example(agent): add basic Camel example

.gitignore

@@ -170,4 +170,8 @@ _build/
 
 logs/
 
-.DS_Store
+.DS_Store
+
+# RAG data
+local_data/
+vim_docs/

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/__init__.py

@@ -13,6 +13,7 @@
 # =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
 # ruff: noqa: F401
 from .camel_model import CamelModel
+from .camel_rag_model import CamelRAGModel
 from .claude_model import ClaudeModel
 from .gemini_model import GeminiModel
 from .openai_model import OpenAIModel

crab/agents/backend_models/camel_rag_model.py

@@ -0,0 +1,128 @@
+# =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
+from typing import Any, List, Optional, Tuple
+
+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
+    from camel.retrievers import VectorRetriever
+    from camel.storages import QdrantStorage
+    RAG_ENABLED = True
+except ImportError:
+    RAG_ENABLED = False
+
+
+class CamelRAGModel(CamelModel):
+    def __init__(
+        self,
+        model: str,
+        model_platform: str,
+        parameters: dict[str, Any] | None = None,
+        history_messages_len: int = 0,
+        embedding_model: Optional[str] = "text-embedding-3-small",
+        collection_name: str = "knowledge_base",
+        vector_storage_path: str = "local_data",
+        top_k: int = 3,
+        similarity_threshold: float = 0.75,
+    ) -> None:
+        if not RAG_ENABLED:
+            raise ImportError(
+                "Please install RAG dependencies: "
+                "pip install camel-ai[embeddings,retrievers,storages]"
+            )
+
+        super().__init__(model, model_platform, parameters, history_messages_len)
+
+        self.embedding_model = OpenAIEmbedding() if embedding_model else None
+
+        if self.embedding_model:
+            self.vector_storage = QdrantStorage(
+                vector_dim=self.embedding_model.get_output_dim(),
+                path=vector_storage_path,
+                collection_name=collection_name,
+            )
+            self.retriever = VectorRetriever(
+                embedding_model=self.embedding_model
+            )
+        else:
+            self.vector_storage = None
+            self.retriever = None
+
+        self.top_k = top_k
+        self.similarity_threshold = similarity_threshold
+
+    def process_documents(self, content_path: str) -> None:
+        if not self.retriever or not self.vector_storage:
+            raise ValueError("RAG components not initialized")
+
+        self.retriever.process(
+            content=content_path,
+            storage=self.vector_storage, 
+        )
+
+    def _enhance_with_context(self, messages: List[Tuple[str, MessageType]]) -> List[Tuple[str, MessageType]]:
+        if not self.retriever or not self.vector_storage:
+            return messages
+
+        query = next(
+            (msg[0] for msg in messages if msg[1] != MessageType.IMAGE_JPG_BASE64),
+            ""
+        )
+
+        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[0].get('payload'):
+            return messages
+
+        context = "Relevant context:\n\n"
+        for info in retrieved_info:
+            context += f"From {info.get('content path', 'unknown')}:\n"
+            context += f"{info.get('text', '')}\n\n"
+
+        enhanced_messages = []
+        enhanced_messages.append((context, MessageType.TEXT))
+        enhanced_messages.extend(messages)
+
+        return enhanced_messages
+
+    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

@@ -0,0 +1,79 @@
+# =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
+from termcolor import colored
+import os
+
+from crab import Benchmark, create_benchmark
+from crab.agents.backend_models.camel_model import CamelModel
+from crab.agents.policies import SingleAgentPolicy
+from crab.benchmarks.template import template_benchmark_config
+from camel.types import ModelType, ModelPlatformType
+from camel.models import ModelFactory
+
+
+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}")
+        response = agent.chat(
+            {
+                "template_env": [
+                    (f"Current environment observation: {observation}", 0),
+                ]
+            }
+        )
+        print(colored(f"Agent take action: {response}", "blue"))
+
+        for action in response:
+            response = benchmark.step(
+                action=action.name,
+                parameters=action.arguments,
+                env_name=action.env,
+            )
+            print(
+                colored(
+                    f'Action "{action.name}" success, stat: '
+                    f"{response.evaluation_results}",
+                    "green",
+                )
+            )
+            if response.terminated:
+                print("=" * 40)
+                print(
+                    colored(
+                        f"Task finished, result: {response.evaluation_results}", "green"
+                    )
+                )
+                return
+
+
+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()
+
+    # TODO: Use local model
+    camel_model = CamelModel(
+        model="gpt-4o",
+        model_platform=ModelPlatformType.OPENAI,
+        parameters={"temperature": 0.7},  
+    )
+    agent = SingleAgentPolicy(model_backend=camel_model)
+    agent.reset(task.description, action_space, env_descriptions)
+    print("Start performing task: " + colored(f'"{task.description}"', "green"))
+    start_benchmark(benchmark, agent)
+    benchmark.reset()