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https://github.com/hoshikawa2/openclaw-oci.git
synced 2026-03-06 10:11:06 +00:00
Linux commands OK
This commit is contained in:
@@ -16,6 +16,417 @@ import os
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import requests
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# ============================================================
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# CONFIG
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# ============================================================
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OCI_CONFIG_FILE = os.getenv("OCI_CONFIG_FILE", os.path.expanduser("~/.oci/config"))
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OCI_PROFILE = os.getenv("OCI_PROFILE", "DEFAULT")
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OCI_COMPARTMENT_ID = os.getenv("OCI_COMPARTMENT_ID", "<YOUR_OCI_COMPARTMENT_ID>")
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OCI_GENAI_ENDPOINT = os.getenv(
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"OCI_GENAI_ENDPOINT",
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"https://inference.generativeai.<oci_region>.oci.oraclecloud.com"
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)
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if not OCI_COMPARTMENT_ID:
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raise RuntimeError("OCI_COMPARTMENT_ID not defined")
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OPENCLAW_TOOLS_ACTIVE = True
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# ============================================================
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# PROMPTS to adapt for OCI
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# ============================================================
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SYSTEM_AGENT_PROMPT = """
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You are an autonomous software agent.
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You have full access to the local machine.
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Available tools:
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- weather(city: string)
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- exec(command: string)
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If a system command is required, respond ONLY with:
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{
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"action": "call_tool",
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"tool": "exec",
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"arguments": {
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"command": "<shell command>"
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}
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}
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***VERY IMPORTANT***: A TASK IS CONSIDERED COMPLETED WHEN IT RESULTS IN A ARTIFACT ASKED FROM THE USER
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If task is completed:
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{
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"action": "final_answer",
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"content": "<result>"
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}
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"""
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RUNNER_PROMPT = (
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"You are a Linux execution agent.\n"
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"\n"
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"OUTPUT CONTRACT (MANDATORY):\n"
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"- You must output EXACTLY ONE of the following per response:\n"
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" A) (exec <command>)\n"
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" B) (done <final answer>)\n"
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"\n"
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"STRICT RULES:\n"
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"1) NEVER output raw commands without (exec <command>). Raw commands will be ignored.\n"
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"2) NEVER output explanations, markdown, code fences, bullets, or extra text.\n"
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"3) If you need to create multi-line files, you MUST use heredoc inside (exec <command>), e.g.:\n"
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" (exec cat > file.py << 'EOF'\n"
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" ...\n"
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" EOF)\n"
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"4) If the previous tool result shows an error, your NEXT response must be (exec <command>) to fix it.\n"
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"5) When the artifact is created successfully, end with (done ...).\n"
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"\n"
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"REMINDER: Your response must be only a single parenthesized block."
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)
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# Mapeamento OpenAI → OCI
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MODEL_MAP = {
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"gpt-5": "openai.gpt-4.1",
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"openai/gpt-5": "openai.gpt-4.1",
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"openai-compatible/gpt-5": "openai.gpt-4.1",
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}
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# ============================================================
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# FASTAPI APP
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# ============================================================
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app = FastAPI(title="OCI OpenAI-Compatible Gateway")
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# ============================================================
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# OCI SIGNER
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# ============================================================
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def get_signer():
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config = oci.config.from_file(OCI_CONFIG_FILE, OCI_PROFILE)
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return oci.signer.Signer(
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tenancy=config["tenancy"],
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user=config["user"],
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fingerprint=config["fingerprint"],
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private_key_file_location=config["key_file"],
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pass_phrase=config.get("pass_phrase"),
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)
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# ============================================================
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# OCI CHAT CALL (OPENAI FORMAT)
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# ============================================================
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def _openai_messages_to_generic(messages: list) -> list:
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"""
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OpenAI: {"role":"user","content":"..."}
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Generic: {"role":"USER","content":[{"type":"TEXT","text":"..."}]}
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"""
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out = []
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for m in messages or []:
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role = (m.get("role") or "user").upper()
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# OCI GENERIC geralmente espera USER/ASSISTANT
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if role == "SYSTEM":
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role = "USER"
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elif role == "TOOL":
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role = "USER"
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content = m.get("content", "")
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# Se vier lista (OpenAI multimodal), extrai texto
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if isinstance(content, list):
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parts = []
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for item in content:
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if isinstance(item, dict) and item.get("type") in ("text", "TEXT"):
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parts.append(item.get("text", ""))
