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- with_config() allows binding any config values to a Runnable, like
.bind() does for kwargs
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gets announced and you'd like a mention, we'll gladly shout you out!
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submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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tests, lint, etc:
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1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. These live is docs/extras
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This PR fixes an issues I found when upgrading to a more recent version
of Langchain. I was using 0.0.142 before, and this issue popped up
already when the `_custom_parser` was added to `output_parsers/json`.
Anyway, the issue is that the parser tries to escape quotes when they
are double-escaped (e.g. `\\"`), leading to OutputParserException.
This is particularly undesired in my app, because I have an Agent that
uses a single input Tool, which expects as input a JSON string with the
structure:
```python
{
"foo": string,
"bar": string
}
```
The LLM (GPT3.5) response is (almost) always something like
`"action_input": "{\\"foo\\": \\"bar\\", \\"bar\\": \\"foo\\"}"` and
since the upgrade this is not correctly parsed.
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
This fixes the exampe import line in the general "cassandra" doc page
mdx file. (it was erroneously a copy of the chat message history import
statement found below).
Description: updated the prompt name in a sequential chain example so
that it is not overwritten by the same prompt name in the next chain
(this is a sequential chain example)
Issue: n/a
Dependencies: none
Tag maintainer: not known
Twitter handle: not on twitter, feel free to use my git username for
anything
Adds a call to Pydantic's `update_forward_refs` for the `Run` class (in
addition to the `ChainRun` and `ToolRun` classes, for which that method
is already called). Without it, the self-reference of child classes
(type `List[Run]`) is problematic. For example:
```python
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from wandb.integration.langchain import WandbTracer
llm = OpenAI()
prompt = PromptTemplate.from_template("1 + {number} = ")
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[StdOutCallbackHandler(), WandbTracer()])
print(chain.run(number=2))
```
results in the following output before the change
```
WARNING:root:Error in on_chain_start callback: field "child_runs" not yet prepared so type is still a ForwardRef, you might need to call Run.update_forward_refs().
> Entering new LLMChain chain...
Prompt after formatting:
1 + 2 =
WARNING:root:Error in on_chain_end callback: No chain Run found to be traced
> Finished chain.
3
```
but afterwards the callback error messages are gone.
Hi there！
I'm excited to open this PR to add support for using 'Tencent Cloud
VectorDB' as a vector store.
Tencent Cloud VectorDB is a fully-managed, self-developed,
enterprise-level distributed database service designed for storing,
retrieving, and analyzing multi-dimensional vector data. The database
supports multiple index types and similarity calculation methods, with a
single index supporting vector scales up to 1 billion and capable of
handling millions of QPS with millisecond-level query latency. Tencent
Cloud VectorDB not only provides external knowledge bases for large
models to improve their accuracy, but also has wide applications in AI
fields such as recommendation systems, NLP services, computer vision,
and intelligent customer service.
The PR includes:
Implementation of Vectorstore.
I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
make format
make lint
make coverage
make test
This PR brings structural updates to `PlaywrightURLLoader`, aiming at
making the code more readable and extensible through the abstraction of
page evaluation logic. These changes also align this implementation with
a similar structure used in LangChain.js.
The key enhancements include:
1. Introduction of 'PlaywrightEvaluator', an abstract base class for all
evaluators.
2. Creation of 'UnstructuredHtmlEvaluator', a concrete class
implementing 'PlaywrightEvaluator', which uses `unstructured` library
for processing page's HTML content.
3. Extension of 'PlaywrightURLLoader' constructor to optionally accept
an evaluator of the type 'PlaywrightEvaluator'. It defaults to
'UnstructuredHtmlEvaluator' if no evaluator is provided.
4. Refactoring of 'load' and 'aload' methods to use the 'evaluate' and
'evaluate_async' methods of the provided 'PageEvaluator' for page
content handling.
This update brings flexibility to 'PlaywrightURLLoader' as it can now
utilize different evaluators for page processing depending on the
requirement. The abstraction also improves code maintainability and
readability.
Twitter: @ywkim
- Description: Add bloomz_7b, llama-2-7b, llama-2-13b, llama-2-70b to
ErnieBotChat, which only supported ERNIE-Bot-turbo and ERNIE-Bot.
- Issue: #10022,
- Dependencies: no extra dependencies
---------
Co-authored-by: hetianfeng <hetianfeng@meituan.com>
- Description: A change in the documentation example for Azure Cognitive
Vector Search with Scoring Profile so the example works as written
- Issue: #10015
- Dependencies: None
- Tag maintainer: @baskaryan @ruoccofabrizio
- Twitter handle: @poshporcupine
