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Diffstat (limited to 'rag/generator/ollama.py')
-rw-r--r-- | rag/generator/ollama.py | 71 |
1 files changed, 71 insertions, 0 deletions
diff --git a/rag/generator/ollama.py b/rag/generator/ollama.py new file mode 100644 index 0000000..ec6a55f --- /dev/null +++ b/rag/generator/ollama.py @@ -0,0 +1,71 @@ +import os +from typing import Any, Generator, List + +import ollama +from loguru import logger as log + +from .prompt import Prompt +from .abstract import AbstractGenerator + +try: + from rag.retriever.vector import Document +except ModuleNotFoundError: + from retriever.vector import Document + +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"] |