Develop -> main

.gitignore

@@ -6,3 +6,4 @@ dist/
 .env
 .samples
 .session
+.rsazure

CHANGELOG.md

@@ -3,6 +3,32 @@
 All notable changes to this project will be documented in this file.
 ___
 
+## [0.6.2] - 2025-04-07
+
+### Changed
+- Refactored all template files in `samples/templates/` to align with the modular CLI architecture introduced in v0.6.0
+  - Replaced all uses of `call_azure_openai_handler()` with `main()` function
+  - Replaced manual print formatting with `ChatResult().print()` for consistent output
+  - Replaced direct logging calls with `get_logger()` and `result.to_log_dict()`
+  - Replaced hardcoded message structures with `get_context_messages()` for context support
+  - Added defensive `response.choices` check and fallback token estimation
+  - Standardized import style: `import rsazure_openai_toolkit as rschat`
+  - Wrapped logic in `main()` blocks for reuse and testability
+
+### Affected Templates
+- `basic_usage.py.j2`
+- `chat_loop_usage.py.j2`
+- `env_usage.py.j2`
+- `env_chat_loop_usage.py.j2`
+
+> These templates are now fully aligned with the official CLI (`rschat`) and offer consistent logging, context persistence, and safety across all use cases.
+
+### Notes
+- No breaking changes
+- Recommended: regenerate examples using `rschat-tools samples`
+- Version bumped to 0.6.2
+___
+
 ## [0.6.1] - 2025-04-05
 
 ### Changed

README.md

@@ -59,14 +59,18 @@ A fast, modular, secure, and auditable Python toolkit to integrate with Azure Op
 
 ___
 
-## 🚀 What's New in v0.6.0
++ ## 🚀 What's New in v0.6.x
 
 - Object-oriented CLI (`rschat`, `rschat-tools`) — easier to test, extend and reuse
 - Persistent session context — with system prompt validation and full/trimmed history tracking
 - Reproducible model config — centralized via `get_model_config()` and `ModelConfig`
 - Transparent logging — structured logs via `InteractionLogger` (CSV/JSONL)
 - Modular architecture — folders like `core/`, `session/`, `logging/`, `model_config/`, etc.
 
+### v0.6.2
+- All sample templates have been fully aligned with the core architecture and CLI
+- Logging, context, result formatting, and safety checks now follow production-grade standards
+
 > Check the full [CHANGELOG](https://github.com/renan-siqueira/rsazure-openai-toolkit/blob/main/CHANGELOG.md) for details.
 ___
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "rsazure-openai-toolkit"
-version = "0.6.1"
+version = "0.6.2"
 description = "A fast, modular, secure, and auditable toolkit to integrate with Azure OpenAI — with a friendly CLI and dev-first architecture."
 authors = [{ name = "Renan Siqueira Antonio", email = "[email protected]" }]
 readme = { file = "README.md", content-type = "text/markdown" }

src/rsazure_openai_toolkit/__init__.py

@@ -33,7 +33,7 @@
 - rschat.ContextInfo(...)
 """
 
-__version__ = "0.6.1"
+__version__ = "0.6.2"
 __description__ = "A fast, modular, secure, and auditable toolkit to integrate with Azure OpenAI — with a friendly CLI and dev-first architecture."
 __author__ = "Renan Siqueira Antonio"
 __license__ = "MIT"

src/rsazure_openai_toolkit/samples/templates/basic_usage.py.j2

@@ -1,56 +1,64 @@
 import time
-from rsazure_openai_toolkit import call_azure_openai_handler
-from rsazure_openai_toolkit.utils import get_model_config
-from rsazure_openai_toolkit.logging.interaction_logger import InteractionLogger
-
-
-messages = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Say hello!"}
-]
-
-model_config = get_model_config()
-start = time.time()
-
-response = call_azure_openai_handler(
-    api_key="your-api-key",
-    azure_endpoint="https://your-resource.openai.azure.com/",
-    api_version="2023-12-01-preview",
-    deployment_name="your-deployment-name",
-    messages=messages,
-    **model_config
-)
-
-elapsed = round(time.time() - start, 2)
-content = response.choices[0].message.content
-usage = response.usage.model_dump() if response.usage else {}
-model_used = response.model
-seed_used = model_config.get("seed")
-
-input_tokens = usage.get("prompt_tokens", 0)
-output_tokens = usage.get("completion_tokens", 0)
-total_tokens = usage.get("total_tokens", input_tokens + output_tokens)
-
-print(f"\nAssistant:\n\t{content}")
-print("\n----- REQUEST INFO -----")
-print(f"📤 Input tokens: {input_tokens}")
-print(f"📥 Output tokens: {output_tokens}")
-print(f"🧾 Total tokens: {total_tokens}")
-print(f"🧠 Model: {model_used}")
-print(f"🎲 Seed: {seed_used}")
-print(f"⏱️ Time: {elapsed}s\n")
-
-# Optional logging mode: none [default], jsonl, csv
-logger = InteractionLogger(mode="none", path="chat_logs.jsonl")
-
-if logger.enabled:
-    logger.log({
-        "question": "Say hello!",
-        "response": content,
-        "model": model_used,
-        "usage": usage,
-        "model_config": model_config,
-        "raw_response": response.model_dump()
-    })
-else:
-    print("📭 Logging is disabled (RSCHAT_LOG_MODE is 'none' or not configured)\n")
+import rsazure_openai_toolkit as rschat
+
+
+def main():
+    user_input = "Say hello!"
+    rschat.load_env()
+
+    context_data = rschat.get_context_messages(user_input=user_input)
+    messages = context_data["messages"]
+
+    model_config = rschat.get_model_config()
+    config = rschat.get_cli_config()
+
+    start = time.time()
+    response = rschat.main(
+        api_key=config["api_key"],
+        azure_endpoint=config["endpoint"],
+        api_version=config["version"],
+        deployment_name=config["deployment_name"],
+        messages=messages,
+        **model_config
+    )
+    elapsed = round(time.time() - start, 2)
+
+    if not response.choices:
+        print("❌ No response received from the model.")
+        return
+
+    content = response.choices[0].message.content
+    usage = response.usage.model_dump() if response.usage else {}
+
+    input_tokens = usage.get("prompt_tokens", 0)
+    output_tokens = usage.get("completion_tokens") or rschat.estimate_input_tokens(
+        messages=[{"role": "assistant", "content": content}],
+        deployment_name=config["deployment_name"]
+    )
+    total_tokens = usage.get("total_tokens", input_tokens + output_tokens)
+
+    result = rschat.ChatResult(
+        question=user_input,
+        response_text=content,
+        system_prompt=config["system_prompt"],
+        model=response.model,
+        seed=model_config.get("seed"),
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        total_tokens=total_tokens,
+        elapsed_time=elapsed,
+        model_config=model_config,
+        raw_response=response.model_dump()
+    )
+
+    result.print()
+
+    logger = rschat.get_logger()
+    if logger.enabled:
+        logger.log(result.to_log_dict())
+    else:
+        print(f"📭 Logging is disabled ({logger})\n")
+
+
+if __name__ == "__main__":
+    main()

