rag-solution/services/rag/langchain/vector_storage.py

"""Vector storage module using Qdrant and configurable embeddings for the RAG solution."""

import os
from typing import Optional

from dotenv import load_dotenv
from langchain_qdrant import QdrantVectorStore
from langchain_core.documents import Document
from langchain_ollama import OllamaEmbeddings
from langchain_openai import OpenAIEmbeddings
from qdrant_client import QdrantClient

# Load environment variables
load_dotenv()

# Qdrant configuration
QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
QDRANT_REST_PORT = int(os.getenv("QDRANT_REST_PORT", 6333))
QDRANT_GRPC_PORT = int(os.getenv("QDRANT_GRPC_PORT", 6334))

# Embedding model configuration
EMBEDDING_STRATEGY = os.getenv("EMBEDDING_STRATEGY", "ollama").lower()
OLLAMA_EMBEDDING_MODEL = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
OPENAI_EMBEDDING_MODEL = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-ada-002")
OPENAI_EMBEDDING_BASE_URL = os.getenv("OPENAI_EMBEDDING_BASE_URL")
OPENAI_EMBEDDING_API_KEY = os.getenv("OPENAI_EMBEDDING_API_KEY")


def initialize_vector_store(
    collection_name: str = "documents_langchain", recreate_collection: bool = False
) -> QdrantVectorStore:
    """
    Initialize and return a Qdrant vector store with configurable embeddings.

    Args:
        collection_name: Name of the Qdrant collection to use
        recreate_collection: Whether to recreate the collection if it exists

    Returns:
        Initialized Qdrant vector store
    """
    # Determine which embedding strategy to use
    if EMBEDDING_STRATEGY == "openai":
        # Validate required OpenAI embedding variables
        if not OPENAI_EMBEDDING_API_KEY or not OPENAI_EMBEDDING_BASE_URL:
            raise ValueError("OPENAI_EMBEDDING_API_KEY and OPENAI_EMBEDDING_BASE_URL must be set when using OpenAI embedding strategy")

        # Initialize OpenAI embeddings
        embeddings = OpenAIEmbeddings(
            model=OPENAI_EMBEDDING_MODEL,
            openai_api_base=OPENAI_EMBEDDING_BASE_URL,
            openai_api_key=OPENAI_EMBEDDING_API_KEY,
        )
    else:  # Default to ollama
        # Initialize Ollama embeddings
        embeddings = OllamaEmbeddings(
            model=OLLAMA_EMBEDDING_MODEL,
            base_url="http://localhost:11434",  # Default Ollama URL
        )

    # Check if collection exists and create if needed
    client = QdrantClient(
        host=QDRANT_HOST,
        port=QDRANT_REST_PORT,
    )

    collection_exists = False
    try:
        client.get_collection(collection_name)
        collection_exists = True
    except Exception:
        # Collection doesn't exist, we'll create it
        collection_exists = False

    if recreate_collection and collection_exists:
        client.delete_collection(collection_name)
        collection_exists = False

    # If collection doesn't exist, create it using the client directly
    if not collection_exists:
        # Create collection using the Qdrant client directly
        from qdrant_client.http.models import Distance, VectorParams

        # First, we need to determine the embedding size by creating a sample embedding
        sample_embedding = embeddings.embed_query("sample text for dimension detection")
        vector_size = len(sample_embedding)

        # Create the collection with appropriate vector size
        client.create_collection(
            collection_name=collection_name,
            vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
        )

    # Create the Qdrant instance connected to the collection
    vector_store = QdrantVectorStore(
        client=client,
        collection_name=collection_name,
        embedding=embeddings,
    )

    return vector_store


def add_documents_to_vector_store(
    vector_store: QdrantVectorStore, documents: list[Document], batch_size: int = 10
) -> None:
    """
    Add documents to the vector store.

    Args:
        vector_store: Initialized Qdrant vector store
        documents: List of documents to add
        batch_size: Number of documents to add in each batch
    """
    # Add documents to the vector store in batches
    for i in range(0, len(documents), batch_size):
        batch = documents[i : i + batch_size]
        vector_store.add_documents(batch)


def search_vector_store(vector_store: QdrantVectorStore, query: str, top_k: int = 5) -> list:
    """
    Search the vector store for similar documents.

    Args:
        vector_store: Initialized Qdrant vector store
        query: Query string to search for
        top_k: Number of top results to return

    Returns:
        List of similar documents
    """
    return vector_store.similarity_search(query, k=top_k)


# Just in case add possibility to connect via openai embedding, using openrouter api key.
# Comment this section, so it can be used in the future.
"""
# Alternative implementation using OpenAI embeddings via OpenRouter
# Uncomment and configure as needed

import os
from langchain_openai import OpenAIEmbeddings

OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
OPENROUTER_EMBEDDING_MODEL = os.getenv("OPENROUTER_EMBEDDING_MODEL", "openai/text-embedding-ada-002")

def initialize_vector_store_with_openrouter(
    collection_name: str = "documents_langchain"
) -> QdrantVectorStore:
    # Initialize Qdrant client
    client = QdrantClient(
        host=QDRANT_HOST,
        port=QDRANT_REST_PORT,
    )

    # Initialize OpenAI embeddings via OpenRouter
    embeddings = OpenAIEmbeddings(
        model=OPENROUTER_EMBEDDING_MODEL,
        openai_api_key=OPENROUTER_API_KEY,
        openai_api_base="https://openrouter.ai/api/v1"
    )

    # Create or get the vector store
    vector_store = QdrantVectorStore(
        client=client,
        collection_name=collection_name,
        embeddings=embeddings,
    )

    return vector_store
"""


if __name__ == "__main__":
    # Example usage
    print(
        f"Initializing vector store with Ollama embedding model: {OLLAMA_EMBEDDING_MODEL}"
    )
    vector_store = initialize_vector_store()
    print("Vector store initialized successfully!")