initial

2025-11-19 20:33:36 -05:00
commit f15846f3f4
9 changed files with 549 additions and 0 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
--- a/.python-version
+++ b/.python-version
@@ -0,0 +1 @@
+3.14
--- a/README.md
+++ b/README.md
--- a/chunker.py
+++ b/chunker.py
@@ -0,0 +1,141 @@
+import os
+import re
+from math import ceil
+from typing import Union
+from uuid import UUID, uuid4
+
+from chromadb.utils.embedding_functions.openai_embedding_function import (
+    OpenAIEmbeddingFunction,
+)
+from dotenv import load_dotenv
+from llm import LLMClient
+
+load_dotenv()
+
+ollama_client = Client(
+    host=os.getenv("OLLAMA_HOST", "http://localhost:11434"), timeout=1.0
+)
+
+
+def remove_headers_footers(text, header_patterns=None, footer_patterns=None):
+    if header_patterns is None:
+        header_patterns = [r"^.*Header.*$"]
+    if footer_patterns is None:
+        footer_patterns = [r"^.*Footer.*$"]
+
+    for pattern in header_patterns + footer_patterns:
+        text = re.sub(pattern, "", text, flags=re.MULTILINE)
+
+    return text.strip()
+
+
+def remove_special_characters(text, special_chars=None):
+    if special_chars is None:
+        special_chars = r"[^A-Za-z0-9\s\.,;:\'\"\?\!\-]"
+
+    text = re.sub(special_chars, "", text)
+    return text.strip()
+
+
+def remove_repeated_substrings(text, pattern=r"\.{2,}"):
+    text = re.sub(pattern, ".", text)
+    return text.strip()
+
+
+def remove_extra_spaces(text):
+    text = re.sub(r"\n\s*\n", "\n\n", text)
+    text = re.sub(r"\s+", " ", text)
+
+    return text.strip()
+
+
+def preprocess_text(text):
+    # Remove headers and footers
+    text = remove_headers_footers(text)
+
+    # Remove special characters
+    text = remove_special_characters(text)
+
+    # Remove repeated substrings like dots
+    text = remove_repeated_substrings(text)
+
+    # Remove extra spaces between lines and within lines
+    text = remove_extra_spaces(text)
+
+    # Additional cleaning steps can be added here
+
+    return text.strip()
+
+
+class Chunk:
+    def __init__(
+        self,
+        text: str,
+        size: int,
+        document_id: UUID,
+        chunk_id: int,
+        embedding,
+    ):
+        self.text = text
+        self.size = size
+        self.document_id = document_id
+        self.chunk_id = chunk_id
+        self.embedding = embedding
+
+
+class Chunker:
+    def __init__(self, collection) -> None:
+        self.collection = collection
+        self.llm_client = LLMClient()
+
+    def embedding_fx(self, inputs):
+        openai_embedding_fx = OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model_name="text-embedding-3-small",
+        )
+        return openai_embedding_fx(inputs)
+
+    def chunk_document(
+        self,
+        document: str,
+        chunk_size: int = 1000,
+        metadata: dict[str, Union[str, float]] = {},
+    ) -> list[Chunk]:
+        doc_uuid = uuid4()
+
+        chunk_size = min(chunk_size, len(document)) or 1
+
+        chunks = []
+        num_chunks = ceil(len(document) / chunk_size)
+        document_length = len(document)
+
+        for i in range(num_chunks):
+            curr_pos = i * num_chunks
+            to_pos = (
+                curr_pos + chunk_size
+                if curr_pos + chunk_size < document_length
+                else document_length
+            )
+            text_chunk = self.clean_document(document[curr_pos:to_pos])
+
+            embedding = self.embedding_fx([text_chunk])
+            self.collection.add(
+                ids=[str(doc_uuid) + ":" + str(i)],
+                documents=[text_chunk],
+                embeddings=embedding,
+                metadatas=[metadata],
+            )
+
+        return chunks
+
+    def clean_document(self, document: str) -> str:
+        """This function will remove information that is noise or already known.
