Populate DB Chromadb

.gitignore

@@ -66,3 +66,4 @@ venv.bak/
 # Tauri
 
 bun.lock
+dataset/

backend/requirements.txt

@@ -1,12 +1,14 @@
 flask
-google-genai
 gunicorn
-pymongo
 ultralytics
 opencv-python-headless
 transformers
 torch
 pandas
 pypdf
+openpyxl
 python-dotenv
 flask-cors
+ollama
+chromadb-client
+pymongo

backend/scripts/populate_db.py

@@ -1,8 +1,8 @@
 import os
 import sys
+import argparse
 from pathlib import Path
 
-# Add backend directory to path so we can import src
 sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 
 from dotenv import load_dotenv
@@ -10,21 +10,23 @@ load_dotenv()
 
 from src.rag.ingest import process_file
 from src.rag.store import ingest_documents
-from src.mongo.vector_store import is_file_processed, log_processed_file
+from src.mongo.metadata import is_file_processed, log_processed_file
 
-def populate_from_dataset(dataset_dir):
+def populate_from_dataset(dataset_dir, category=None):
     dataset_path = Path(dataset_dir)
     if not dataset_path.exists():
         print(f"Dataset directory not found: {dataset_dir}")
         return
 
     print(f"Scanning {dataset_dir}...")
+    if category:
+        print(f"Category: {category}")
     
     total_chunks = 0
     files_processed = 0
 
     for file_path in dataset_path.glob('*'):
-        if file_path.is_file() and file_path.suffix.lower() in ['.csv', '.pdf']:
+        if file_path.is_file() and file_path.suffix.lower() in ['.csv', '.pdf', '.txt', '.xlsx']:
             if is_file_processed(file_path.name):
                 print(f"Skipping {file_path.name} (already processed)")
                 continue
@@ -33,10 +35,10 @@ def populate_from_dataset(dataset_dir):
             try:
                 chunks = process_file(str(file_path))
                 if chunks:
-                    count = ingest_documents(chunks)
+                    count = ingest_documents(chunks, source_file=file_path.name, category=category)
                     print(f"  Ingested {count} chunks.")
                     if count > 0:
-                        log_processed_file(file_path.name)
+                        log_processed_file(file_path.name, category=category, chunk_count=count)
                         total_chunks += count
                         files_processed += 1
                 else:
@@ -47,6 +49,14 @@ def populate_from_dataset(dataset_dir):
     print(f"\nFinished! Processed {files_processed} files. Total chunks ingested: {total_chunks}")
 
 if __name__ == "__main__":
-    # Assuming run from backend/
+    parser = argparse.ArgumentParser(description="Populate vector database from dataset files")
-    dataset_dir = os.path.join(os.path.dirname(__file__), '../dataset')
+    parser.add_argument("--category", "-c", type=str, help="Category to assign to ingested documents")
-    populate_from_dataset(dataset_dir)
+    parser.add_argument("--dir", "-d", type=str, default=None, help="Dataset directory path")
+    args = parser.parse_args()
+    
+    if args.dir:
+        dataset_dir = args.dir
+    else:
+        dataset_dir = os.path.join(os.path.dirname(__file__), '../dataset')
+    
+    populate_from_dataset(dataset_dir, category=args.category)

backend/src/chroma/vector_store.py

@@ -0,0 +1,69 @@
+import chromadb
+
+CHROMA_HOST = "http://chroma.sirblob.co"
+COLLECTION_NAME = "rag_documents"
+
+_client = None
+
+def get_chroma_client():
+    global _client
+    if _client is None:
+        _client = chromadb.HttpClient(host=CHROMA_HOST)
+    return _client
+
+def get_collection(collection_name=COLLECTION_NAME):
+    client = get_chroma_client()
+    return client.get_or_create_collection(name=collection_name)
+
+def insert_documents(texts, embeddings, collection_name=COLLECTION_NAME, metadata_list=None):
+    collection = get_collection(collection_name)
+    
+    ids = [f"doc_{i}_{hash(text)}" for i, text in enumerate(texts)]
+    
+    if metadata_list:
+        collection.add(
+            ids=ids,
+            embeddings=embeddings,
+            documents=texts,
+            metadatas=metadata_list
+        )
+    else:
+        collection.add(
+            ids=ids,
+            embeddings=embeddings,
+            documents=texts
+        )
+    
+    return len(texts)
+
+def search_documents(query_embedding, collection_name=COLLECTION_NAME, num_results=5, filter_metadata=None):
+    collection = get_collection(collection_name)
+    
+    query_params = {
+        "query_embeddings": [query_embedding],
+        "n_results": num_results
+    }
+    
+    if filter_metadata:
+        query_params["where"] = filter_metadata
+    
+    results = collection.query(**query_params)
+    
+    output = []
+    if results and results["documents"]:
+        for i, doc in enumerate(results["documents"][0]):
+            score = results["distances"][0][i] if "distances" in results else None
+            output.append({
+                "text": doc,
+                "score": score
+            })
+    
+    return output
+
+def delete_documents_by_source(source_file, collection_name=COLLECTION_NAME):
+    collection = get_collection(collection_name)
+    results = collection.get(where={"source": source_file})
+    if results["ids"]:
+        collection.delete(ids=results["ids"])
+        return len(results["ids"])
+    return 0

