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First commit
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# Sample guideline, please follow similar structure for guideline with code samples
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# 1. Suggest using streams instead of simple loops for better readability.
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# <example>
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# *Comment:
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# Category: Minor
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# Issue: Use streams instead of a loop for better readability.
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# Code Block:
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#
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# ```java
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# // Calculate squares of numbers
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# for (int number : numbers) {
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# squares.add(number * number);
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# }
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# ```
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# Recommendation:
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#
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# ```java
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# // Calculate squares of numbers
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# List<Integer> squares = Arrays.stream(numbers)
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# .map(n -> n * n) // Map each number to its square
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# .toList();
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# ```
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# </example>
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README.md
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README.md
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# Generate PowerPoint Presentations with OpenClaw and Oracle Cloud Generative AI
|
||||
|
||||
## Enterprise AI Power, Open Ecosystem, Zero Compromise
|
||||
|
||||
The rapid evolution of AI orchestration tools has reshaped how companies build intelligent systems. Among these tools, OpenClaw has emerged as a powerful open-source platform designed to simplify the creation of AI agents, conversational workflows, and multi-channel integrations.
|
||||
|
||||
OpenClaw is not just another wrapper around LLM APIs. It is:
|
||||
|
||||
* Modular
|
||||
* Plugin-driven
|
||||
* Open-source
|
||||
* OpenAI-compatible
|
||||
* Community-powered
|
||||
|
||||
Its OpenAI-compatible design makes it instantly interoperable with the entire AI tooling ecosystem — SDKs, automation frameworks, browser clients, bots, and custom agent pipelines.
|
||||
|
||||
And because it is open source, innovation happens in public.
|
||||
|
||||
There is an active and growing community contributing:
|
||||
|
||||
* New plugins
|
||||
* Messaging integrations (WhatsApp, web, etc.)
|
||||
* Tool execution engines
|
||||
* Agent frameworks
|
||||
* Workflow automation patterns
|
||||
* Performance optimizations
|
||||
|
||||
This means OpenClaw evolves continuously — without vendor lock-in.
|
||||
|
||||
But while agility and innovation are essential, enterprises require something more:
|
||||
* Security
|
||||
* Governance
|
||||
* Compliance
|
||||
* Regional data sovereignty
|
||||
* Observability
|
||||
* Controlled network exposure
|
||||
* Predictable scalability
|
||||
|
||||
This is where Oracle Cloud Infrastructure (OCI) Generative AI becomes the strategic enterprise choice.
|
||||
|
||||
⸻
|
||||
|
||||
## The Power of Ecosystem + Enterprise Security
|
||||
|
||||
### OpenClaw: Open Ecosystem Advantage
|
||||
|
||||
Because OpenClaw is:
|
||||
* Open-source
|
||||
* Community-driven
|
||||
* Plugin-extensible
|
||||
* OpenAI-protocol compatible
|
||||
|
||||
You benefit from:
|
||||
|
||||
* Rapid innovation
|
||||
* Transparent architecture
|
||||
* Community-tested integrations
|
||||
* Zero dependency on a single SaaS provider
|
||||
* Full customization capability
|
||||
|
||||
You are not locked into one AI vendor.
|
||||
You control your orchestration layer.
|
||||
|
||||
This flexibility is critical in a world where models evolve rapidly and enterprises need adaptability.
|
||||
|
||||
⸻
|
||||
|
||||
## OCI Generative AI: Enterprise Trust Layer
|
||||
|
||||
Oracle Cloud Infrastructure adds what large organizations require:
|
||||
* Fine-grained IAM control
|
||||
* Signed API requests (no exposed API keys)
|
||||
* Dedicated compartments
|
||||
* Private VCN networking
|
||||
* Sovereign cloud regions
|
||||
* Enterprise SLAs
|
||||
* Monitoring & logging integration
|
||||
* Production-ready inference endpoints
|
||||
|
||||
OCI Generative AI supports powerful production-grade models such as:
|
||||
* Cohere Command
|
||||
* LLaMA family
|
||||
* Embedding models
|
||||
* Custom enterprise deployments
|
||||
* OpenAI-compatible models via mapping
|
||||
|
||||
This creates a secure AI backbone inside your own tenancy.
|
||||
|
||||
⸻
|
||||
|
||||
## Why This Combination Is Strategically Powerful
|
||||
|
||||
By implementing a local OpenAI-compatible gateway backed by OCI:
|
||||
|
||||
OpenClaw continues to behave exactly as designed —
|
||||
while inference happens securely inside Oracle Cloud.
|
||||
|
||||
You gain:
|
||||
* Full OpenAI protocol compatibility
|
||||
* Enterprise security boundaries
|
||||
* Cloud tenancy governance
|
||||
* Scalable AI inference
|
||||
* Ecosystem extensibility
|
||||
* Open-source flexibility
|
||||
|
||||
Without rewriting your agents.
|
||||
Without breaking plugins.
|
||||
Without sacrificing innovation.
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
# Why Use OCI Generative AI?
|
||||
|
||||
Oracle Cloud Infrastructure provides:
|
||||
|
||||
- Enterprise security (IAM, compartments, VCN)
|
||||
- Flexible model serving (ON_DEMAND, Dedicated)
|
||||
- High scalability
|
||||
- Cost control
|
||||
- Regional deployment control
|
||||
- Native integration with Oracle ecosystem
|
||||
|
||||
By building an OpenAI-compatible proxy, we combine:
|
||||
|
||||
OpenClaw flexibility + OCI enterprise power
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
|
||||
# OpenClaw + OCI Generative AI Gateway **and** PPTX Template Builder
|
||||
|
||||
|
||||
## About the tutorial
|
||||
|
||||
|
||||
### OpenAI-compatible endpoint
|
||||
|
||||
This tutorial is based on [Integrating OpenClaw with Oracle Cloud Generative AI (OCI)](https://github.com/hoshikawa2/openclaw-oci) tutorial and explains how to integrate **OpenClaw** with **Oracle Cloud
|
||||
Infrastructure (OCI) Generative AI** by building an OpenAI-compatible
|
||||
API gateway using FastAPI.
|
||||
|
||||
Instead of modifying OpenClaw's core, we expose an **OpenAI-compatible
|
||||
endpoint** (`/v1/chat/completions`) that internally routes requests to
|
||||
OCI Generative AI.
|
||||
|
||||
This approach provides:
|
||||
|
||||
- ✅ Full OpenClaw compatibility
|
||||
- ✅ Control over OCI model mapping
|
||||
- ✅ Support for streaming responses
|
||||
- ✅ Enterprise-grade OCI infrastructure
|
||||
- ✅ Secure request signing via OCI SDK
|
||||
|
||||
### PPTX Builder
|
||||
|
||||
**A PPTX builder** will generate a professional **PowerPoint deck from a template** (`.pptx`) + a structured `content.json`
|
||||
|
||||
The goal is to keep **OpenClaw** fully compatible with the OpenAI protocol while moving inference to **OCI** and enabling **artifact generation (PPTX)** using a repeatable, governed pipeline.
