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feat: extract_text tool for LangGraph agent
Browse files- retriever.py +31 -29
retriever.py
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from smolagents import Tool
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from langchain_community.retrievers import BM25Retriever
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from langchain.docstore.document import Document
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import datasets
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description = "Retrieves detailed information about gala guests based on their name or relation."
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inputs = {
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"query": {
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"type": "string",
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"description": "The name or relation of the guest you want information about."
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}
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}
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output_type = "string"
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def __init__(self, docs):
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self.is_initialized = False
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self.retriever = BM25Retriever.from_documents(docs)
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def forward(self, query: str):
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results = self.retriever.get_relevant_documents(query)
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if results:
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return "\n\n".join([doc.page_content for doc in results[:3]])
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else:
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return "No matching guest information found."
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def load_guest_dataset():
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guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
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# Convert dataset entries into Document objects
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for guest in guest_dataset
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# Return the
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return
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import datasets
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from langchain.docstore.document import Document
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from langchain_community.retrievers import BM25Retriever
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from langchain.tools import Tool
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def load_guest_dataset():
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"""Loads the guest dataset and converts it into Document objects."""
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guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
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# Convert dataset entries into Document objects
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for guest in guest_dataset
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]
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# Return the documents
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return docs
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# Load the dataset
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docs = load_guest_dataset()
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# Initialize the retriever
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bm25_retriever = BM25Retriever.from_documents(docs)
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def extract_text(query: str) -> str:
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"""Retrieves detailed information about gala guests based on their name or relation."""
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results = bm25_retriever.invoke(query)
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if results:
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return results[0].page_content # [doc.page_content for doc in results[:1]]), :3
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else:
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return "No matching guest information found."
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guest_info_tool = Tool(
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name="guest_info_retriever",
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func=extract_text,
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description="Retrieves detailed information about gala guests based on their name or relation."
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)
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if __name__ == "__main__":
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query = "Marie"
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print(f"query: {query}:\nretrieval: {extract_text(query)}")
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