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content = "\n".join(parts)
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out.append({
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"role": role,
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"content": [{"type": "TEXT", "text": str(content)}]
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})
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return out
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def build_generic_messages(openai_messages: list, system_prompt: str) -> list:
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out = []
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# 1) Injeta o system como PRIMEIRA mensagem USER, com prefixo fixo
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out.append({
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"role": "USER",
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"content": [{"type":"TEXT","text": "SYSTEM:\n" + system_prompt.strip()}]
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})
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# 2) Depois converte o resto, ignorando systems originais
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for m in openai_messages or []:
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role = (m.get("role") or "user").lower()
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if role == "system":
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continue
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r = "USER" if role in ("user", "tool") else "ASSISTANT"
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content = m.get("content", "")
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if isinstance(content, list):
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parts = []
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for item in content:
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if isinstance(item, dict) and item.get("type") in ("text","TEXT"):
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parts.append(item.get("text",""))
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content = "\n".join(parts)
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out.append({
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"role": r,
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"content": [{"type":"TEXT","text": str(content)}]
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})
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return out
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def call_oci_chat(body: dict, system_prompt: str):
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signer = get_signer()
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model = body.get("model")
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oci_model = MODEL_MAP.get(model, model)
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url = f"{OCI_GENAI_ENDPOINT}/20231130/actions/chat"
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# generic_messages = _openai_messages_to_generic(body.get("messages", []))
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generic_messages = build_generic_messages(body.get("messages", []), system_prompt)
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payload = {
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"compartmentId": OCI_COMPARTMENT_ID,
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"servingMode": {
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"servingType": "ON_DEMAND",
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"modelId": oci_model
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},
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"chatRequest": {
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"apiFormat": "GENERIC",
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"messages": generic_messages,
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"maxTokens": int(body.get("max_tokens", 4000)),
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"temperature": float(body.get("temperature", 0.0)),
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"topP": float(body.get("top_p", 1.0)),
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}
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}
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# ⚠️ IMPORTANTÍSSIMO:
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# Em GENERIC, NÃO envie tools/tool_choice/stream (você orquestra tools no proxy)
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# Se você mandar, pode dar 400 "correct format of request".
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# print("\n=== PAYLOAD FINAL (GENERIC) ===")
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# print(json.dumps(payload, indent=2, ensure_ascii=False))
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r = requests.post(url, json=payload, auth=signer)
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if r.status_code != 200:
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print("OCI ERROR:", r.text)
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raise HTTPException(status_code=r.status_code, detail=r.text)
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return r.json()["chatResponse"]
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def detect_tool_call(text: str):
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pattern = r"exec\s*\(\s*([^\s]+)\s*(.*?)\s*\)"
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match = re.search(pattern, text)
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if not match:
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return None
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tool_name = "exec"
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command = match.group(1)
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args = match.group(2)
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return {
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"tool": tool_name,
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"args_raw": f"{command} {args}".strip()
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}
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def execute_exec_command(command: str):
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try:
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print(f"LOG: EXEC COMMAND: {command}")
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p = subprocess.run(
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command,
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shell=True,
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capture_output=True,
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text=True,
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timeout=120 # ajuste
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)
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out = (p.stdout or "") + (p.stderr or "")
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return out if out.strip() else f"(no output) exit={p.returncode}"
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except subprocess.TimeoutExpired:
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return "ERROR: command timed out"
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TOOLS = {
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"weather": lambda city: get_weather_from_api(city),
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"exec": lambda command: execute_exec_command(command)
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}
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def execute_real_tool(name, args):
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if name == "weather":
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city = args.get("city")
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return get_weather_from_api(city)
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return "Tool not implemented"
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def _extract_generic_text(oci_message: dict) -> str:
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content = oci_message.get("content")
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if isinstance(content, list):
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r = "".join([i.get("text", "") for i in content if isinstance(i, dict) and i.get("type") == "TEXT"])
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# print("r", r)
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return r
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if isinstance(content, str):
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# print("content", content)
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return content
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return str(content)
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def agent_loop(body: dict, max_iterations=10000):
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# Trabalhe sempre com OpenAI messages internamente,
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# mas call_oci_chat converte pra GENERIC.