|
||||
|
||||
---
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
OpenClaw
|
||||
↓ (OpenAI protocol)
|
||||
OpenAI-compatible Gateway (FastAPI)
|
||||
↓ (signed OCI REST)
|
||||
OCI Generative AI (chat endpoint)
|
||||
↓
|
||||
LLM response
|
||||
|
||||
(Optional)
|
||||
Material (URL / file / text)
|
||||
↓
|
||||
content.json (validated / governed)
|
||||
↓
|
||||
PPTX Builder (template + content.json)
|
||||
↓
|
||||
openclaw_oci_apresentacao.pptx
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Project structure
|
||||
|
||||
```
|
||||
project/
|
||||
├── oci_openai_proxy.py # FastAPI OpenAI-compatible gateway -> OCI GenAI
|
||||
├── pptx_runner_policy_strict.txt # Strict policy for extracting/validating material -> content.json
|
||||
├── openclaw.json # Example OpenClaw config using the gateway
|
||||
└── README.md
|
||||
AND these files:
|
||||
├── generate_openclaw_ppt_template.py # PPTX generator (template + content.json)
|
||||
├── read_url_and_read_file.sh # Helper script to create read_url/read_file in OpenClaw workspace
|
||||
└── template_openclaw_oci_clean.pptx # You MUST have one template here
|
||||
|
||||
|
||||
Move these files to:
|
||||
$HOME/.openclaw/workspace/openclaw_folder
|
||||
├── generate_openclaw_ppt_template.py # PPTX generator (template + content.json)
|
||||
├── read_url_and_read_file.sh # Helper script to create read_url/read_file in OpenClaw workspace
|
||||
└── template_openclaw_oci_clean.pptx # You MUST have one template here
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# Part A — OpenAI-compatible Gateway (OpenClaw → OCI GenAI)
|
||||
|
||||
## Why OCI Generative AI?
|
||||
|
||||
OCI provides what enterprises usually need:
|
||||
|
||||
- IAM & compartments
|
||||
- Signed requests (no API key leakage)
|
||||
- Regional control / sovereignty
|
||||
- VCN options
|
||||
- Observability integration
|
||||
- Production-grade inference endpoints
|
||||
|
||||
By putting an OpenAI-compatible API in front of OCI, you get:
|
||||
|
||||
- ✅ OpenClaw compatibility
|
||||
- ✅ Model mapping (OpenAI names → OCI modelIds)
|
||||
- ✅ Streaming compatibility (simulated if OCI returns full text)
|
||||
- ✅ Governance inside your tenancy
|
||||
|
||||
---
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python 3.10+ (recommended)
|
||||
- OCI config file (`~/.oci/config`) + API key
|
||||
- Network access to OCI GenAI endpoint
|
||||
|
||||
Install dependencies:
|
||||
|
||||
```bash
|
||||
|
||||
pip install fastapi uvicorn requests oci pydantic
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Configuration (environment variables)
|
||||
|
||||
The gateway reads OCI configuration using environment variables (defaults shown):
|
||||
|
||||
```bash
|
||||
|
||||
export OCI_CONFIG_FILE="$HOME/.oci/config"
|
||||
export OCI_PROFILE="DEFAULT"
|
||||
export OCI_COMPARTMENT_ID="ocid1.compartment.oc1..."
|
||||
export OCI_GENAI_ENDPOINT="https://inference.generativeai.<region>.oci.oraclecloud.com"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Run the server
|
||||
|
||||
```bash
|
||||
|
||||
uvicorn oci_openai_proxy:app --host 0.0.0.0 --port 8050
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Test with curl
|
||||
|
||||
```bash
|
||||
|
||||
curl http://127.0.0.1:8050/v1/chat/completions -H "Content-Type: application/json" -d '{
|
||||
"model": "gpt-5",
|
||||
"messages": [{"role": "user", "content": "Hello"}]
|
||||
}'
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## OpenClaw configuration (openclaw.json)
|
||||
|
||||
Point OpenClaw to the gateway:
|
||||
|
||||
- `baseUrl` → your local gateway (port 8050)
|
||||
- `api` → **openai-completions**
|
||||
- `model id` → must match a `MODEL_MAP` key inside `oci_openai_proxy.py`
|
||||
|
||||
Example provider block:
|
||||
|
||||
```json
|
||||
{
|
||||
"models": {
|
||||
"providers": {
|
||||
"openai-compatible": {
|
||||
"baseUrl": "http://127.0.0.1:8050/v1",
|
||||
"apiKey": "sk-test",
|
||||
"api": "openai-completions"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# Part B — PPTX generation from a template (Template → Deck)
|
||||
|
||||
## What it does
|
||||
|
||||
`generate_openclaw_ppt_template.py` builds a **fixed 7-slide** strategic deck:
|
||||
|
||||
1. Cover
|
||||
2. Intro (use case)
|
||||
3. Technologies
|
||||
4. Architecture
|
||||
5. Problems
|
||||
6. Demo (includes the source link)
|
||||
7. Conclusion
|
||||
|
||||
The deck is generated from:
|
||||
|
||||
- a **PPTX template** (with expected layouts),
|
||||
- a `content.json` file,
|
||||
- and a `OCI_LINK_DEMO` link (material source shown on the Demo slide).
|
||||
|
||||
---
|
||||
|
||||
## Inputs
|
||||
|
||||
### 1) PPTX template
|
||||
|
||||
You MUST have a PowerPoint template named **template_openclaw_oci_clean.pptx** with some master layout slides.
|
||||
|
||||
Default expected layout names inside the template:
|
||||
|
||||
- `Cover 1 - Full Image`
|
||||
- `Full Page - Light`
|
||||
|
||||
You can change the template by passing `--template` or `PPTX_TEMPLATE_PATH`.
|
||||
|
||||
### 2) content.json
|
||||
|
||||
`content.json` must contain:
|
||||
|
||||
- `cover_title` (string)
|
||||
- `introduction`, `technologies`, `architecture`, `problems`, `demo`, `conclusion` (objects)
|
||||
|
||||
Each section object must include:
|
||||
|
||||
- `bullets`: 3–6 short bullets
|
||||
- `keywords`: 5–12 keywords that appear literally in the material
|
||||
- `evidence`: 2–4 short excerpts (10–25 words) extracted from the material (no HTML)
|
||||
|
||||
The strict validation rules are described in `pptx_runner_policy_strict.txt`.
|
||||
|
||||
---
|
||||
|
||||
## Configure paths
|
||||
|
||||
Create a folder named **openclaw_folder** inside the $HOME/.openclaw/workspace.
|
||||
|
||||
``` bash
|
||||
|
||||
cd $HOME/.openclaw
|
||||
mkdir openclaw_folder
|
||||
cd openclaw_folder
|
||||
```
|
||||
|
||||
Put these files into the openclaw_folder:
|
||||
|
||||
````
|
||||
generate_openclaw_ppt_template.py
|
||||
read_url_and_read_file.sh
|
||||
template_openclaw_oci_clean.pptx (Your PPTX template if you have)
|
||||
````
|
||||
|
||||
Run this command only one time:
|
||||
```
|
||||
bash read_url_and_read_file.sh
|
||||
```
|
||||
This will generate the read_url and read_file tools.
|
||||
|
||||
|
||||
You can run everything **without hardcoded paths** using either CLI flags or environment variables.
