openai-cookbook

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OpenAI API 사용을 위한 예제 및 가이드

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- - feature_request.md
  - get-help.md
  - problem-report.md
- - check_notebooks.py
- - stale.yaml
  - validate-notebooks.yaml
- authors_schema.json
- CODEOWNERS
- pull_request_template.md
- registry_schema.json
- - build-your-own-fact-checker-cerebras.ipynb
  - fine-tune-korean.ipynb
  - fine-tune-transfomers.ipynb
  - handle-raw-cot.md
  - run-colab.ipynb
  - run-locally-lmstudio.md
  - run-locally-ollama.md
  - run-nvidia.ipynb
  - run-transformers.md
  - run-vllm.md
  - verifying-implementations.md
- chatgpt-agents-sales-meeting-prep.md
- codex_exec_plans.md
- gpt-oss-safeguard-guide.md
- how_to_work_with_large_language_models.md
- openai-harmony.md
- per_run_spending_controller_responses_api.md
- related_resources.md
- techniques_to_improve_reliability.md
- text_comparison_examples.md
- what_is_new_with_dalle_3.mdx
- what_makes_documentation_good.md
- - agentkit_walkthrough.ipynb
- - agent_improvement_loop.ipynb
  - app_assistant_voice_agents.ipynb
  - building_reliable_agents_memory_compaction.ipynb
  - context_personalization.ipynb
  - dispute_agent.ipynb
  - evaluate_agents.ipynb
  - parallel_agents.ipynb
  - session_memory.ipynb
- - chat.ipynb
  - chat_with_your_own_data.ipynb
  - embeddings.ipynb
  - functions.ipynb
  - whisper.ipynb
- - translate_latex_book.ipynb
- - building_w_rt_mini.ipynb
- - Autofix-github-actions.ipynb
  - build_code_review_with_codex_sdk.md
  - Build_iterative_repair_loops_with_Codex.ipynb
  - code_modernization.md
  - iterating-development-workflows-with-codex.md
  - jira-github.ipynb
  - secure_quality_gitlab.md
  - using_goals_in_codex.ipynb
- - How_to_create_dynamic_masks_with_DALL-E_and_Segment_Anything.ipynb
  - Image_generations_edits_and_variations_with_DALL-E.ipynb
- - 25000_spend_dataset_current.csv
  - AG_news_samples.csv
  - amazon_furniture_dataset.csv
  - amazon_product_kg.json
  - artificial_intelligence_wikipedia.txt
  - bison.mp4
  - Chinook.db
  - cookbook_recipes_nlg_10k.csv
  - created_slides.pptx
  - dbpedia_samples.jsonl
  - drone_training.jsonl
  - example_events_openapi.json
  - example_requests_to_parallel_process.jsonl
  - fia_f1_power_unit_financial_regulations_issue_1_-_2022-08-16.pdf
  - fine_food_reviews_1k.csv
  - fine_food_reviews_with_embeddings_1k.csv
  - helpcenter_articles.csv
  - imdb_top_1000.csv
  - items_tagged_and_captioned.csv
  - items_tagged_and_captioned_embeddings.csv
  - labelled_transactions.csv
  - language_models_are_unsupervised_multitask_learners.pdf
  - library_transactions_with_embeddings_359.csv
  - marketing_sample_for_amazon_com-ecommerce__20200101_20200131__10k_data.csv
  - NotRealCorp_financial_data.json
  - parsed_pdf_docs.json
  - parsed_pdf_docs_with_embeddings.csv
  - recommendations_embeddings_cache.pkl
  - scifact_claims.jsonl
  - scifact_corpus.jsonl
  - snli_1.0_train_2k.csv
  - styles_2k.csv
  - styles_44k.csv
  - toy_chat_fine_tuning.jsonl
  - winter_olympics_2022.csv
- - introduction_to_deep_research_api.ipynb
  - introduction_to_deep_research_api_agents.ipynb
- - Building_resilient_prompts_using_an_evaluation_flywheel.md
  - Evaluate_RAG_with_LlamaIndex.ipynb
  - Getting_Started_with_OpenAI_Evals.ipynb
  - How_to_eval_abstractive_summarization.ipynb
  - How_to_evaluate_LLMs_for_SQL_generation.ipynb
  - moving-from-openai-evals-to-promptfoo.md
- - answers_with_ft.py
  - ft_retrieval_augmented_generation_qdrant.ipynb
  - olympics-1-collect-data.ipynb
  - olympics-2-create-qa.ipynb
  - olympics-3-train-qa.ipynb
  - reinforcement_finetuning_healthbench.ipynb
- - apply_patch.py
  - codex_prompting_guide.ipynb
  - gpt-5-1_prompting_guide.ipynb
  - gpt-5-2_prompting_guide.ipynb
  - gpt-5_frontend.ipynb
  - gpt-5_new_params_and_tools.ipynb
  - gpt-5_prompting_guide.ipynb
  - gpt-5_troubleshooting_guide.ipynb
  - prompt-optimization-cookbook.ipynb
  - prompt_personalities.ipynb
- - introduction_to_gpt4o.ipynb
- - bag.png
  - cat_portrait_pixel.jpg
  - cat_with_hat.jpg
  - desk.png
  - glorptak.jpg
  - hat.png
  - logo.png
  - mask.png
  - mask_alpha.png
  - mask_edit.png
  - model.png
  - woman_portrait.png
  - woman_smiling.jpg
- - databricks_mcp_cookbook.ipynb