backend/src/mongo/metadata.py

@@ -0,0 +1,62 @@
+from .connection import get_mongo_client
+from datetime import datetime
+
+DB_NAME = "hoya_metadata"
+
+def get_datasets_collection():
+    client = get_mongo_client()
+    db = client.get_database(DB_NAME)
+    return db["datasets"]
+
+def get_categories_collection():
+    client = get_mongo_client()
+    db = client.get_database(DB_NAME)
+    return db["categories"]
+
+def is_file_processed(filename):
+    collection = get_datasets_collection()
+    return collection.find_one({"filename": filename}) is not None
+
+def log_processed_file(filename, category=None, chunk_count=0):
+    collection = get_datasets_collection()
+    doc = {
+        "filename": filename,
+        "category": category,
+        "chunk_count": chunk_count,
+        "processed_at": datetime.utcnow(),
+        "status": "processed"
+    }
+    collection.insert_one(doc)
+
+def get_all_datasets():
+    collection = get_datasets_collection()
+    return list(collection.find({}, {"_id": 0}))
+
+def get_datasets_by_category(category):
+    collection = get_datasets_collection()
+    return list(collection.find({"category": category}, {"_id": 0}))
+
+def delete_dataset_record(filename):
+    collection = get_datasets_collection()
+    result = collection.delete_one({"filename": filename})
+    return result.deleted_count > 0
+
+def create_category(name, description=""):
+    collection = get_categories_collection()
+    if collection.find_one({"name": name}):
+        return False
+    collection.insert_one({
+        "name": name,
+        "description": description,
+        "created_at": datetime.utcnow()
+    })
+    return True
+
+def get_all_categories():
+    collection = get_categories_collection()
+    return list(collection.find({}, {"_id": 0}))
+
+def delete_category(name):
+    collection = get_categories_collection()
+    result = collection.delete_one({"name": name})
+    return result.deleted_count > 0

backend/src/mongo/vector_store.py

@@ -46,4 +46,4 @@ def log_processed_file(filename, log_collection="ingested_files", db_name="vecto
     client = get_mongo_client()
     db = client.get_database(db_name)
     collection = db[log_collection]
-    collection.insert_one({"filename": filename, "processed_at": 1}) # keeping it simple
+    collection.insert_one({"filename": filename, "processed_at": 1})

backend/src/rag/embeddings.py

@@ -1,26 +1,32 @@
-from google import genai
+import ollama
 import os
 
-def get_embedding(text, model="gemini-embedding-001"):
+client = ollama.Client(host="https://ollama.sirblob.co")
-    api_key = os.environ.get("GOOGLE_API_KEY")
+DEFAULT_MODEL = "nomic-embed-text:latest"
-    if not api_key:
-        raise ValueError("GOOGLE_API_KEY environment variable not set")
 
-    client = genai.Client(api_key=api_key)
+def get_embedding(text, model=DEFAULT_MODEL):
-    result = client.models.embed_content(
+    try:
-        model=model,
+        response = client.embeddings(model=model, prompt=text)
-        contents=text
+        return response["embedding"]
-    )
+    except Exception as e:
-    return result.embeddings[0].values
+        print(f"Error getting embedding from Ollama: {e}")
+        raise e
 
-def get_embeddings_batch(texts, model="gemini-embedding-001"):
+def get_embeddings_batch(texts, model=DEFAULT_MODEL, batch_size=50):
-    api_key = os.environ.get("GOOGLE_API_KEY")
+    all_embeddings = []
-    if not api_key:
-        raise ValueError("GOOGLE_API_KEY environment variable not set")
     
