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Update documentation for Vertex AI

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Rhys Tyers
2023-11-15 10:24:56 +00:00
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17
Usage.md
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@ -303,6 +303,23 @@ key = ...
Also review the [AiHandler](pr_agent/algo/ai_handler.py) file for instruction how to set keys for other models.
#### Vertex AI
To use Google's Vertex AI platform and its associated models (chat-bison/codechat-bison) set:
```
[config] # in configuration.toml
model = "vertex_ai/codechat-bison"
[vertexai] # in .secrets.toml
vertex_project = "my-google-cloud-project"
vertex_location = ""
```
Your [application default credentials](https://cloud.google.com/docs/authentication/application-default-credentials) will be used for authentication so there is no need to set explicit credentials in most environments.
If you do want to set explicit credentials then you can use the `GOOGLE_APPLICATION_CREDENTIALS` environment variable set to a path to a json credentials file.
### Working with large PRs
The default mode of CodiumAI is to have a single call per tool, using GPT-4, which has a token limit of 8000 tokens.

4
pr_agent/settings/.secrets_template.toml
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@ -36,6 +36,10 @@ api_base = "" # the base url for your huggingface inference endpoint
[ollama]
api_base = "" # the base url for your local Llama 2, Code Llama, and other models inference endpoint. Acquire through https://ollama.ai/
[vertexai]
vertex_project = "" # the google cloud platform project name for your vertexai deployment
vertex_location = "" # the google cloud platform location for your vertexai deployment
[github]
# ---- Set the following only for deployment type == "user"
user_token = "" # A GitHub personal access token with 'repo' scope.