Backwards compatible change that converts pydantic extras to literals
which is consistent with pydantic 2 usage.
- fireworks
- voyage ai
- mistralai
- mistral ai
- together ai
- huggigng face
- pinecone
**Description**
Fix the asyncronous methods to retrieve documents from AzureSearch
VectorStore. The previous changes from [this
commit](ffe6ca986e)
create a similar code for the syncronous methods and the asyncronous
ones but the asyncronous client return an asyncronous iterator
"AsyncSearchItemPaged" as said in the issue #24740.
To solve this issue, the syncronous iterators in asyncronous methods
where changed to asyncronous iterators.
@chrislrobert said in [this
comment](https://github.com/langchain-ai/langchain/issues/24740#issuecomment-2254168302)
that there was a still a flaw due to `with` blocks that close the client
after each call. I removed this `with` blocks in the `async_client`
following the same pattern as the sync `client`.
In order to close up the connections, a __del__ method is included to
gently close up clients once the vectorstore object is destroyed.
**Issue:** #24740 and #24064
**Dependencies:** No new dependencies for this change
**Example notebook:** I created a notebook just to test the changes work
and gives the same results as the syncronous methods for vector and
hybrid search. With these changes, the asyncronous methods in the
retriever work as well.
**Lint and test**: Passes the tests and the linter
This adds `args_schema` member to `SearxSearchResults` tool. This member
is already present in the `SearxSearchRun` tool in the same file.
I was having `TypeError: Type is not JSON serializable:
AsyncCallbackManagerForToolRun` being thrown in langserve playground
when I was using `SearxSearchResults` tool as a part of chain there.
This fixes the issue, so the error is not raised anymore.
This is a example langserve app that was giving me the error, but it
works properly after the proposed fix:
```python
#!/usr/bin/env python
from fastapi import FastAPI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain_community.utilities import SearxSearchWrapper
from langchain_community.tools.searx_search.tool import SearxSearchResults
from langserve import add_routes
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
s = SearxSearchWrapper(searx_host="http://localhost:8080")
search = SearxSearchResults(wrapper=s)
search_chain = (
{"context": search, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
app = FastAPI()
add_routes(
app,
search_chain,
path="/chain",
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="localhost", port=8000)
```
- **Description:** Standardize SparkLLM, include:
- docs, the issue #24803
- to support stream
- update api url
- model init arg names, the issue #20085
- **Description:** This PR implements the `bind_tool` functionality for
ChatZhipuAI as requested by the user. ChatZhipuAI models support tool
calling according to the `OpenAI` tool format, as outlined in their
official documentation [here](https://open.bigmodel.cn/dev/api#glm-4).
- **Issue:** ##23868
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- In the in ` embedding-3 ` and later models of Zhipu AI, it is
supported to specify the dimensions parameter of Embedding. Ref:
https://bigmodel.cn/dev/api#text_embedding-3 .
- Add test case for `embedding-3` model by assigning dimensions.
This PR deprecates the beta upsert APIs in vectorstore.
We'll introduce them in a V2 abstraction instead to keep the existing
vectorstore implementations lighter weight.
The main problem with the existing APIs is that it's a bit more
challenging to
implement the correct behavior w/ respect to IDs since ID can be present
in
both the function signature and as an optional attribute on the document
object.
But VectorStores that pass the standard tests should have implemented
the semantics properly!
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR gets rid `root_validators(allow_reuse=True)` logic used in
EdenAI Tool in preparation for pydantic 2 upgrade.
- add another test to secret_from_env_factory
**Description:**
The get time point method in the _consume() method of
core.rate_limiters.InMemoryRateLimiter uses time.time(), which can be
affected by system time backwards. Therefore, it is recommended to use
the monotonically increasing monotonic() to obtain the time
```python
with self._consume_lock:
now = time.time() # time.time() -> time.monotonic()
# initialize on first call to avoid a burst
if self.last is None:
self.last = now
elapsed = now - self.last # when use time.time(), elapsed may be negative when system time backwards
```
Thank you for contributing to LangChain!
- [X] **PR title**: "community: fix valueerror mentions wrong argument
missing"
- 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:** when faiss.py has a None relevance_score_fn it raises
a ValueError that says a normalize_fn_score argument is needed.
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:** This minor PR aims to add `llm_extraction` to Firecrawl
loader. This feature is supported on API and PythonSDK, but the
langchain loader omits adding this to the response.
**Twitter handle:** [scalable_pizza](https://x.com/scalablepizza)
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Description: As described in the related issue: There is an error
occuring when using langchain-openai>=0.1.17 which can be attributed to
the following PR: #23691
Here, the parameter logprobs is added to requests per default.
However, AzureOpenAI takes issue with this parameter as stated here:
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?tabs=python-new&pivots=programming-language-chat-completions
-> "If you set any of these parameters, you get an error."
Therefore, this PR changes the default value of logprobs parameter to
None instead of False. This results in it being filtered before the
request is sent.
- Issue: #24880
- Dependencies: /
Co-authored-by: blaufink <sebastian.brueckner@outlook.de>
