Adding samples

Author: aayush3011

Python/CosmosDB-MongoDB-vCore-Integrations/LangChain-AzureCognitiveSearch-AzureOpenAI.ipynb

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+## Notebooks for Retrieval Augmented Generation with CosmosDB for MongoDB vCore and Azure Cognitive Search  using different integrations
+This folder contains end-to-end walk through of how to perform 
+Retrieval Augmented Generation (RAG) with Azure Cosmos DB for MongoDB 
+vCore and Azure Cognitive Search in a small demo. 
+
+All the samples use abstracts of papers(PDF format) presented at 
+the Computer Vision and Pattern Recognition Conference 2019 (CVPR19). 
+
+1. LangChain + Azure Cosmos MongoDB vCore + Azure Open AI 
+2. LangChain + Azure Cognitive Search + Azure Open AI
+3. Semantic Kernel + Azure Cognitive Search + Azure Open AI 

Python/CosmosDB-MongoDB-vCore-Integrations/LangChain-CosmosDBMongovCoreVectorSearch-AzureOpenAI.ipynb

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+AZCOSMOS_API=mongo
+AZCOSMOS_CONNSTR=<Mongo_vCore_Connection_String>
+OPENAI_API_KEY=
+OPENAI_API_TYPE = "azure"
+OPENAI_API_VERSION = "2023-05-15"
+OPENAI_API_BASE = <Your_Open_Ai_Endpoint>
+OPENAI_EMBEDDINGS_MODEL_NAME = "text-embedding-ada-002"
+OPENAI_EMBEDDINGS_MODEL_DEPLOYMENT = "smart-agent-embedding-ada"
+OPENAI_COMPELETIONS_MODEL_NAME = "gpt-3.5-turbo"
+OPENAI_COMPELETIONS_MODEL_DEPLOYMENT = "smart-agent-completions-gpt35"
+COG_SEARCH_ENDPOINT=<Your_Cognitive_Search_Endpoint>
+COG_SEARCH_KEY=