Add pipeline for QnA use-case

qna/README.md

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+# QnA Pipeline
+
+This pipeline shows how an external API can be used to build a QnA app with ChatGPT and ReactiveSearch

qna/fetchFromNewsAPI.js

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+async function handleRequest() {
+  const responseBody = JSON.parse(context.response.body);
+
+  let queriesParsed = [];
+  try {
+    const queriesReturned = responseBody.choices[0].message.content;
+    queriesParsed = JSON.parse(queriesReturned)["queries"];
+  } catch (e) {
+    throw Error("Error while parsing ChatGPT response: " + e);
+  }
+
+  /* Using the parsed queries, make calls to News API */
+
+  const totalCalls = queriesParsed.length;
+  const newsAPIKey = context.envs.NEWS_API_KEY;
+  console.log(totalCalls);
+
+  if (!newsAPIKey) {
+    throw Error("News API key needs to be defined!");
+  }
+
+  let topTwoEach = await Promise.all(
+    queriesParsed.map(async (query) => {
+      const res = await fetch(
+        `https://newsapi.org/v2/everything?q=${query}&apiKey=${newsAPIKey}&pageSize=2&sortBy=relevancy`
+      );
+      const jsonResponse = await res.json();
+
+      /* Prepare the top 2 results for each query */
+      return jsonResponse.articles.map((a) => {
+        a["query"] = query;
+        return a;
+      });
+    })
+  );
+
+  /* Merge the articles */
+  let finalArticles = [];
+  topTwoEach.forEach((r) => {
+    finalArticles.push(...r);
+  });
+
+  const urlKeyObj = {};
+
+  finalArticles.forEach((a) => {
+    if (a.title == null) return;
+
+    urlKeyObj[a.url] = a;
+  });
+
+  topTwoEach = finalArticles;
+
+  return {
+    queriesParsed: JSON.stringify(queriesParsed),
+    topTwoEach: JSON.stringify(topTwoEach),
+  };
+}

qna/mergeResponse.js

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+function handleRequest() {
+  const responseBody = JSON.parse(context.customResponse);
+
+  /* Read the AI Answer response */
+  const answerBody = JSON.parse(context.answerCall);
+  const answerMessage = answerBody.choices[0].message.content;
+
+  responseBody["answer"] = answerMessage;
+
+  return {
+    response: {
+      ...context.response,
+      body: JSON.stringify(responseBody),
+    },
+  };
+}

qna/pipeline.yaml

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+enabled: true
+description: QNA Pipeline
+routes:
+  - path: "/qna/_reactivesearch"
+    method: POST
+    classify:
+      category: reactivesearch
+envs:
+  NEWS_API_KEY: ${{ NEWS_API_KEY }}
+
+stages:
+  - id: auth
+    use: authorization
+  - use: useCache
+  - id: translateUserQuery
+    scriptRef: translateUserQuery
+  - id: queryGenRequest
+    use: httpRequest
+    inputs:
+      url: https://api.openai.com/v1/chat/completions
+      headers:
+        Content-Type: application/json
+        Authorization: Bearer {{openAIConfig.open_ai_key}}
+      method: POST
+      body: "{{{queryGenBody}}}"
+  - id: fetch from news API
+    scriptRef: fetchFromNewsAPI
+    continueOnError: false
+    async: true
+  - id: synthesize answer
+    needs:
+      - fetch from news API
+    async: true
+    scriptRef: synthesizeAnswer
+    continueOnError: false
+  - id: merge response
+    needs:
+      - synthesize answer
+    continueOnError: false
+    scriptRef: mergeResponse

