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Merge branch 'development' of github.com:Scale3-Labs/langtrace-python-sdk into ali/s3en-2136-instrument-litellm
2 parents b3dbcfa + fc1b186 commit 31628a7

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‎README.md‎

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@@ -232,23 +232,27 @@ prompt = get_prompt_from_registry(<Registry ID>, options={"prompt_version": 1, "
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Langtrace automatically captures traces from the following vendors:
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| Vendor | Type | Typescript SDK | Python SDK |
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| ------------ | --------------- | ------------------ | ------------------ |
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| OpenAI | LLM | :white_check_mark: | :white_check_mark: |
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| Anthropic | LLM | :white_check_mark: | :white_check_mark: |
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| Azure OpenAI | LLM | :white_check_mark: | :white_check_mark: |
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| Cohere | LLM | :white_check_mark: | :white_check_mark: |
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| Groq | LLM | :x: | :white_check_mark: |
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| Langchain | Framework | :x: | :white_check_mark: |
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| Langgraph | Framework | :x: | :white_check_mark: |
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| LlamaIndex | Framework | :white_check_mark: | :white_check_mark: |
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| DSPy | Framework | :x: | :white_check_mark: |
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| CrewAI | Framework | :x: | :white_check_mark: |
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| Ollama | Framework | :x: | :white_check_mark: |
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| Pinecone | Vector Database | :white_check_mark: | :white_check_mark: |
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| ChromaDB | Vector Database | :white_check_mark: | :white_check_mark: |
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| Weaviate | Vector Database | :white_check_mark: | :white_check_mark: |
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| QDrant | Vector Database | :x: | :white_check_mark: |
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| Vendor | Type | Typescript SDK | Python SDK |
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| ------------ | --------------- | ------------------ | ------------------------------- |
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| OpenAI | LLM | :white_check_mark: | :white_check_mark: |
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| Anthropic | LLM | :white_check_mark: | :white_check_mark: |
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| Azure OpenAI | LLM | :white_check_mark: | :white_check_mark: |
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| Cohere | LLM | :white_check_mark: | :white_check_mark: |
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| Groq | LLM | :x: | :white_check_mark: |
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| Perplexity | LLM | :white_check_mark: | :white_check_mark: |
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| Gemini | LLM | :x: | :white_check_mark: |
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| Langchain | Framework | :x: | :white_check_mark: |
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| LlamaIndex | Framework | :white_check_mark: | :white_check_mark: |
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| Langgraph | Framework | :x: | :white_check_mark: |
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| DSPy | Framework | :x: | :white_check_mark: |
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| CrewAI | Framework | :x: | :white_check_mark: |
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| Ollama | Framework | :x: | :white_check_mark: |
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| VertexAI | Framework | :x: | :white_check_mark: |
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| Pinecone | Vector Database | :white_check_mark: | :white_check_mark: |
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| ChromaDB | Vector Database | :white_check_mark: | :white_check_mark: |
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| QDrant | Vector Database | :white_check_mark: | :white_check_mark: |
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| Weaviate | Vector Database | :white_check_mark: | :white_check_mark: |
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| PGVector | Vector Database | :white_check_mark: | :white_check_mark: (SQLAlchemy) |
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---
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‎pyproject.toml‎

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"qdrant_client",
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"weaviate-client",
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"ollama",
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"groq"
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"groq",
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"google-generativeai",
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"google-cloud-aiplatform"
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]
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test = [

‎src/examples/gemini_example/__init__.py‎

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from .main import basic
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class GeminiRunner:
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def run(self):
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basic()

