Bibliography summarization and Azure openai API change (#2)

* changed code to work with azure openai instead of openai. 
* added a function that will summarize a list of numbered abstract from the bibliography

README.md

@@ -2,6 +2,19 @@
 
 This repository holds the Open Targets AI API router.
 
+For this API to function an .env file has to be added in the root folder of this project. For the values of these variables please contact the Open Targets team of The Hyve or create your own Azure OpenAI deployment.
+
+### .env variables
+```
+AZURE_OPENAI_ENDPOINT = ...
+AZURE_OPENAI_API_KEY = ...
+AZURE_OPENAI_API_DEPLOYMENT_NAME = ...
+AZURE_OPENAI_API_VERSION = ...
+AZURE_OPENAI_API_INSTANCE_NAME = ...
+MAX_LLM_REQUESTS = ...
+HYVE_AI_API = ...
+```
+
 ### Required stack
 
 - [NodeJS >= v18](https://nodejs.org/en/)

app.js

@@ -12,10 +12,15 @@ const port = normalizePort(process.env.PORT || "8080");
 const app = express();
 
 app.use(httpLogger);
-app.use(express.json());
+app.use(express.json({limit: '50mb'}));
 
 if (isDevelopment) {
-  app.use(cors());
+  app.use(cors({
+    origin: [
+      process.env.SERVER_NAME,
+    ]
+  }
+  ));
 }
 
 if (isProduction) {
@@ -26,6 +31,7 @@ if (isProduction) {
         "https://partner-platform.dev.opentargets.xyz",
         "https://platform.opentargets.org",
         "https://platform.dev.opentargets.xyz",
+        process.env.SERVER_NAME
       ],
     })
   );

controllers/publicationSummary.js

@@ -1,5 +1,5 @@
 import { loadQAMapReduceChain } from "langchain/chains";
-import { OpenAI } from "langchain/llms/openai";
+import { AzureChatOpenAI } from "@langchain/openai";
 import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
 import wandb from "@wandb/sdk";
 
@@ -10,10 +10,13 @@ dotenv.config();
 
 // query setup
 // summarization docs https://js.langchain.com/docs/api/chains/functions/loadQAMapReduceChain
-const model = new OpenAI({
-  modelName: "gpt-4o-mini",
-  openAIApiKey: process.env.OPENAI_TOKEN,
-  temperature: 0.5,
+const model = new AzureChatOpenAI({
+  temperature: .9,
+  azureOpenAIEndpoint: process.env.AZURE_OPENAI_ENDPOINT,
+  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
+  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
+  azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION,
+  azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME,
 });
 
 const createPrompt = ({ targetSymbol, diseaseName }) => {
@@ -41,6 +44,36 @@ export const streamTest = ({ res }) => {
   sendAndSleep(res, 1);
 };
 
+export const getMulitpleAbstractSummary = async ({
+  name,
+  entity,
+  abstracts,
+}) => {  
+  var prompt = 
+  `You are given abstracts related to ${name} ${entity}. Use information found in the abstracts to inform me about this ${entity}.
+  Combine the information found in the following abstracts into a single story.\n
+  Format the output as plaintext.\n
+  If abstract 1 is used as information source, add a citation [1] to the text if an abstact is used.\n
+  Only use abstracts that are listed as context as an abstract, do not make up numbers.
+  Never add a list of citations at the end of the story.
+  `
+
+  for(let i = 0; i < abstracts.length; i++) {
+    // if the abstracts are sent from the bibliography section
+    if(abstracts[i].hasOwnProperty("publication").hasOwnProperty("abstract")){
+      prompt = prompt.concat("\nAbstract [", abstracts[i].publication.number, "]\n Title:\n", abstracts[i].publication.title, "\nAbstract:\n", abstracts[i].publication.abstract.replace(/<[^>]*>?/gm, ''))
+    // if the abstracts are sent from the EuropePMC section
+    } else if(abstracts[i].hasOwnProperty("abstract")) {
+      prompt = prompt.concat("\nAbstract [", abstracts[i].number, "]\n Title:\n", abstracts[i].title, "\nAbstract:\n", abstracts[i].abstract.replace(/<[^>]*>?/gm, ''))
+    }
+  }
+  const apiResponse = await model.invoke(prompt);
+
+  return apiResponse
+};
+
+
+
 export const getPublicationSummary = async ({
   text,
   targetSymbol,