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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>
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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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docs/tools/ragtool.mdx
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@@ -8,8 +8,8 @@ icon: vector-square
## Description
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.
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.
This tool is particularly useful for applications that require access to a vast array of information and need to provide contextually relevant answers.
## Example
@@ -138,7 +138,7 @@ config = {
"model": "gpt-4",
}
},
"embedder": {
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-ada-002"
@@ -151,4 +151,4 @@ rag_tool = RagTool(config=config, summarize=True)
## 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.