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import os
from typing import Any, Generator, List
import ollama
from loguru import logger as log
from rag.retriever.vector import Document
from .abstract import AbstractGenerator
from .prompt import Prompt
SYSTEM_PROMPT = (
"# System Preamble"
"## Basic Rules"
"When you answer the user's requests, you cite your sources in your answers, according to those instructions."
"Answer the following question using the provided context.\n"
"## Style Guide"
"Unless the user asks for a different style of answer, you should answer "
"in full sentences, using proper grammar and spelling."
)
class Ollama(metaclass=AbstractGenerator):
def __init__(self) -> None:
self.model = os.environ["GENERATOR_MODEL"]
def __context(self, documents: List[Document]) -> str:
results = [
f"Document: {i}\ntitle: {doc.title}\n{doc.text}"
for i, doc in enumerate(documents)
]
return "\n".join(results)
def __metaprompt(self, prompt: Prompt) -> str:
# Include sources
metaprompt = (
f'Question: "{prompt.query.strip()}"\n\n'
"Context:\n"
"<result>\n"
f"{self.__context(prompt.documents)}\n\n"
"</result>\n"
"Carefully perform the following instructions, in order, starting each "
"with a new line.\n"
"Firstly, Decide which of the retrieved documents are relevant to the "
"user's last input by writing 'Relevant Documents:' followed by "
"comma-separated list of document numbers.\n If none are relevant, you "
"should instead write 'None'.\n"
"Secondly, Decide which of the retrieved documents contain facts that "
"should be cited in a good answer to the user's last input by writing "
"'Cited Documents:' followed a comma-separated list of document numbers. "
"If you dont want to cite any of them, you should instead write 'None'.\n"
"Thirdly, Write 'Answer:' followed by a response to the user's last input "
"in high quality natural english. Use the retrieved documents to help you. "
"Do not insert any citations or grounding markup.\n"
"Finally, Write 'Grounded answer:' followed by a response to the user's "
"last input in high quality natural english. Use the symbols <co: doc> and "
"</co: doc> to indicate when a fact comes from a document in the search "
"result, e.g <co: 0>my fact</co: 0> for a fact from document 0."
)
return metaprompt
def generate(self, prompt: Prompt) -> Generator[Any, Any, Any]:
log.debug("Generating answer...")
metaprompt = self.__metaprompt(prompt)
for chunk in ollama.generate(
model=self.model, prompt=metaprompt, system=SYSTEM_PROMPT, stream=True
):
yield chunk["response"]
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