chore: import upstream snapshot with attribution

deploy-agentic-rag/server.py

@@ -0,0 +1,64 @@
+# rename .env.example to .env and add the following:
+# SERPER_API_KEY=your_serper_api_key
+# OPENAI_API_KEY=your_openai_api_key
+
+from crewai import Crew, Agent, Task, LLM
+import litserve as ls
+from crewai_tools import SerperDevTool
+
+# If you'd like, you can use a local LLM as well through Ollama. Do this:
+# ollama pull qwen3 in the command line.
+
+# Uncomment the following line and also the llm=llm line in the Agents definitions.
+# llm = LLM(model="ollama/qwen3")
+
+class AgenticRAGAPI(ls.LitAPI):
+    def setup(self, device):
+        researcher_agent = Agent(
+            role="Researcher",
+            goal="Research about the user's query and generate insights",
+            backstory="You are a helpful assistant that can answer questions about the document.",
+            verbose=True,
+            tools=[SerperDevTool()],
+            # llm=llm
+        )
+
+        writer_agent = Agent(
+            role="Writer",
+            goal="Use the available insights to write a concise and informative response to the user's query",
+            backstory="You are a helpful assistant that can write a report about the user's query",
+            verbose=True,
+            # llm=llm
+        )
+        
+        researcher_task = Task(
+            description="Research about the user's query and generate insights: {query}",
+            expected_output="A concise and informative report about the user's query",
+            agent=researcher_agent,
+        )
+
+        writer_task = Task(
+            description="Use the available insights to write a concise and informative response to the user's query: {query}",
+            expected_output="A concise and informative response to the user's query",
+            agent=writer_agent,
+        )
+        
+        self.crew = Crew(
+            agents=[researcher_agent, writer_agent],
+            tasks=[researcher_task, writer_task],
+            verbose=True,
+        )
+
+    def decode_request(self, request):
+        return request["query"]
+
+    def predict(self, query):
+        return self.crew.kickoff(inputs={"query": query})
+
+    def encode_response(self, output):
+        return {"output": output}
+
+if __name__ == "__main__":
+    api = AgenticRAGAPI()
+    server = ls.LitServer(api)
+    server.run(port=8000)