Monorepo Integration: Unified Backend, Frontend & Documentation

- Reorganize project into monorepo structure - backend/app/ - New FastAPI backend (modular with src/) - backend/legacy/ - Legacy database modules (relational & vector) - frontend/ - React text editor application - Add launcher.py for easy full-stack startup - Complete documentation in README.md - Quick start guide - API endpoints reference - Development setup - Troubleshooting - Refactor main.py to 35 lines (app configuration only) - Update .gitignore for full-stack project - Add CHANGELOG.md with version history (v0.1.0-v0.1.1) Structure is now clean and ready for team collaboration.
2026-04-09 17:06:59 +02:00
parent fddaad962b
commit 6bbb24e633
55 changed files with 808 additions and 93 deletions
--- a/backend/legacy/local_model_miniLM/1_Pooling/config.json
+++ b/backend/legacy/local_model_miniLM/1_Pooling/config.json
@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}
--- a/backend/legacy/local_model_miniLM/README.md
+++ b/backend/legacy/local_model_miniLM/README.md
@@ -0,0 +1,173 @@
+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+datasets:
+- s2orc
+- flax-sentence-embeddings/stackexchange_xml
+- ms_marco
+- gooaq
+- yahoo_answers_topics
+- code_search_net
+- search_qa
+- eli5
+- snli
+- multi_nli
+- wikihow
+- natural_questions
+- trivia_qa
+- embedding-data/sentence-compression
+- embedding-data/flickr30k-captions
+- embedding-data/altlex
+- embedding-data/simple-wiki
+- embedding-data/QQP
+- embedding-data/SPECTER
+- embedding-data/PAQ_pairs
+- embedding-data/WikiAnswers
+pipeline_tag: sentence-similarity
+---
+
+
+# all-MiniLM-L6-v2
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+------
+
+## Background
+
+The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
+contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 
+1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
+
+We developed this model during the 
+[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
+organized by Hugging Face. We developed this model as part of the project:
+[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
+
+## Intended uses
+
+Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
+We then apply the cross entropy loss by comparing with true pairs.
+
+#### Hyper parameters
+
+We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
+We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
+a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
+
+#### Training data
+
+We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
+We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
+
+
+| Dataset                                                  | Paper                                    | Number of training tuples  |
+|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
+| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
+| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
+| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
+| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
+| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
+| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
+| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
+| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
+| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
+| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
+| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
+| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
+| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
+| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
+| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
+| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
+| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
+| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
+| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
+| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
+| **Total** | | **1,170,060,424** |
--- a/backend/legacy/local_model_miniLM/config.json
+++ b/backend/legacy/local_model_miniLM/config.json
@@ -0,0 +1,30 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}
--- a/backend/legacy/local_model_miniLM/config_sentence_transformers.json
+++ b/backend/legacy/local_model_miniLM/config_sentence_transformers.json
@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "5.3.0",
+    "pytorch": "2.10.0+cpu"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}
--- a/backend/legacy/local_model_miniLM/model.safetensors
+++ b/backend/legacy/local_model_miniLM/model.safetensors
--- a/backend/legacy/local_model_miniLM/modules.json
+++ b/backend/legacy/local_model_miniLM/modules.json
@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]
--- a/backend/legacy/local_model_miniLM/sentence_bert_config.json
+++ b/backend/legacy/local_model_miniLM/sentence_bert_config.json
@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}
--- a/backend/legacy/local_model_miniLM/tokenizer.json
+++ b/backend/legacy/local_model_miniLM/tokenizer.json
--- a/backend/legacy/local_model_miniLM/tokenizer_config.json
+++ b/backend/legacy/local_model_miniLM/tokenizer_config.json
@@ -0,0 +1,23 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}
--- a/backend/legacy/relational_database.py
+++ b/backend/legacy/relational_database.py
@@ -0,0 +1,101 @@
+import sqlite3
+import json
+import os
+from fastapi import FastAPI, Body, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DB_FILE = os.path.join(BASE_DIR, "archivium.db")
+
+
+def get_db_connection():
+    conn = sqlite3.connect(DB_FILE)
+    conn.execute("PRAGMA journal_mode=WAL;")
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def init_db():
+    with get_db_connection() as conn:
+        conn.execute("""
+                     CREATE TABLE IF NOT EXISTS archive
+                     (
+                         id
+                         INTEGER
+                         PRIMARY
+                         KEY
+                         AUTOINCREMENT,