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messages = []
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messages.append({"role": "system", "content": SYSTEM_AGENT_PROMPT})
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messages.extend(body.get("messages", []))
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for _ in range(max_iterations):
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response = call_oci_chat({**body, "messages": messages}, SYSTEM_AGENT_PROMPT)
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oci_choice = response["choices"][0]
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oci_message = oci_choice["message"]
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text = _extract_generic_text(oci_message)
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try:
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agent_output = json.loads(text)
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except:
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# modelo não retornou JSON (quebrou regra)
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return response
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if agent_output.get("action") == "call_tool":
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tool_name = agent_output.get("tool")
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args = agent_output.get("arguments", {})
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if tool_name not in TOOLS:
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# devolve pro modelo como erro
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messages.append({"role": "assistant", "content": text})
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messages.append({"role": "user", "content": json.dumps({
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"tool_error": f"Tool '{tool_name}' not implemented"
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})})
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continue
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tool_result = TOOLS[tool_name](**args)
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# Mantém o histórico: (1) decisão do agente, (2) resultado do tool
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messages.append({"role": "assistant", "content": text})
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messages.append({"role": "user", "content": json.dumps({
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"tool_result": {
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"tool": tool_name,
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"arguments": args,
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"result": tool_result
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}
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}, ensure_ascii=False)})
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continue
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if agent_output.get("action") == "final_answer":
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return response
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return response
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EXEC_RE = re.compile(r"\(exec\s+(.+?)\)\s*$", re.DOTALL)
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DONE_RE = re.compile(r"\(done\s+(.+?)\)\s*$", re.MULTILINE)
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def run_exec_loop(body: dict, max_steps: int = 10000) -> dict:
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# Histórico OpenAI-style
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messages = [{"role":"system"}]
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messages.extend(body.get("messages", []))
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last = None
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for _ in range(max_steps):
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last = call_oci_chat({**body, "messages": messages}, RUNNER_PROMPT)
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print('LLM Result', last)
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msg = last["choices"][0]["message"]
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text = _extract_generic_text(msg) or ""
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m_done = DONE_RE.search(text)
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print("DONE_RE", text)
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print("m_done", m_done)
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if m_done:
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final_text = m_done.group(1).strip()
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# devolve em formato OpenAI no fim
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return {
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**last,
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"choices": [{
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**last["choices"][0],
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"message": {"role":"assistant","content": final_text},
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"finishReason": "stop"
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}]
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}
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m_exec = EXEC_RE.search(text)
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if m_exec:
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command = m_exec.group(1).strip()
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result = execute_exec_command(command)
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messages.append({"role":"assistant","content": text})
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messages.append({"role":"user","content": f"Tool result:\n{result}"})
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continue
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# Se o modelo quebrou o protocolo:
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messages.append({"role":"assistant","content": text})
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messages.append({"role":"user","content": (
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"Protocol error. You MUST reply ONLY with (exec <command>) or (done <final answer>)."
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)})
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continue
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# estourou steps: devolve última resposta (melhor do que travar)
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return last
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def verify_task_completion(original_task: str, assistant_output: str) -> bool:
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"""
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Retorna True se tarefa estiver concluída.
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Retorna False se ainda precisar continuar.
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"""
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verifier_prompt = [
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{
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"role": "system",
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"content": (
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"You are a strict task completion validator.\n"
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"Answer ONLY with DONE or CONTINUE.\n"
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"DONE = the task is fully completed.\n"
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"CONTINUE = more steps are required.\n"
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),
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},
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{
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"role": "user",
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"content": f"""
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Original task:
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{original_task}
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Last assistant output:
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{assistant_output}
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Is the task fully completed?
