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import pandas as pd
from tqdm import tqdm
from scipy.sparse import csr_matrix
from llama_cpp import Llama
from typing import Mapping, List, Tuple, Any, Union, Callable
from bertopic.representation._base import BaseRepresentation
from bertopic.representation._utils import truncate_document
DEFAULT_PROMPT = """
Q: I have a topic that contains the following documents:
[DOCUMENTS]
The topic is described by the following keywords: '[KEYWORDS]'.
Based on the above information, can you give a short label of the topic?
A: """
class LlamaCPP(BaseRepresentation):
""" A llama.cpp implementation to use as a representation model.
Arguments:
model: Either a string pointing towards a local LLM or a
`llama_cpp.Llama` object.
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.
pipeline_kwargs: Kwargs that you can pass to the `llama_cpp.Llama`
when it is called such as `max_tokens` to be generated.
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 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 a llama.cpp, first download the LLM:
```bash
wget https://huggingface.co/TheBloke/zephyr-7B-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q4_K_M.gguf
```
Then, we can now use the model the model with BERTopic in just a couple of lines:
```python
from bertopic import BERTopic
from bertopic.representation import LlamaCPP
# Use llama.cpp to load in a 4-bit quantized version of Zephyr 7B Alpha
representation_model = LlamaCPP("zephyr-7b-alpha.Q4_K_M.gguf")
# Create our BERTopic model
topic_model = BERTopic(representation_model=representation_model, verbose=True)
```
If you want to have more control over the LLMs parameters, you can run it like so:
```python
from bertopic import BERTopic
from bertopic.representation import LlamaCPP
from llama_cpp import Llama
# Use llama.cpp to load in a 4-bit quantized version of Zephyr 7B Alpha
llm = Llama(model_path="zephyr-7b-alpha.Q4_K_M.gguf", n_gpu_layers=-1, n_ctx=4096, stop="Q:")
representation_model = LlamaCPP(llm)
# Create our BERTopic model
topic_model = BERTopic(representation_model=representation_model, verbose=True)
```
"""
def __init__(self,
model: Union[str, Llama],
prompt: str = None,
pipeline_kwargs: Mapping[str, Any] = {},
nr_docs: int = 4,
diversity: float = None,
doc_length: int = None,
tokenizer: Union[str, Callable] = None
):
if isinstance(model, str):
self.model = Llama(model_path=model, n_gpu_layers=-1, stop="Q:")
elif isinstance(model, Llama):
self.model = model
else:
raise ValueError("Make sure that the model that you"
"pass is either a string referring to a"
"local LLM or a ` llama_cpp.Llama` object.")
self.prompt = prompt if prompt is not None else DEFAULT_PROMPT
self.default_prompt_ = DEFAULT_PROMPT
self.pipeline_kwargs = pipeline_kwargs
self.nr_docs = nr_docs
self.diversity = diversity
self.doc_length = doc_length
self.tokenizer = tokenizer
self.prompts_ = []
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 topic representations and return a single label
Arguments:
topic_model: A BERTopic model
documents: Not used
c_tf_idf: Not used
topics: The candidate topics as calculated with c-TF-IDF
Returns:
updated_topics: Updated topic representations
"""
# Extract the top 4 representative documents per topic
repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs(
c_tf_idf,
documents,
topics,
500,
self.nr_docs,
self.diversity
)
updated_topics = {}
for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
# Prepare prompt
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)
# Extract result from generator and use that as label
topic_description = self.model(prompt, **self.pipeline_kwargs)['choices']
topic_description = [(description["text"].replace(prompt, ""), 1) for description in topic_description]
if len(topic_description) < 10:
topic_description += [("", 0) for _ in range(10-len(topic_description))]
updated_topics[topic] = topic_description
return updated_topics
def _create_prompt(self, docs, topic, topics):
keywords = ", ".join(list(zip(*topics[topic]))[0])
# Use the default prompt and replace keywords
if self.prompt == DEFAULT_PROMPT:
prompt = self.prompt.replace("[KEYWORDS]", keywords)
# Use a prompt that leverages either keywords or documents in
# a custom location
else:
prompt = self.prompt
if "[KEYWORDS]" in prompt:
prompt = prompt.replace("[KEYWORDS]", keywords)
if "[DOCUMENTS]" in prompt:
to_replace = ""
for doc in docs:
to_replace += f"- {doc}\n"
prompt = prompt.replace("[DOCUMENTS]", to_replace)
return prompt
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