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from langchain.embeddings.base import Embeddings |
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from langchain_community.vectorstores import Qdrant |
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from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store |
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from langflow.helpers.data import docs_to_data |
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from langflow.io import ( |
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DataInput, |
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DropdownInput, |
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HandleInput, |
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IntInput, |
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MultilineInput, |
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SecretStrInput, |
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StrInput, |
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) |
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from langflow.schema import Data |
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class QdrantVectorStoreComponent(LCVectorStoreComponent): |
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display_name = "Qdrant" |
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description = "Qdrant Vector Store with search capabilities" |
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documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant" |
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icon = "Qdrant" |
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inputs = [ |
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StrInput(name="collection_name", display_name="Collection Name", required=True), |
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StrInput(name="host", display_name="Host", value="localhost", advanced=True), |
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IntInput(name="port", display_name="Port", value=6333, advanced=True), |
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IntInput(name="grpc_port", display_name="gRPC Port", value=6334, advanced=True), |
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SecretStrInput(name="api_key", display_name="API Key", advanced=True), |
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StrInput(name="prefix", display_name="Prefix", advanced=True), |
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IntInput(name="timeout", display_name="Timeout", advanced=True), |
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StrInput(name="path", display_name="Path", advanced=True), |
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StrInput(name="url", display_name="URL", advanced=True), |
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DropdownInput( |
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name="distance_func", |
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display_name="Distance Function", |
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options=["Cosine", "Euclidean", "Dot Product"], |
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value="Cosine", |
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advanced=True, |
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), |
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StrInput(name="content_payload_key", display_name="Content Payload Key", value="page_content", advanced=True), |
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StrInput(name="metadata_payload_key", display_name="Metadata Payload Key", value="metadata", advanced=True), |
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MultilineInput(name="search_query", display_name="Search Query"), |
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DataInput( |
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name="ingest_data", |
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display_name="Ingest Data", |
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is_list=True, |
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), |
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HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]), |
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IntInput( |
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name="number_of_results", |
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display_name="Number of Results", |
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info="Number of results to return.", |
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value=4, |
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advanced=True, |
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), |
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] |
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@check_cached_vector_store |
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def build_vector_store(self) -> Qdrant: |
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qdrant_kwargs = { |
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"collection_name": self.collection_name, |
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"content_payload_key": self.content_payload_key, |
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"metadata_payload_key": self.metadata_payload_key, |
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} |
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server_kwargs = { |
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"host": self.host or None, |
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"port": int(self.port), |
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"grpc_port": int(self.grpc_port), |
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"api_key": self.api_key, |
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"prefix": self.prefix, |
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"timeout": int(self.timeout) if self.timeout else None, |
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"path": self.path or None, |
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"url": self.url or None, |
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} |
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server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None} |
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documents = [] |
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for _input in self.ingest_data or []: |
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if isinstance(_input, Data): |
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documents.append(_input.to_lc_document()) |
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else: |
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documents.append(_input) |
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if not isinstance(self.embedding, Embeddings): |
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msg = "Invalid embedding object" |
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raise TypeError(msg) |
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if documents: |
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qdrant = Qdrant.from_documents(documents, embedding=self.embedding, **qdrant_kwargs, **server_kwargs) |
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else: |
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from qdrant_client import QdrantClient |
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client = QdrantClient(**server_kwargs) |
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qdrant = Qdrant(embeddings=self.embedding, client=client, **qdrant_kwargs) |
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return qdrant |
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def search_documents(self) -> list[Data]: |
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vector_store = self.build_vector_store() |
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if self.search_query and isinstance(self.search_query, str) and self.search_query.strip(): |
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docs = vector_store.similarity_search( |
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query=self.search_query, |
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k=self.number_of_results, |
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) |
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data = docs_to_data(docs) |
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self.status = data |
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return data |
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return [] |
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