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OpenAI

Using the OpenAI API to generate topic labels based on one of their Completion of ChatCompletion models.

The default method is `openai.Completion` if `chat=False`.
The prompts will also need to follow a completion task. If you
are looking for a more interactive chats, use `chat=True`
with `model=gpt-3.5-turbo`.

For an overview see:
https://platform.openai.com/docs/models

!!! arguments
    client: A `openai.OpenAI` client
    !!! model "Model to use within OpenAI, defaults to `"text-ada-001"`."
           NOTE: If a `gpt-3.5-turbo` model is used, make sure to set
           `chat` to True.
    !!! generator_kwargs "Kwargs passed to `openai.Completion.create`"
                      for fine-tuning the output.
    !!! prompt "The prompt to be used in the model. If no prompt is given,"
            `self.default_prompt_` is used instead.
            NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
            to decide where the keywords and documents need to be
            inserted.
    !!! delay_in_seconds "The delay in seconds between consecutive prompts"
                      in order to prevent RateLimitErrors.
    !!! exponential_backoff "Retry requests with a random exponential backoff."
                         A short sleep is used when a rate limit error is hit,
                         then the requests is retried. Increase the sleep length
                         if errors are hit until 10 unsuccesfull requests.
                         If True, overrides `delay_in_seconds`.
    !!! chat "Set this to True if a GPT-3.5 model is used."
          See: https://platform.openai.com/docs/models/gpt-3-5
    !!! nr_docs "The number of documents to pass to OpenAI if a prompt"
             with the `["DOCUMENTS"]` tag is used.
    !!! diversity "The diversity of documents to pass to OpenAI."
               Accepts values between 0 and 1. A higher
               values results in passing more diverse documents
               whereas lower values passes more similar documents.
    !!! doc_length "The maximum length of each document. If a document is longer,"
                it will be truncated. If None, the entire document is passed.
    !!! tokenizer "The tokenizer used to calculate to split the document into segments"
               used to count the length of a document.
                   * If tokenizer is 'char', then the document is split up
                     into characters which are counted to adhere to `doc_length`
                   * If tokenizer is 'whitespace', the document is split up
                     into words separated by whitespaces. These words are counted
                     and truncated depending on `doc_length`
                   * If tokenizer is 'vectorizer', then the internal CountVectorizer
                     is used to tokenize the document. These tokens are counted
                     and trunctated depending on `doc_length`
                   * If tokenizer is a callable, then that callable is used to tokenize
                     the document. These tokens are counted and truncated depending
                     on `doc_length`

Usage:

To use this, you will need to install the openai package first:

`pip install openai`

Then, get yourself an API key and use OpenAI's API as follows:

```python
import openai
from bertopic.representation import OpenAI
from bertopic import BERTopic

# Create your representation model
client = openai.OpenAI(api_key=MY_API_KEY)
representation_model = OpenAI(client, delay_in_seconds=5)

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)
```

