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import weaviate |
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from langchain_community.vectorstores import Weaviate |
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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 BoolInput, DataInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput |
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from langflow.schema import Data |
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class WeaviateVectorStoreComponent(LCVectorStoreComponent): |
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display_name = "Weaviate" |
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description = "Weaviate Vector Store with search capabilities" |
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documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate" |
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name = "Weaviate" |
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icon = "Weaviate" |
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inputs = [ |
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StrInput(name="url", display_name="Weaviate URL", value="http://localhost:8080", required=True), |
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SecretStrInput(name="api_key", display_name="API Key", required=False), |
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StrInput( |
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name="index_name", |
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display_name="Index Name", |
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required=True, |
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info="Requires capitalized index name.", |
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), |
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StrInput(name="text_key", display_name="Text Key", value="text", 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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BoolInput(name="search_by_text", display_name="Search By Text", advanced=True), |
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] |
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@check_cached_vector_store |
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def build_vector_store(self) -> Weaviate: |
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if self.api_key: |
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auth_config = weaviate.AuthApiKey(api_key=self.api_key) |
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client = weaviate.Client(url=self.url, auth_client_secret=auth_config) |
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else: |
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client = weaviate.Client(url=self.url) |
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if self.index_name != self.index_name.capitalize(): |
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msg = f"Weaviate requires the index name to be capitalized. Use: {self.index_name.capitalize()}" |
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raise ValueError(msg) |
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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 documents and self.embedding: |
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return Weaviate.from_documents( |
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client=client, |
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index_name=self.index_name, |
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documents=documents, |
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embedding=self.embedding, |
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by_text=self.search_by_text, |
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) |
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return Weaviate( |
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client=client, |
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index_name=self.index_name, |
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text_key=self.text_key, |
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embedding=self.embedding, |
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by_text=self.search_by_text, |
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) |
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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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