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docs: Update JSONSearchTool and RagTool configuration parameter from 'embedder' to 'embedding_model' (#2311)

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Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
This commit is contained in:
Lorenze Jay
2025-03-20 10:11:37 -07:00
committed by GitHub
parent 794574957e
commit 2155acb3a3
2 changed files with 10 additions and 8 deletions
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10
docs/tools/jsonsearchtool.mdx
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@@ -7,8 +7,10 @@ icon: file-code
# `JSONSearchTool` # `JSONSearchTool`
<Note> <Note>
The JSONSearchTool is currently in an experimental phase. This means the tool is under active development, and users might encounter unexpected behavior or changes. The JSONSearchTool is currently in an experimental phase. This means the tool
We highly encourage feedback on any issues or suggestions for improvements. is under active development, and users might encounter unexpected behavior or
changes. We highly encourage feedback on any issues or suggestions for
improvements.
</Note> </Note>
## Description ## Description
@@ -60,7 +62,7 @@ tool = JSONSearchTool(
# stream=true, # stream=true,
}, },
}, },
"embedder": { "embedding_model": {
"provider": "google", # or openai, ollama, ... "provider": "google", # or openai, ollama, ...
"config": { "config": {
"model": "models/embedding-001", "model": "models/embedding-001",
@@ -70,4 +72,4 @@ tool = JSONSearchTool(
}, },
} }
) )
``` ```

8
docs/tools/ragtool.mdx
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@@ -8,8 +8,8 @@ icon: vector-square
## Description ## Description
The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain. The `RagTool` is designed to answer questions by leveraging the power of Retrieval-Augmented Generation (RAG) through EmbedChain.
It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources. It provides a dynamic knowledge base that can be queried to retrieve relevant information from various data sources.
This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers. This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
## Example ## Example
@@ -138,7 +138,7 @@ config = {
"model": "gpt-4", "model": "gpt-4",
} }
}, },
"embedder": { "embedding_model": {
"provider": "openai", "provider": "openai",
"config": { "config": {
"model": "text-embedding-ada-002" "model": "text-embedding-ada-002"
@@ -151,4 +151,4 @@ rag_tool = RagTool(config=config, summarize=True)
## Conclusion ## Conclusion
The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses. The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.