+                         filename
+                         TEXT
+                         UNIQUE,
+                         ocr_text
+                         TEXT,
+                         metadata
+                         TEXT,
+                         created_at
+                         TIMESTAMP
+                         DEFAULT
+                         CURRENT_TIMESTAMP
+                     )
+                     """)
+        conn.commit()
+
+
+init_db()
+
+
+@app.post("/save-document")
+async def save_document(data: dict = Body(...)):
+    title = data.get("title")
+    content = data.get("content")
+
+    if not title or content is None:
+        raise HTTPException(status_code=400, detail="Missing title or content")
+
+    content_str = json.dumps(content)
+
+    try:
+        with get_db_connection() as conn:
+            conn.execute("""
+                         INSERT INTO archive (filename, ocr_text)
+                         VALUES (?, ?) ON CONFLICT(filename) DO
+                         UPDATE SET
+                             ocr_text=excluded.ocr_text
+                         """, (title, content_str))
+            conn.commit()
+        return {"status": "success"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/load-document")
+async def load_document(title: str = None):
+    with get_db_connection() as conn:
+        if title:
+            row = conn.execute("SELECT filename, ocr_text FROM archive WHERE filename = ?", (title,)).fetchone()
+        else:
+            row = conn.execute("SELECT filename, ocr_text FROM archive ORDER BY id DESC LIMIT 1").fetchone()
+
+    if row:
+        try:
+            content_val = json.loads(row['ocr_text'])
+        except:
+            content_val = row['ocr_text']
+
+        return {"title": row['filename'], "content": content_val}
+
+    raise HTTPException(status_code=404, detail="Document not found")
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8000)
--- a/backend/legacy/vector_database.py
+++ b/backend/legacy/vector_database.py
@@ -0,0 +1,134 @@
+import sqlite3
+import os
+import numpy as np
+from fastapi import FastAPI, Body, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from sentence_transformers import SentenceTransformer
+import uvicorn
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DB_FILE = os.path.join(BASE_DIR, "assets.db")
+MODEL_DIR = os.path.join(BASE_DIR, "local_model_miniLM")
+
+
+if not os.path.exists(MODEL_DIR):
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    model.save(MODEL_DIR)
+else:
+    model = SentenceTransformer(MODEL_DIR)
+
+
+def get_db_connection():
+    conn = sqlite3.connect(DB_FILE)
+
+    conn.execute("PRAGMA journal_mode=WAL;")
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def init_db():
+    with get_db_connection() as conn:
+        conn.execute("""
+                     CREATE TABLE IF NOT EXISTS documents
+                     (
+                         id
+                         INTEGER
+                         PRIMARY
+                         KEY
+                         AUTOINCREMENT,
+                         title
+                         TEXT
+                         UNIQUE,
+                         content
+                         BLOB,
+                         content_type
+                         TEXT,
+                         embedding
+                         BLOB
+                     )
+                     """)
+        conn.commit()
+
+
+init_db()
+
+
+@app.post("/save-document")
+async def save_document(
+        title: str = Body(...),
+        content: str = Body(...),
+        content_type: str = Body("text/plain")
+):
+
+    vector = model.encode(f"{title} {content}").astype(np.float32).tobytes()
+
+    try:
+        with get_db_connection() as conn:
+            conn.execute("""
+                         INSERT INTO documents (title, content, content_type, embedding)
+                         VALUES (?, ?, ?, ?) ON CONFLICT(title) DO
+                         UPDATE SET
+                             content=excluded.content,
+                             content_type=excluded.content_type,
+                             embedding=excluded.embedding
+                         """, (title, content.encode('utf-8'), content_type, vector))
+            conn.commit()
+        return {"status": "success", "message": f"Dokument '{title}' zapisany."}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/search")
+async def search_similar(query: str = Body(..., embed=True), top_k: int = 3):
+    """Wyszukiwanie semantyczne (Vector Search)"""
+    query_vector = model.encode(query).astype(np.float32)
+
+    with get_db_connection() as conn:
+        cursor = conn.execute("SELECT title, content, embedding FROM documents")
+        rows = cursor.fetchall()
+
+    results = []
+    for row in rows:
+        db_vector = np.frombuffer(row['embedding'], dtype=np.float32)
+
+
+        score = np.dot(query_vector, db_vector) / (np.linalg.norm(query_vector) * np.linalg.norm(db_vector))
+
+        results.append({
+            "title": row['title'],
+            "content": row['content'].decode('utf-8', errors='ignore'),
+            "score": float(score)
+        })
+
+
+    results = sorted(results, key=lambda x: x['score'], reverse=True)[:top_k]
+    return {"results": results}
+
+
+@app.get("/load-document")
+async def load_document(title: str = None):
+    with get_db_connection() as conn:
+        if title:
+            row = conn.execute("SELECT title, content FROM documents WHERE title = ?", (title,)).fetchone()
+        else:
+            row = conn.execute("SELECT title, content FROM documents ORDER BY id DESC LIMIT 1").fetchone()
+
+    if row:
+        return {
+            "title": row['title'],
+            "content": row['content'].decode('utf-8', errors='ignore')
+        }
+    return {"error": "Nie znaleziono dokumentu"}
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8000)