+
+        Example: We already know all the things in here are Simba-related, so we don't need things like
+        "Sumamry of simba's visit"
+        """
+
+        document = document.replace("\\n", "")
+        document = document.strip()
+
+        return preprocess_text(document)
--- a/main.py
+++ b/main.py
@@ -0,0 +1,58 @@
+from chromadb import HttpClient
+from quart import Quart, jsonify, request
+from tortoise.contrib.quart import register_tortoise
+
+app = Quart(__name__)
+
+TORTOISE_CONFIG = {
+    "connections": {
+        "default": f"postgresql://postgres:password@localhost?statusColor=&env=local&name=simbarag&tLSMode=0&usePrivateKey=false&safeModeLevel=0&advancedSafeModeLevel=0&driverVersion=0&showSystemSchemas=0&driverVersion=0&lazyload=False"
+    },
+    "apps": {
+        "models": {
+            "models": [
+                "blueprints.conversation.models",
+                "blueprints.users.models",
+                "aerich.models",
+            ]
+        },
+    },
+}
+
+chroma_client = HttpClient(host="localhost", port=8333)
+chroma_client.heartbeat()
+
+register_tortoise(
+    app,
+    config=TORTOISE_CONFIG,
+    generate_schemas=False,  # Disabled - using Aerich for migrations
+)
+
+
+@app.route("/api/query")
+async def query():
+    data = await request.get_json()
+    return jsonify("asdf")
+
+
+@app.route("/api/index")
+async def index():
+    """
+    This function adds the corpus of text attached to the ChromaDB library
+    """
+
+    return ""
+
+
+@app.route("/api/index/paperless/all")
+async def reindex_all_documents():
+    return ""
+
+
+@app.route("/api/index/paperless/single", methods=["POST"])
+async def index_single_document():
+    return ""
+
+
+if __name__ == "__main__":
+    app.run()
--- a/models.py
+++ b/models.py
@@ -0,0 +1,15 @@
+import enum
+
+from tortoise import fields
+from tortoise.models import Model
+
+
+class EmbeddingEngine(enum.Enum):
+    OPENAI = "openai"
+    OLLAMA = "ollama"
+
+
+class PaperlessDocument(Model):
+    id = fields.UUIDField(primary_key=True)
+    paperless_id = fields.CharField(unique=True)
+    indexing_engine = fields.CharEnumField(enum_type=EmbeddingEngine)
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "simbarag-index"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "quart>=0.20.0",
+]
--- a/request.py
+++ b/request.py
@@ -0,0 +1,86 @@
+import logging
+import os
+import tempfile
+
+import httpx
+from dotenv import load_dotenv
+
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO)
+
+
+class PaperlessNGXService:
+    def __init__(self):
+        self.base_url = os.getenv("BASE_URL")
+        self.token = os.getenv("PAPERLESS_TOKEN")
+        self.url = f"http://{os.getenv('BASE_URL')}/api/documents/?tags__id=8"
+        self.headers = {"Authorization": f"Token {os.getenv('PAPERLESS_TOKEN')}"}
+
+    def get_data(self):
+        print(f"Getting data from: {self.url}")
+        r = httpx.get(self.url, headers=self.headers)
+        results = r.json()["results"]
+
+        nextLink = r.json().get("next")
+
+        while nextLink:
+            r = httpx.get(nextLink, headers=self.headers)
+            results += r.json()["results"]
+            nextLink = r.json().get("next")
+
+        return results
+
+    def get_doc_by_id(self, doc_id: int):
+        url = f"http://{os.getenv('BASE_URL')}/api/documents/{doc_id}/"
+        r = httpx.get(url, headers=self.headers)
+        return r.json()
+
+    def download_pdf_from_id(self, id: int) -> str:
+        download_url = f"http://{os.getenv('BASE_URL')}/api/documents/{id}/download/"
+        response = httpx.get(
+            download_url, headers=self.headers, follow_redirects=True, timeout=30
+        )
+        response.raise_for_status()
+        # Use a temporary file for the downloaded PDF
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+        temp_file.write(response.content)
+        temp_file.close()
+        temp_pdf_path = temp_file.name
+        pdf_to_process = temp_pdf_path
+        return pdf_to_process
+
+    def upload_cleaned_content(self, document_id, data):
+        PUTS_URL = f"http://{os.getenv('BASE_URL')}/api/documents/{document_id}/"
+        r = httpx.put(PUTS_URL, headers=self.headers, data=data)
+        r.raise_for_status()
+
+    def upload_description(self, description_filepath, file, title, exif_date: str):
+        POST_URL = f"http://{os.getenv('BASE_URL')}/api/documents/post_document/"
+        files = {"document": ("description_filepath", file, "application/txt")}
+        data = {
+            "title": title,
+            "create": exif_date,
+            "document_type": 3,
+            "tags": [7],
+        }
+
+        r = httpx.post(POST_URL, headers=self.headers, data=data, files=files)
+        r.raise_for_status()
+
+    def get_tags(self):
+        GET_URL = f"http://{os.getenv('BASE_URL')}/api/tags/"
+        r = httpx.get(GET_URL, headers=self.headers)
+        data = r.json()
+        return {tag["id"]: tag["name"] for tag in data["results"]}
+
+    def get_doctypes(self):
+        GET_URL = f"http://{os.getenv('BASE_URL')}/api/document_types/"
+        r = httpx.get(GET_URL, headers=self.headers)
+        data = r.json()
+        return {doctype["id"]: doctype["name"] for doctype in data["results"]}
+
+
+if __name__ == "__main__":
+    pp = PaperlessNGXService()
+    pp.get_data()
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+++ b/uv.lock
@@ -0,0 +1,229 @@
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