beta 2 build changes

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.0.1-dev0
+current_version = 0.0.1.b3
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
 serialize = 

.gitignore

@@ -19,6 +19,7 @@ lib64/
 parts/
 sdist/
 var/
+venv/
 wheels/
 share/python-wheels/
 *.egg-info/
@@ -77,4 +78,7 @@ target/
 
 # pyenv
 .python-versi
+
+# VSCode
+.vscode/
 .ruff_cache

Makefile

@@ -17,7 +17,7 @@ format-fix: ## Fix formatting issues
 fix: lint-fix format-fix ## Fix all linting and formatting issues
 
 install: ## Install dependencies with poetry
-	poetry install
+	poetry install -E "openai" -E "bedrock"
 
 test: ## Run unit tests
 	poetry run pytest -vvv -s -o log_level=INFO -o log_cli=true tests/
@@ -28,6 +28,9 @@ integ: ## Run integration tests
 dist: ## Build the distribution
 	poetry build
 
+clean: ## remove build artifacts
+	rm -rf ./dist/*
+
 bump-patch: ## Bump the patch version (_._.X) everywhere it appears in the project
 	@$(call i, Bumping the patch number)
 	poetry run bumpversion patch --allow-dirty

README.md

@@ -1,6 +1,6 @@
-# llm-traceguard
+# OpenLLMTelemetry
 
-**llm-traceguard** is an open-source Python library that provides Open Telemetry integration with Large Language Models (LLMs). It is designed to facilitate tracing and safeguarding applications that leverage LLMs, ensuring better monitoring and reliability.
+`openllmtelemetry` is an open-source Python library that provides Open Telemetry integration with Large Language Models (LLMs). It is designed to facilitate tracing applications that leverage LLMs and Generative AI, ensuring better observability and monitoring.
 
 ## Features
 
@@ -10,20 +10,102 @@
 
 ## Installation
 
-To install **llm-traceguard**, simply use pip:
+To install `openllmtelemetry` simply use pip:
 
 ```bash
-pip install llm-traceguard
+pip install openllmtelemetry
 ```
 
 ## Usage 🚀
 
-Here's a basic example of how to use **llm-traceguard** in your project:
+Here's a basic example of how to use **OpenLLMTelemetry** in your project:
+
+First you need to setup a few environment variables to specify where you want your LLM telemetry to be sent, and make sure you also have any API keys set for interacting with your LLM and for sending the telemetry to [WhyLabs](https://whylabs.ai/free?utm_source=openllmtelemetry-Github&utm_medium=openllmtelemetry-readme&utm_campaign=WhyLabs_Secure)
+
+
+
+```python
+import os
+
+os.environ["WHYLABS_DEFAULT_DATASET_ID"] = "your-model-id" #  e.g. model-1 
+os.environ["WHYLABS_API_KEY"] = "replace-with-your-whylabs-api-key"
+
+```
+
+After you verify your env variables are set you can now instrument your app by running the following:
+
+```python
+import openllmtelemetry
+
+openllmtelemetry.instrument()
+```
+
+This will automatically instrument your calls to LLMs to gather open telemetry traces and send these to WhyLabs.
+
+## Integration: OpenAI
+Integration with an OpenAI application is straightforward with `openllmtelemetry` package.
+
+First, you need to set a few environment variables. This can be done via your container set up or via code. 
 
 ```python
-import llm_traceguard
+import os 
 
-llm_traceguard.instrument()
+os.environ["WHYLABS_API_KEY"] = "<your-whylabs-api-key>"
+os.environ["WHYLABS_DEFAULT_DATASET_ID"] = "<your-llm-resource-id>"
+os.environ["WHYLABS_GUARD_ENDPOINT"] = "<your container endpoint>"
+os.environ["WHYLABS_GUARD_API_KEY"] = "internal-secret-for-whylabs-Secure"
+```
+
+Once this is done, all of your OpenAI interactions will be automatically traced. If you have rulesets enabled for blocking in WhyLabs Secure policy, the library will block requests accordingly
+
+```python
+from openai import OpenAI
+client = OpenAI()
+
+response = client.chat.completions.create(
+  model="gpt-3.5-turbo",
+  messages=[
+    {
+      "role": "system",
+      "content": "You are a helpful chatbot. "
+    },
+    {
+      "role": "user",
+      "content": "Aren't noodles amazing?"
+    }
+  ],
+  temperature=0.7,
+  max_tokens=64,
+  top_p=1
+)
+```
+
+## Integration: Amazon Bedrock
+
+One of the nice things about `openllmtelemetry` is that a single call to intrument your app can work across various LLM providers, using the same instrument call above, you can also invoke models using the boto3 client's bedrock-runtime and interaction with LLMs such as Titan and you get the same level of telemetry extracted and sent to WhyLabs
+
+Note: you may have to test that your boto3 credentials are working to be able to use the below example
+For details see [boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
+
+```python
+import json
+import boto3
+
+
+def bedrock_titan(prompt: str):
+    try:
+        model_id = 'amazon.titan-text-express-v1'
+        brt = boto3.client(service_name='bedrock-runtime')
+        response = brt.invoke_model(body=json.dumps({"inputText": prompt}), modelId=model_id)
+        response_body = json.loads(response.get("body").read())
+
+    except Exception as error:
+        logger.error(f"A client error occurred:{error}")
+
+    return response_body
+
+response = bedrock_titan("What is your name and what is the origin and reason for that name?")
+print(response)
 ```
 
