**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>
