From 91ddb3672e514fa9824609ff047d7cab0c65631a Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Tue, 9 Apr 2024 00:14:00 +0200
Subject: Refactor

---
 rag/llm/ollama_generator.py | 76 ---------------------------------------------
 1 file changed, 76 deletions(-)
 delete mode 100644 rag/llm/ollama_generator.py

(limited to 'rag/llm/ollama_generator.py')

diff --git a/rag/llm/ollama_generator.py b/rag/llm/ollama_generator.py
deleted file mode 100644
index dd17f8d..0000000
--- a/rag/llm/ollama_generator.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-from dataclasses import dataclass
-from typing import Any, Generator, List
-
-import ollama
-from loguru import logger as log
-
-try:
-    from rag.db.vector import Document
-except ModuleNotFoundError:
-    from db.vector import Document
-
-
-@dataclass
-class Prompt:
-    query: str
-    documents: List[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 OllamaGenerator:
-    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
-- 
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