|
||||
|
||||
### Environment variables
|
||||
|
||||
```bash
|
||||
# Optional: where your files live (default: current directory)
|
||||
export OPENCLAW_WORKDIR="$HOME/.openclaw/workspace/openclaw_folder"
|
||||
|
||||
# Template + output
|
||||
export PPTX_TEMPLATE_PATH="$OPENCLAW_WORKDIR/template_openclaw_oci_clean.pptx"
|
||||
export PPTX_OUTPUT_PATH="$OPENCLAW_WORKDIR/openclaw_oci_apresentacao.pptx"
|
||||
|
||||
# Content JSON (if not set, defaults to $OPENCLAW_WORKDIR/content.json)
|
||||
export OCI_CONTENT_FILE="$OPENCLAW_WORKDIR/content.json"
|
||||
|
||||
# Source link shown on the Demo slide
|
||||
export OCI_LINK_DEMO="https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm"
|
||||
```
|
||||
|
||||
### CLI usage
|
||||
|
||||
```bash
|
||||
python generate_openclaw_ppt_template.py --template "$PPTX_TEMPLATE_PATH" --output "$PPTX_OUTPUT_PATH" --content "$OCI_CONTENT_FILE" --link "$OCI_LINK_DEMO"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## End-to-end pipeline (URL → content.json → PPTX)
|
||||
|
||||
A typical (strict) flow:
|
||||
|
||||
1) **Read material** (URL or local file)
|
||||
2) **Generate `content.json`** following the strict policy
|
||||
3) **Validate JSON**
|
||||
4) **Generate PPTX**
|
||||
|
||||
### Helper scripts (read_url / read_file)
|
||||
|
||||
The repository includes `read_url e read_file.sh` to install helper scripts into your OpenClaw workspace.
|
||||
|
||||
Example:
|
||||
|
||||
```bash
|
||||
bash "read_url e read_file.sh"
|
||||
```
|
||||
|
||||
Then:
|
||||
|
||||
```bash
|
||||
# Read URL
|
||||
~/.openclaw/workspace/openclaw_folder/read_url "https://example.com" > material_raw.txt
|
||||
|
||||
# Read local file
|
||||
~/.openclaw/workspace/openclaw_folder/read_file "/path/to/file.pdf" > material_raw.txt
|
||||
```
|
||||
|
||||
### Validate JSON
|
||||
|
||||
```bash
|
||||
python -m json.tool "$OCI_CONTENT_FILE" >/dev/null
|
||||
```
|
||||
|
||||
### Generate PPTX
|
||||
|
||||
```bash
|
||||
python gera_oci_ppt_openclaw_template.py --link "$OCI_LINK_DEMO"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Deploying (common options)
|
||||
|
||||
### Option 1 — Run locally (developer laptop)
|
||||
|
||||
- Run the gateway with `uvicorn`
|
||||
- Generate decks on demand in the workspace folder
|
||||
|
||||
### Option 2 — Server VM (systemd for gateway)
|
||||
|
||||
Create a systemd service (example):
|
||||
|
||||
```ini
|
||||
[Unit]
|
||||
Description=OpenAI-compatible OCI GenAI Gateway
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
WorkingDirectory=/opt/openclaw-oci
|
||||
Environment=OCI_CONFIG_FILE=/home/ubuntu/.oci/config
|
||||
Environment=OCI_PROFILE=DEFAULT
|
||||
Environment=OCI_COMPARTMENT_ID=ocid1.compartment...
|
||||
Environment=OCI_GENAI_ENDPOINT=https://inference.generativeai.<region>.oci.oraclecloud.com
|
||||
ExecStart=/usr/bin/python -m uvicorn oci_openai_proxy:app --host 0.0.0.0 --port 8050
|
||||
Restart=always
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
```
|
||||
|
||||
### Option 3 — Containerize
|
||||
|
||||
- Put `oci_openai_proxy.py` inside an image
|
||||
- Mount `~/.oci/config` read-only
|
||||
- Pass the same env vars above
|
||||
|
||||
(Exact Dockerfile depends on how you manage OCI config and keys in your environment.)
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### PPTX builder errors
|
||||
|
||||
- **Layout not found**: your template does not have the expected layout names.
|
||||
- **Too few placeholders**: your selected layout must have at least 2 text placeholders.
|
||||
- **Exactly 7 slides**: the generator enforces the fixed structure.
|
||||
|
||||
### Content issues
|
||||
|
||||
- If `content.json` has generic bullets/keywords not present in the material, the strict policy should fail validation.
|
||||
- If you cannot extract enough literal keywords, re-check your material extraction (HTML removal, raw GitHub URL, etc.).
|
||||
|
||||
---
|
||||
|
||||
## Test the Solution
|
||||
|
||||
Go to the openclaw dashboard:
|
||||
|
||||
```
|
||||
openclaw dashboard
|
||||
```
|
||||
|
||||
|
||||
|
||||
Try this:
|
||||
|
||||
```
|
||||
generate a pptx based on this material https://github.com/hoshikawa2/openclaw-oci
|
||||
```
|
||||
|
||||
|
||||
|
||||
And you get a temporary OCI Object Storage link:
|
||||
|
||||
|
||||
|
||||
This is the oci_openai_proxy.py monitoring output:
|
||||
|
||||
|
||||
|
||||
And the Presentation generated is:
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
# Final Notes
|
||||
|
||||
You now have:
|
||||
|
||||
✔ OpenClaw fully integrated\
|
||||
✔ OCI Generative AI backend\
|
||||
✔ Streaming compatibility\
|
||||
✔ Enterprise-ready architecture
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
# Reference
|
||||
|
||||
- [Integrating OpenClaw with Oracle Cloud Generative AI (OCI)](https://github.com/hoshikawa2/openclaw-oci)
|
||||
- [Installing the OCI CLI](https://docs.oracle.com/en-us/iaas/private-cloud-appliance/pca/installing-the-oci-cli.htm)
|
||||
- [Oracle Cloud Generative AI](https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/)
|
||||
- [OpenClaw](https://openclaw.ai/)
|
||||
|
||||
# Acknowledgments
|
||||
|
||||
- **Author** - Cristiano Hoshikawa (Oracle LAD A-Team Solution Engineer)
|
||||
BIN
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|
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|
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|
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|
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|
After Width: | Height: | Size: 422 KiB |
232
project/generate_openclaw_ppt_template.py
Normal file
232
project/generate_openclaw_ppt_template.py
Normal file
@@ -0,0 +1,232 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, Optional
|
||||
|
||||
from pptx import Presentation
|
||||
|
||||
|
||||
# ============================================================
|
||||
# PATHS / CONFIG (flexible via env vars and CLI)
|
||||
# ============================================================
|
||||
|
||||
def _env_path(name: str, default: Optional[str] = None) -> Optional[Path]:
|
||||
v = os.getenv(name, default)
|
||||
return Path(v).expanduser() if v else None
|
||||
|
||||
|
||||
OPENCLAW_WORKDIR = _env_path("OPENCLAW_WORKDIR", ".") # default: current directory
|
||||
PPTX_TEMPLATE_PATH = _env_path(
|
||||
"PPTX_TEMPLATE_PATH",
|
||||
str(OPENCLAW_WORKDIR / "template_openclaw_oci_clean.pptx"),
|
||||
)
|
||||
PPTX_OUTPUT_PATH = _env_path(
|
||||
"PPTX_OUTPUT_PATH",
|
||||
str(OPENCLAW_WORKDIR / "openclaw_oci_presentation.pptx"),
|
||||
)
|
||||
|
||||
# Prefer OCI_CONTENT_FILE (policy name) but accept PPTX_CONTENT_PATH too
|
||||
PPTX_CONTENT_PATH = _env_path(
|
||||
"OCI_CONTENT_FILE",
|
||||
os.getenv("PPTX_CONTENT_PATH", str(OPENCLAW_WORKDIR / "content.json")),
|
||||
)
|
||||
|
||||
DEFAULT_LINK = "https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm"
|
||||
DEFAULT_PRESENTER = os.getenv("PPTX_PRESENTER", "Cristiano Hoshikawa")
|
||||
DEFAULT_COVER_SUBTITLE = os.getenv("PPTX_COVER_SUBTITLE", "Architecture")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# TEMPLATE ENGINE
|
||||
# ============================================================
|
||||
|
||||
class RedwoodSafePPT:
|
||||
"""
|
||||
Loads a PPTX template, wipes all existing slides safely, and builds a new deck
|
||||
using named layouts from the template.
