**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:** Runhouse recently migrated from Read the Docs to a
self-hosted solution. This PR updates a broken link from the old docs to
www.run.house/docs. Also changed "The Runhouse" to "Runhouse" (it's
cleaner).
- **Issue:** None
- **Dependencies:** None
- **Description:** Standardize SparkLLM, include:
- docs, the issue #24803
- to support stream
- update api url
- model init arg names, the issue #20085
Cleaned up the "Tying it Together" section of the Conversational RAG
tutorial by removing unnecessary imports that were not used. This
reduces confusion and makes the code more concise.
Thank you for contributing to LangChain!
PR title: docs: remove unused imports in Conversational RAG tutorial
PR message:
Description: Removed unnecessary imports from the "Tying it Together"
section of the Conversational RAG tutorial. These imports were not used
in the code and created confusion. The updated code is now more concise
and easier to understand.
Issue: N/A
Dependencies: None
LinkedIn handle: [Hassan
Memon](https://www.linkedin.com/in/hassan-memon-a109b3257/)
Add tests and docs:
Hi [LangChain Team Member’s Name],
I hope you're doing well! I’m thrilled to share that I recently made my
second contribution to the LangChain project. If possible, could you
give me a shoutout on LinkedIn? It would mean a lot to me and could help
inspire others to contribute to the community as well.
Here’s my LinkedIn profile: [Hassan
Memon](https://www.linkedin.com/in/hassan-memon-a109b3257/).
Thank you so much for your support and for creating such a great
platform for learning and collaboration. I'm looking forward to
contributing more in the future!
Best regards,
Hassan Memon
fix: #25137
`SqliteSaver.from_conn_string()` has been changed to a `contextmanager`
method in `langgraph >= 0.2.0`, the original usage is no longer
applicable.
Refer to
<https://github.com/langchain-ai/langgraph/pull/1271#issue-2454736415>
modification method to replace `SqliteSaver` with `MemorySaver`.
- **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>
…ctions to match LangGraph v2 documentation. Corrected code snippet to
prevent validation errors.
Here's how you can fill out the provided template for your pull request:
---
**Thank you for contributing to LangChain!**
- [ ] **PR title**: `docs: update checkpointer example in Conversational
RAG tutorial`
- [ ] **PR message**:
- **Description:** Updated the Conversational RAG tutorial to correct
the checkpointer example by replacing `SqliteSaver` with `MemorySaver`.
Added installation instructions for `langgraph-checkpoint-memory` to
match LangGraph v2 documentation and prevent validation errors.
- **Issue:** N/A
- **Dependencies:** `langgraph-checkpoint-memory`
- **Twitter handle:** N/A
- [ ] **Add tests and docs**:
1. No new integration tests are required.
2. Updated documentation in the Conversational RAG tutorial.
- [ ] **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: [LangChain Contribution
Guidelines](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 rearranges the examples in Upstash Vector
integration documentation to describe how to use namespaces and improve
the description of metadata filtering.
Thank you for contributing to LangChain!
- **Description:** Fixing package install bug in cookbook
- **Issue:** zsh:1: no matches found: unstructured[all-docs]
- **Dependencies:** N/A
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- **Description:** Fix link for API reference of Gmail Toolkit
- **Issue:** I've just found this issue while I'm reading the doc
- **Dependencies:** N/A
- **Twitter handle:** [@soichisumi](https://x.com/soichisumi)
TODO: If no one reviews your PR within a few days, please @-mention one
of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- 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>