You can also use a custom prompt:

```python
prompt = "I have the following documents: [DOCUMENTS]

These documents are about the following topic: '" representation_model = OpenAI(client, prompt=prompt, delay_in_seconds=5) If you want to use OpenAI's ChatGPT model:python representation_model = OpenAI(client, model="gpt-3.5-turbo", delay_in_seconds=10, chat=True) ```

Source code in bertopic\representation\_openai.py
class OpenAI(BaseRepresentation):
    """ Using the OpenAI API to generate topic labels based
    on one of their Completion of ChatCompletion models.

    The default method is `openai.Completion` if `chat=False`.
    The prompts will also need to follow a completion task. If you
    are looking for a more interactive chats, use `chat=True`
    with `model=gpt-3.5-turbo`.

    For an overview see:
    https://platform.openai.com/docs/models

    Arguments:
        client: A `openai.OpenAI` client
        model: Model to use within OpenAI, defaults to `"text-ada-001"`.
               NOTE: If a `gpt-3.5-turbo` model is used, make sure to set
               `chat` to True.
        generator_kwargs: Kwargs passed to `openai.Completion.create`
                          for fine-tuning the output.
        prompt: The prompt to be used in the model. If no prompt is given,
                `self.default_prompt_` is used instead.
                NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt
                to decide where the keywords and documents need to be
                inserted.
        delay_in_seconds: The delay in seconds between consecutive prompts
                          in order to prevent RateLimitErrors.
        exponential_backoff: Retry requests with a random exponential backoff.
                             A short sleep is used when a rate limit error is hit,
                             then the requests is retried. Increase the sleep length
                             if errors are hit until 10 unsuccesfull requests.
                             If True, overrides `delay_in_seconds`.
        chat: Set this to True if a GPT-3.5 model is used.
              See: https://platform.openai.com/docs/models/gpt-3-5
        nr_docs: The number of documents to pass to OpenAI if a prompt
                 with the `["DOCUMENTS"]` tag is used.
        diversity: The diversity of documents to pass to OpenAI.
                   Accepts values between 0 and 1. A higher
                   values results in passing more diverse documents
                   whereas lower values passes more similar documents.
        doc_length: The maximum length of each document. If a document is longer,
                    it will be truncated. If None, the entire document is passed.
        tokenizer: The tokenizer used to calculate to split the document into segments
                   used to count the length of a document.
                       * If tokenizer is 'char', then the document is split up
                         into characters which are counted to adhere to `doc_length`
                       * If tokenizer is 'whitespace', the document is split up
                         into words separated by whitespaces. These words are counted
                         and truncated depending on `doc_length`
                       * If tokenizer is 'vectorizer', then the internal CountVectorizer
                         is used to tokenize the document. These tokens are counted
                         and trunctated depending on `doc_length`
                       * If tokenizer is a callable, then that callable is used to tokenize
                         the document. These tokens are counted and truncated depending
                         on `doc_length`

    Usage:

    To use this, you will need to install the openai package first:

    `pip install openai`

    Then, get yourself an API key and use OpenAI's API as follows:

    ```python
    import openai
    from bertopic.representation import OpenAI
    from bertopic import BERTopic

    # Create your representation model
    client = openai.OpenAI(api_key=MY_API_KEY)
    representation_model = OpenAI(client, delay_in_seconds=5)

    # Use the representation model in BERTopic on top of the default pipeline
    topic_model = BERTopic(representation_model=representation_model)
    ```

    You can also use a custom prompt:

    ```python
    prompt = "I have the following documents: [DOCUMENTS] \nThese documents are about the following topic: '"
    representation_model = OpenAI(client, prompt=prompt, delay_in_seconds=5)
    ```

    If you want to use OpenAI's ChatGPT model:

    ```python
    representation_model = OpenAI(client, model="gpt-3.5-turbo", delay_in_seconds=10, chat=True)
    ```
    """
    def __init__(self,
                 client,
                 model: str = "text-embedding-3-small",
                 prompt: str = None,
                 generator_kwargs: Mapping[str, Any] = {},
                 delay_in_seconds: float = None,
                 exponential_backoff: bool = False,
                 chat: bool = False,
                 nr_docs: int = 4,
                 diversity: float = None,
                 doc_length: int = None,
                 tokenizer: Union[str, Callable] = None
                 ):
        self.client = client
        self.model = model

        if prompt is None:
            self.prompt = DEFAULT_CHAT_PROMPT if chat else DEFAULT_PROMPT
        else:
            self.prompt = prompt

        self.default_prompt_ = DEFAULT_CHAT_PROMPT if chat else DEFAULT_PROMPT
        self.delay_in_seconds = delay_in_seconds
        self.exponential_backoff = exponential_backoff
        self.chat = chat
        self.nr_docs = nr_docs
        self.diversity = diversity
        self.doc_length = doc_length
        self.tokenizer = tokenizer
        self.prompts_ = []

        self.generator_kwargs = generator_kwargs
        if self.generator_kwargs.get("model"):
            self.model = generator_kwargs.get("model")
            del self.generator_kwargs["model"]
        if self.generator_kwargs.get("prompt"):
            del self.generator_kwargs["prompt"]
        if not self.generator_kwargs.get("stop") and not chat:
            self.generator_kwargs["stop"] = "\n"

    def extract_topics(self,
