Follow up on https://github.com/langchain-ai/langchain/pull/13048.
This PR intends to simplify the Qdrant async implementation by replacing
the internal GRPC methods with the `QdrantAsyncClient` methods.
This is a backward compatible change with no additional steps required
after merge.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes#14347
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- **Description:** Added the traceback of the previous error to keep the
initial error type,
- **Issue:** #14347 ,
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---------
Co-authored-by: Julien Raffy <julien.raffy@emeria.eu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** the ability to add all extra parameter of vectorstore
and using them SemanticSimilarityExampleSelector.
- **Issue:** #14583
- **Dependencies:** no dependensies
- **Tag maintainer:**
- **Twitter handle:** @AmirMalekiz
---------
Co-authored-by: Amir Maleki <amaleki@fb.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Add support for setting the `score_threshold` for
similarity search in SupabaseVectoreStore.
This pull request addresses issue #14438
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** changed json.py to handle additional cases of partial
json string to be parsed, basically by dropping the last character in
the string until a valid json string is found or the string is empty.
Also added additional test cases.
- **Issue:** function parse_partial_json could not parse cases where the
key is present but the value is not.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Because Milvus' collection_name doesn't support UFT8 characters in other
languages, I want the `collection_descriotion`.
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**Description:** Fix for processing for serpapi response for Google Maps
API
**Issue:** Due to the fact corresponding
[api](https://serpapi.com/google-maps-api) returns 'local_results' as
list, and old version requested `res["local_results"].keys()` of the
list. As the result we got exception: ```AttributeError: 'list' object
has no attribute 'keys'```.
Way to reproduce wrong behaviour:
```
params = {
"engine": "google_maps",
"type": "search",
"google_domain": "google.de",
"ll": "@51.1917,10.525,14z",
"hl": "de",
"gl": "de",
}
search = SerpAPIWrapper(params=params)
results = search.run("cafe")
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Ran <rccalman@gmail.com>
Because Milvus doesn't support nullable fields, but document metadata is
very rich, so it makes more sense to store it as json.
https://github.com/milvus-io/pymilvus/issues/1705#issuecomment-1731112372
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
BigQuery vector search lets you use GoogleSQL to do semantic search,
using vector indexes for fast but approximate results, or using brute
force for exact results.
This PR integrates LangChain vectorstore with BigQuery Vector Search.
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---------
Co-authored-by: Vlad Kolesnikov <vladkol@google.com>
- **Description:** replace score_threshold with args
- **Issue:** needs a way to pass more options to similarity search
- **Dependencies:** None
- **Twitter handle:** @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
- **Description:** Tool now supports querying over 200 million
scientific articles, vastly expanding its reach beyond the 2 million
articles accessible through Arxiv. This update significantly broadens
access to the entire scope of scientific literature.
- **Dependencies:** semantischolar
https://github.com/danielnsilva/semanticscholar
- **Twitter handle:** @shauryr
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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…tch]: import models from community
ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
```
- easier to write custom logic/loops with automatic tracing
- if you don't want to streaming support write a regular function and
pass to RunnableLambda
- if you do want streaming write a generator and pass it to
RunnableGenerator
```py
import json
from typing import AsyncIterator
from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable, RunnableGenerator, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.tools.render import format_tool_to_openai_function
def _get_tavily():
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
tavily_search = TavilySearchAPIWrapper()
return TavilySearchResults(api_wrapper=tavily_search)
async def _agent_executor_generator(
input: AsyncIterator[list[BaseMessage]],
*,
max_iterations: int = 10,
tools: dict[str, BaseTool],
agent: Runnable[list[BaseMessage], BaseMessage],
parser: Runnable[BaseMessage, AgentAction | AgentFinish],
) -> AsyncIterator[BaseMessage]:
messages = [m async for mm in input for m in mm]
for _ in range(max_iterations):
next_message = await agent.ainvoke(messages)
yield next_message
messages.append(next_message)
parsed = await parser.ainvoke(next_message)
if isinstance(parsed, AgentAction):
result = await tools[parsed.tool].ainvoke(parsed.tool_input)
next_message = FunctionMessage(name=parsed.tool, content=json.dumps(result))
yield next_message
messages.append(next_message)
elif isinstance(parsed, AgentFinish):
return
def get_agent_executor(tools: list[BaseTool], system_message: str):
llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_message),
MessagesPlaceholder(variable_name="messages"),
]
)
llm_with_tools = llm.bind(
functions=[format_tool_to_openai_function(t) for t in tools]
)
agent = {"messages": RunnablePassthrough()} | prompt | llm_with_tools
parser = OpenAIFunctionsAgentOutputParser()
executor = RunnableGenerator(_agent_executor_generator)
return executor.bind(
tools={tool.name for tool in tools}, agent=agent, parser=parser
)
agent = get_agent_executor([_get_tavily()], "You are a very nice agent!")
