> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Enrich

| Step                                                                                 | Fast | Description                                                                                                             |
| ------------------------------------------------------------------------------------ | ---- | ----------------------------------------------------------------------------------------------------------------------- |
| [caption\_images](/api-docs/prepare/enrich/caption_images)                           |      | Predict image captions using pretrained DL models                                                                       |
| [classify\_text](/api-docs/prepare/enrich/classify_text)                             |      | Classify texts using any model from the [Hugging Face hub](https://huggingface.co/models)                               |
| [clean\_categories](/api-docs/prepare/enrich/clean_categories)                       |      | Clean a given column of categories or lists of categories using OpenAI                                                  |
| [describe\_clusters](/api-docs/prepare/enrich/describe_clusters)                     |      | Describe your clusters from their given relevant TF-IDF terms using OpenAI                                              |
| [explore\_database](/api-docs/prepare/enrich/explore_database)                       |      | Explore database structure — generates a dataset of column metadata with statistics, relationships, and semantic descr… |
| [fetch\_apify\_dataset](/api-docs/prepare/enrich/fetch_apify_dataset)                |      | Fetch data from an existing Apify dataset                                                                               |
| [fetch\_demographics\_es](/api-docs/prepare/enrich/fetch_demographics_es)            |      | Fetch Spanish demographic census data given a geographical location in each row                                         |
| [fetch\_from\_sql](/api-docs/prepare/enrich/fetch_from_sql)                          |      | Fetch data from a SQL database query and return it as a dataset                                                         |
| [fetch\_full\_contact\_domains](/api-docs/prepare/enrich/fetch_full_contact_domains) |      | Enrich a dataset containing links (URLs) to companies' online presence using the FullContact service                    |
| [fetch\_full\_contact\_emails](/api-docs/prepare/enrich/fetch_full_contact_emails)   |      | Enrich a dataset containing email addresses with personal information using the *FullContact* service                   |
| [fetch\_google\_places](/api-docs/prepare/enrich/fetch_google_places)                |      | Fetch information about the most relevant places surrounding a location                                                 |
| [fetch\_google\_vision](/api-docs/prepare/enrich/fetch_google_vision)                |      | Analyze images given their URL using the Google Vision API                                                              |
| [fetch\_location](/api-docs/prepare/enrich/fetch_location)                           |      | Extract formatted address, locality, area, state, country and geographical coordinates from one or more address columns |
| [fetch\_openreview](/api-docs/prepare/enrich/fetch_openreview)                       |      | Fetch publications submitted to one or more conferences via [OpenReview](https://openreview.net/)                       |
| [fetch\_social\_shares](/api-docs/prepare/enrich/fetch_social_shares)                |      | Fetch the number of times a Url was shared on Facebook                                                                  |
| [fetch\_twitter](/api-docs/prepare/enrich/fetch_twitter)                             |      | Enriches a dataset containing tweets with information about their authors                                               |
| [fetch\_twitter\_api\_io](/api-docs/prepare/enrich/fetch_twitter_api_io)             |      | Fetch tweets from Twitter/X using TwitterAPI.io advanced search                                                         |
| [fetch\_url\_content](/api-docs/prepare/enrich/fetch_url_content)                    |      | Fetch the main text from a web URL, and return its title, author, content, excerpt and domain                           |
| [infer\_aspect\_polarity](/api-docs/prepare/enrich/infer_aspect_polarity)            |      | Extract aspect-based polarity from texts using OpenAI. Identifies entities mentioned in textsand separates them into p… |
| [infer\_aspect\_sentiment](/api-docs/prepare/enrich/infer_aspect_sentiment)          |      | Extract aspect-based sentiments from texts using OpenAI. Identifies entities mentioned in textswith clear positive or … |
| [infer\_gender](/api-docs/prepare/enrich/infer_gender)                               |      | Try to infer a person's gender given a first name                                                                       |
| [infer\_language](/api-docs/prepare/enrich/infer_language)                           |      | Detect the language used for each text in the input column                                                              |
| [infer\_missing](/api-docs/prepare/enrich/infer_missing)                             |      | Train and use a machine learning model to predict (impute) the missing values in a column                               |
| [infer\_missing\_with\_probs](/api-docs/prepare/enrich/infer_missing_with_probs)     |      | Train and use a machine learning model to predict (impute) the missing values in a column                               |
| [infer\_sentiment](/api-docs/prepare/enrich/infer_sentiment)                         |      | Parse text and calculate the overall positive or negative sentiment polarity                                            |
| [infer\_topics](/api-docs/prepare/enrich/infer_topics)                               |      | Generate topics and subtopics for given texts using OpenAI. Infers a hierarchical topic structurefrom (a sample of) th… |
| [prompt\_ai](/api-docs/prepare/enrich/prompt_ai)                                     |      | Call OpenAI's models on each row of the dataset for a given prompt                                                      |
| [run\_apify\_actor](/api-docs/prepare/enrich/run_apify_actor)                        |      | Run an Apify actor and fetch the resulting dataset                                                                      |
| [zeroshot\_classify\_text](/api-docs/prepare/enrich/zeroshot_classify_text)          |      | Classify texts using custom labels/categories                                                                           |