qna/pipeline_oneclick.json

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+{
+    "enabled": true,
+    "description": "QNA Pipeline",
+    "routes": [
+      {
+        "path": "/qna/_reactivesearch",
+        "method": "POST",
+        "classify": {
+          "category": "reactivesearch"
+        }
+      }
+    ],
+    "global_envs": [
+        {
+            "key": "NEWS_API_KEY",
+            "value": "",
+            "description": "API Key for the News API",
+            "label": "News API Key"
+        }
+    ],
+    "envs": {
+      "NEWS_API_KEY": "${{ NEWS_API_KEY }}"
+    },
+    "stages": [
+      {
+        "id": "auth",
+        "use": "authorization"
+      },
+      {
+        "use": "useCache"
+      },
+      {
+        "id": "translateUserQuery",
+        "script": "const queriesInput = `You have access to a search API that returns recent news articles.\r\nGenerate an array of search queries that are relevant to this question.\r\nUse a variation of related keywords for the queries, trying to be as general as possible.\r\nInclude as many queries as you can think of, including and excluding terms.\r\nFor example, include queries like ['keyword_1 keyword_2', 'keyword_1', 'keyword_2'].\r\nBe creative. Return a maximum of 5 queries and chose to represent all of the user question in these.\r\n\r\nUser question: {USER_INPUT}\r\n\r\nFormat: {{\"queries\": [\"query_1\", \"query_2\", \"query_3\"]}}\r\n`;\r\n\r\nfunction handleRequest() {\r\n    const query = context.envs.query;\r\n\r\n    if (!query) {\r\n        throw Error('`value` is required to search!');\r\n    }\r\n\r\n    const chatInputWithQuery = queriesInput.replace('{USER_INPUT}', query);\r\n    return {\r\n        queryGenBody: JSON.stringify({\r\n            model: 'gpt-3.5-turbo',\r\n            temperature: 0,\r\n            messages: [\r\n                {\r\n                    role: 'system',\r\n                    content: 'You are a helpful assistant.',\r\n                },\r\n                {\r\n                    role: 'user',\r\n                    content: chatInputWithQuery,\r\n                },\r\n            ],\r\n        }),\r\n    };\r\n}\r\n"
+      },
+      {
+        "id": "queryGenRequest",
+        "use": "httpRequest",
+        "inputs": {
+          "url": "https://api.openai.com/v1/chat/completions",
+          "headers": {
+            "Content-Type": "application/json",
+            "Authorization": "Bearer {{openAIConfig.open_ai_key}}"
+          },
+          "method": "POST",
+          "body": "{{{queryGenBody}}}"
+        }
+      },
+      {
+        "id": "fetch from news API",
+        "script": "async function handleRequest() {\r\n    const responseBody = JSON.parse(context.response.body);\r\n\r\n    let queriesParsed = [];\r\n    try {\r\n        const queriesReturned = responseBody.choices[0].message.content;\r\n        queriesParsed = JSON.parse(queriesReturned)['queries'];\r\n    } catch (e) {\r\n        throw Error('Error while parsing ChatGPT response: ' + e);\r\n    }\r\n\r\n    \/* Using the parsed queries, make calls to News API *\/\r\n\r\n    const totalCalls = queriesParsed.length;\r\n    const newsAPIKey = context.envs.NEWS_API_KEY;\r\n    console.log(totalCalls);\r\n\r\n    if (!newsAPIKey) {\r\n        throw Error('News API key needs to be defined!');\r\n    }\r\n\r\n    let topTwoEach = await Promise.all(\r\n        queriesParsed.map(async (query) => {\r\n            const res = await fetch(\r\n                `https:\/\/newsapi.org\/v2\/everything?q=${query}&apiKey=${newsAPIKey}&pageSize=2&sortBy=relevancy`\r\n            );\r\n            const jsonResponse = await res.json();\r\n\r\n            \/* Prepare the top 2 results for each query *\/\r\n            return jsonResponse.articles.map((a) => {\r\n                a['query'] = query;\r\n                return a;\r\n            });\r\n        })\r\n    );\r\n\r\n    \/* Merge the articles *\/\r\n    let finalArticles = [];\r\n    topTwoEach.forEach((r) => {\r\n        finalArticles.push(...r);\r\n    });\r\n\r\n    const urlKeyObj = {};\r\n\r\n    finalArticles.forEach((a) => {\r\n        if (a.title == null) return;\r\n\r\n        urlKeyObj[a.url] = a;\r\n    });\r\n\r\n    topTwoEach = finalArticles;\r\n\r\n    return {\r\n        queriesParsed: JSON.stringify(queriesParsed),\r\n        topTwoEach: JSON.stringify(topTwoEach),\r\n    };\r\n}\r\n",
+        "continueOnError": false,
+        "async": true
+      },
+      {
+        "id": "synthesize answer",
+        "needs": [
+          "fetch from news API"
+        ],
+        "async": true,
+        "script": "const ANSWER_PROMPT = `\r\nGenerate an answer to the user's question based on the given search results. \r\nTOP_RESULTS: {results}\r\nUSER_QUESTION: {USER_QUESTION}\r\n\r\nInclude as much information as possible in the answer. Reference the relevant search result urls as markdown links.\r\n`;\r\n\r\nasync function handleRequest() {\r\n    const openAIKey = context.envs.openAIConfig.open_ai_key;\r\n    const queriesParsed = JSON.parse(context.queriesParsed);\r\n    const topTwoEach = JSON.parse(context.topTwoEach);\r\n\r\n    const bodyToReturn = {\r\n        results: {\r\n            hits: topTwoEach.slice(0, 10),\r\n            count: topTwoEach.length,\r\n        },\r\n        queries: {\r\n            count: queriesParsed.length,\r\n            hits: queriesParsed,\r\n        },\r\n        resultsByQuery: topTwoEach,\r\n    };\r\n\r\n    const formattedResults = topTwoEach.slice(0, 10).map((a) => {\r\n        return JSON.stringify({\r\n            title: a['title'],\r\n            description: a['description'],\r\n            url: a['url'],\r\n        });\r\n    });\r\n\r\n    const query = context.envs.query;\r\n\r\n    let promptWithFormat = ANSWER_PROMPT.replace(\r\n        '{results}',\r\n        formattedResults.toString()\r\n    );\r\n    promptWithFormat = promptWithFormat.replace('{USER_QUESTION}', query);\r\n\r\n    \/* Make the answer call *\/\r\n    const answerResponse = await makeAnswerCall(\r\n        openAIKey,\r\n        JSON.stringify({\r\n            model: 'gpt-3.5-turbo',\r\n            temperature: 0.5,\r\n            messages: [\r\n                {\r\n                    role: 'system',\r\n                    content: 'You are a helpful assistant.',\r\n                },\r\n                {\r\n                    role: 'user',\r\n                    content: promptWithFormat,\r\n                },\r\n            ],\r\n        })\r\n    );\r\n\r\n    return {\r\n        customResponse: JSON.stringify(bodyToReturn),\r\n        answerCall: JSON.stringify(answerResponse),\r\n    };\r\n}\r\n\r\nasync function makeAnswerCall(apiKey, body) {\r\n    const answerResponse = await fetch(\r\n        'https:\/\/api.openai.com\/v1\/chat\/completions',\r\n        {\r\n            method: 'POST',\r\n            headers: {\r\n                'Content-Type': 'application\/json',\r\n                Authorization: `Bearer ${apiKey}`,\r\n            },\r\n            body: body,\r\n        }\r\n    );\r\n    const response = await answerResponse.json();\r\n    return response;\r\n}\r\n",
+        "continueOnError": false
+      },
+      {
+        "id": "merge response",
+        "needs": [
+          "synthesize answer"
+        ],
+        "continueOnError": false,
+        "script": "function handleRequest() {\r\n    const responseBody = JSON.parse(context.customResponse);\r\n\r\n    \/* Read the AI Answer response *\/\r\n    const answerBody = JSON.parse(context.answerCall);\r\n    const answerMessage = answerBody.choices[0].message.content;\r\n\r\n    responseBody['answer'] = answerMessage;\r\n\r\n    return {\r\n        response: {\r\n            ...context.response,\r\n            body: JSON.stringify(responseBody),\r\n        },\r\n    };\r\n}\r\n"
+      }
+    ]
+  }