-    client = genai.Client(api_key=api_key)
+    for i in range(0, len(texts), batch_size):
-    result = client.models.embed_content(
+        batch = texts[i:i + batch_size]
-        model=model,
+        try:
-        contents=texts
+            response = client.embed(model=model, input=batch)
-    )
+            
-    return [emb.values for emb in result.embeddings]
+            if "embeddings" in response:
+                all_embeddings.extend(response["embeddings"])
+            else:
+                raise ValueError("Unexpected response format from client.embed")
+                
+        except Exception as e:
+            print(f"Error embedding batch {i}-{i+batch_size}: {e}")
+            raise e
+            
+    return all_embeddings

backend/src/rag/ingest.py

@@ -3,6 +3,40 @@ from pypdf import PdfReader
 import io
 import os
 
+def chunk_text(text, target_length=2000, overlap=100):
+    if not text:
+        return []
+        
+    chunks = []
+    
+    paragraphs = text.split('\n\n')
+    current_chunk = ""
+    
+    for para in paragraphs:
+        if len(current_chunk) + len(para) > target_length:
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            
+            if len(para) > target_length:
+                start = 0
+                while start < len(para):
+                    end = start + target_length
+                    chunks.append(para[start:end].strip())
+                    start += (target_length - overlap)
+                current_chunk = "" 
+            else:
+                current_chunk = para
+        else:
+            if current_chunk:
+                current_chunk += "\n\n" + para
+            else:
+                current_chunk = para
+                
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+        
+    return chunks
+
 def load_csv(file_path):
     df = pd.read_csv(file_path)
     return df.apply(lambda x: ' | '.join(x.astype(str)), axis=1).tolist()
@@ -13,14 +47,52 @@ def load_pdf(file_path):
     for page in reader.pages:
         text = page.extract_text()
         if text:
-            text_chunks.append(text)
+            if len(text) > 4000:
+                text_chunks.extend(chunk_text(text))
+            else:
+                text_chunks.append(text)
     return text_chunks
 
+def load_txt(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+    
+    return chunk_text(content)
+
+def load_xlsx(file_path):
+    all_rows = []
+    try:
+        sheets = pd.read_excel(file_path, sheet_name=None)
+    except Exception as e:
+        raise ValueError(f"Pandas read_excel failed: {e}")
+    
+    for sheet_name, df in sheets.items():
+        if df.empty:
+            continue
+            
+        df = df.fillna("")
+        
+        for row in df.values:
+            row_items = [str(x) for x in row if str(x).strip() != ""]
+            if row_items:
+                row_str = f"Sheet: {str(sheet_name)} | " + " | ".join(row_items)
+                
+                if len(row_str) > 8000:
+                    all_rows.extend(chunk_text(row_str))
+                else:
+                    all_rows.append(row_str)
+        
+    return all_rows
+
 def process_file(file_path):
     ext = os.path.splitext(file_path)[1].lower()
     if ext == '.csv':
         return load_csv(file_path)
     elif ext == '.pdf':
         return load_pdf(file_path)
+    elif ext == '.txt':
+        return load_txt(file_path)
+    elif ext == '.xlsx':
+        return load_xlsx(file_path)
     else:
         raise ValueError(f"Unsupported file type: {ext}")

backend/src/rag/store.py

@@ -1,18 +1,27 @@
 from .embeddings import get_embeddings_batch, get_embedding
-from ..mongo.vector_store import insert_rag_documents, search_rag_documents
+from ..chroma.vector_store import insert_documents, search_documents
 
-def ingest_documents(text_chunks, collection_name="rag_documents"):
+def ingest_documents(text_chunks, collection_name="rag_documents", source_file=None, category=None):
     embeddings = get_embeddings_batch(text_chunks)
     
-    documents = []
+    metadata_list = None
-    for text, embedding in zip(text_chunks, embeddings):
+    if source_file or category:
-        documents.append({
+        metadata_list = []
-            "text": text,
+        for _ in text_chunks:
-            "embedding": embedding
+            meta = {}
-        })
+            if source_file:
+                meta["source"] = source_file
+            if category:
+                meta["category"] = category
+            metadata_list.append(meta)
     
-    return insert_rag_documents(documents, collection_name=collection_name)
+    return insert_documents(text_chunks, embeddings, collection_name=collection_name, metadata_list=metadata_list)
 
-def vector_search(query_text, collection_name="rag_documents", num_results=5):
+def vector_search(query_text, collection_name="rag_documents", num_results=5, category=None):
     query_embedding = get_embedding(query_text)
-    return search_rag_documents(query_embedding, collection_name=collection_name, num_results=num_results)
+    
+    filter_metadata = None
+    if category:
+        filter_metadata = {"category": category}
+    
+    return search_documents(query_embedding, collection_name=collection_name, num_results=num_results, filter_metadata=filter_metadata)