|
||||
"""
|
||||
|
||||
LAYOUT_COVER = "Cover 1 - Full Image"
|
||||
LAYOUT_CONTENT = "Full Page - Light"
|
||||
|
||||
def __init__(self, template_path: Path):
|
||||
template_path = Path(template_path).expanduser()
|
||||
if not template_path.exists():
|
||||
raise FileNotFoundError(f"Template not found: {template_path}")
|
||||
|
||||
self.prs = Presentation(str(template_path))
|
||||
|
||||
# Remove ALL slides
|
||||
while len(self.prs.slides) > 0:
|
||||
rId = self.prs.slides._sldIdLst[0].rId
|
||||
self.prs.part.drop_rel(rId)
|
||||
del self.prs.slides._sldIdLst[0]
|
||||
|
||||
self.layouts = {layout.name: layout for layout in self.prs.slide_layouts}
|
||||
|
||||
def _layout(self, name: str):
|
||||
if name not in self.layouts:
|
||||
available = ", ".join(sorted(self.layouts.keys()))
|
||||
raise ValueError(f"Layout '{name}' not found in template. Available: {available}")
|
||||
return self.layouts[name]
|
||||
|
||||
def add_content(self, title: str, subhead: str, body: str):
|
||||
slide = self.prs.slides.add_slide(self._layout(self.LAYOUT_CONTENT))
|
||||
|
||||
text_placeholders = [ph for ph in slide.placeholders if getattr(ph, "has_text_frame", False)]
|
||||
if len(text_placeholders) < 2:
|
||||
raise RuntimeError("Content layout must have at least 2 text placeholders.")
|
||||
|
||||
text_placeholders[0].text = title
|
||||
text_placeholders[1].text = f"{subhead}\n\n{body}"
|
||||
|
||||
def add_cover(self, title: str, subtitle: str, presenter: str):
|
||||
slide = self.prs.slides.add_slide(self._layout(self.LAYOUT_COVER))
|
||||
|
||||
text_placeholders = [ph for ph in slide.placeholders if getattr(ph, "has_text_frame", False)]
|
||||
if len(text_placeholders) < 2:
|
||||
raise RuntimeError("Cover layout must have at least 2 text placeholders.")
|
||||
|
||||
text_placeholders[0].text = title
|
||||
text_placeholders[1].text = subtitle
|
||||
|
||||
# Optional placeholders by name
|
||||
for ph in text_placeholders:
|
||||
name = (getattr(ph, "name", "") or "").lower()
|
||||
if "date" in name:
|
||||
ph.text = datetime.now().strftime("%d %b %Y")
|
||||
if "presenter" in name:
|
||||
ph.text = presenter
|
||||
|
||||
def save(self, output_path: Path):
|
||||
output_path = Path(output_path).expanduser()
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
if output_path.exists():
|
||||
output_path.unlink()
|
||||
self.prs.save(str(output_path))
|
||||
|
||||
|
||||
# ============================================================
|
||||
# DECK (fixed 7 slides)
|
||||
# ============================================================
|
||||
|
||||
class OCIStrategicArchitectDeck:
|
||||
def __init__(self, template_path: Path):
|
||||
self.ppt = RedwoodSafePPT(template_path)
|
||||
|
||||
def _format_section(self, section: Dict[str, Any]) -> str:
|
||||
bullets = section.get("bullets", []) or []
|
||||
evidence = section.get("evidence", []) or []
|
||||
keywords = section.get("keywords", []) or []
|
||||
|
||||
lines = []
|
||||
for b in bullets:
|
||||
lines.append(f"• {str(b).strip()}")
|
||||
|
||||
if evidence:
|
||||
lines.append("")
|
||||
lines.append("Evidence:")
|
||||
for e in evidence[:2]:
|
||||
lines.append(f"- {str(e).strip()}")
|
||||
|
||||
if keywords:
|
||||
lines.append("")
|
||||
lines.append("Keywords: " + ", ".join([str(k).strip() for k in keywords[:8]]))
|
||||
|
||||
return "\n".join(lines).strip()
|
||||
|
||||
def build(self, material_link: str, content: Dict[str, Any], presenter: str, cover_subtitle: str):
|
||||
# 1) Cover
|
||||
self.ppt.add_cover(
|
||||
title=str(content["cover_title"]).strip(),
|
||||
subtitle=cover_subtitle,
|
||||
presenter=presenter,
|
||||
)
|
||||
|
||||
# 2) Intro
|
||||
self.ppt.add_content(
|
||||
title="Intro",
|
||||
subhead="Context and Motivation",
|
||||
body=self._format_section(content["introduction"]),
|
||||
)
|
||||
|
||||
# 3) Technologies
|
||||
self.ppt.add_content(
|
||||
title="Technologies",
|
||||
subhead="Stack OCI",
|
||||
body=self._format_section(content["technologies"]),
|
||||
)
|
||||
|
||||
# 4) Architecture
|
||||
self.ppt.add_content(
|
||||
title="Architecture",
|
||||
subhead="Architecture Flow",
|
||||
body=self._format_section(content["architecture"]),
|
||||
)
|
||||
|
||||
# 5) Problems
|
||||
self.ppt.add_content(
|
||||
title="Problems",
|
||||
subhead="Technical Challenges",
|
||||
body=self._format_section(content["problems"]),
|
||||
)
|
||||
|
||||
# 6) Demo
|
||||
self.ppt.add_content(
|
||||
title="Demo",
|
||||
subhead="Materials",
|
||||
body=f"{material_link}\n\n{self._format_section(content['demo'])}",
|
||||
)
|
||||
|
||||
# 7) Conclusion
|
||||
self.ppt.add_content(
|
||||
title="Conclusion",
|
||||
subhead="Strategies",
|
||||
body=self._format_section(content["conclusion"]),
|
||||
)
|
||||
|
||||
if len(self.ppt.prs.slides) != 7:
|
||||
raise RuntimeError("Deck must contain exactly 7 slides.")
|
||||
|
||||
def save(self, output_path: Path):
|
||||
self.ppt.save(output_path)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# CLI
|
||||
# ============================================================
|
||||
|
||||
def _load_json(path: Path) -> Dict[str, Any]:
|
||||
path = Path(path).expanduser()
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"content.json not found: {path}")
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Generate a 7-slide OCI strategic PPTX from a template + content.json.")
|
||||
parser.add_argument("--template", default=str(PPTX_TEMPLATE_PATH), help="Path to the PPTX template file.")
|
||||
parser.add_argument("--output", default=str(PPTX_OUTPUT_PATH), help="Path to the output PPTX to be written.")
|
||||
parser.add_argument("--content", default=str(PPTX_CONTENT_PATH), help="Path to content.json.")
|
||||
parser.add_argument("--link", default=os.getenv("OCI_LINK_DEMO", DEFAULT_LINK), help="Source link shown on Demo slide.")
|
||||
parser.add_argument("--presenter", default=DEFAULT_PRESENTER, help="Presenter name on cover (if placeholder exists).")
|
||||
parser.add_argument("--cover-subtitle", default=DEFAULT_COVER_SUBTITLE, help="Cover subtitle.")
