Update camel_example and camel_rag_example

Author: anjieyang

crab-benchmark-v0/dataset/ubuntu/camel-task-example.json

@@ -0,0 +1,15 @@
+{
+  "description": "Using Firefox, navigate to the CAMEL-AI GitHub repository (https://github.com/camel-ai/camel). Download the example code file 'examples/role_playing.py', then use Visual Studio Code to open it and save a copy to '/home/crab/camel_examples/role_playing.py'.",
+  "tasks": [
+    {
+      "task": "a313ea4d-e501-4971-b4fe-db2aad19eac1",
+      "attribute": {
+        "url": "https://raw.githubusercontent.com/camel-ai/camel/master/examples/role_playing.py",
+        "file_path": "/home/crab/camel_examples/role_playing.py"
+      },
+      "output": "/home/crab/camel_examples/role_playing.py"
+    }
+  ],
+  "adjlist": "0",
+  "id": "camel-example-001"
+}

crab/agents/backend_models/camel_rag_model.py

@@ -16,6 +16,7 @@
 from crab import BackendOutput, MessageType
 from crab.agents.backend_models.camel_model import CamelModel
 from camel.messages import BaseMessage
+from langchain.schema import Document 
 
 try:
     from camel.embeddings import OpenAIEmbedding
@@ -83,13 +84,19 @@ def _enhance_with_context(self, messages: List[Tuple[str, MessageType]]) -> List
             ""
         )
 
-        retrieved_info = self.retriever.query(
-            query=query,
-            top_k=self.top_k,
-            similarity_threshold=self.similarity_threshold,
-        )
+        try:
+            retrieved_info = self.retriever.query(
+                query=query,
+                top_k=self.top_k,
+                similarity_threshold=self.similarity_threshold,
+            )
+        except Exception:
+            return messages
+
+        if not retrieved_info:
+            return messages
 
-        if not retrieved_info or retrieved_info[0].get('text', '').startswith('No suitable information'):
+        if not retrieved_info[0].get('payload'):
             return messages
 
         context = "Relevant context:\n\n"
@@ -106,3 +113,16 @@ def _enhance_with_context(self, messages: List[Tuple[str, MessageType]]) -> List
     def chat(self, messages: List[Tuple[str, MessageType]]) -> BackendOutput:
         enhanced_messages = self._enhance_with_context(messages)
         return super().chat(enhanced_messages)
+
+    def get_relevant_content(self, query: str) -> List[Document]:
+        if not self.vector_storage: 
+            return []
+        
+        try:
+            return self.retriever.query(
+                query=query,
+                top_k=self.top_k,
+                similarity_threshold=self.similarity_threshold,
+            )
+        except Exception:
+            return []

examples/camel_example.py

@@ -62,6 +62,7 @@ def start_benchmark(benchmark: Benchmark, agent: SingleAgentPolicy):
 
 if __name__ == "__main__":
     benchmark = create_benchmark(template_benchmark_config)
+    #TODO: Use new task config
     task, action_space = benchmark.start_task("0")
     env_descriptions = benchmark.get_env_descriptions()
 

examples/camel_rag_example.py

@@ -23,21 +23,38 @@
 from crab.benchmarks.template import template_benchmark_config
 from camel.types import ModelType, ModelPlatformType
 
-
+# TODO: Add new benchmark template
 def start_benchmark(benchmark: Benchmark, agent: SingleAgentPolicy):
     for step in range(20):
         print("=" * 40)
         print(f"Start agent step {step}:")
         observation = benchmark.observe()["template_env"]
         print(f"Current environment observation: {observation}")
+        
+        try:
+            rag_content = agent.model_backend.get_relevant_content(str(observation))
+            print(colored("\nRelevant RAG content:", "magenta"))
+            if rag_content:
+                for idx, content in enumerate(rag_content, 1):
+                    print(colored(f"\nDocument {idx}:", "magenta"))
+                    if isinstance(content, dict):
+                        print(colored(f"Source: {content.get('content path', 'Unknown')}", "yellow"))
+                        print(colored(f"Content: {content.get('text', '')[:500]}...", "white"))
+                    else:
+                        print(colored(f"Content: {str(content)[:500]}...", "white"))
+            else:
+                print(colored("No relevant content found", "yellow"))
+        except Exception as e:
+            print(colored(f"Error retrieving RAG content: {str(e)}", "red"))
+        
         response = agent.chat(
             {
                 "template_env": [
                     (f"Current environment observation: {observation}", 0),
                 ]
             }
         )
-        print(colored(f"Agent take action: {response}", "blue"))
+        print(colored(f"\nAgent take action: {response}", "blue"))
 
         for action in response:
             response = benchmark.step(
@@ -74,7 +91,6 @@ def prepare_vim_docs():
     response = requests.get(base_url)
     soup = BeautifulSoup(response.text, 'html.parser')
 
-    # Process the main page first
     main_content = soup.get_text(separator='\n', strip=True)
     with open(os.path.join(content_dir, "main.txt"), 'w', encoding='utf-8') as f:
         f.write(f"Source: {base_url}\n\n{main_content}")
@@ -91,14 +107,12 @@ def prepare_vim_docs():
         try:
             print(colored(f"Processing page {idx}/{total_links}: {href}", "yellow"))
 
-            # Fetch and process page
             page_response = requests.get(full_url)
             page_soup = BeautifulSoup(page_response.text, 'html.parser')
             for tag in page_soup(['script', 'style']):
                 tag.decompose()
             content = page_soup.get_text(separator='\n', strip=True)
 
-            # Save content
             filename = os.path.join(content_dir, f"{href.replace('/', '_')}.txt")
             with open(filename, 'w', encoding='utf-8') as f:
                 f.write(f"Source: {full_url}\n\n{content}")
@@ -115,7 +129,6 @@ def prepare_vim_docs():
 if __name__ == "__main__":
     print(colored("=== Starting RAG-enhanced benchmark ===", "cyan"))
 
-    # Initialize benchmark and environment
     print(colored("\nInitializing benchmark environment...", "yellow"))
     benchmark = create_benchmark(template_benchmark_config)
     task, action_space = benchmark.start_task("0")