qasper eval

langchain/indexes/vectorstore.py

@@ -4,6 +4,7 @@ from pydantic import BaseModel, Extra, Field
 
 from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain
 from langchain.chains.vector_db_qa.base import VectorDBQA
+from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
 from langchain.embeddings.base import Embeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
@@ -60,13 +61,22 @@ class VectorstoreIndexCreator(BaseModel):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
-    def from_loaders(self, loaders: List[BaseLoader]) -> VectorStoreIndexWrapper:
-        """Create a vectorstore index from loaders."""
-        docs = []
-        for loader in loaders:
-            docs.extend(loader.load())
+    def _from_docs(self, docs: List[Document]) -> VectorStoreIndexWrapper:
         sub_docs = self.text_splitter.split_documents(docs)
         vectorstore = self.vectorstore_cls.from_documents(
             sub_docs, self.embedding, **self.vectorstore_kwargs
         )
         return VectorStoreIndexWrapper(vectorstore=vectorstore)
+
+    def from_text(
+        self, text: str, metadata: Optional[dict] = None
+    ) -> VectorStoreIndexWrapper:
+        doc = Document(page_content=text, metadata=metadata or {})
+        return self._from_docs([doc])
+
+    def from_loaders(self, loaders: List[BaseLoader]) -> VectorStoreIndexWrapper:
+        """Create a vectorstore index from loaders."""
+        docs = []
+        for loader in loaders:
+            docs.extend(loader.load())
+        return self._from_docs(docs)