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"""
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}
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]
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response = call_oci_chat({
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"model": "openai-compatible/gpt-5",
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"messages": verifier_prompt,
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"temperature": 0
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}, verifier_prompt[0]["content"])
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text = _extract_generic_text(response["choices"][0]["message"]).strip().upper()
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return text == "DONE"
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# ============================================================
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# ENTERPRISE TOOLS
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# Set the OPENCLAW_TOOLS_ACTIVE = True to automatize OpenClaw execution Tools
|
||||
# Set the OPENCLAW_TOOLS_ACTIVE = False and implement your own Tools
|
||||
# ============================================================
|
||||
|
||||
def get_weather_from_api(city: str) -> str:
|
||||
"""
|
||||
Consulta clima atual usando Open-Meteo (100% free, sem API key)
|
||||
@@ -78,245 +489,6 @@ def get_weather_from_api(city: str) -> str:
|
||||
|
||||
except Exception as e:
|
||||
return f"Weather tool error: {str(e)}"
|
||||
# ============================================================
|
||||
# CONFIG
|
||||
# ============================================================
|
||||
|
||||
OCI_CONFIG_FILE = os.getenv("OCI_CONFIG_FILE", os.path.expanduser("~/.oci/config"))
|
||||
OCI_PROFILE = os.getenv("OCI_PROFILE", "DEFAULT")
|
||||
OCI_COMPARTMENT_ID = os.getenv("OCI_COMPARTMENT_ID", "<YOUR_OCI_COMPARTMENT_ID>")
|
||||
OCI_GENAI_ENDPOINT = os.getenv(
|
||||
"OCI_GENAI_ENDPOINT",
|
||||
"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
|
||||
)
|
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|
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OPENCLAW_TOOLS_ACTIVE = True
|
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|
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SYSTEM_AGENT_PROMPT = """
|
||||
You are an enterprise AI agent.
|
||||
|
||||
You MUST respond ONLY in valid JSON.
|
||||
|
||||
Available tools:
|
||||
- weather(city: string)
|
||||
|
||||
Response format:
|
||||
|
||||
If you need to call a tool:
|
||||
{
|
||||
"action": "call_tool",
|
||||
"tool": "<tool_name>",
|
||||
"arguments": { ... }
|
||||
}
|
||||
|
||||
If you are returning a final answer:
|
||||
{
|
||||
"action": "final_answer",
|
||||
"content": "<final user answer>"
|
||||
}
|
||||
|
||||
Never include explanations outside JSON.
|
||||
"""
|
||||
|
||||
TOOLS = {
|
||||
"weather": lambda city: get_weather_from_api(city)
|
||||
}
|
||||
|
||||
if not OCI_COMPARTMENT_ID:
|
||||
raise RuntimeError("OCI_COMPARTMENT_ID not defined")
|
||||
|
||||
# Mapeamento OpenAI → OCI
|
||||
MODEL_MAP = {
|
||||
"gpt-5": "openai.gpt-4.1",
|
||||
"openai/gpt-5": "openai.gpt-4.1",
|
||||
"openai-compatible/gpt-5": "openai.gpt-4.1",
|
||||
}
|
||||
|
||||
app = FastAPI(title="OCI OpenAI-Compatible Gateway")
|
||||
|
||||
# ============================================================
|
||||
# OCI SIGNER
|
||||
# ============================================================
|
||||
|
||||
def get_signer():
|
||||
config = oci.config.from_file(OCI_CONFIG_FILE, OCI_PROFILE)
|
||||
return oci.signer.Signer(
|
||||
tenancy=config["tenancy"],
|
||||
user=config["user"],
|
||||
fingerprint=config["fingerprint"],
|
||||
private_key_file_location=config["key_file"],
