new file: .dockerignore
new file: .env.example new file: Dockerfile new file: app.py new file: blueprints/__init__.py new file: blueprints/auth.py new file: blueprints/chat.py new file: blueprints/context.py new file: blueprints/documents.py new file: blueprints/main.py new file: config.py new file: docker-compose.yml new file: models/__init__.py new file: models/chat_session.py new file: models/document.py new file: models/user.py new file: requirements.txt new file: services/__init__.py new file: services/document_parser.py new file: services/llm_service.py new file: services/rag_service.py new file: services/url_scraper.py new file: static/css/style.css new file: static/js/chat.js new file: static/js/inline_chat.js new file: static/js/main.js new file: templates/base.html new file: templates/document_view.html new file: templates/index.html new file: templates/login.html new file: templates/register.html
This commit is contained in:
SimolZimol
1
services/__init__.py
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services/__init__.py
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# services package
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services/document_parser.py
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services/document_parser.py
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import os
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import io
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def parse_document(file_path: str, file_type: str) -> str:
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"""Extract plain text from a document file."""
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ext = file_type.lower().lstrip(".")
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if ext == "txt" or ext == "md":
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with open(file_path, "r", encoding="utf-8", errors="replace") as f:
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return f.read()
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if ext == "pdf":
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return _parse_pdf(file_path)
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if ext == "docx":
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return _parse_docx(file_path)
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raise ValueError(f"Unsupported file type: {ext}")
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def _parse_pdf(file_path: str) -> str:
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from pdfminer.high_level import extract_text
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text = extract_text(file_path)
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return text or ""
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def _parse_docx(file_path: str) -> str:
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from docx import Document
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doc = Document(file_path)
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paragraphs = [p.text for p in doc.paragraphs if p.text.strip()]
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return "\n".join(paragraphs)
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109
services/llm_service.py
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services/llm_service.py
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"""
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Abstracted LLM service.
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Supports AI_PROVIDER=lmstudio (default) or AI_PROVIDER=openai.
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Both use the openai Python client — only base_url / api_key differ.
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"""
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from typing import Optional
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from flask import current_app
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_client = None
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def _get_client():
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global _client
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if _client is not None:
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return _client
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import openai
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provider = current_app.config.get("AI_PROVIDER", "lmstudio").lower()
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if provider == "openai":
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api_key = current_app.config.get("OPENAI_API_KEY", "")
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_client = openai.OpenAI(api_key=api_key)
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else:
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# LM Studio or any OpenAI-compatible endpoint
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base_url = current_app.config.get("LM_STUDIO_URL", "http://localhost:1234")
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_client = openai.OpenAI(
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base_url=f"{base_url.rstrip('/')}/v1",
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api_key="lm-studio",
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)
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return _client
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def _get_model() -> str:
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provider = current_app.config.get("AI_PROVIDER", "lmstudio").lower()
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if provider == "openai":
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return current_app.config.get("OPENAI_MODEL", "gpt-4o")
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return current_app.config.get("LM_STUDIO_MODEL", "local-model")
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def ask(
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user_message: str,
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context_chunks: Optional[list[str]] = None,
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history: Optional[list[dict]] = None,
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system_extra: Optional[str] = None,
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) -> str:
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"""
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Send a message to the LLM with optional RAG context and chat history.
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Returns the assistant reply as a string.
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"""
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client = _get_client()
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model = _get_model()
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system_parts = [
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"You are a helpful AI assistant. Answer questions accurately based on the provided context.",
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"If the context does not contain enough information, say so clearly.",
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]
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if context_chunks:
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context_text = "\n\n---\n\n".join(context_chunks)
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system_parts.append(f"\n\n## Context\n\n{context_text}")
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if system_extra:
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system_parts.append(system_extra)
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messages = [{"role": "system", "content": "\n".join(system_parts)}]
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if history:
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messages.extend(history)
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messages.append({"role": "user", "content": user_message})
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=0.7,
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)
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return response.choices[0].message.content.strip()
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def ask_inline(selected_text: str, question: str) -> str:
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"""
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Inline chat: use selected_text directly as context — no RAG lookup.
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"""
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system = (
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"You are a helpful AI assistant. The user has selected the following text "
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"and has a question about it. Answer specifically about the selected content."
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)
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messages = [
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{"role": "system", "content": system},
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{
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"role": "user",
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"content": f"## Selected text\n\n{selected_text}\n\n## Question\n\n{question}",
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},
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]
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client = _get_client()
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model = _get_model()
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=0.7,
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)
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return response.choices[0].message.content.strip()
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135
services/rag_service.py
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135
services/rag_service.py
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"""
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RAG service using ChromaDB + sentence-transformers.
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Each chunk is stored with metadata: user_id, source_id, source_type (doc|url).
