## Description
This PR introduces a minor change to the TitanTakeoff integration.
Instead of specifying a port on localhost, this PR will allow users to
specify a baseURL instead. This will allow users to use the integration
if they have TitanTakeoff deployed externally (not on localhost). This
removes the hardcoded reference to localhost "http://localhost:{port}".
### Info about Titan Takeoff
Titan Takeoff is an inference server created by
[TitanML](https://www.titanml.co/) that allows you to deploy large
language models locally on your hardware in a single command. Most
generative model architectures are included, such as Falcon, Llama 2,
GPT2, T5 and many more.
Read more about Titan Takeoff here:
-
[Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e)
- [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started)
### Dependencies
No new dependencies are introduced. However, users will need to install
the titan-iris package in their local environment and start the Titan
Takeoff inferencing server in order to use the Titan Takeoff
integration.
Thanks for your help and please let me know if you have any questions.
cc: @hwchase17 @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Hi,
this PR contains loader / parser for Azure Document intelligence which
is a ML-based service to ingest arbitrary PDFs / images, even if
scanned. The loader generates Documents by pages of the original
document. This is my first contribution to LangChain.
Unfortunately I could not find the correct place for test cases. Happy
to add one if you can point me to the location, but as this is a
cloud-based service, a test would require network access and credentials
- so might be of limited help.
Dependencies: The needed dependency was already part of pyproject.toml,
no change.
Twitter: feel free to mention @LarsAC on the announcement
This small PR aims at supporting the following missing parameters in the
`HuggingfaceTextGen` LLM:
- `return_full_text` - sometimes useful for completion tasks
- `do_sample` - quite handy to control the randomness of the model.
- `watermark`
@hwchase17 @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR follows the **Eden AI (LLM + embeddings) integration**. #8633
We added an optional parameter to choose different AI models for
providers (like 'text-bison' for provider 'google', 'text-davinci-003'
for provider 'openai', etc.).
Usage:
```python
llm = EdenAI(
feature="text",
provider="google",
params={
"model": "text-bison", # new
"temperature": 0.2,
"max_tokens": 250,
},
)
```
You can also change the provider + model after initialization
```python
llm = EdenAI(
feature="text",
provider="google",
params={
"temperature": 0.2,
"max_tokens": 250,
},
)
prompt = """
hi
"""
llm(prompt, providers='openai', model='text-davinci-003') # change provider & model
```
The jupyter notebook as been updated with an example well.
Ping: @hwchase17, @baskaryan
---------
Co-authored-by: RedhaWassim <rwasssim@gmail.com>
Co-authored-by: sam <melaine.samy@gmail.com>
Adapting Microsoft Presidio to other languages requires a bit more work,
so for now it will be good idea to remove the language option to choose,
so as not to cause errors and confusion.
https://microsoft.github.io/presidio/analyzer/languages/
I will handle different languages after the weekend 😄
This adds sqlite-vss as an option for a vector database. Contains the
code and a few tests. Tests are passing and the library sqlite-vss is
added as optional as explained in the contributing guidelines. I
adjusted the code for lint/black/ and mypy. It looks that everything is
currently passing.
Adding sqlite-vss was mentioned in this issue:
https://github.com/langchain-ai/langchain/issues/1019.
Also mentioned here in the sqlite-vss repo for the curious:
https://github.com/asg017/sqlite-vss/issues/66
Maintainer tag: @baskaryan
---------
Co-authored-by: Philippe Oger <philippe.oger@adevinta.com>
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>
