# Add caching to BaseChatModel
Fixes#1644
(Sidenote: While testing, I noticed we have multiple implementations of
Fake LLMs, used for testing. I consolidated them.)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR targets the `API Reference` documentation.
- Several classes and functions missed `docstrings`. These docstrings
were created.
- In several places this
```
except ImportError:
raise ValueError(
```
was replaced to
```
except ImportError:
raise ImportError(
```
vertex Ai chat is broken right now. That is because context is in params
and chat.send_message doesn't accept that as a params.
- Closes issue [ChatVertexAI Error: _ChatSessionBase.send_message() got
an unexpected keyword argument 'context'
#6610](https://github.com/hwchase17/langchain/issues/6610)
Added the functionality to leverage 3 new Codey models from Vertex AI:
- code-bison - Code generation using the existing LLM integration
- code-gecko - Code completion using the existing LLM integration
- codechat-bison - Code chat using the existing chat_model integration
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Already supported in the reverse operation in
`_convert_message_to_dict()`, this just provides parity.
@hwchase17
@agola11
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Fixes: ChatAnthropic was mutating the input message list during
formatting which isn't ideal bc you could be changing the behavior for
other chat models when using the same input
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Related to this https://github.com/hwchase17/langchain/issues/6225
Just copied the implementation from `generate` function to `agenerate`
and tested it.
Didn't run any official tests thought
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Fixes#6225
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Fixes # (issue)
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Trying to call `ChatOpenAI.get_num_tokens_from_messages` returns the
following error for the newly announced models `gpt-3.5-turbo-0613` and
`gpt-4-0613`:
```
NotImplementedError: get_num_tokens_from_messages() is not presently implemented for model gpt-3.5-turbo-0613.See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.
```
This adds support for counting tokens for those models, by counting
tokens the same way they're counted for the previous versions of
`gpt-3.5-turbo` and `gpt-4`.
#### reviewers
- @hwchase17
- @agola11
# Ensure parameters are used by vertexai chat models (PaLM2)
The current version of the google aiplatform contains a bug where
parameters for a chat model are not used as intended.
See https://github.com/googleapis/python-aiplatform/issues/2263
Params can be passed both to start_chat() and send_message(); however,
the parameters passed to start_chat() will not be used if send_message()
is called without the overrides. This is due to the defaults in
send_message() being global values rather than None (there is code in
send_message() which would use the params from start_chat() if the param
passed to send_message() evaluates to False, but that won't happen as
the defaults are global values).
Fixes # 5531
@hwchase17
@agola11
# Lint sphinx documentation and fix broken links
This PR lints multiple warnings shown in generation of the project
documentation (using "make docs_linkcheck" and "make docs_build").
Additionally documentation internal links to (now?) non-existent files
are modified to point to existing documents as it seemed the new correct
target.
The documentation is not updated content wise.
There are no source code changes.
Fixes # (issue)
- broken documentation links to other files within the project
- sphinx formatting (linting)
## Before submitting
No source code changes, so no new tests added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add async versions of predict() and predict_messages()
#4615 introduced a unifying interface for "base" and "chat" LLM models
via the new `predict()` and `predict_messages()` methods that allow both
types of models to operate on string and message-based inputs,
respectively.
This PR adds async versions of the same (`apredict()` and
`apredict_messages()`) that are identical except for their use of
`agenerate()` in place of `generate()`, which means they repurpose all
existing work on the async backend.
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@hwchase17 (follows his work on #4615)
@agola11 (async)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# changed ValueError to ImportError
Code cleaning.
Fixed inconsistencies in ImportError handling. Sometimes it raises
ImportError and sometime ValueError.
I've changed all cases to the `raise ImportError`
Also:
- added installation instruction in the error message, where it missed;
- fixed several installation instructions in the error message;
- fixed several error handling in regards to the ImportError
The Anthropic classes used `BaseLanguageModel.get_num_tokens` because of
an issue with multiple inheritance. Fixed by moving the method from
`_AnthropicCommon` to both its subclasses.
This change will significantly speed up token counting for Anthropic
users.
This PR adds exponential back-off to the Google PaLM api to gracefully
handle rate limiting errors.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Add Invocation Params to Logged Run
Adds an llm type to each chat model as well as an override of the dict()
method to log the invocation parameters for each call
---------
Co-authored-by: Ankush Gola <ankush.gola@gmail.com>
### Add on_chat_message_start to callback manager and base tracer
Goal: trace messages directly to permit reloading as chat messages
(store in an integration-agnostic way)
Add an `on_chat_message_start` method. Fall back to `on_llm_start()` for
handlers that don't have it implemented.
Does so in a non-backwards-compat breaking way (for now)
- Add langchain.llms.GooglePalm for text completion,
- Add langchain.chat_models.ChatGooglePalm for chat completion,
- Add langchain.embeddings.GooglePalmEmbeddings for sentence embeddings,
- Add example field to HumanMessage and AIMessage so that users can feed
in examples into the PaLM Chat API,
- Add system and unit tests.
Note async completion for the Text API is not yet supported and will be
included in a future PR.
Happy for feedback on any aspect of this PR, especially our choice of
adding an example field to Human and AI Message objects to enable
passing example messages to the API.
With longer context and completions, gpt-3.5-turbo and, especially,
gpt-4, will more times than not take > 60seconds to respond.
Based on some other discussions, it seems like this is an increasingly
common problem, especially with summarization tasks.
- https://github.com/hwchase17/langchain/issues/3512
- https://github.com/hwchase17/langchain/issues/3005
OpenAI's max 600s timeout seems excessive, so I settled on 120, but I do
run into generations that take >240 seconds when using large prompts and
completions with GPT-4, so maybe 240 would be a better compromise?
re
https://github.com/hwchase17/langchain/issues/439#issuecomment-1510442791
I think it's not polite for a library to use the root logger
both of these forms are also used:
```
logger = logging.getLogger(__name__)
logger = logging.getLogger(__file__)
```
I am not sure if there is any reason behind one vs the other? (...I am
guessing maybe just contributed by different people)
it seems to me it'd be better to consistently use
`logging.getLogger(__name__)`
this makes it easier for consumers of the library to set up log
handlers, e.g. for everything with `langchain.` prefix
Harrison Chase