From f70aa82c844d28e1a36a5dbda6a17a0ed691184d Mon Sep 17 00:00:00 2001
From: Shaurya Rohatgi <shauryr@gmail.com>
Date: Mon, 13 Nov 2023 03:12:46 -0500
Subject: [PATCH] Update README.md - Added notebook for extraction_openai_tools
  (#13205)

added Parallel Function Calling for Structured Data Extraction notebook

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---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 cookbook/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cookbook/README.md b/cookbook/README.md
index 5ea01df373fa5..9130c629dae4a 100644
--- a/cookbook/README.md
+++ b/cookbook/README.md
@@ -20,6 +20,7 @@ Notebook | Description
 [databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
 [deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
 [elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
+[extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools
 [forward_looking_retrieval_augm...](https://github.com/langchain-ai/langchain/tree/master/cookbook/forward_looking_retrieval_augmented_generation.ipynb) | Implement the forward-looking active retrieval augmented generation (flare) method, which generates answers to questions, identifies uncertain tokens, generates hypothetical questions based on these tokens, and retrieves relevant documents to continue generating the answer.
 [generative_agents_interactive_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb) | Implement a generative agent that simulates human behavior, based on a research paper, using a time-weighted memory object backed by a langchain retriever.
 [gymnasium_agent_simulation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/gymnasium_agent_simulation.ipynb) | Create a simple agent-environment interaction loop in simulated environments like text-based games with gymnasium.