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@jbrry Could you please add Some sample PRs:
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NISH1001
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[Initial superficial ocmments]
…vector_database_tool
movinam
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Thanks for the PR! I think the text splitter tool is not needed - we can just use the langchain implementation/or any existing implementation and pass the documents to the vector db to be indexed. Please see comments.
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@jbrry There's merge conflict with develop. Could you pull in develop latest changes and resolve? |
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akd/tools/vector_db_tool.py
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| description="Path to the persistent ChromaDB directory.", | ||
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| collection_name: str = Field( | ||
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Can we rename the default name to something else? like akd_vdb or something?
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Summary 📝
This PR adds
FactCheckToolwhich is a wrapper to call the hosted FactReasoner service.It also adds some helper tools which enable further downstream analysis: a
VectorDBToolwhich is based on a Chroma vector database is added so that search artefacts can be stored for downstream analysis or so that FactReasoner can use it for context retrieval.Details
VectorDBTool:akd/tools/vector_db_tool.pyused for interacting with a vector database. It uses ChromaDB for persistent storage and sentence-transformers for creating embeddings. The.index()method is used for adding documents, and_arunruns the retrieval based on a query.DeepLitSearchAgentIntegration: The new functionality has been added as part of a DeepLitSearchAgent run. This shows how we can use the new tools to work along with theDeepLitSearchAgent.Usage
Documentation for the backend deployment is available here.
Standalone example:
It returns the factuality score based on the question and response pair:
I am also listing the test files for each of these tools, which cover initialization and running of the tools and can serve as documentation:
VectorDBTool: test script exampleFactCheckTool: test script example.