qna/synthesizeAnswer.js

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+const ANSWER_PROMPT = `
+Generate an answer to the user's question based on the given search results. 
+TOP_RESULTS: {results}
+USER_QUESTION: {USER_QUESTION}
+
+Include as much information as possible in the answer. Reference the relevant search result urls as markdown links.
+`;
+
+async function handleRequest() {
+  const openAIKey = context.envs.openAIConfig.open_ai_key;
+  const queriesParsed = JSON.parse(context.queriesParsed);
+  const topTwoEach = JSON.parse(context.topTwoEach);
+
+  const bodyToReturn = {
+    results: {
+      hits: topTwoEach.slice(0, 10),
+      count: topTwoEach.length,
+    },
+    queries: {
+      count: queriesParsed.length,
+      hits: queriesParsed,
+    },
+    resultsByQuery: topTwoEach,
+  };
+
+  const formattedResults = topTwoEach.slice(0, 10).map((a) => {
+    return JSON.stringify({
+      title: a["title"],
+      description: a["description"],
+      url: a["url"],
+    });
+  });
+
+  const query = context.envs.query;
+
+  let promptWithFormat = ANSWER_PROMPT.replace(
+    "{results}",
+    formattedResults.toString()
+  );
+  promptWithFormat = promptWithFormat.replace("{USER_QUESTION}", query);
+
+  /* Make the answer call */
+  const answerResponse = await makeAnswerCall(
+    openAIKey,
+    JSON.stringify({
+      model: "gpt-3.5-turbo",
+      temperature: 0.5,
+      messages: [
+        {
+          role: "system",
+          content: "You are a helpful assistant.",
+        },
+        {
+          role: "user",
+          content: promptWithFormat,
+        },
+      ],
+    })
+  );
+
+  return {
+    customResponse: JSON.stringify(bodyToReturn),
+    answerCall: JSON.stringify(answerResponse),
+  };
+}
+
+async function makeAnswerCall(apiKey, body) {
+  const answerResponse = await fetch(
+    "https://api.openai.com/v1/chat/completions",
+    {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: `Bearer ${apiKey}`,
+      },
+      body: body,
+    }
+  );
+  const response = await answerResponse.json();
+  return response;
+}

qna/translateUserQuery.js

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+const queriesInput = `You have access to a search API that returns recent news articles.
+Generate an array of search queries that are relevant to this question.
+Use a variation of related keywords for the queries, trying to be as general as possible.
+Include as many queries as you can think of, including and excluding terms.
+For example, include queries like ['keyword_1 keyword_2', 'keyword_1', 'keyword_2'].
+Be creative. Return a maximum of 5 queries and chose to represent all of the user question in these.
+
+User question: {USER_INPUT}
+
+Format: {{"queries": ["query_1", "query_2", "query_3"]}}
+`;
+
+function handleRequest() {
+  const query = context.envs.query;
+
+  if (!query) {
+    throw Error("`value` is required to search!");
+  }
+
+  const chatInputWithQuery = queriesInput.replace("{USER_INPUT}", query);
+  return {
+    queryGenBody: JSON.stringify({
+      model: "gpt-3.5-turbo",
+      temperature: 0,
+      messages: [
+        {
+          role: "system",
+          content: "You are a helpful assistant.",
+        },
+        {
+          role: "user",
+          content: chatInputWithQuery,
+        },
+      ],
+    }),
+  };
+}