 ## Requirements 📋
@@ -38,8 +120,11 @@ Contributions are welcome! For major changes, please open an issue first to disc
 
 ## License 📄
 
-**llm-traceguard** is licensed under the Apache-2.0 License. See [LICENSE](LICENSE) for more details.
+**OpenLLMTelemetry** is licensed under the Apache-2.0 License. See [LICENSE](LICENSE) for more details.
 
 ## Contact 📧
 
-For support or any questions, feel free to contact us at [email protected].
+For support or any questions, feel free to contact us at [email protected].
+
+## Documentation
+More documentation can be found here on WhyLabs site: https://whylabs.ai/docs/

notebooks/test.ipynb

@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ['TRACE_PROMPT_AND_RESPONSE'] = 'true'\n",
+    "os.environ[\"WHYLABS_DEFAULT_ORG_ID\"] = \"\" # set your org id\n",
+    "os.environ[\"WHYLABS_API_KEY\"] = \"\"\n",
+    "os.environ[\"WHYLABS_DEFAULT_DATASET_ID\"] = \"\" #\n",
+    "# os.environ[\"WHYLABS_API_ENDPOINT\"] = \"\"\n",
+    "# os.environ[\"WHYLABS_TRACES_ENDPOINT\"] =\"\"\n",
+    "# os.environ[\"OPENAI_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<opentelemetry.trace.ProxyTracer at 0x7fd920137400>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import openllmtelemetry\n",
+    "\n",
+    "openllmtelemetry.instrument()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChatCompletion(id='chatcmpl-95PI3UrvG8c5bbHLDf197AcdaPLK8', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Positive', role='assistant', function_call=None, tool_calls=None))], created=1711075883, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint='fp_3bc1b5746c', usage=CompletionUsage(completion_tokens=1, prompt_tokens=40, total_tokens=41))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from openai import OpenAI\n",
+    "client = OpenAI()\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "  model=\"gpt-3.5-turbo\",\n",
+    "  messages=[\n",
+    "    {\n",
+    "      \"role\": \"system\",\n",
+    "      \"content\": \"You will be provided with a tweet, and your task is to classify its sentiment as positive, neutral, or negative.\"\n",
+    "    },\n",
+    "    {\n",
+    "      \"role\": \"user\",\n",
+    "      \"content\": \"I love lasagna!\"\n",
+    "    }\n",
+    "  ],\n",
+    "  temperature=0.7,\n",
+    "  max_tokens=64,\n",
+    "  top_p=1\n",
+    ")\n",
+    "\n",
+    "response"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openllmtelemetry-D7f_qCE2-py3.9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

openllmtelemetry/__init__.py

@@ -0,0 +1,3 @@
+from openllmtelemetry.instrument import instrument
+
+__ALL__ = [instrument]