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81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
from utils import get_embedding
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from flask import jsonify
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from config import *
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from flask import current_app
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import openai
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from config import *
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TOP_K = 10
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def get_answer_from_files(question, session_id, pinecone_index):
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logging.info(f"Getting answer for question: {question}")
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search_query_embedding = get_embedding(question, EMBEDDINGS_MODEL)
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try:
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query_response = pinecone_index.query(
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namespace=session_id,
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top_k=TOP_K,
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include_values=False,
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include_metadata=True,
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vector=search_query_embedding,
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)
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logging.info(
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f"[get_answer_from_files] received query response from Pinecone: {query_response}")
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files_string = ""
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file_text_dict = current_app.config["file_text_dict"]
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for i in range(len(query_response.matches)):
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result = query_response.matches[i]
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file_chunk_id = result.id
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score = result.score
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filename = result.metadata["filename"]
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file_text = file_text_dict.get(file_chunk_id)
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file_string = f"###\n\"{filename}\"\n{file_text}\n"
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if score < COSINE_SIM_THRESHOLD and i > 0:
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logging.info(
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f"[get_answer_from_files] score {score} is below threshold {COSINE_SIM_THRESHOLD} and i is {i}, breaking")
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break
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files_string += file_string
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# Note: this is not the proper way to use the ChatGPT conversational format, but it works for now
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messages = [
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{
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"role": "system",
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"content": f"Given a question, try to answer it using the content of the file extracts below, and if you cannot answer, or find " \
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f"a relevant file, just output \"I couldn't find the answer to that question in your files.\".\n\n" \
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f"If the answer is not contained in the files or if there are no file extracts, respond with \"I couldn't find the answer " \
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f"to that question in your files.\" If the question is not actually a question, respond with \"That's not a valid question.\"\n\n" \
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f"In the cases where you can find the answer, first give the answer. Then explain how you found the answer from the source or sources, " \
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f"and use the exact filenames of the source files you mention. Do not make up the names of any other files other than those mentioned "\
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f"in the files context. Give the answer in markdown format." \
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f"Use the following format:\n\nQuestion: <question>\n\nFiles:\n<###\n\"filename 1\"\nfile text>\n<###\n\"filename 2\"\nfile text>...\n\n"\
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f"Answer: <answer or \"I couldn't find the answer to that question in your files\" or \"That's not a valid question.\">\n\n" \
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f"Question: {question}\n\n" \
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f"Files:\n{files_string}\n" \
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f"Answer:"
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},
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]
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response = openai.ChatCompletion.create(
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messages=messages,
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model=GENERATIVE_MODEL,
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max_tokens=1000,
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temperature=0,
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)
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choices = response["choices"] # type: ignore
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answer = choices[0].message.content.strip()
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logging.info(f"[get_answer_from_files] answer: {answer}")
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return jsonify({"answer": answer})
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except Exception as e:
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logging.info(f"[get_answer_from_files] error: {e}")
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return str(e)
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