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import os
import pandas as pd
from typing import List
import cohere
from langchain.embeddings.cohere import CohereEmbeddings
from langchain.llms import Cohere
from langchain.prompts import PromptTemplate
from langchain.vectorstores import Qdrant
from langchain.chains.question_answering import load_qa_chain
from .constants import SUMMARIZATION_MODEL, EXAMPLES_FILE_PATH
# load environment variables
QDRANT_HOST = os.environ.get("QDRANT_HOST")
QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY")
COHERE_API_KEY = os.environ.get("COHERE_API_KEY")
def summarize(
document: str,
summary_length: str,
summary_format: str,
extractiveness: str = "high",
temperature: float = 0.6,
) -> str:
"""
Generates a summary for the input document using Cohere's summarize API.
Args:
document (`str`):
The document given by the user for which summary must be generated.
summary_length (`str`):
A value such as 'short', 'medium', 'long' indicating the length of the summary.
summary_format (`str`):
This indicates whether the generated summary should be in 'paragraph' format or 'bullets'.
extractiveness (`str`, *optional*, defaults to 'high'):
A value such as 'low', 'medium', 'high' indicating how close the generated summary should be in meaning to the original text.
temperature (`str`):
This controls the randomness of the output. Lower values tend to generate more “predictable” output, while higher values tend to generate more “creative” output.
Returns:
generated_summary (`str`):
The generated summary from the summarization model.
"""
summary_response = cohere.Client(COHERE_API_KEY).summarize(
text=document,
length=summary_length,
format=summary_format,
model=SUMMARIZATION_MODEL,
extractiveness=extractiveness,
temperature=temperature,
)
generated_summary = summary_response.summary
return generated_summary
def question_answer(input_document: str, history: List) -> str:
"""
Generates an appropriate answer for the question asked by the user based on the input document.
Args:
input_document (`str`):
The document given by the user for which summary must be generated.
history (`List[List[str,str]]`):
A list made up of pairs of input question asked by the user & corresponding generated answers. It is used to keep track of the history of the chat between the user and the model.
Returns:
answer (`str`):
The generated answer corresponding to the input question and document received from the user.
"""
context = input_document
# The last element of the `history` list contains the most recent question asked by the user whose answer needs to be generated.
question = history[-1][0]
texts = [context[k : k + 256] for k in range(0, len(context.split()), 256)]
embeddings = CohereEmbeddings(
model="multilingual-22-12", cohere_api_key=COHERE_API_KEY
)
context_index = Qdrant.from_texts(
texts, embeddings, host=QDRANT_HOST, api_key=QDRANT_API_KEY, port=443,
)
prompt_template = """Text: {context}
Question: {question}
Answer the question based on the text provided. If the text doesn't contain the answer, reply that the answer is not available."""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
# Generate the answer given the context
chain = load_qa_chain(
Cohere(
model="command-xlarge-nightly", temperature=0, cohere_api_key=COHERE_API_KEY
),
chain_type="stuff",
prompt=PROMPT,
)
relevant_context = context_index.similarity_search(question)
answer = chain.run(input_documents=relevant_context, question=question)
answer = answer.replace("\n", "").replace("Answer:", "")
return answer
def load_gpl_license():
"""
Read GPL license document and sample question to be loaded as an example on the UI.
Returns:
gpl_doc (`str`):
GPL license document which will be loaded as an example on the UI.
sample_question (`str`):
A sample question related to GPL license document.
"""
examples_df = pd.read_csv(EXAMPLES_FILE_PATH)
gpl_doc = examples_df["doc"].iloc[0]
sample_question = examples_df["question"].iloc[0]
return gpl_doc, sample_question
def load_pokemon_license():
"""
Read PokemonGo license document and sample question to be loaded as an example on the UI.
Returns:
pokemon_license (`str`):
PokemonGo license document which will be loaded as an example on the UI.
sample_question (`str`):
A sample question related to PokemonGo license document.
"""
examples_df = pd.read_csv(EXAMPLES_FILE_PATH)
pokemon_license = examples_df["doc"].iloc[1]
sample_question = examples_df["question"].iloc[1]
return pokemon_license, sample_question
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