Solves https://github.com/hwchase17/langchain/issues/1412
Currently `OpenAIChat` inherits the way it calculates the number of
tokens, `get_num_token`, from `BaseLLM`.
In the other hand `OpenAI` inherits from `BaseOpenAI`.
`BaseOpenAI` and `BaseLLM` uses different methodologies for doing this.
The first relies on `tiktoken` while the second on `GPT2TokenizerFast`.
The motivation of this PR is:
1. Bring consistency about the way of calculating number of tokens
`get_num_token` to the `OpenAI` family, regardless of `Chat` vs `non
Chat` scenarios.
2. Give preference to the `tiktoken` method as it's serverless friendly.
It doesn't require downloading models which might make it incompatible
with `readonly` filesystems.
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":')
```
Dennis Aumiller