Refactor

4 files changed, 0 insertions, 152 deletions

diff --git a/rag/llm/__init__.py b/rag/llm/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/rag/llm/__init__.py
+++ /dev/null
diff --git a/rag/llm/cohere_generator.py b/rag/llm/cohere_generator.py
deleted file mode 100644
index a6feacd..0000000
--- a/rag/llm/cohere_generator.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os
-from typing import Any, Generator
-import cohere
-
-from dataclasses import asdict
-try:
-    from rag.llm.ollama_generator import Prompt
-except ModuleNotFoundError:
-    from llm.ollama_generator import Prompt
-from loguru import logger as log
-
-
-class CohereGenerator:
-    def __init__(self) -> None:
-        self.client = cohere.Client(os.environ["COHERE_API_KEY"])
-
-    def generate(self, prompt: Prompt) -> Generator[Any, Any, Any]:
-        log.debug("Generating answer from cohere")
-        for event in self.client.chat_stream(
-            message=prompt.query,
-            documents=[asdict(d) for d in prompt.documents],
-            prompt_truncation="AUTO",
-        ):
-            if event.event_type == "text-generation":
-                yield event.text
-            elif event.event_type == "citation-generation":
-                yield event.citations
-            elif event.event_type == "stream-end":
-                yield event.finish_reason
diff --git a/rag/llm/encoder.py b/rag/llm/encoder.py
deleted file mode 100644
index a59b1b4..0000000
--- a/rag/llm/encoder.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-from pathlib import Path
-from typing import List, Dict
-from uuid import uuid4
-
-import ollama
-from langchain_core.documents import Document
-from loguru import logger as log
-from qdrant_client.http.models import StrictFloat
-
-
-try:
-    from rag.db.vector import Point
-except ModuleNotFoundError:
-    from db.vector import Point
-
-
-class Encoder:
-    def __init__(self) -> None:
-        self.model = os.environ["ENCODER_MODEL"]
-        self.query_prompt = "Represent this sentence for searching relevant passages: "
-
-    def __encode(self, prompt: str) -> List[StrictFloat]:
-        return list(ollama.embeddings(model=self.model, prompt=prompt)["embedding"])
-
-    def __get_source(self, metadata: Dict[str, str]) -> str:
-        source = metadata["source"]
-        return Path(source).name
-
-    def encode_document(self, chunks: List[Document]) -> List[Point]:
-        log.debug("Encoding document...")
-        return [
-            Point(
-                id=uuid4().hex,
-                vector=self.__encode(chunk.page_content),
-                payload={
-                    "text": chunk.page_content,
-                    "source": self.__get_source(chunk.metadata),
-                },
-            )
-            for chunk in chunks
-        ]
-
-    def encode_query(self, query: str) -> List[StrictFloat]:
-        log.debug(f"Encoding query: {query}")
-        query = self.query_prompt + query
-        return self.__encode(query)
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