of `langchain_community.llms.ollama.Ollama` to
specify the `auth` parameter, which is then forwarded to all internal
calls of `requests.request`. This works in the same way as the existing
`headers` parameters. The auth parameter enables the usage of the given
class with Ollama instances, which are secured by more complex
authentication mechanisms, that do not only rely on static headers. An
example are AWS API Gateways secured by the IAM authorizer, which
expects signatures dynamically calculated on the specific HTTP request.
**Issue:**
Integrating a remote LLM running through Ollama using
`langchain_community.llms.ollama.Ollama` only allows setting static HTTP
headers with the parameter `headers`. This does not work, if the given
instance of Ollama is secured with an authentication mechanism that
makes use of dynamically created HTTP headers which for example may
depend on the content of a given request.
**Dependencies:**
None
**Twitter handle:**
None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
### Description
* support asynchronous in InMemoryVectorStore
* since embeddings might be possible to call asynchronously, ensure that
both asynchronous and synchronous functions operate correctly.
This PR introduces the following Runnables:
1. BaseRateLimiter: an abstraction for specifying a time based rate
limiter as a Runnable
2. InMemoryRateLimiter: Provides an in-memory implementation of a rate
limiter
## Example
```python
from langchain_core.runnables import InMemoryRateLimiter, RunnableLambda
from datetime import datetime
foo = InMemoryRateLimiter(requests_per_second=0.5)
def meow(x):
print(datetime.now().strftime("%H:%M:%S.%f"))
return x
chain = foo | meow
for _ in range(10):
print(chain.invoke('hello'))
```
Produces:
```
17:12:07.530151
hello
17:12:09.537932
hello
17:12:11.548375
hello
17:12:13.558383
hello
17:12:15.568348
hello
17:12:17.578171
hello
17:12:19.587508
hello
17:12:21.597877
hello
17:12:23.607707
hello
17:12:25.617978
hello
```
## Interface
The rate limiter uses the following interface for acquiring a token:
```python
class BaseRateLimiter(Runnable[Input, Output], abc.ABC):
@abc.abstractmethod
def acquire(self, *, blocking: bool = True) -> bool:
"""Attempt to acquire the necessary tokens for the rate limiter.```
```
The flag `blocking` has been added to the abstraction to allow
supporting streaming (which is easier if blocking=False).
## Limitations
- The rate limiter is not designed to work across different processes.
It is an in-memory rate limiter, but it is thread safe.
- The rate limiter only supports time-based rate limiting. It does not
take into account the size of the request or any other factors.
- The current implementation does not handle streaming inputs well and
will consume all inputs even if the rate limit has been reached. Better
support for streaming inputs will be added in the future.
- When the rate limiter is combined with another runnable via a
RunnableSequence, usage of .batch() or .abatch() will only respect the
average rate limit. There will be bursty behavior as .batch() and
.abatch() wait for each step to complete before starting the next step.
One way to mitigate this is to use batch_as_completed() or
abatch_as_completed().
## Bursty behavior in `batch` and `abatch`
When the rate limiter is combined with another runnable via a
RunnableSequence, usage of .batch() or .abatch() will only respect the
average rate limit. There will be bursty behavior as .batch() and
.abatch() wait for each step to complete before starting the next step.
This becomes a problem if users are using `batch` and `abatch` with many
inputs (e.g., 100). In this case, there will be a burst of 100 inputs
into the batch of the rate limited runnable.
1. Using a RunnableBinding
The API would look like:
```python
from langchain_core.runnables import InMemoryRateLimiter, RunnableLambda
rate_limiter = InMemoryRateLimiter(requests_per_second=0.5)
def meow(x):
return x
rate_limited_meow = RunnableLambda(meow).with_rate_limiter(rate_limiter)
```
2. Another option is to add some init option to RunnableSequence that
changes `.batch()` to be depth first (e.g., by delegating to
`batch_as_completed`)
```python
RunnableSequence(first=rate_limiter, last=model, how='batch-depth-first')
```
Pros: Does not require Runnable Binding
Cons: Feels over-complicated
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>
- [ ] **PR title**: "experimental: Adding compatibility for
OllamaFunctions with ImagePromptTemplate"
- [ ] **PR message**:
- **Description:** Removes the outdated
`_convert_messages_to_ollama_messages` method override in the
`OllamaFunctions` class to ensure that ollama multimodal models can be
invoked with an image.
- **Issue:** #24174
---------
Co-authored-by: Joel Akeret <joel.akeret@ti&m.com>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
add dynamic field feature to langchain_milvus
more unittest, more robustic
plan to deprecate the `metadata_field` in the future, because it's
function is the same as `enable_dynamic_field`, but the latter one is a
more advanced concept in milvus
Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This linter is meant to move development to use __init__ instead of
root_validator and validator.
We need to investigate whether we need to lint some of the functionality
of Field (e.g., `lt` and `gt`, `alias`)
`alias` is the one that's most popular:
(community) ➜ community git:(eugene/add_linter_to_community) ✗ git grep
" Field(" | grep "alias=" | wc -l
144
(community) ➜ community git:(eugene/add_linter_to_community) ✗ git grep
" Field(" | grep "ge=" | wc -l
10
(community) ➜ community git:(eugene/add_linter_to_community) ✗ git grep
" Field(" | grep "gt=" | wc -l
4
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!
- This PR adds vector search filtering for Azure Cosmos DB Mongo vCore
and NoSQL.
- [ ] **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**
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>
In some lines its trying to read a key that do not exists yet. In this
cases I changed the direct access to dict.get() method
- [ 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/
The previous implementation would never be called.
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.
---------
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
