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| from abc import abstractmethod | |
| import os | |
| from qdrant_client import QdrantClient | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.vectorstores import Qdrant, ElasticVectorSearch, VectorStore | |
| from qdrant_client.models import VectorParams, Distance | |
| embeddings = OpenAIEmbeddings() | |
| class ToyVectorStore: | |
| def get_instance(): | |
| vector_store = os.getenv("STORE") | |
| if vector_store == "ELASTIC": | |
| return ElasticVectorStore() | |
| elif vector_store == "QDRANT": | |
| return QdrantVectorStore() | |
| else: | |
| raise ValueError(f"Invalid vector store {vector_store}") | |
| def get_collection(self, collection: str="test") -> VectorStore: | |
| """ | |
| get an instance of vector store | |
| of collection | |
| """ | |
| pass | |
| def create_collection(self, collection: str) -> None: | |
| """ | |
| create an instance of vector store | |
| with collection name | |
| """ | |
| pass | |
| class ElasticVectorStore(ToyVectorStore): | |
| def get_collection(self, collection:str) -> ElasticVectorSearch: | |
| return ElasticVectorSearch(elasticsearch_url= os.getenv("ES_URL"), | |
| index_name= collection, embedding=embeddings) | |
| def create_collection(self, collection: str) -> None: | |
| store = self.get_collection(collection) | |
| store.create_index(store.client,collection, dict()) | |
| class QdrantVectorStore(ToyVectorStore): | |
| def __init__(self): | |
| self.client = QdrantClient(url=os.getenv("QDRANT_URL"), | |
| api_key=os.getenv("QDRANT_API_KEY")) | |
| def get_collection(self, collection: str) -> Qdrant: | |
| return Qdrant(client=self.client,collection_name=collection,embeddings=embeddings) | |
| def create_collection(self, collection: str) -> None: | |
| self.client.create_collection(collection_name=collection, | |
| vectors_config=VectorParams(size=1536, distance=Distance.COSINE)) | |