|
||||
pass_phrase=config.get("pass_phrase"),
|
||||
)
|
||||
|
||||
# ============================================================
|
||||
# OCI CHAT CALL (OPENAI FORMAT)
|
||||
# ============================================================
|
||||
|
||||
def _openai_messages_to_generic(messages: list) -> list:
|
||||
"""
|
||||
OpenAI: {"role":"user","content":"..."}
|
||||
Generic: {"role":"USER","content":[{"type":"TEXT","text":"..."}]}
|
||||
"""
|
||||
out = []
|
||||
for m in messages or []:
|
||||
role = (m.get("role") or "user").upper()
|
||||
|
||||
# OCI GENERIC geralmente espera USER/ASSISTANT
|
||||
if role == "SYSTEM":
|
||||
role = "USER"
|
||||
elif role == "TOOL":
|
||||
role = "USER"
|
||||
|
||||
content = m.get("content", "")
|
||||
|
||||
# Se vier lista (OpenAI multimodal), extrai texto
|
||||
if isinstance(content, list):
|
||||
parts = []
|
||||
for item in content:
|
||||
if isinstance(item, dict) and item.get("type") in ("text", "TEXT"):
|
||||
parts.append(item.get("text", ""))
|
||||
content = "\n".join(parts)
|
||||
|
||||
out.append({
|
||||
"role": role,
|
||||
"content": [{"type": "TEXT", "text": str(content)}]
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def call_oci_chat(body: dict):
|
||||
signer = get_signer()
|
||||
|
||||
model = body.get("model")
|
||||
oci_model = MODEL_MAP.get(model, model)
|
||||
|
||||
url = f"{OCI_GENAI_ENDPOINT}/20231130/actions/chat"
|
||||
|
||||
generic_messages = _openai_messages_to_generic(body.get("messages", []))
|
||||
|
||||
payload = {
|
||||
"compartmentId": OCI_COMPARTMENT_ID,
|
||||
"servingMode": {
|
||||
"servingType": "ON_DEMAND",
|
||||
"modelId": oci_model
|
||||
},
|
||||
"chatRequest": {
|
||||
"apiFormat": "GENERIC",
|
||||
"messages": generic_messages,
|
||||
"maxTokens": int(body.get("max_tokens", 1024)),
|
||||
"temperature": float(body.get("temperature", 0.0)),
|
||||
"topP": float(body.get("top_p", 1.0)),
|
||||
}
|
||||
}
|
||||
|
||||
# ⚠️ IMPORTANTÍSSIMO:
|
||||
# Em GENERIC, NÃO envie tools/tool_choice/stream (você orquestra tools no proxy)
|
||||
# Se você mandar, pode dar 400 "correct format of request".
|
||||
|
||||
print("\n=== PAYLOAD FINAL (GENERIC) ===")
|
||||
print(json.dumps(payload, indent=2, ensure_ascii=False))
|
||||
|
||||
r = requests.post(url, json=payload, auth=signer)
|
||||
if r.status_code != 200:
|
||||
print("OCI ERROR:", r.text)
|
||||
raise HTTPException(status_code=r.status_code, detail=r.text)
|
||||
|
||||
return r.json()["chatResponse"]
|
||||
|
||||
def detect_tool_call(text: str):
|
||||
pattern = r"exec\s*\(\s*([^\s]+)\s*(.*?)\s*\)"
|
||||
match = re.search(pattern, text)
|
||||
|
||||
if not match:
|
||||
return None
|
||||
|
||||
tool_name = "exec"
|
||||
command = match.group(1)
|
||||
args = match.group(2)
|
||||
|
||||
return {
|
||||
"tool": tool_name,
|
||||
"args_raw": f"{command} {args}".strip()
|
||||
}
|
||||
|
||||
def execute_exec_command(command: str):
|
||||
try:
|
||||
print(f"LOG: EXEC COMMAND: {command}")
|
||||
result = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
|
||||
return result.decode()
|
||||
except subprocess.CalledProcessError as e:
|
||||
return e.output.decode()
|
||||
|
||||
def execute_real_tool(name, args):
|
||||
|
||||
if name == "weather":
|
||||
city = args.get("city")
|
||||
return get_weather_from_api(city)
|
||||
|
||||
return "Tool not implemented"
|
||||
|
||||
def _extract_generic_text(oci_message: dict) -> str:
|
||||
content = oci_message.get("content")
|
||||
if isinstance(content, list):
|
||||
return "".join([i.get("text", "") for i in content if isinstance(i, dict) and i.get("type") == "TEXT"])
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
return str(content)
|
||||
|
||||
|
||||
def agent_loop(body: dict, max_iterations=5):
