init

functionality/rag/agentic_usage.py

@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+"""The agentic usage example for RAG in AgentScope, where the agent is
+equipped with RAG tools to answer questions based on a knowledge base.
+
+The example is more challenging for the agent, requiring the agent to
+adjust the retrieval parameters to get relevant results.
+"""
+import asyncio
+import os
+
+from agentscope.agent import ReActAgent, UserAgent
+from agentscope.embedding import DashScopeTextEmbedding
+from agentscope.formatter import DashScopeChatFormatter
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+from agentscope.rag import QdrantStore, SimpleKnowledge, TextReader
+from agentscope.tool import Toolkit
+
+# Create a knowledge base instance
+knowledge = SimpleKnowledge(
+    embedding_store=QdrantStore(
+        location=":memory:",
+        collection_name="test_collection",
+        dimensions=1024,  # The dimension of the embedding vectors
+    ),
+    embedding_model=DashScopeTextEmbedding(
+        api_key=os.environ["DASHSCOPE_API_KEY"],
+        model_name="text-embedding-v4",
+    ),
+)
+
+
+async def main() -> None:
+    """The main entry of the agent usage example for RAG in AgentScope."""
+
+    # Store some things into the knowledge base for demonstration
+    # In practice, the VDB store would be pre-filled with relevant data
+    reader = TextReader(chunk_size=1024, split_by="sentence")
+    documents = await reader(
+        text=(
+            # Fake personal profile for demonstration
+            "I'm John Doe, 28 years old. My best friend is James "
+            "Smith. I live in San Francisco. I work at OpenAI as a "
+            "software engineer. I love hiking and photography. "
+            "My father is Michael Doe, a doctor. I'm very proud of him. "
+            "My mother is Sarah Doe, a teacher. She is very kind and "
+            "always helps me with my studies.\n"
+            "I'm now a PhD student at Stanford University, majoring in "
+            "Computer Science. My advisor is Prof. Jane Williams, who is "
+            "a leading expert in artificial intelligence. I have published "
+            "several papers in top conferences, such as NeurIPS and ICML. "
+        ),
+    )
+    await knowledge.add_documents(documents)
+
+    # Create a toolkit and register the RAG tool function
+    toolkit = Toolkit()
+    toolkit.register_tool_function(
+        knowledge.retrieve_knowledge,
+        func_description=(  # Provide a clear description for the tool
+            "Retrieve relevant documents from the knowledge base, which is "
+            "relevant to John Doe's profile. Note the `query` parameter is "
+            "very important for the retrieval quality, and you can try many "
+            "different queries to get the best results. Adjust the `limit` "
+            "and `score_threshold` parameters to get more or fewer results."
+        ),
+    )
+
+    # Create an agent and a user
+    agent = ReActAgent(
+        name="Friday",
+        sys_prompt=(
+            "You're a helpful assistant named Friday. "
+            "You're equipped with a 'retrieve_knowledge' tool to help you "
+            "know about the user named John Doe. "
+            "NOTE to adjust the `score_threshold` parameters when you cannot "
+            "get relevant results. "
+        ),
+        toolkit=toolkit,
+        model=DashScopeChatModel(
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+            model_name="qwen3-max-preview",
+        ),
+        formatter=DashScopeChatFormatter(),
+    )
+    user = UserAgent(name="User")
+
+    # A simple conversation loop beginning with a preset question
+    msg = Msg(
+        "user",
+        "I'm John Doe. Do you know my father?",
+        "user",
+    )
+    while True:
+        msg = await agent(msg)
+        msg = await user(msg)
+        if msg.get_text_content() == "exit":
+            break
+
+
+asyncio.run(main())