|
||||
args = parser.parse_args()
|
||||
|
||||
content = _load_json(Path(args.content))
|
||||
|
||||
deck = OCIStrategicArchitectDeck(Path(args.template))
|
||||
deck.build(args.link, content, presenter=args.presenter, cover_subtitle=args.cover_subtitle)
|
||||
deck.save(Path(args.output))
|
||||
|
||||
print("✅ PPT generated:", Path(args.output).expanduser().resolve())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
752
project/oci_openai_proxy.py
Normal file
752
project/oci_openai_proxy.py
Normal file
@@ -0,0 +1,752 @@
|
||||
import os
|
||||
import time
|
||||
import json
|
||||
import uuid
|
||||
from typing import Optional, List, Dict, Any
|
||||
import re
|
||||
import subprocess
|
||||
|
||||
import requests
|
||||
import oci
|
||||
from fastapi import FastAPI, Request, HTTPException
|
||||
from fastapi.responses import StreamingResponse
|
||||
from pydantic import BaseModel, ConfigDict
|
||||
import requests
|
||||
import os
|
||||
|
||||
import requests
|
||||
|
||||
# ============================================================
|
||||
# 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_COMPARTMENT_ID>")
|
||||
OCI_GENAI_ENDPOINT = os.getenv(
|
||||
"OCI_GENAI_ENDPOINT",
|
||||
"https://inference.generativeai.<region>.oci.oraclecloud.com"
|
||||
)
|
||||
if not OCI_COMPARTMENT_ID:
|
||||
raise RuntimeError("OCI_COMPARTMENT_ID not defined")
|
||||
|
||||
OPENCLAW_TOOLS_ACTIVE = True
|
||||
|
||||
# ============================================================
|
||||
# PROMPTS to adapt for OCI
|
||||
# ============================================================
|
||||
|
||||
SYSTEM_AGENT_PROMPT = """
|
||||
You are an autonomous software agent.
|
||||
|
||||
You have full access to the local machine.
|
||||
|
||||
Available tools:
|
||||
- weather(city: string)
|
||||
- exec(command: string)
|
||||
|
||||
If a system command is required, respond ONLY with:
|
||||
|
||||
{
|
||||
"action": "call_tool",
|
||||
"tool": "exec",
|
||||
"arguments": {
|
||||
"command": "<shell command>"
|
||||
}
|
||||
}
|
||||
|
||||
***VERY IMPORTANT***: A TASK IS CONSIDERED COMPLETED WHEN IT RESULTS IN A ARTIFACT ASKED FROM THE USER
|
||||
|
||||
If task is completed:
|
||||
|
||||
{
|
||||
"action": "final_answer",
|
||||
"content": "<result>"
|
||||
}
|
||||
"""
|
||||
|
||||
|
||||
PROMPT_PATH = os.path.expanduser("pptx_runner_policy_strict.txt")
|
||||
def load_runner_policy():
|
||||
if os.path.exists(PROMPT_PATH):
|
||||
with open(PROMPT_PATH, "r", encoding="utf-8") as f:
|
||||
return f.read()
|
||||
return ""
|
||||
RUNNER_POLICY = load_runner_policy()
|
||||
|
||||
RUNNER_PROMPT = (
|
||||
RUNNER_POLICY + "\n\n"
|
||||
"You are a Linux execution agent.\n"
|
||||
"\n"
|
||||
"OUTPUT CONTRACT (MANDATORY):\n"
|
||||
"- You must output EXACTLY ONE of the following per response:\n"
|
||||
" A) (exec <command>)\n"
|
||||
" B) (done <final answer>)\n"
|
||||
"\n"
|
||||
"STRICT RULES:\n"
|
||||
"1) NEVER output raw commands without (exec <command>). Raw commands will be ignored.\n"
|
||||
"2) NEVER output explanations, markdown, code fences, bullets, or extra text.\n"
|
||||
"3) If you need to create multi-line files, you MUST use heredoc inside (exec <command>), e.g.:\n"
|
||||
" (exec cat > file.py << 'EOF'\n"
|
||||
" ...\n"
|
||||
" EOF)\n"
|
||||
"4) If the previous tool result shows an error, your NEXT response must be (exec <command>) to fix it.\n"
|
||||
"5) When the artifact is created successfully, end with (done ...).\n"
|
||||
"\n"
|
||||
"REMINDER: Your response must be only a single parenthesized block."
|
||||
)
|
||||
|
||||
# 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",
|
||||
}
|
||||
|
||||
# ============================================================
|
||||
# FASTAPI APP
|
||||
# ============================================================
|
||||
|
||||
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 build_generic_messages(openai_messages: list, system_prompt: str) -> list:
|
||||
out = []
|
||||
# 1) Injeta o system como PRIMEIRA mensagem USER, com prefixo fixo
|
||||
out.append({
|
||||
"role": "USER",
|
||||
"content": [{"type":"TEXT","text": "SYSTEM:\n" + system_prompt.strip()}]
|
||||
})
|
||||
|
||||
# 2) Depois converte o resto, ignorando systems originais
|
||||
for m in openai_messages or []:
|
||||
role = (m.get("role") or "user").lower()
|
||||
if role == "system":
|
||||
continue
|
||||
|
||||
r = "USER" if role in ("user", "tool") else "ASSISTANT"
|
||||
content = m.get("content", "")
|
||||
|
||||
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": r,
|
||||
"content": [{"type":"TEXT","text": str(content)}]
|
||||
})
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def call_oci_chat(body: dict, system_prompt: str):
|
||||
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", []))
|
||||
generic_messages = build_generic_messages(body.get("messages", []), system_prompt)
|
||||
|
||||
payload = {
|
||||
"compartmentId": OCI_COMPARTMENT_ID,
|
||||
"servingMode": {
|
||||
"servingType": "ON_DEMAND",
|
||||
"modelId": oci_model
|
||||
},
|
||||
"chatRequest": {
|
||||
"apiFormat": "GENERIC",
|
||||
"messages": generic_messages,
|
||||
"maxTokens": int(body.get("max_tokens", 4000)),
|
||||
"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}")
|
||||
p = subprocess.run(
|
||||
command,
|
||||
shell=True,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=120 # ajuste
|
||||
)
|
||||
out = (p.stdout or "") + (p.stderr or "")
|
||||
return out if out.strip() else f"(no output) exit={p.returncode}"
|
||||
except subprocess.TimeoutExpired:
|
||||
return "ERROR: command timed out"
|
||||
|
||||
TOOLS = {
|
||||
"weather": lambda city: get_weather_from_api(city),
|
||||
"exec": lambda command: execute_exec_command(command)
|
||||
}
|
||||
|
||||
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):
|
||||
r = "".join([i.get("text", "") for i in content if isinstance(i, dict) and i.get("type") == "TEXT"])
|
||||
# print("r", r)
|
||||
return r
|
||||
if isinstance(content, str):
|
||||
# print("content", content)
|
||||
return content
|
||||
return str(content)
|
||||
|
||||
|
||||
def agent_loop(body: dict, max_iterations=10000):
|
||||
|
||||
# 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}, SYSTEM_AGENT_PROMPT)
|
||||
|
||||
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
|
||||
|
||||
EXEC_RE = re.compile(r"\(exec\s+(.+?)\)\s*$", re.DOTALL)
|
||||
DONE_RE = re.compile(r"\(done\s+(.+?)\)\s*$", re.MULTILINE)
|
||||
|
||||
def run_exec_loop(body: dict, max_steps: int = 10000) -> dict:
|
||||
# Histórico OpenAI-style
|
||||
messages = [{"role":"system"}]
|
||||
messages.extend(body.get("messages", []))
|
||||
|
||||
last = None
|
||||
|
||||
last_executed_command = None
|
||||
|
||||
for _ in range(max_steps):
|
||||
last = call_oci_chat({**body, "messages": messages}, RUNNER_PROMPT)
|
||||
print('LLM Result', last)
|
||||
msg = last["choices"][0]["message"]
|
||||
text = _extract_generic_text(msg) or ""
|
||||
|
||||
m_done = DONE_RE.search(text)
|
||||
print("DONE_RE", text)
|
||||
print("m_done", m_done)
|
||||
if m_done:
|
||||
final_text = m_done.group(1).strip()
|
||||
|
||||
# devolve em formato OpenAI no fim
|
||||
return {
|
||||
**last,
|
||||
"choices": [{
|
||||
**last["choices"][0],
|
||||
"message": {"role":"assistant","content": final_text},
|
||||
"finishReason": "stop"
|
||||
}]
|
||||
}
|
||||
|
||||
m_exec = EXEC_RE.search(text)
|
||||
if m_exec:
|
||||
command = m_exec.group(1).strip()
|
||||
|
||||
if command == last_executed_command:
|
||||
print("⚠️ DUPLICATE COMMAND BLOCKED:", command)
|
||||
messages.append({"role":"assistant","content": text})
|
||||
messages.append({"role":"user","content": (
|
||||
"Command already executed. You must proceed or finish with (done ...)."