  - mcp_tool_guide.ipynb
- - agents_sdk_transcription.mmd
  - realtime_api_transcription.mmd
  - speech-to-text-not-streaming.mmd
  - speech-to-text-streaming.mmd
- - document_and_multimodal_understanding_tips.ipynb
  - grounded_spatial_reasoning_layouts.ipynb
  - image-gen-1.5-prompting_guide.ipynb
  - image-gen-models-prompting-guide.ipynb
  - image_evals.ipynb
  - image_understanding_with_rag.ipynb
  - org-chart-sample.jpg
  - transparent-design-template-demo.html
  - transparent-image-assets-for-campaigns-and-presentations.ipynb
  - Using_GPT4_Vision_With_Function_Calling.ipynb
  - Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering.ipynb
- - o3o4-mini_prompting_guide.ipynb
- - Using_chained_calls_for_o1_structured_outputs.ipynb
  - Using_reasoning_for_data_validation.ipynb
  - Using_reasoning_for_routine_generation.ipynb
- - Secure_code_interpreter_tool_for_LLM_agents.ipynb
- - reasoning_items.ipynb
  - responses_api_tool_orchestration.ipynb
  - responses_example.ipynb
- - sora2_prompting_guide.ipynb
- - selecting_a_model_based_on_stripe_conversion.ipynb
- - Code_quality_and_security_scan_with_GitHub_Actions.md
  - financial_document_analysis_with_llamaindex.ipynb
  - GPT_finetuning_with_wandb.ipynb
  - How_to_automate_S3_storage_with_functions.ipynb
  - Openai_monitoring_with_wandb_weave.ipynb
  - Visualizing_embeddings_in_Kangas.ipynb
  - Visualizing_embeddings_in_wandb.ipynb
  - Visualizing_embeddings_with_Atlas.ipynb
  - Web_search_with_google_api_bring_your_own_browser_tool.ipynb
- - embeddings_utils.py
- - README.md
- - one_way_translation_using_realtime_api.mdx
  - realtime_translation_guide.mdx
  - running_realtime_api_speech_on_esp32_arduino_edge_runtime_elatoai.md
  - steering_tts.ipynb
  - voice_translation_into_different_languages_using_GPT-4o.ipynb
- api_request_parallel_processor.py
- Assistants_API_overview_python.ipynb
- batch_processing.ipynb
- Build_a_coding_agent_with_GPT-5.1.ipynb
- Chat_finetuning_data_prep.ipynb
- Classification_using_embeddings.ipynb
- Clustering.ipynb
- Clustering_for_transaction_classification.ipynb
- Code_search_using_embeddings.ipynb
- completions_usage_api.ipynb
- Context_summarization_with_realtime_api.ipynb
- Creating_slides_with_Assistants_API_and_DALL-E3.ipynb
- Custom-LLM-as-a-Judge.ipynb
- custom_image_embedding_search.ipynb
- Customizing_embeddings.ipynb
- Data-intensive-Realtime-apps.ipynb
- Data_extraction_transformation.ipynb
- Developing_hallucination_guardrails.ipynb
- Embedding_long_inputs.ipynb
- Embedding_Wikipedia_articles_for_search.ipynb
- Enhance_your_prompts_with_meta_prompting.ipynb
- Entity_extraction_for_long_documents.ipynb
- File_Search_Responses.ipynb
- Fine-tuned_classification.ipynb
- Fine_tuning_direct_preference_optimization_guide.ipynb
- Fine_tuning_for_function_calling.ipynb
- Function_calling_finding_nearby_places.ipynb
- Function_calling_with_an_OpenAPI_spec.ipynb
- Generate_Images_With_GPT_Image.ipynb
- Generate_Images_With_High_Input_Fidelity.ipynb
- Get_embeddings_from_dataset.ipynb
- gpt4-1_prompting_guide.ipynb
- GPT_with_vision_for_video_understanding.ipynb
- How_to_build_a_tool-using_agent_with_Langchain.ipynb
- How_to_build_an_agent_with_the_node_sdk.mdx
- How_to_call_functions_for_knowledge_retrieval.ipynb
- How_to_call_functions_with_chat_models.ipynb
- How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb
- How_to_count_tokens_with_tiktoken.ipynb
- How_to_finetune_chat_models.ipynb
- How_to_format_inputs_to_ChatGPT_models.ipynb
- How_to_handle_rate_limits.ipynb
- How_to_stream_completions.ipynb
- How_to_use_guardrails.ipynb
- How_to_use_moderation.ipynb
- Leveraging_model_distillation_to_fine-tune_a_model.ipynb
- migrating_from_whisper_to_gpt_transcribe.ipynb
- Multiclass_classification_for_transactions.ipynb
- Named_Entity_Recognition_to_enrich_text.ipynb
- Optimize_Prompts.ipynb
- Orchestrating_agents.ipynb
- Parse_PDF_docs_for_RAG.ipynb
- Prompt_Caching101.ipynb
- Prompt_Caching_201.ipynb
- Prompt_migration_guide.ipynb
- Question_answering_using_a_search_API.ipynb
- Question_answering_using_embeddings.ipynb
- RAG_with_graph_db.ipynb
- Realtime_eval_guide.ipynb
- Realtime_out_of_band_transcription.ipynb
- Realtime_prompting_guide.ipynb
- reasoning_function_calls.ipynb