|
||||
|
||||
# Trabalhe sempre com OpenAI messages internamente,
|
||||
# mas call_oci_chat converte pra GENERIC.
|
||||
messages = []
|
||||
messages.append({"role": "system", "content": SYSTEM_AGENT_PROMPT})
|
||||
messages.extend(body.get("messages", []))
|
||||
|
||||
for _ in range(max_iterations):
|
||||
|
||||
response = call_oci_chat({**body, "messages": messages})
|
||||
|
||||
oci_choice = response["choices"][0]
|
||||
oci_message = oci_choice["message"]
|
||||
|
||||
text = _extract_generic_text(oci_message)
|
||||
|
||||
try:
|
||||
agent_output = json.loads(text)
|
||||
except:
|
||||
# modelo não retornou JSON (quebrou regra)
|
||||
return response
|
||||
|
||||
if agent_output.get("action") == "call_tool":
|
||||
tool_name = agent_output.get("tool")
|
||||
args = agent_output.get("arguments", {})
|
||||
|
||||
if tool_name not in TOOLS:
|
||||
# devolve pro modelo como erro
|
||||
messages.append({"role": "assistant", "content": text})
|
||||
messages.append({"role": "user", "content": json.dumps({
|
||||
"tool_error": f"Tool '{tool_name}' not implemented"
|
||||
})})
|
||||
continue
|
||||
|
||||
tool_result = TOOLS[tool_name](**args)
|
||||
|
||||
# Mantém o histórico: (1) decisão do agente, (2) resultado do tool
|
||||
messages.append({"role": "assistant", "content": text})
|
||||
messages.append({"role": "user", "content": json.dumps({
|
||||
"tool_result": {
|
||||
"tool": tool_name,
|
||||
"arguments": args,
|
||||
"result": tool_result
|
||||
}
|
||||
}, ensure_ascii=False)})
|
||||
|
||||
continue
|
||||
|
||||
if agent_output.get("action") == "final_answer":
|
||||
return response
|
||||
|
||||
return response
|
||||
|
||||
# ============================================================
|
||||
# STREAMING ADAPTER
|
||||
@@ -387,35 +559,12 @@ async def chat_completions(request: Request):
|
||||
# chat_response = agent_loop(body)
|
||||
|
||||
if OPENCLAW_TOOLS_ACTIVE:
|
||||
chat_response = call_oci_chat(body)
|
||||
|
||||
oci_choice = chat_response["choices"][0]
|
||||
oci_message = oci_choice["message"]
|
||||
|
||||
content_text = _extract_generic_text(oci_message)
|
||||
|
||||
# 🔥 DETECT EXEC
|
||||
exec_match = re.search(r"\(exec\s+(.*?)\)", content_text)
|
||||
|
||||
if exec_match:
|
||||
command = exec_match.group(1)
|
||||
result = execute_exec_command(command)
|
||||
|
||||
# Injeta resultado e chama novamente
|
||||
new_messages = body["messages"] + [
|
||||
{"role": "assistant", "content": content_text},
|
||||
{"role": "user", "content": f"Tool result:\n{result}"}
|
||||
]
|
||||
|
||||
chat_response = call_oci_chat({
|
||||
**body,
|
||||
"messages": new_messages
|
||||
})
|
||||
chat_response = run_exec_loop(body, max_steps=10000)
|
||||
else:
|
||||
# 🔥 Modo enterprise → seu agent_loop controla tools
|
||||
chat_response = agent_loop(body)
|
||||
|
||||
print("FINAL RESPONSE:", json.dumps(chat_response, indent=2))
|
||||
# print("FINAL RESPONSE:", json.dumps(chat_response, indent=2))
|
||||
|
||||
oci_choice = chat_response["choices"][0]
|
||||
oci_message = oci_choice["message"]
|
||||
@@ -566,15 +715,15 @@ async def responses(request: Request):
|
||||
|
||||
@app.middleware("http")
|
||||
async def log_requests(request: Request, call_next):
|
||||
print("\n>>> ENDPOINT:", request.method, request.url.path)
|
||||
# print("\n>>> ENDPOINT:", request.method, request.url.path)
|
||||
|
||||
body = await request.body()
|
||||
try:
|
||||
body_json = json.loads(body.decode())
|
||||
print(">>> BODY:", json.dumps(body_json, indent=2))
|
||||
# print(">>> BODY:", json.dumps(body_json, indent=2))
|
||||
except:
|
||||
print(">>> BODY RAW:", body.decode())
|
||||
|
||||
response = await call_next(request)
|
||||
print(">>> STATUS:", response.status_code)
|
||||
# print(">>> STATUS:", response.status_code)
|
||||
return response
|
||||
Reference in New Issue
Block a user