|
||||
)})
|
||||
continue
|
||||
|
||||
last_executed_command = command
|
||||
|
||||
result = execute_exec_command(command)
|
||||
|
||||
messages.append({"role":"assistant","content": text})
|
||||
messages.append({"role":"user","content": f"Tool result:\n{result}"})
|
||||
continue
|
||||
|
||||
# Se o modelo quebrou o protocolo:
|
||||
messages.append({"role":"assistant","content": text})
|
||||
messages.append({"role":"user","content": (
|
||||
"Protocol error. You MUST reply ONLY with (exec <command>) or (done <final answer>)."
|
||||
)})
|
||||
continue
|
||||
|
||||
# estourou steps: devolve última resposta (melhor do que travar)
|
||||
return last
|
||||
|
||||
def verify_task_completion(original_task: str, assistant_output: str) -> bool:
|
||||
"""
|
||||
Retorna True se tarefa estiver concluída.
|
||||
Retorna False se ainda precisar continuar.
|
||||
"""
|
||||
|
||||
verifier_prompt = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a strict task completion validator.\n"
|
||||
"Answer ONLY with DONE or CONTINUE.\n"
|
||||
"DONE = the task is fully completed.\n"
|
||||
"CONTINUE = more steps are required.\n"
|
||||
),
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"""
|
||||
Original task:
|
||||
{original_task}
|
||||
|
||||
Last assistant output:
|
||||
{assistant_output}
|
||||
|
||||
Is the task fully completed?
|
||||
"""
|
||||
}
|
||||
]
|
||||
|
||||
response = call_oci_chat({
|
||||
"model": "openai-compatible/gpt-5",
|
||||
"messages": verifier_prompt,
|
||||
"temperature": 0
|
||||
}, verifier_prompt[0]["content"])
|
||||
|
||||
text = _extract_generic_text(response["choices"][0]["message"]).strip().upper()
|
||||
|
||||
return text == "DONE"
|
||||
|
||||
# ============================================================
|
||||
# ENTERPRISE TOOLS
|
||||
# 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)
|
||||
"""
|
||||
print("LOG: EXECUTE TOOL WEATHER")
|
||||
try:
|
||||
# 1️⃣ Geocoding (cidade -> lat/lon)
|
||||
geo_url = "https://geocoding-api.open-meteo.com/v1/search"
|
||||
geo_params = {
|
||||
"name": city,
|
||||
"count": 1,
|
||||
"language": "pt",
|
||||
"format": "json"
|
||||
}
|
||||
|
||||
geo_response = requests.get(geo_url, params=geo_params, timeout=10)
|
||||
|
||||
if geo_response.status_code != 200:
|
||||
return f"Erro geocoding: {geo_response.text}"
|
||||
|
||||
geo_data = geo_response.json()
|
||||
|
||||
if "results" not in geo_data or len(geo_data["results"]) == 0:
|
||||
return f"Cidade '{city}' não encontrada."
|
||||
|
||||
location = geo_data["results"][0]
|
||||
latitude = location["latitude"]
|
||||
longitude = location["longitude"]
|
||||
resolved_name = location["name"]
|
||||
country = location.get("country", "")
|
||||
|
||||
# 2️⃣ Clima atual
|
||||
weather_url = "https://api.open-meteo.com/v1/forecast"
|
||||
weather_params = {
|
||||
"latitude": latitude,
|
||||
"longitude": longitude,
|
||||
"current_weather": True,
|
||||
"timezone": "auto"
|
||||
}
|
||||
|
||||
weather_response = requests.get(weather_url, params=weather_params, timeout=10)
|
||||
|
||||
if weather_response.status_code != 200:
|
||||
return f"Erro clima: {weather_response.text}"
|
||||
|
||||
weather_data = weather_response.json()
|
||||
|
||||
current = weather_data.get("current_weather")
|
||||
|
||||
if not current:
|
||||
return "Dados de clima indisponíveis."
|
||||
|
||||
temperature = current["temperature"]
|
||||
windspeed = current["windspeed"]
|
||||
|
||||
return (
|
||||
f"Temperatura atual em {resolved_name}, {country}: {temperature}°C.\n"
|
||||
f"Velocidade do vento: {windspeed} km/h."
