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/generator/ollama.py | 71 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 rag/generator/ollama.py

(limited to 'rag/generator/ollama.py')

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"]
-- 
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