Add Exa search connector

python/.env.example

@@ -21,6 +21,7 @@ WEAVIATE_URL=""
 WEAVIATE_API_KEY=""
 GOOGLE_SEARCH_ENGINE_ID=""
 BRAVE_API_KEY=""
+EXA_API_KEY=""
 REDIS_CONNECTION_STRING=""
 AZCOSMOS_API=""
 AZCOSMOS_CONNSTR=""

python/samples/concepts/README.md

@@ -229,6 +229,7 @@
 
 - [Bing Text Search as Plugin](./search/bing_text_search_as_plugin.py)
 - [Brave Text Search as Plugin](./search/brave_text_search_as_plugin.py)
+- [Exa Text Search as Plugin](./search/exa_text_search_as_plugin.py)
 - [Google Text Search as Plugin](./search/google_text_search_as_plugin.py)
 
 ### Service Selector - Shows how to create and use a custom service selector class

python/samples/concepts/search/exa_text_search_as_plugin.py

@@ -0,0 +1,125 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from collections.abc import Awaitable, Callable
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
+from semantic_kernel.connectors.exa import ExaSearch
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
+from semantic_kernel.functions import KernelArguments, KernelParameterMetadata
+
+"""
+This project demonstrates how to integrate the Exa Search API as a plugin into the Semantic Kernel
+framework to enable conversational AI capabilities with real-time web information.
+
+To use Exa Search, you need an API key, which can be obtained by login to
+https://dashboard.exa.ai/api-keys and creating a key.
+After that store it under the name `EXA_API_KEY` in a .env file or your environment variables.
+"""
+
+kernel = Kernel()
+kernel.add_service(OpenAIChatCompletion(service_id="chat"))
+kernel.add_function(
+    plugin_name="exa",
+    function=ExaSearch().create_search_function(
+        function_name="exa_search",
+        description="Get details about Semantic Kernel concepts.",
+        parameters=[
+            KernelParameterMetadata(
+                name="query",
+                description="The search query.",
+                type="str",
+                is_required=True,
+                type_object=str,
+            ),
+            KernelParameterMetadata(
+                name="top",
+                description="The number of results to return.",
+                type="int",
+                is_required=False,
+                default_value=2,
+                type_object=int,
+            ),
+        ],
+    ),
+)
+chat_function = kernel.add_function(
+    prompt="{{$chat_history}}{{$user_input}}",
+    plugin_name="ChatBot",
+    function_name="Chat",
+)
+execution_settings = OpenAIChatPromptExecutionSettings(
+    service_id="chat",
+    max_tokens=2000,
+    temperature=0.7,
+    top_p=0.8,
+    function_choice_behavior=FunctionChoiceBehavior.Auto(auto_invoke=True),
+)
+
+history = ChatHistory()
+system_message = """
+You are a chat bot, specialized in Semantic Kernel, Microsoft LLM orchestration SDK.
+Assume questions are related to that, and use the Exa search plugin to find answers.
+"""
+history.add_system_message(system_message)
+history.add_user_message("Hi there, who are you?")
+history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
+
+arguments = KernelArguments(settings=execution_settings)
+
+
+@kernel.filter(filter_type=FilterTypes.FUNCTION_INVOCATION)
+async def log_exa_filter(
+    context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]]
+):
+    if context.function.plugin_name == "exa":
+        print("Calling Exa search with arguments:")
+        if "query" in context.arguments:
+            print(f'  Query: "{context.arguments["query"]}"')
+        if "top" in context.arguments:
+            print(f'  Top: "{context.arguments["top"]}"')
+        await next(context)
+        print("Exa search completed.")
+    else:
+        await next(context)
+
+
+async def chat() -> bool:
+    try:
+        user_input = input("User:> ")
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+
+    if user_input == "exit":
+        print("\n\nExiting chat...")
+        return False
+    arguments["user_input"] = user_input
+    arguments["chat_history"] = history
+    result = await kernel.invoke(chat_function, arguments=arguments)
+    print(f"Mosscap:> {result}")
+    history.add_user_message(user_input)
+    history.add_assistant_message(str(result))
+    return True
+
+
+async def main():
+    chatting = True
+    print(
+        "Welcome to the chat bot!\
+        \n  Type 'exit' to exit.\
+        \n  Try to find out more about the inner workings of Semantic Kernel."
+    )
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())