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
return f"Weather tool error: {str(e)}"
|
||||
|
||||
# ============================================================
|
||||
# STREAMING ADAPTER
|
||||
# ============================================================
|
||||
|
||||
def stream_openai_format(chat_response: dict, model: str):
|
||||
|
||||
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
|
||||
created = int(time.time())
|
||||
|
||||
content = chat_response["choices"][0]["message"]["content"]
|
||||
|
||||
yield f"data: {json.dumps({
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'created': created,
|
||||
'model': model,
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'delta': {'role': 'assistant'},
|
||||
'finish_reason': None
|
||||
}]
|
||||
})}\n\n"
|
||||
|
||||
for i in range(0, len(content), 60):
|
||||
chunk = content[i:i+60]
|
||||
yield f"data: {json.dumps({
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'created': created,
|
||||
'model': model,
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'delta': {'content': chunk},
|
||||
'finish_reason': None
|
||||
}]
|
||||
})}\n\n"
|
||||
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
# ============================================================
|
||||
# ENDPOINTS
|
||||
# ============================================================
|
||||
|
||||
@app.get("/health")
|
||||
def health():
|
||||
return {"status": "ok"}
|
||||
|
||||
@app.get("/v1/models")
|
||||
def list_models():
|
||||
return {
|
||||
"object": "list",
|
||||
"data": [
|
||||
{"id": k, "object": "model", "owned_by": "oci"}
|
||||
for k in MODEL_MAP.keys()
|
||||
],
|
||||
}
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# CHAT COMPLETIONS
|
||||
# ------------------------------------------------------------
|
||||
|
||||
@app.post("/v1/chat/completions")
|
||||
async def chat_completions(request: Request):
|
||||
|
||||
body = await request.json()
|
||||
# chat_response = call_oci_chat(body)
|
||||
# chat_response = agent_loop(body)
|
||||
|
||||
if OPENCLAW_TOOLS_ACTIVE:
|
||||
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))
|
||||
|
||||
oci_choice = chat_response["choices"][0]
|
||||
oci_message = oci_choice["message"]
|
||||
|
||||
# 🔥 SE É TOOL CALL → RETORNA DIRETO
|
||||
if oci_message.get("tool_calls"):
|
||||
return chat_response
|
||||
|
||||
content_text = ""
|
||||
|
||||
content = oci_message.get("content")
|
||||
|
||||
if isinstance(content, list):
|
||||
for item in content:
|
||||
if isinstance(item, dict) and item.get("type") == "TEXT":
|
||||
content_text += item.get("text", "")
|
||||
elif isinstance(content, str):
|
||||
content_text = content
|
||||
else:
|
||||
content_text = str(content)
|
||||
|
||||
finish_reason = oci_choice.get("finishReason", "stop")
|
||||
|
||||
# 🔥 SE STREAMING
|
||||
if body.get("stream"):
|
||||
async def event_stream():
|
||||
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
|
||||
created = int(time.time())
|
||||
|
||||
# role chunk
|
||||
yield f"data: {json.dumps({
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'created': created,
|
||||
'model': body['model'],
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'delta': {'role': 'assistant'},
|
||||
'finish_reason': None
|
||||
}]
|
||||
})}\n\n"
|
||||
|
||||
# content chunks
|
||||
for i in range(0, len(content_text), 50):
|
||||
chunk = content_text[i:i+50]
|
||||
|
||||
yield f"data: {json.dumps({
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'created': created,
|
||||
'model': body['model'],
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'delta': {'content': chunk},
|
||||
'finish_reason': None
|
||||
}]
|
||||
})}\n\n"
|
||||
|
||||
# final chunk
|
||||
yield f"data: {json.dumps({
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion.chunk',
|
||||
'created': created,
|
||||
'model': body['model'],
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'delta': {},
|
||||
'finish_reason': finish_reason
|
||||
}]
|
||||
})}\n\n"
|
||||
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
return StreamingResponse(
|
||||
event_stream(),
|
||||
media_type="text/event-stream"
|
||||
)
|
||||
|
||||
# 🔥 SE NÃO FOR STREAM
|
||||
return {
|
||||
"id": f"chatcmpl-{uuid.uuid4().hex}",
|
||||
"object": "chat.completion",
|
||||
"created": int(time.time()),
|
||||
"model": body["model"],
|
||||
"choices": [{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": content_text
|
||||
},
|
||||
"finish_reason": finish_reason
|
||||
}],
|
||||
"usage": {
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"total_tokens": 0
|
||||
}
|
||||
}
|
||||
# ------------------------------------------------------------
|
||||
# RESPONSES (OpenAI 2024 format)
|
||||
# ------------------------------------------------------------
|
||||
|
||||
@app.post("/v1/responses")
|
||||
async def responses(request: Request):
|
||||
|
||||
body = await request.json()
|
||||
|
||||
# chat_response = call_oci_chat(body)
|
||||
chat_response = agent_loop(body)
|
||||
|
||||
oci_choice = chat_response["choices"][0]
|
||||
oci_message = oci_choice["message"]
|
||||
|
||||
content_text = ""
|
||||
|
||||
content = oci_message.get("content")
|
||||
|
||||
if isinstance(content, list):
|
||||
for item in content:
|
||||
if item.get("type") == "TEXT":
|
||||
content_text += item.get("text", "")
|
||||
elif isinstance(content, str):
|
||||
content_text = content
|
||||
|
||||
return {
|
||||
"id": f"resp_{uuid.uuid4().hex}",
|
||||
"object": "response",
|
||||
"created": int(time.time()),
|
||||
"model": body.get("model"),
|
||||
"output": [
|
||||
{
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"type": "output_text",
|
||||
"text": content_text
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"usage": {
|
||||
"input_tokens": 0,
|
||||
"output_tokens": 0,
|
||||
"total_tokens": 0
|
||||
}
|
||||
}
|
||||
|
||||
@app.middleware("http")
|
||||
async def log_requests(request: Request, call_next):
|
||||
# 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))
|
||||
except:
|
||||
print(">>> BODY RAW:", body.decode())
|
||||
|
||||
response = await call_next(request)
|
||||
# print(">>> STATUS:", response.status_code)
|
||||
return response
|
||||
89
project/openclaw.json
Normal file
89
project/openclaw.json
Normal file
@@ -0,0 +1,89 @@
|
||||
{
|
||||
"meta": {
|
||||
"lastTouchedVersion": "2026.2.1",
|
||||
"lastTouchedAt": "2026-02-14T03:24:55.922Z"
|
||||
},
|
||||
"wizard": {
|
||||
"lastRunAt": "2026-02-14T03:24:55.917Z",
|
||||
"lastRunVersion": "2026.2.1",
|
||||
"lastRunCommand": "onboard",
|
||||
"lastRunMode": "local"
|
||||
},
|
||||
"models": {
|
||||
"providers": {
|
||||
"openai-compatible": {
|
||||
"baseUrl": "http://127.0.0.1:8050/v1",
|
||||
"apiKey": "sk-test",
|
||||
"api": "openai-completions",
|
||||
"models": [
|
||||
{
|
||||
"id": "gpt-5",
|
||||
"name": "gpt-5" ,
|
||||
"reasoning": false,
|
||||
"input": ["text"],
|
||||
"cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 },
|
||||
"contextWindow": 200000,
|
||||
"maxTokens": 8192
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"agents": {
|
||||
"defaults": {
|
||||
"model": {
|
||||
"primary": "openai-compatible/gpt-5"
|
||||
},
|
||||
"models": {
|
||||
"openai-compatible/gpt-5": {}
|
||||
},
|
||||
"workspace": "/home/hoshikawa2/.openclaw/workspace",
|
||||
"compaction": {
|
||||
"mode": "safeguard"
|
||||
},
|
||||
"maxConcurrent": 4,
|
||||
"subagents": {
|
||||
"maxConcurrent": 8
|
||||
}
|
||||
}
|
||||
},
|
||||
"messages": {
|
||||
"ackReactionScope": "group-mentions"
|
||||
},
|
||||
"commands": {
|
||||
"native": "auto",
|
||||
"nativeSkills": "auto"
|
||||
},
|
||||
"channels": {
|
||||
"whatsapp": {
|
||||
"dmPolicy": "allowlist",
|
||||
"selfChatMode": true,
|
||||
"allowFrom": [
|
||||
"+5511999961711"
|
||||
],
|
||||
"groupPolicy": "allowlist",
|
||||
"mediaMaxMb": 50,
|
||||
"debounceMs": 0
|
||||
}
|
||||
},
|
||||
"gateway": {
|
||||
"port": 18789,
|
||||
"mode": "local",
|
||||
"bind": "loopback",
|
||||
"auth": {
|
||||
"mode": "token",
|
||||
"token": "5459cc59afcb0a4de09e0ce23ef6409090059a7d35df1740"
|
||||
},
|
||||
"tailscale": {
|
||||
"mode": "off",
|
||||
"resetOnExit": false
|
||||
}
|
||||
},
|
||||
"plugins": {
|
||||
"entries": {
|
||||
"whatsapp": {
|
||||
"enabled": true
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
100
project/pptx_runner_policy_strict.txt
Normal file
100
project/pptx_runner_policy_strict.txt
Normal file
@@ -0,0 +1,100 @@
|
||||
Whenever the user requests PPTX generation with external material (link, file, or text):
|
||||
|
||||
----------------------------------------------
|
||||
STEP 0 – FIXED WORKING DIRECTORY (MANDATORY)
|
||||
----------------------------------------------
|
||||
|
||||
All operations MUST occur inside:
|
||||
$HOME/.openclaw/workspace/openclaw_folder
|
||||
|
||||
Execute:
|
||||
cd $HOME/.openclaw/workspace/openclaw_folder
|
||||
|
||||
STEP 1 – PREPARATION (MANDATORY)
|
||||
|
||||
The file generate_openclaw_ppt_template.py is located in $HOME/.openclaw/workspace/openclaw_folder
|
||||
The file read_url is located in $HOME/.openclaw/workspace/openclaw_folder
|
||||
The file read_file is located in $HOME/.openclaw/workspace/openclaw_folder
|
||||
|
||||
Required:
|
||||
|
||||
read_url for links
|
||||
read_file for local files
|
||||
|
||||
🔄 GITHUB LINK HANDLING (REQUIRED)
|
||||
|
||||
If the link contains:
|
||||
github.com/.../blob/...