async def main():
async for message in agent.astream(
[HumanMessage(content="whats the weather in sf tomorrow?")]
):
print(message)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
results in this trace
https://smith.langchain.com/public/fa17f05d-9724-4d08-8fa1-750f8fcd051b/r
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- **Description:** SingleFileFacebookMessengerChatLoader did not handle
the case for when messages had stickers and/or photos so fixed that.
- **Issue:** #15356
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** updates/enhancements to IBM
[watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider
(prompt tuned models and prompt templates deployments support)
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan
- **Twitter handle:** details in comment below.
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally. ✅
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The fix#14221 has broken default gitlab url which is forcing the users
to specify GITLAB_URL for default one. With this fix if GITLAB_URL is
not set, the default gitlab url will be taken.
- **Description:** Add the GITHUB URL instead of None
- **Issue:** the issue #14221 has broken the default github URL
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** manjunath_shiva
- **Description:** This PR adds `api_base` to `_client_params` in the
`chat_model` of LiteLLM to ensure it's included in API calls.
Previously, `api_base` was set on the client but was not included in the
parameters passed to the completion function. This change ensures that
`api_base` is correctly passed to all API calls.
- **Issue:** #14338
- **Tag maintainer:** @hwchase17 @agola11
- **Twitter handle:** @LMS_David_RS
Sometimes, the tool_schema is like:
` {'action_name': 'search_items', 'action': {'term': 'pizza'}}`
sometimes, specially with gpt3.5 it comes like:
`{'action_name': 'search_items', 'term': 'pizza'}`
and it fails.
This PR is a way to make it work in both scenarios.
issues releated: #6624
Please make sure your PR is passing linting and testing before
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-->
Co-authored-by: Lucca Zenobio <lucca.zenobio@ifood.com.br>
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This change addresses the issue where DashScopeEmbeddingAPI limits
requests to 25 lines of data, and DashScopeEmbeddings did not handle
cases with more than 25 lines, leading to errors. I have implemented a
fix to manage data exceeding this limit efficiently.
---------
Co-authored-by: xuxiang <xuxiang@aliyun.com>
Adding to my previously, already merged PR I made some further
improvements:
* Added documentation to the existing Pydantic Parser notebook, with an
example using LCEL and `with_retry()` on `OutputParserException`.
* Added an additional output example to the prompt
* More lenient parser in terms of LLM output format
* Amended unit test
FYI @hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Update _retrieve_ref inside json_schema.py to include
an isdigit() check
- **Issue:** This library is used inside dereference_refs inside
langchain_community.agent_toolkits.openapi.spec. When I read in a yaml
file which has references for "400", "401" etc; the line "out =
out[component]" causes a KeyError. The isdigit() check ensures that if
it is an integer like "400" or "401"; it converts it into integer before
using it as a key to prevent the error.
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description: _python-lint_
This agent writes Python code that is formatted and linted using
`black`, `ruff`, and `mypy`, but does not execute the code. It writes
the code to a temporary file and then runs the linters. Once these
checks pass, the code is returned.
# Dependencies
- black
- ruff
- mypy
# Demo
The functionality can be seen here:
https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo
Added some Headers in steam tool notebook to match consistency with the
other toolkit notebooks
- Dependencies: no new dependencies
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
