support Bedrock #81
support Bedrock #81
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hi @JackHopkins How can I can contact you ...etc (discord invite is invalid) thanks |
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Hey Kevin -
You can join here: https://discord.gg/ymmV6FKg
LMK if you can't and I can add you via email!
Daniel Kwak, CEO
Paperplane ( https://www.paperplane.ai/ )
404-704-2999
…On Tue, Nov 21, 2023 at 6:01 PM, Kevin Truong < ***@***.*** > wrote:
hi @ JackHopkins ( https://github.com/JackHopkins )
WIP for bedrock support
I'm testing at my side, working. But need align with you to make a good
progress for the MR.
How can I can contact you ...etc (discord invite is invalid)
thanks
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Can you have a look at these comments and let me know what you think?
Additionally, it'd be really helpful to have some automated tests for Bedrock (possibly mocking out the Bedrock client) so we can ensure that we don't break anything as we continue development.
| if ("error" in response and | ||
| "code" in response["error"] and | ||
| response["error"]["code"] == 'invalid_api_key'): | ||
| raise Exception(f"The supplied OpenAI API key {self.api_key} is invalid") |
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Please change to "Anthropic API key"
| continue | ||
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| if not choice: | ||
| raise Exception("OpenAI API failed to generate a response") |
| modelId=model, | ||
| contentType="application/json", | ||
| accept="application/json") | ||
| return json.loads(response.get('body').read().decode())['completion'] |
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We shouldn't be returning this here - we should loop the counter below in the case where the bedrock_runtime throws an exception.
| return 'anthropic.claude-instant-v1' | ||
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| elif api_model_name == 'openai': | ||
| return "" |
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Let me double check to ensure whether this works.
| "Content-Type": "application/json", | ||
| } | ||
| response = requests.post( | ||
| OPENAI_URL, headers=openai_headers, json=params, timeout=50 |
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We should replace this with the invoke_model you have above.
| align_dataset_size = self.dataset_sizes["alignments"][func_hash] if func_hash in self.dataset_sizes[ | ||
| "alignments"] else 0 | ||
| patch_dataset_size = self.dataset_sizes["patches"][func_hash] if func_hash in self.dataset_sizes[ | ||
| "patches"] else 0 | ||
| total_dataset_size = align_dataset_size + patch_dataset_size | ||
| training_file_id = response["id"] | ||
| # submit the finetuning job | ||
| try: | ||
| finetuning_response = openai.FineTuningJob.create(training_file=training_file_id, model="gpt-3.5-turbo", |
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We need to figure out a way to call the logic to trigger a finetune in the relevant LLM provider class (i.e OpenAI, Bedrock, etc) - this is not the right place for this logic to live now we are adding support for more providers.
| from monkey_patch.language_models.llm_api_abc import LLM_Api | ||
| import os | ||
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| OPENAI_URL = "https://api.openai.com/v1/chat/completions" |
| OPENAI_URL = "https://api.openai.com/v1/chat/completions" | ||
| import requests | ||
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| AWS_REGION = os.environ.get("AWS_DEFAULT_REGION", "us-east-1") |
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We should instantiate all envvars / bedrock specific logic inside the init of the Bedrock class - catching and reraising any thrown exceptions.
| from monkey_patch.language_models.openai_api import Openai_API | ||
| from monkey_patch.models.language_model_output import LanguageModelOutput | ||
| from monkey_patch.utils import approximate_token_count | ||
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| class ApiModelFactory: |
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Can we please have this factory inside its own file.
src/monkey_patch/monkey.py
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| @@ -68,10 +68,9 @@ class Monkey: | |||
| logging.addLevelName(PATCH_LEVEL_NUM, "PATCH") | |||
| logging.basicConfig(level=ALIGN_LEVEL_NUM) | |||
| logger = logger_factory(__name__) | |||
| language_modeler = LanguageModel() | |||
| language_modeler = LanguageModel(api_model=os.getenv('API_MODEL')) | |||
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We should add the API_MODEL envvar to .example.env to make it obvious that it is required.
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