When I try to import the Class HuggingFaceEndpoint I get an Import
Error: cannot import name 'HuggingFaceEndpoint' from 'langchain'.
(langchain version 0.0.88)
These two imports work fine: from langchain import HuggingFacePipeline
and from langchain import HuggingFaceHub.
So I corrected the import statement in the example. There is probably a
better solution to this, but this fixes the Error for me.
This import works fine:
```python
from langchain import Anthropic
```
This import does not:
```python
from langchain import AI21
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'AI21' from 'langchain' (/opt/anaconda3/envs/fed_nlp/lib/python3.9/site-packages/langchain/__init__.py)
```
I think there is a slight documentation inconsistency here:
https://langchain.readthedocs.io/en/latest/reference/modules/llms.html
This PR starts to solve that. Should all the import examples be
`from langchain.llms import X` instead of `from langchain import X`?
This PR updates `PromptLayerOpenAI` to now support requests using the
[Async
API](https://langchain.readthedocs.io/en/latest/modules/llms/async_llm.html)
It also updates the documentation on Async API to let users know that
PromptLayerOpenAI also supports this.
`PromptLayerOpenAI` now redefines `_agenerate` a similar was to how it
redefines `_generate`
Supporting asyncio in langchain primitives allows for users to run them
concurrently and creates more seamless integration with
asyncio-supported frameworks (FastAPI, etc.)
Summary of changes:
**LLM**
* Add `agenerate` and `_agenerate`
* Implement in OpenAI by leveraging `client.Completions.acreate`
**Chain**
* Add `arun`, `acall`, `_acall`
* Implement them in `LLMChain` and `LLMMathChain` for now
**Agent**
* Refactor and leverage async chain and llm methods
* Add ability for `Tools` to contain async coroutine
* Implement async SerpaPI `arun`
Create demo notebook.
Open questions:
* Should all the async stuff go in separate classes? I've seen both
patterns (keeping the same class and having async and sync methods vs.
having class separation)
Was passing prompt in directly as string and getting nonsense outputs.
Had to inspect source code to realize that first arg should be a list.
Could be nice if there was an explicit error or warning, seems like this
could be a common mistake.
text-davinci-003 supports a context size of 4097 tokens so return 4097
instead of 4000 in modelname_to_contextsize() for text-davinci-003
Co-authored-by: Bill Kish <bill@cogniac.co>
* add implementations of `BaseCallbackHandler` to support tracing:
`SharedTracer` which is thread-safe and `Tracer` which is not and is
meant to be used locally.
* Tracers persist runs to locally running `langchain-server`
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Since the tokenizer and model are constructed manually, model_kwargs
needs to
be passed to their constructors. Additionally, the pipeline has a
specific
named parameter to pass these with, which can provide forward
compatibility if
they are used for something other than tokenizer or model construction.
Add
[`logit_bias`](https://beta.openai.com/docs/api-reference/completions/create#completions/create-logit_bias)
params to OpenAI
See [here](https://beta.openai.com/tokenizer) for the tokenizer.
NB: I see that others (like Cohere) have the same parameter, but since I
don't have an access to it, I don't want to make a mistake.
---
Just to make sure the default "{}" works for openai:
```
from langchain.llms import OpenAI
OPENAI_API_KEY="XXX"
llm = OpenAI(openai_api_key=OPENAI_API_KEY)
llm.generate('Write "test":')
llm = OpenAI(openai_api_key=OPENAI_API_KEY, logit_bias={'9288': -100, '1332': -100, '14402': -100, '6208': -100})
llm.generate('Write "test":')
```
Add `finish_reason` to `Generation` as well as extend
`BaseOpenAI._generate` to include it in the output. This can be useful
for usage in downstream tasks when we need to filter for only
generations that finished because of `"stop"` for example. Maybe we
should add this to `LLMChain` as well?
For more details, see
https://beta.openai.com/docs/guides/completion/best-practices
Signed-off-by: Diwank Singh Tomer <diwank.singh@gmail.com>
this is the second PR of #519.
in #519 I suggested deleting Extra.forbid.
I was very confused but I replaced Extra.forbid to Extra.ignore, which
is the default of pydantic.
Since the
[BaseLLM](4b7b8229de/langchain/llms/base.py (L20))
from which it is inherited is set in Extra.forbid, I wanted to avoid
having the Extra.forbid settings inherited by simply deleting it.
As talking #519, I made 2 PRs.
this is the first PR for adding a logger.
I am concerned about the following two points and would appreciate your
opinion.
1. Since the logger is not formatted, the statement itself is output
like a print statement, and I thought it was difficult to understand
that it was a warning, so I put WARNING! at the beginning of the warning
statement. After the logger formatting is done properly, the word
WARNING can be repeated.
2. Statement `Please confirm that {field_name} is what you intended.`
can be replaced like `If {field_name} is intended parameters, enter it
to model_kwargs`
thank you!
Yongtae
Hi! This PR adds support for the Azure OpenAI service to LangChain.
I've tried to follow the contributing guidelines.
Co-authored-by: Keiji Kanazawa <{ID}+{username}@users.noreply.github.com>
Harrison Chase
Enrico Shippole
Iskren Ivov Chernev
Dennis Antela Martinez
Konstantin Hebenstreit
Zach Schillaci
trigaten
Jonathan Pedoeem
Ankush Gola
Enrico Shippole
Oliver Klingefjord
Bagatur
bair82
Bill Kish
xloem
Benjamin
Diwank Singh Tomer
Yongtae723
Keiji Kanazawa
mrbean