Ejemplo n.º 1
0
def get_vector(folder, file):
    f = open(folder + file, "r")
    embeddings_data = f.read()

    api = API(user_key="", service_url="http://localhost:8181/rest/v1/")
    params = DocumentParameters()
    params["content"] = embeddings_data

    try:
        vector = api.text_embedding(params)
        save_vector(file, vector, "doc", "")
    except RosetteException as exception:
        print(exception)
    except:
        print('unkown error in get vector')

    try:
        sentences = api.sentences(params)
        sentences = sentences.get('sentences')
        for x in sentences:
            params["content"] = x
            sentence_vector = api.text_embedding(params)
            save_vector(file, sentence_vector, "sen", x)
    except RosetteException as exception:
        print(exception)
    except:
        print('unkown sentence error')
Ejemplo n.º 2
0
def run(key, altUrl='https://api.rosette.com/rest/v1/'):
    # Create an API instance
    api = API(user_key=key, service_url=altUrl)
    embeddings_data = "Cambridge, Massachusetts"
    params = DocumentParameters()
    params["content"] = embeddings_data
    return api.text_embedding(params)
Ejemplo n.º 3
0
def vectorize_text(text, key, url='https://api.rosette.com/rest/v1/'):
    """
    Return the vector representation of the input text (as a list of floats).
    """
    api = API(user_key=key, service_url=url)
    params = DocumentParameters()
    params["content"] = text
    return api.text_embedding(params)["embedding"]
Ejemplo n.º 4
0
def run(key, alt_url='https://api.rosette.com/rest/v1/'):
    """ Run the example """
    # Create an API instance
    api = API(user_key=key, service_url=alt_url)
    embeddings_data = "Cambridge, Massachusetts"
    params = DocumentParameters()
    params["content"] = embeddings_data
    try:
        return api.text_embedding(params)
    except RosetteException as exception:
        print(exception)
Ejemplo n.º 5
0
def get_input():
    search = raw_input('Input your search term: ')
    #distance = input('Select similarity distance from 0 to 10 (0=close, 10=far): ')

    api = API(user_key="", service_url="http://localhost:8181/rest/v1/")
    params = DocumentParameters()
    params["content"] = search

    try:
        vector = api.text_embedding(params)
        vector = vector.get("embedding")
        return vector
    except RosetteException as exception:
        print(exception)
    except:
        print('unkown error in get vector')