RAG mit einem externen Datenanbieter

import requests
import json
import os

# Set up the headers with the API key
headers = {
    'X-API-KEY': "YOUR OWN API KEY"
}

# Specify the fields you want to fetch for each recommended paper
fields = "paperId,title,authors,abstract,url,referenceCount"

# Define the limit for the number of recommendations to return
limit = 20

# Make the request to get paper recommendations
response = requests.get(
    'https://api.semanticscholar.org/graph/v1/paper/search',
    headers=headers,
    # json=payload,
    params={'fields': fields, 'limit': limit, 'query':'multifidelity optimization, gaussian processes' }
)

# Check if the request was successful
if response.status_code == 200:
    print(json.dumps(response.json(), indent=2))
else:
    print(f"Error: {response.status_code}")
    # It's better to print response.text for non-200 responses, as they may not be in JSON format
    print(response.text)

{
  "total": 12096,
  "offset": 0,
  "next": 20,
  "data": [
    {
      "paperId": "b108e6e11f4a96d5058945f3b582a032e8204ade",
      "url": "https://www.semanticscholar.org/paper/b108e6e11f4a96d5058945f3b582a032e8204ade",
      "title": "Multifidelity Gaussian processes for failure boundary andprobability estimation",
      "abstract": "...",
      "referenceCount": 49,
      "authors": [
        { "authorId": "98543101", "name": "Ashwin Renganathan" },
        { "authorId": "144321616", "name": "Vishwas Rao" },
        { "authorId": "143672238", "name": "Ionel M. Navon" }
      ]
    },
    {
      "paperId": "963a5c60ada159d27641a284008f57d6419b26f2",
      "url": "https://www.semanticscholar.org/paper/963a5c60ada159d27641a284008f57d6419b26f2",
      "title": "Generative Transfer Optimization for Aerodynamic Design",
      "abstract": "...",
      "referenceCount": 16,
      "authors": [
        { "authorId": "2149505113", "name": "Zhendong Guo" },
        ...
      ]
    }
    ...
  ]
}

Function name: paper_relevance_search

Function description: 
This function searches Semantic Scholar, a scholarly literature database. The input parameters will be defined based on extracted information from conversation context. It will return search results containing academic paper metadata (with abstracts) in a JSON format.
The returned fields are defined as follows:
- title: paper title
- abstract: paper abstract
- paperId: paper uuid
- authors: paper authors
- year: paper publication year
- url: paper link
- referenceCount: number of references included in the paper
- citationCount: number of citations of this paper

https://api.semanticscholar.org/graph/v1/paper/search

https://api.app.outscraper.com/maps/search-v3?async=false&fields=name,full_address,phone,site

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The keywords, author names or exact paper IDs to search for"
    },
    "fields": {
      "type": "string",
      "description": "The information to be returned, default as 'title,abstract,paperId,authors,year,url,referenceCount,citationCount'"
    },
    "limit": {
      "type": "string",
      "description": "The number of papers users want to get, default as 2. Use the default for all cases."
    }
  },
  "required": [
    "query"
  ]
}

{
  "type": "object",
  "properties": {
    "tags": {
      "type": "array",
      "description": "labels that should be assigned to the user’s request",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "The name of the label"
          },
          "color": {
            "type": "string",
            "description": "The color of the label"
          }
        },
        "required": ["name", "color"]
      }
    }
  },
  "required": ["tags"]
}