Agent with Video: add docs context

agent_chat/src/agents/agent.py

@@ -1,7 +1,7 @@
 from datetime import timedelta
 
 from pydantic import BaseModel
-from restack_ai.agent import agent, import_functions, log
+from restack_ai.agent import agent, import_functions, log, AgentError
 
 with import_functions():
     from src.functions.llm_chat import LlmChatInput, Message, llm_chat
@@ -23,15 +23,22 @@ def __init__(self) -> None:
 
     @agent.event
     async def messages(self, messages_event: MessagesEvent) -> list[Message]:
-        log.info(f"Received messages: {messages_event.messages}")
-        self.messages.extend(messages_event.messages)
-        assistant_message = await agent.step(
-            function=llm_chat,
-            function_input=LlmChatInput(messages=self.messages),
-            start_to_close_timeout=timedelta(seconds=120),
-        )
-        self.messages.append(assistant_message)
-        return self.messages
+        try:
+            log.info(f"Received messages: {messages_event.messages}")
+            self.messages.extend(messages_event.messages)
+
+            log.info(f"Calling llm_chat with messages: {self.messages}")
+            assistant_message = await agent.step(
+                function=llm_chat,
+                function_input=LlmChatInput(messages=self.messages),
+                start_to_close_timeout=timedelta(seconds=120),
+            )
+
+            self.messages.append(assistant_message)
+            return self.messages
+        except Exception as e:
+                log.error(f"Error in messages: {e}")
+                raise AgentError(f"Error in messages: {e}")
 
     @agent.event
     async def end(self, end: EndEvent) -> EndEvent:

agent_telephony/twilio/livekit-trunk-setup/.gitignore

@@ -2,3 +2,4 @@
 .venv
 .env
 uv.lock
+inbound_trunk.json

agent_video/src/agents/agent.py

@@ -5,7 +5,7 @@
 
 with import_functions():
     from src.functions.llm_chat import LlmChatInput, Message, llm_chat
-
+    from src.functions.context_docs import context_docs
 
 class MessagesEvent(BaseModel):
     messages: list[Message]
@@ -42,4 +42,11 @@ async def end(self, end: EndEvent) -> EndEvent:
 
     @agent.run
     async def run(self) -> None:
+        docs = await agent.step(function=context_docs)
+        system_prompt=f"""
+        You are an interactive video assistant, your answers will be used in text to speech so try to keep answers short and concise so that interaction is seamless.
+        You can answer questions about the following documentation:
+        {docs}
+        """
+        self.messages.append(Message(role="system", content=system_prompt))
         await agent.condition(lambda: self.end)

agent_video/src/functions/context_docs.py

@@ -0,0 +1,25 @@
+from restack_ai.function import function, log
+import aiohttp
+
+
+async def fetch_content_from_url(url: str) -> str:
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url) as response:
+            if response.status == 200:
+                return await response.text()
+            else:
+                log.error("Failed to fetch content", status=response.status)
+                raise Exception(f"Failed to fetch content: {response.status}")
+
+
+@function.defn()
+async def context_docs() -> str:
+    try:
+        docs_content = await fetch_content_from_url("https://docs.restack.io/llms-full.txt")
+        log.info("Fetched content from URL", content=len(docs_content))
+
+        return docs_content
+
+    except Exception as e:
+        log.error("llm_chat function failed", error=str(e))
+        raise

agent_video/src/services.py

@@ -10,6 +10,7 @@
 from src.functions.llm_chat import llm_chat
 from src.functions.pipeline import pipecat_pipeline
 from src.workflows.room import RoomWorkflow
+from src.functions.context_docs import context_docs
 
 
 async def main() -> None:
@@ -19,6 +20,7 @@ async def main() -> None:
         functions=[
             llm_chat,
             pipecat_pipeline,
+            context_docs,
         ],
     )