Updated the MongoDB Atlas Vector Search docs to indicate the service is
Generally Available, updated the example to use the new index
definition, and added an example that uses metadata pre-filtering for
semantic search
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Updated provider page by adding LLM and ChatLLM references; removed a
content that is duplicate text from the LLM referenced page.
Updated the collback page
Many jupyter notebooks didn't pass linting. List of these files are
presented in the [tool.ruff.lint.per-file-ignores] section of the
pyproject.toml . Addressed these bugs:
- fixed bugs; added missed imports; updated pyproject.toml
Only the `document_loaders/tensorflow_datasets.ipyn`,
`cookbook/gymnasium_agent_simulation.ipynb` are not completely fixed.
I'm not sure about imports.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The namespaces like `langchain.agents.format_scratchpad` clogging the
API Reference sidebar.
This change removes those 3-level namespaces from sidebar (this issue
was discussed with @efriis )
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This reverts commit 38813d7090. This is a
temporary fix, as I don't see a clear way on how to use multiple keys
with `Qdrant.from_texts`.
Context: #14378
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---------
Co-authored-by: Brace Sproul <braceasproul@gmail.com>
Keeping it simple for now.
Still iterating on our docs build in pursuit of making everything mdxv2
compatible for docusaurus 3, and the fewer custom scripts we're reliant
on through that, the less likely the docs will break again.
Other things to consider in future:
Quarto rewriting in ipynbs:
https://quarto.org/docs/extensions/nbfilter.html (but this won't do
md/mdx files)
Docusaurus plugins for rewriting these paths
- **Description:** In Qdrant allows to input list of keys as the
content_payload_key to retrieve multiple fields (the generated document
will contain the dictionary {field: value} in a string),
- **Issue:** Previously we were able to retrieve only one field from the
vector database when making a search
- **Dependencies:**
- **Tag maintainer:**
- **Twitter handle:** @jb_dlb
---------
Co-authored-by: Jean Baptiste De La Broise <jeanbaptiste.delabroise@mdpi.com>
Description: This PR masked baidu qianfan - Chat_Models API Key and
added unit tests.
Issue: the issue langchain-ai#12165.
Tag maintainer: @eyurtsev
---------
Co-authored-by: xiayi <xiayi@bytedance.com>
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We found a request with `max_tokens=None` results in the following error
in Anthropic:
```
HTTPError: 400 Client Error: Bad Request for url: https://oregon.staging.cloud.databricks.com/serving-endpoints/corey-anthropic/invocations.
Response text: {"error_code":"INVALID_PARAMETER_VALUE","message":"INVALID_PARAMETER_VALUE: max_tokens was not of type Integer: null"}
```
This PR excludes `max_tokens` if it's None.
- **Description:** new parameters in OpenAIEmbeddings() constructor
(retry_min_seconds and retry_max_seconds) that allow parametrization by
the user of the former min_seconds and max_seconds that were hidden in
_create_retry_decorator() and _async_retry_decorator()
- **Issue:** #9298, #12986
- **Dependencies:** none
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @adumont
make format ✅
make lint ✅
make test ✅
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description :
Updated the functions with new Clarifai python SDK.
Enabled initialisation of Clarifai class with model URL.
Updated docs with new functions examples.
Remove whitespaces from the input of the ListSQLDatabaseTool for better
support.
for example, the input "table1,table2,table3" will throw an exception
whiteout the change although it's a valid input.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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This PR adds support for metadata filters of the form:
`{"filter": {"key": { "NIN" : ["list", "of", "values"]}}}`
"IN" is already supported, so this is a quick & related update to add
"NIN"
- **Description:**
1. Add system parameters to the ERNIE LLM API to set the role of the
LLM.
2. Add support for the ERNIE-Bot-turbo-AI model according from the
document https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Alp0kdm0n.
3. For the function call of ErnieBotChat, align with the
QianfanChatEndpoint.
With this PR, the `QianfanChatEndpoint()` can use the `function calling`
ability with `create_ernie_fn_chain()`. The example is as the following:
```
from langchain.prompts import ChatPromptTemplate
import json
from langchain.prompts.chat import (
ChatPromptTemplate,
)
from langchain.chat_models import QianfanChatEndpoint
from langchain.chains.ernie_functions import (
create_ernie_fn_chain,
)
def get_current_news(location: str) -> str:
"""Get the current news based on the location.'
Args:
location (str): The location to query.
Returs:
str: Current news based on the location.
"""
news_info = {
"location": location,
"news": [
"I have a Book.",
"It's a nice day, today."
]
}
return json.dumps(news_info)
def get_current_weather(location: str, unit: str="celsius") -> str:
"""Get the current weather in a given location
Args:
location (str): location of the weather.
unit (str): unit of the tempuature.
Returns:
str: weather in the given location.
"""
weather_info = {
"location": location,
"temperature": "27",
"unit": unit,
"forecast": ["sunny", "windy"],
}
return json.dumps(weather_info)
template = ChatPromptTemplate.from_messages([
("user", "{user_input}"),
])
chat = QianfanChatEndpoint(model="ERNIE-Bot-4")
chain = create_ernie_fn_chain([get_current_weather, get_current_news], chat, template, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```
The result of the above code:
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？
> Finished chain.
{'name': 'get_current_news', 'arguments': {'location': '北京'}}
```
For the `ErnieBotChat`, now can use the `system` parameter to set the
role of the LLM.
```
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from langchain.chat_models import ErnieBotChat
llm = ErnieBotChat(model_name="ERNIE-Bot-turbo-AI", system="你是一个能力很强的机器人，你的名字叫 小叮当。无论问你什么问题，你都可以给出答案。")
prompt = ChatPromptTemplate.from_messages(
[
("human", "{query}"),
]
)
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
res = chain.run(query="你是谁？")
print(res)
```
The result of the above code:
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 你是谁？
> Finished chain.
我是小叮当，一个智能机器人。我可以为你提供各种服务，包括回答问题、提供信息、进行计算等。如果你需要任何帮助，请随时告诉我，我会尽力为你提供最好的服务。
```
