OpenAI

Bases: 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]

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(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:

Name	Type	Description
updated_topics	Mapping[str, List[Tuple[str, float]]]	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