Custom model docs (#368)

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Gui Vieira
2024-03-22 03:01:34 -03:00
committed by GitHub
parent 637bd885cf
commit aa0eb02968
17 changed files with 440 additions and 88 deletions

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@@ -1,8 +1,5 @@
# GitHubSearchTool
!!! note "Depend on OpenAI"
All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
@@ -40,3 +37,31 @@ tool = GitHubSearchTool(
## Arguments
- `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
- `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.
## Custom model and embeddings
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
```python
tool = GitHubSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
```