- Recommendation_using_embeddings.ipynb
- Regression_using_embeddings.ipynb
- Reinforcement_Fine_Tuning.ipynb
- Reproducible_outputs_with_the_seed_parameter.ipynb
- SDG1.ipynb
- Search_reranking_with_cross-encoders.ipynb
- Semantic_text_search_using_embeddings.ipynb
- skills_in_api.ipynb
- Speech_transcription_methods.ipynb
- Structured_Outputs_Intro.ipynb
- Structured_outputs_multi_agent.ipynb
- Summarizing_long_documents.ipynb
- Tag_caption_images_with_GPT4V.ipynb
- Unit_test_writing_using_a_multi-step_prompt.ipynb
- Unit_test_writing_using_a_multi-step_prompt_with_older_completions_API.ipynb
- User_and_product_embeddings.ipynb
- Using_embeddings.ipynb
- Using_logprobs.ipynb
- Using_tool_required_for_customer_service.ipynb
- Visualizing_embeddings_in_2D.ipynb
- Visualizing_embeddings_in_3D.ipynb
- Whisper_correct_misspelling.ipynb
- Whisper_processing_guide.ipynb
- Whisper_prompting_guide.ipynb
- Zero-shot_classification_with_embeddings.ipynb
- 01_alti_agent_governance.png
- 01_benefit_of_temporal_kb.jpg
- 02_alti_stack.png
- 02_question_types_for_temporal_kbs.jpg
- 03_alti_promptfoo_dash.png
- 03_statement_invalidation.png
- 04_temporal_agent.png
- 05_temporal_agent_arch.png
- 06_temporal_agent_chunker.png
- 07_temporal_agent_class.png
- 08_invalidation_agent.png
- 09_full_pipeline.png
- 10_multi_step_retrieval.png
- 11_retrieval_agent.png
- 12_spectrum_of_tools.png
- 2.2_model_evolution.png
- 3A_rag_hierarchical_router.png
- 3A_rag_task_card.png
- 3B_coscientist_architecture.png
- 3B_reasoning_task_card.png
- 3C_insurance_architecture.png
- 3C_insurance_form.png
- 3C_insurance_task_card.png
- 3D_model_selection_flowchart.png
- access_token.png
- add_label.png
- agent-improvement-loop-flywheel.svg
- agent-improvement-loop-human-gates.svg
- agent_dr.png
- agent_instructions.png
- agent_log_traces.png
- agent_trace_details.png
- agentic-system-architecture.svg
- agents_sdk_transcription.png
- api-trigger-check.svg
- api_channel.png
- app_assistant_voice_agents.png
- app_assistant_voice_agents_arch.png
- app_assistant_voice_agents_arch_2.png
- app_assistant_voice_agents_trace.png
- assistants_overview_assistants_dashboard.png
- assistants_overview_assistants_playground.png
- assistants_overview_diagram.png
- assistants_overview_enable_code_interpreter.png
- assistants_overview_enable_function.png
- assistants_overview_enable_retrieval.png
- assistants_overview_new_assistant.png
- aws_lambda_1.png
- aws_lambda_10.png
- aws_lambda_11.png
- aws_lambda_12.png
- aws_lambda_13.png
- aws_lambda_14.png
- aws_lambda_15.png
- aws_lambda_16.png
- aws_lambda_17.png
- aws_lambda_18.png
- aws_lambda_19.png
- aws_lambda_20.png
- aws_lambda_3.png
- aws_lambda_4.png
- aws_lambda_8.png
- aws_lambda_9.png
- axial-coding.png
- azure-rag-architecture.png
- azure-rag-quickstart-gpt.png
- base_woman.png
- baseline_agent.png
- bigquery_rag_architecture.png
- billboard.png
- billboard_gpt-image-2.png
- billboard_winter.png
- billboard_winter_gpt-image-2.png
- boots copy.png
- boots.png
- business-technical-bridge.svg
- byo_realtime_diagram.png
- byo_realtime_starting.png
- canvas_lms_list_of_tokens.png
- canvas_lms_new_token.png
- canvas_lms_sample_conversation.png
- canvas_lms_settings_link.png
- carbon_cycle_technical_diagram.png
- chain_of_thought_fig1.png
- chain_of_thought_fig11.png
- chain_of_thought_fig3.png
- chain_of_thought_fig5.png
- chatgpt-auth-config.png
- childrens_book_illustration_1.png
- childrens_book_illustration_1_gpt-image-2.png
- childrens_book_illustration_2.png
- childrens_book_illustration_2_gpt-image-2.png
- christmas_collectible_toy_airplane.png
- christmas_collectible_toy_airplane_gpt-image-2.png
- christmas_holiday_card_teddy.png
- christmas_holiday_card_teddy_gpt-image-2.png
- ci-codex-workflow.png
- code-modernization-phases.png
- codex-pr.png
- codex-workflow.png
- Custom-LLM-as-a-Judge-Output.png
- Custom-LLM-as-a-Judge-Partial-Credit.gif
- Custom-LLM-as-a-Judge-Reasoning.png
- ELT_table.png
- NotRealCorp_chart.png
- Supplies_20240322_220858_Raven_Scan_3_jpeg.rf.50852940734939c8838819d7795e1756.jpg
- System_flow_partner_mcp.png
- Traces-1_partner.png
- Traces-2_partner.png
.gitignore
AGENTS.md
authors.yaml
CONTRIBUTING.md
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/openai/openai-cookbook