‎src/examples/gemini_example/function_tools.py‎

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tools = [
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{
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"function_declarations": [
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{
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"name": "find_movies",
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"description": "find movie titles currently playing in theaters based on any description, genre, title words, etc.",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616",
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},
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"description": {
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"type": "string",
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"description": "Any kind of description including category or genre, title words, attributes, etc.",
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},
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},
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"required": ["description"],
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},
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},
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{
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"name": "find_theaters",
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"description": "find theaters based on location and optionally movie title which is currently playing in theaters",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616",
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},
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"movie": {"type": "string", "description": "Any movie title"},
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},
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"required": ["location"],
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},
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},
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{
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"name": "get_showtimes",
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"description": "Find the start times for movies playing in a specific theater",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616",
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},
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"movie": {"type": "string", "description": "Any movie title"},
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"theater": {
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"type": "string",
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"description": "Name of the theater",
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},
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"date": {
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"type": "string",
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"description": "Date for requested showtime",
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},
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},
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"required": ["location", "movie", "theater", "date"],
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},
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},
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]
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}
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]

‎src/examples/gemini_example/main.py‎

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from langtrace_python_sdk import langtrace
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import google.generativeai as genai
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from dotenv import load_dotenv
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import os
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import asyncio
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import pathlib
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from .function_tools import tools
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load_dotenv()
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langtrace.init(write_spans_to_console=False, batch=False)
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genai.configure(api_key=os.environ["GEMINI_API_KEY"])
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async def async_demo():
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task1 = asyncio.create_task(async_generate())
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task2 = asyncio.create_task(async_generate(stream=True))
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return await asyncio.gather(task1, task2)
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def basic():
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generate()
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generate(stream=True, with_tools=True)
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# image_to_text()
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# audio_to_text()
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asyncio.run(async_demo())
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def generate(stream=False, with_tools=False):
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model = genai.GenerativeModel(
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"gemini-1.5-pro", system_instruction="You are a cat. Your name is Neko."
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)
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response = model.generate_content(
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"Write a story about a AI and magic",
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stream=stream,
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tools=tools if with_tools else None,
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)
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if stream:
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for res in response:
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if res.text:
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print(res.text)
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else:
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print(response.text)
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async def async_generate(stream=False):
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model = genai.GenerativeModel(
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"gemini-1.5-pro", system_instruction="You are a cat. Your name is Neko."
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)
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response = await model.generate_content_async(
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"Write a story about a AI and magic", stream=stream
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)
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if stream:
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async for chunk in response:
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if chunk.text:
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print(chunk.text)
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else:
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print(response.text)
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def image_to_text(stream=False):
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model = genai.GenerativeModel("gemini-1.5-flash")
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image1 = {
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"mime_type": "image/jpeg",
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"data": pathlib.Path("src/examples/gemini_example/jetpack.jpg").read_bytes(),
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}
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prompt = "Describe me this picture. What do you see in it."
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response = model.generate_content([prompt, image1], stream=stream)
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if stream:
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for res in response:
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print(res.text)
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else:
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print(response.text)
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# def audio_to_text(stream=False):
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# model = genai.GenerativeModel("gemini-1.5-flash")
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# audio = genai.upload_file(
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# pathlib.Path("src/examples/gemini_example/voice_note.mp3")
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# )
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# prompt = "Summarize this voice recording."
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# response = model.generate_content([prompt, audio], stream=stream)
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# if stream:
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# for res in response:
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# print(res.text)
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# else:
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# print(response.text)

‎src/examples/openai_example/embeddings_create.py‎

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result = client.embeddings.create(
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model="text-embedding-ada-002",
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input="Once upon a time, there was a pirate.",
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encoding_format="float",
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)
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return result

‎src/examples/openai_example/images_edit.py‎

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response = client.images.edit(
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model="dall-e-2",
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image=open("./resources/lounge_flamingo.png", "rb"),
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mask=open("./resources/mask.png", "rb"),
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image=open("src/examples/openai_example/resources/lounge_flamingo.png", "rb"),
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mask=open("src/examples/openai_example/resources/mask.png", "rb"),
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prompt="A sunlit indoor lounge area with a pool and duck standing in side with flamingo.",
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n=1,
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size="1024x1024",

‎src/examples/vertexai_example/__init__.py‎

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from .main import basic
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class VertexAIRunner:
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def run(self):
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basic()

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