src/rsazure_openai_toolkit/samples/templates/chat_loop_usage.py.j2

@@ -1,72 +1,81 @@
 import time
-from rsazure_openai_toolkit import call_azure_openai_handler
-from rsazure_openai_toolkit.utils.utils import get_model_config
-from rsazure_openai_toolkit.logging.interaction_logger import InteractionLogger
+import rsazure_openai_toolkit as rschat
 
 
-def chat():
+def chat_loop():
     print("🔁 Chat loop started")
     print("💡 Type 'exit' to quit\n")
 
-    # Optional logging mode: none [default], jsonl, csv
-    logger = InteractionLogger(mode="none", path="chat_logs.jsonl")
+    # Carrega variáveis de ambiente
+    rschat.load_env()
+    config = rschat.get_cli_config()
+    logger = rschat.get_logger()
 
     while True:
-        user_input = input("You: ")
-        if user_input.strip().lower() == "exit":
+        user_input = input("You: ").strip()
+        if user_input.lower() == "exit":
             print("👋 Goodbye!")
             break
-        if not user_input.strip():
+        if not user_input:
             continue
 
-        messages = [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": user_input}
-        ]
+        # Prepara mensagens com ou sem contexto persistente
+        context_data = rschat.get_context_messages(user_input=user_input)
+        messages = context_data["messages"]
 
-        model_config = get_model_config()
-        start = time.time()
+        model_config = rschat.get_model_config()
 
-        response = call_azure_openai_handler(
-            api_key="your-api-key",
-            azure_endpoint="https://your-resource.openai.azure.com/",
-            api_version="2023-12-01-preview",
-            deployment_name="your-deployment-name",
+        start = time.time()
+        response = rschat.main(
+            api_key=config["api_key"],
+            azure_endpoint=config["endpoint"],
+            api_version=config["version"],
+            deployment_name=config["deployment_name"],
             messages=messages,
             **model_config
         )
-
         elapsed = round(time.time() - start, 2)
+
+        if not response.choices:
+            print("❌ No response received from the model.")
+            continue
+
         content = response.choices[0].message.content
         usage = response.usage.model_dump() if response.usage else {}
-        model_used = response.model
-        seed_used = model_config.get("seed")
 
         input_tokens = usage.get("prompt_tokens", 0)
-        output_tokens = usage.get("completion_tokens", 0)
+        output_tokens = usage.get("completion_tokens") or rschat.estimate_input_tokens(
+            messages=[{"role": "assistant", "content": content}],
+            deployment_name=config["deployment_name"]
+        )
         total_tokens = usage.get("total_tokens", input_tokens + output_tokens)
 
-        print(f"\nAssistant:\n\t{content}")
-        print("\n----- REQUEST INFO -----")
-        print(f"📤 Input tokens: {input_tokens}")
-        print(f"📥 Output tokens: {output_tokens}")
-        print(f"🧾 Total tokens: {total_tokens}")
-        print(f"🧠 Model: {model_used}")
-        print(f"🎲 Seed: {seed_used}")
-        print(f"⏱️ Time: {elapsed}s\n")
+        # Constrói resultado e imprime no formato padrão
+        result = rschat.ChatResult(
+            question=user_input,
+            response_text=content,
+            system_prompt=config["system_prompt"],
+            model=response.model,
+            seed=model_config.get("seed"),
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            total_tokens=total_tokens,
+            elapsed_time=elapsed,
+            model_config=model_config,
+            raw_response=response.model_dump()
+        )
+
+        result.print()
 
         if logger.enabled:
-            logger.log({
-                "question": user_input,
-                "response": content,
-                "model": model_used,
-                "usage": usage,
-                "model_config": model_config,
-                "raw_response": response.model_dump()
-            })
+            logger.log(result.to_log_dict())
         else:
-            print("📭 Logging is disabled (RSCHAT_LOG_MODE is 'none' or not configured)\n")
+            print(f"📭 Logging is disabled ({logger})\n")
+
+
+def main():
+    chat_loop()
 
 
 if __name__ == "__main__":
-    chat()
+    main()