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd openai-cookbook

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Docker

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker compose -f examples/vector_databases/milvus/docker-compose.yaml up -d --build

compose 설정 파일에 정의된 서비스들을 대상으로 명령을 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

3. Node.js

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Node.js npm 명령어를 쓰려면 Node.js가 설치되어 있어야 합니다. LTS(장기지원) 버전을 추천합니다.

cd examples/agents_sdk/deployment_manager

이 프로젝트의 관련 파일이 하위 폴더 안에 있어서, 먼저 그 폴더로 이동합니다.

npm install

package.json에 명시된 라이브러리들을 내려받아 설치합니다.

npm start

개발/실행 서버를 켭니다.

✅ 명령어 실행 후 터미널에 나타나는 주소(보통 http://localhost:3000 형태)를 브라우저에서 열어보세요.

4. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r examples/agents_sdk/computer_use_with_daytona/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

5. Make

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.

cd examples/agents_sdk/deployment_manager

이 프로젝트의 관련 파일이 하위 폴더 안에 있어서, 먼저 그 폴더로 이동합니다.

make

생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.

✅ 에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.

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# 설치 가이드

2. Docker

3. Node.js

4. Python

5. Make

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openai-cookbook

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# 설치 가이드

2. Docker

3. Node.js

4. Python

5. Make

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