|
||||
Automatically convert to:
|
||||
raw.githubusercontent.com/USER/REPO/BRANCH/PATH
|
||||
BEFORE calling read_url.
|
||||
|
||||
Example:
|
||||
Original:
|
||||
https://github.com/user/repo/blob/main/app.py
|
||||
Convert to:
|
||||
https://raw.githubusercontent.com/user/repo/main/app.py
|
||||
Then call:
|
||||
read_url <raw_url>
|
||||
|
||||
If the returned content contains <html or <script>, extract only visible text, removing HTML tags.
|
||||
|
||||
* If the content cannot be read successfully → ABORT.
|
||||
|
||||
MANDATORY PIPELINE:
|
||||
|
||||
1) Save material to file:
|
||||
(exec read_url <url> > $HOME/.openclaw/workspace/openclaw_folder/material_raw.txt)
|
||||
|
||||
2) Analyze material_raw.txt and generate content.json explicitly:
|
||||
(exec cat > $HOME/.openclaw/workspace/openclaw_folder/content.json << 'EOF'
|
||||
<valid JSON only>
|
||||
EOF)
|
||||
|
||||
cover_title (string)
|
||||
introduction, technologies, architecture, problems, demo, conclusion (objects)
|
||||
- Each chapter object MUST have:
|
||||
bullets: 3–6 bullets (short, objective)
|
||||
keywords: 5–12 terms that appear literally in the material
|
||||
evidence: 2–4 short excerpts (10–25 words) taken from the material, without HTML
|
||||
- It is FORBIDDEN to use generic bullets without keywords from the material.
|
||||
- VALIDATION: if it is not possible to extract at least 20 unique keywords from the total material → ABORT.
|
||||
|
||||
3) Validate JSON:
|
||||
(exec python -m json.tool $HOME/.openclaw/workspace/openclaw_folder/content.json)
|
||||
|
||||
Only after successful validation:
|
||||
(exec export OCI_LINK_DEMO="<url>")
|
||||
(exec python generate_openclaw_ppt_template.py)
|
||||
|
||||
STEP 4 – MODIFICATION VALIDATION [STRICT VERSION]
|
||||
|
||||
Before running:
|
||||
|
||||
- Verify that each chapter contains at least 1 literal keyword from the material.
|
||||
- Verify that at least 8 keywords appear in 4 or more slides.
|
||||
- Verify that each chapter contains at least 1 piece of evidence.
|
||||
If it fails → ABORT.
|
||||
|
||||
STEP 5 – EXECUTION
|
||||
|
||||
Only now execute:
|
||||
|
||||
SET THE ENVIRONMENT VARIABLE WITH THE URL PASSED AS A BASIS FOR DOCUMENTATION: `export OCI_LINK_DEMO=<link passed as documentation>`
|
||||
|
||||
SET THE ENVIRONMENT VARIABLE WITH THE FILE NAME GENERATED WITH CONTENT READ FROM THE LINK: `export OCI_CONTENT_FILE=<NAME OF THE GENERATED FILE>`
|
||||
|
||||
`python $HOME/.openclaw/workspace/openclaw_folder/generate_openclaw_ppt_template.py`
|
||||
|
||||
STEP 6 – UPLOAD
|
||||
|
||||
First, delete the file in object storage: `openclaw_oci_presentation.pptx`
|
||||
|
||||
And only then upload it to Object Storage: `oci os object put \
|
||||
--bucket-name hoshikawa_template \
|
||||
--file` $HOME/.openclaw/workspace/openclaw_folder/openclaw_oci_presentation.pptx \
|
||||
|
||||
--force
|
||||
|
||||
STEP 7 – GENERATE PRE-AUTH LINK
|
||||
oci os preauth-request create ...
|
||||
84
project/read_url_and_read_file.sh
Normal file
84
project/read_url_and_read_file.sh
Normal file
@@ -0,0 +1,84 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
# Where to install scripts (default matches typical OpenClaw workspace folder)
|
||||
TARGET_DIR="${OPENCLAW_WORKDIR:-$HOME/.openclaw/workspace/openclaw_folder}"
|
||||
mkdir -p "$TARGET_DIR"
|
||||
|
||||
cat > "$TARGET_DIR/read_url" << 'EOF'
|
||||
#!/usr/bin/env python3
|
||||
import sys
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
def normalize_github_blob(url: str) -> str:
|
||||
# Convert github.com/.../blob/... to raw.githubusercontent.com/.../.../...
|
||||
if "github.com/" in url and "/blob/" in url:
|
||||
parts = url.split("github.com/", 1)[1].split("/blob/", 1)
|
||||
repo = parts[0].strip("/")
|
||||
rest = parts[1].lstrip("/")
|
||||
return f"https://raw.githubusercontent.com/{repo}/{rest}"
|
||||
return url
|
||||
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: read_url <url>", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
url = normalize_github_blob(sys.argv[1])
|
||||
|
||||
try:
|
||||
r = requests.get(url, timeout=30)
|
||||
r.raise_for_status()
|
||||
content = r.text
|
||||
|
||||
# If HTML, extract visible text
|
||||
if "<html" in content.lower() or "<body" in content.lower():
|
||||
soup = BeautifulSoup(content, "html.parser")
|
||||
content = soup.get_text("\n")
|
||||
|
||||
print(content)
|
||||
|
||||
except Exception as e:
|
||||
print(f"ERROR: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
EOF
|
||||
|
||||
cat > "$TARGET_DIR/read_file" << 'EOF'
|
||||
#!/usr/bin/env python3
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
def read_pdf(path: Path) -> str:
|
||||
try:
|
||||
import fitz # PyMuPDF
|
||||
except Exception:
|
||||
raise RuntimeError("PyMuPDF (fitz) not installed. Install with: pip install pymupdf")
|
||||
doc = fitz.open(str(path))
|
||||
out = []
|
||||
for i in range(doc.page_count):
|
||||
out.append(doc.load_page(i).get_text("text"))
|
||||
return "\n".join(out)
|
||||
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: read_file <path>", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
p = Path(sys.argv[1]).expanduser()
|
||||
if not p.exists():
|
||||
print(f"ERROR: file not found: {p}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
suffix = p.suffix.lower()
|
||||
try:
|
||||
if suffix == ".pdf":
|
||||
print(read_pdf(p))
|
||||
else:
|
||||
print(p.read_text(encoding="utf-8", errors="replace"))
|
||||
except Exception as e:
|
||||
print(f"ERROR: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
EOF
|
||||
|
||||
chmod +x "$TARGET_DIR/read_url" "$TARGET_DIR/read_file"
|
||||
|
||||
echo "✅ Installed: $TARGET_DIR/read_url and $TARGET_DIR/read_file"
|
||||
Reference in New Issue
Block a user