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"""
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import os
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import re
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from typing import Optional
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from flask import current_app
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_chroma_client = None
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_collection = None
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_embedder = None
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def _get_embedder():
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global _embedder
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if _embedder is None:
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from sentence_transformers import SentenceTransformer
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cache = current_app.config.get("TRANSFORMERS_CACHE", ".cache")
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_embedder = SentenceTransformer("all-MiniLM-L6-v2", cache_folder=cache)
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return _embedder
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def _get_collection():
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global _chroma_client, _collection
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if _collection is None:
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import chromadb
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path = current_app.config["VECTORDB_PATH"]
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_chroma_client = chromadb.PersistentClient(path=path)
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_collection = _chroma_client.get_or_create_collection(
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name="ki_context",
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metadata={"hnsw:space": "cosine"},
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)
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return _collection
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def chunk_text(text: str, chunk_size: int, overlap: int) -> list[str]:
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"""Split text into overlapping word-based chunks."""
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words = text.split()
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chunks = []
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start = 0
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while start < len(words):
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end = start + chunk_size
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chunks.append(" ".join(words[start:end]))
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start += chunk_size - overlap
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return [c for c in chunks if c.strip()]
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def index_source(
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text: str,
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user_id: int,
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source_id: int,
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source_type: str, # "doc" | "url"
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chunk_size: int = 500,
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chunk_overlap: int = 50,
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):
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"""Chunk, embed and store text in ChromaDB. Replaces existing chunks for this source."""
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collection = _get_collection()
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embedder = _get_embedder()
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# Remove old chunks for this source first
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delete_source(user_id, source_id, source_type)
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chunks = chunk_text(text, chunk_size, chunk_overlap)
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if not chunks:
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return
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embeddings = embedder.encode(chunks, show_progress_bar=False).tolist()
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ids = [f"{source_type}_{source_id}_chunk_{i}" for i in range(len(chunks))]
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metadatas = [
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{"user_id": str(user_id), "source_id": str(source_id), "source_type": source_type}
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for _ in chunks
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]
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collection.add(documents=chunks, embeddings=embeddings, ids=ids, metadatas=metadatas)
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def delete_source(user_id: int, source_id: int, source_type: str):
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"""Remove all chunks belonging to a source from ChromaDB."""
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collection = _get_collection()
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try:
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collection.delete(
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where={
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"$and": [
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{"user_id": {"$eq": str(user_id)}},
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{"source_id": {"$eq": str(source_id)}},
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{"source_type": {"$eq": source_type}},
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]
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}
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)
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except Exception:
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pass
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def similarity_search(
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query: str,
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user_id: int,
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source_ids: Optional[list[int]] = None,
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source_type: Optional[str] = None,
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top_k: int = 5,
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) -> list[str]:
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"""
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Search for relevant chunks.
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Optionally filter by specific source_ids and/or source_type.
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Returns list of chunk texts.
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"""
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collection = _get_collection()
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embedder = _get_embedder()
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query_embedding = embedder.encode([query], show_progress_bar=False).tolist()[0]
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# Build where filter
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conditions = [{"user_id": {"$eq": str(user_id)}}]
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if source_ids is not None and len(source_ids) > 0:
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conditions.append(
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{"source_id": {"$in": [str(sid) for sid in source_ids]}}
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)
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if source_type:
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conditions.append({"source_type": {"$eq": source_type}})
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where = {"$and": conditions} if len(conditions) > 1 else conditions[0]
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try:
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results = collection.query(
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query_embeddings=[query_embedding],
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n_results=top_k,
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where=where,
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)
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return results["documents"][0] if results["documents"] else []
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except Exception:
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return []
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31
services/url_scraper.py
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31
services/url_scraper.py
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import requests
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from bs4 import BeautifulSoup
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def scrape_url(url: str, timeout: int = 15) -> tuple[str, str]:
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"""
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Fetch a URL and return (title, plain_text).
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Raises requests.RequestException on network errors.
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"""
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headers = {
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"User-Agent": (
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"Mozilla/5.0 (compatible; KIContextTool/1.0; "
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"+https://github.com/user/ki-context-tool)"
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)
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}
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resp = requests.get(url, headers=headers, timeout=timeout)
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resp.raise_for_status()
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soup = BeautifulSoup(resp.text, "html.parser")
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# Remove script/style noise
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for tag in soup(["script", "style", "nav", "footer", "header", "aside"]):
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tag.decompose()
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title = soup.title.string.strip() if soup.title and soup.title.string else url
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# Extract readable text
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text = soup.get_text(separator="\n", strip=True)
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# Collapse excessive blank lines
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lines = [line for line in text.splitlines() if line.strip()]
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return title, "\n".join(lines)
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