                       topic_model,
                       documents: pd.DataFrame,
                       c_tf_idf: csr_matrix,
                       topics: Mapping[str, List[Tuple[str, float]]]
                       ) -> Mapping[str, List[Tuple[str, float]]]:
        """ Extract topics

        Arguments:
            topic_model: A BERTopic model
            documents: All input documents
            c_tf_idf: The topic c-TF-IDF representation
            topics: The candidate topics as calculated with c-TF-IDF

        Returns:
            updated_topics: Updated topic representations
        """
        # Extract the top n representative documents per topic
        repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity)

        # Generate using OpenAI's Language Model
        updated_topics = {}
        for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
            truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
            prompt = self._create_prompt(truncated_docs, topic, topics)
            self.prompts_.append(prompt)

            # Delay
            if self.delay_in_seconds:
                time.sleep(self.delay_in_seconds)

            if self.chat:
                messages = [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": prompt}
                ]
                kwargs = {"model": self.model, "messages": messages, **self.generator_kwargs}
                if self.exponential_backoff:
                    response = chat_completions_with_backoff(self.client, **kwargs)
                else:
                    response = self.client.chat.completions.create(**kwargs)

                # Check whether content was actually generated
                # Adresses #1570 for potential issues with OpenAI's content filter
                if hasattr(response.choices[0].message, "content"):
                    label = response.choices[0].message.content.strip().replace("topic: ", "")
                else:
                    label = "No label returned"
            else:
                if self.exponential_backoff:
                    response = completions_with_backoff(self.client, model=self.model, prompt=prompt, **self.generator_kwargs)
                else:
                    response = self.client.completions.create(model=self.model, prompt=prompt, **self.generator_kwargs)
                label = response.choices[0].text.strip()

            updated_topics[topic] = [(label, 1)]

        return updated_topics

    def _create_prompt(self, docs, topic, topics):
        keywords = list(zip(*topics[topic]))[0]

        # Use the Default Chat Prompt
        if self.prompt == DEFAULT_CHAT_PROMPT or self.prompt == DEFAULT_PROMPT:
            prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
            prompt = self._replace_documents(prompt, docs)

        # Use a custom prompt that leverages keywords, documents or both using
        # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively
        else:
            prompt = self.prompt
            if "[KEYWORDS]" in prompt:
                prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords))
            if "[DOCUMENTS]" in prompt:
                prompt = self._replace_documents(prompt, docs)

        return prompt

    @staticmethod
    def _replace_documents(prompt, docs):
        to_replace = ""
        for doc in docs:
            to_replace += f"- {doc}\n"
        prompt = prompt.replace("[DOCUMENTS]", to_replace)
        return prompt

extract_topics(self, topic_model, documents, c_tf_idf, topics)

Extract topics

Parameters:

Name Type Description Default
topic_model

A BERTopic model

required
documents DataFrame

All input documents

required
c_tf_idf csr_matrix

The topic c-TF-IDF representation

required
topics Mapping[str, List[Tuple[str, float]]]

The candidate topics as calculated with c-TF-IDF

required

Returns:

Type Description
updated_topics

Updated topic representations

Source code in bertopic\representation\_openai.py
def extract_topics(self,
                   topic_model,
                   documents: pd.DataFrame,
                   c_tf_idf: csr_matrix,
                   topics: Mapping[str, List[Tuple[str, float]]]
                   ) -> Mapping[str, List[Tuple[str, float]]]:
    """ Extract topics

    Arguments:
        topic_model: A BERTopic model
        documents: All input documents
        c_tf_idf: The topic c-TF-IDF representation
        topics: The candidate topics as calculated with c-TF-IDF

    Returns:
        updated_topics: Updated topic representations
    """
    # Extract the top n representative documents per topic
    repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity)

    # Generate using OpenAI's Language Model
    updated_topics = {}
    for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
        truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
        prompt = self._create_prompt(truncated_docs, topic, topics)
        self.prompts_.append(prompt)

        # Delay
        if self.delay_in_seconds:
            time.sleep(self.delay_in_seconds)

        if self.chat:
            messages = [
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": prompt}
            ]
            kwargs = {"model": self.model, "messages": messages, **self.generator_kwargs}
            if self.exponential_backoff:
                response = chat_completions_with_backoff(self.client, **kwargs)
            else:
                response = self.client.chat.completions.create(**kwargs)

            # Check whether content was actually generated
            # Adresses #1570 for potential issues with OpenAI's content filter
            if hasattr(response.choices[0].message, "content"):
                label = response.choices[0].message.content.strip().replace("topic: ", "")
            else:
                label = "No label returned"
        else:
            if self.exponential_backoff:
                response = completions_with_backoff(self.client, model=self.model, prompt=prompt, **self.generator_kwargs)
            else:
                response = self.client.completions.create(model=self.model, prompt=prompt, **self.generator_kwargs)
            label = response.choices[0].text.strip()

        updated_topics[topic] = [(label, 1)]

    return updated_topics