Search and Knowledge

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from signalwire import AgentBase
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class KnowledgeAgent(AgentBase):
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    def __init__(self):
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        super().__init__(name="knowledge-agent")
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        self.add_language("English", "en-US", "rime.spore")
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        self.prompt_add_section(
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            "Role",
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            "You are a helpful assistant with access to company documentation. "
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            "Use the search_documents function to find relevant information."
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        )
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        # Add search skill with local index
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        self.add_skill(
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            "native_vector_search",
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            index_file="./knowledge.swsearch",
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            count=5,  # Number of results
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            tool_name="search_documents",
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            tool_description="Search the company documentation"
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        )
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if __name__ == "__main__":
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    agent = KnowledgeAgent()
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    agent.run()

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self.add_skill(
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    "native_vector_search",
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    # Index source (choose one)
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    index_file="./knowledge.swsearch",       # Local SQLite index
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    # OR
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    # remote_url="http://search-server:8001", # Remote search server
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    # index_name="default",
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    # Search parameters
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    count=5,                    # Results to return (1-20)
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    distance_threshold=0.0,     # Min score (0.0-1.0)
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    tags=["docs", "api"],       # Filter by tags
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    # Tool configuration
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    tool_name="search_knowledge",
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    tool_description="Search the knowledge base for information"
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)

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self.add_skill(
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    "native_vector_search",
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    backend="pgvector",
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    connection_string="postgresql://user:pass@localhost/db",
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    collection_name="knowledge_base",
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    count=5,
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    tool_name="search_docs"
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)

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#!/usr/bin/env python3
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## documentation_agent.py - Agent that searches documentation
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from signalwire import AgentBase
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from signalwire.core.function_result import FunctionResult
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class DocumentationAgent(AgentBase):
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    """Agent that searches documentation to answer questions"""
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    def __init__(self):
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        super().__init__(name="docs-agent")
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        self.add_language("English", "en-US", "rime.spore")
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        self.prompt_add_section(
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            "Role",
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            "You are a documentation assistant. When users ask questions, "
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            "search the documentation to find accurate answers. Always cite "
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            "the source document when providing information."
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        )
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        self.prompt_add_section(
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            "Instructions",
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            """
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            1. When asked a question, use search_docs to find relevant information
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            2. Review the search results carefully
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            3. Synthesize an answer from the results
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            4. Mention which document the information came from
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            5. If nothing relevant is found, say so honestly
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            """
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        )
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        # Add a simple search function for demonstration
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        # In production, use native_vector_search skill with a .swsearch index:
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        # self.add_skill("native_vector_search", index_file="./docs.swsearch")
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        self.define_tool(
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            name="search_docs",
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            description="Search the documentation for information",
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            parameters={
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                "type": "object",
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                "properties": {
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                    "query": {"type": "string", "description": "Search query"}
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                },
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                "required": ["query"]
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            },
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            handler=self.search_docs
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        )
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    def search_docs(self, args, raw_data):
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        """Stub search function for demonstration"""
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        query = args.get("query", "")
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        return FunctionResult(
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            f"Search results for '{query}': This is a demonstration. "
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            "In production, use native_vector_search skill with a .swsearch index file."
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        )
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if __name__ == "__main__":
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    agent = DocumentationAgent()
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    agent.run()

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## Product documentation
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self.add_skill(
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    "native_vector_search",
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    index_file="./products.swsearch",
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    tool_name="search_products",
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    tool_description="Search product catalog and specifications"
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)
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## Support articles
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self.add_skill(
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    "native_vector_search",
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    index_file="./support.swsearch",
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    tool_name="search_support",
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    tool_description="Search support articles and troubleshooting guides"
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)
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## API documentation
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self.add_skill(
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    "native_vector_search",
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    index_file="./api-docs.swsearch",
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    tool_name="search_api",
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    tool_description="Search API reference documentation"
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)

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self.prompt_add_section(
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    "Search Instructions",
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    """
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    When users ask questions:
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    1. First search the documentation using search_docs
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    2. Review all results before answering
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    3. Cite which document your answer came from
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    4. If results aren't relevant, try a different search query
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    5. If no results help, acknowledge you couldn't find the answer
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    """
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)

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self.add_skill(
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    "native_vector_search",
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    index_file="./knowledge.swsearch",
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    tags=["policies", "returns"],  # Only search these categories
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    tool_name="search_policies"
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)