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| from typing import Any, Dict, List, Optional, Type | |
| from langchain_core.document_loaders import BaseLoader | |
| from langchain_core.documents import Document | |
| from langchain_core.embeddings import Embeddings | |
| from langchain_core.language_models import BaseLanguageModel | |
| from langchain_core.pydantic_v1 import BaseModel, Extra, Field | |
| from langchain_core.vectorstores import VectorStore | |
| from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter | |
| from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain | |
| from langchain.chains.retrieval_qa.base import RetrievalQA | |
| def _get_default_text_splitter() -> TextSplitter: | |
| return RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
| class VectorStoreIndexWrapper(BaseModel): | |
| """Wrapper around a vectorstore for easy access.""" | |
| vectorstore: VectorStore | |
| class Config: | |
| """Configuration for this pydantic object.""" | |
| extra = Extra.forbid | |
| arbitrary_types_allowed = True | |
| def query( | |
| self, | |
| question: str, | |
| llm: Optional[BaseLanguageModel] = None, | |
| retriever_kwargs: Optional[Dict[str, Any]] = None, | |
| **kwargs: Any, | |
| ) -> str: | |
| """Query the vectorstore.""" | |
| if llm is None: | |
| raise NotImplementedError( | |
| "This API has been changed to require an LLM. " | |
| "Please provide an llm to use for querying the vectorstore.\n" | |
| "For example,\n" | |
| "from langchain_openai import OpenAI\n" | |
| "llm = OpenAI(temperature=0)" | |
| ) | |
| retriever_kwargs = retriever_kwargs or {} | |
| chain = RetrievalQA.from_chain_type( | |
| llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs | |
| ) | |
| return chain.invoke({chain.input_key: question})[chain.output_key] | |
| async def aquery( | |
| self, | |
| question: str, | |
| llm: Optional[BaseLanguageModel] = None, | |
| retriever_kwargs: Optional[Dict[str, Any]] = None, | |
| **kwargs: Any, | |
| ) -> str: | |
| """Query the vectorstore.""" | |
| if llm is None: | |
| raise NotImplementedError( | |
| "This API has been changed to require an LLM. " | |
| "Please provide an llm to use for querying the vectorstore.\n" | |
| "For example,\n" | |
| "from langchain_openai import OpenAI\n" | |
| "llm = OpenAI(temperature=0)" | |
| ) | |
| retriever_kwargs = retriever_kwargs or {} | |
| chain = RetrievalQA.from_chain_type( | |
| llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs | |
| ) | |
| return (await chain.ainvoke({chain.input_key: question}))[chain.output_key] | |
| def query_with_sources( | |
| self, | |
| question: str, | |
| llm: Optional[BaseLanguageModel] = None, | |
| retriever_kwargs: Optional[Dict[str, Any]] = None, | |
| **kwargs: Any, | |
| ) -> dict: | |
| """Query the vectorstore and get back sources.""" | |
| if llm is None: | |
| raise NotImplementedError( | |
| "This API has been changed to require an LLM. " | |
| "Please provide an llm to use for querying the vectorstore.\n" | |
| "For example,\n" | |
| "from langchain_openai import OpenAI\n" | |
| "llm = OpenAI(temperature=0)" | |
| ) | |
| retriever_kwargs = retriever_kwargs or {} | |
| chain = RetrievalQAWithSourcesChain.from_chain_type( | |
| llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs | |
| ) | |
| return chain.invoke({chain.question_key: question}) | |
| async def aquery_with_sources( | |
| self, | |
| question: str, | |
| llm: Optional[BaseLanguageModel] = None, | |
| retriever_kwargs: Optional[Dict[str, Any]] = None, | |
| **kwargs: Any, | |
| ) -> dict: | |
| """Query the vectorstore and get back sources.""" | |
| if llm is None: | |
| raise NotImplementedError( | |
| "This API has been changed to require an LLM. " | |
| "Please provide an llm to use for querying the vectorstore.\n" | |
| "For example,\n" | |
| "from langchain_openai import OpenAI\n" | |
| "llm = OpenAI(temperature=0)" | |
| ) | |
| retriever_kwargs = retriever_kwargs or {} | |
| chain = RetrievalQAWithSourcesChain.from_chain_type( | |
| llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs | |
| ) | |
| return await chain.ainvoke({chain.question_key: question}) | |
| def _get_in_memory_vectorstore() -> Type[VectorStore]: | |
| """Get the InMemoryVectorStore.""" | |
| import warnings | |
| try: | |
| from langchain_community.vectorstores.inmemory import InMemoryVectorStore | |
| except ImportError: | |
| raise ImportError( | |
| "Please install langchain-community to use the InMemoryVectorStore." | |
| ) | |
| warnings.warn( | |
| "Using InMemoryVectorStore as the default vectorstore." | |
| "This memory store won't persist data. You should explicitly" | |
| "specify a vectorstore when using VectorstoreIndexCreator" | |
| ) | |
| return InMemoryVectorStore | |
| class VectorstoreIndexCreator(BaseModel): | |
| """Logic for creating indexes.""" | |
| vectorstore_cls: Type[VectorStore] = Field( | |
| default_factory=_get_in_memory_vectorstore | |
| ) | |
| embedding: Embeddings | |
| text_splitter: TextSplitter = Field(default_factory=_get_default_text_splitter) | |
| vectorstore_kwargs: dict = Field(default_factory=dict) | |
| class Config: | |
| """Configuration for this pydantic object.""" | |
| 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()) | |
| return self.from_documents(docs) | |
| async def afrom_loaders(self, loaders: List[BaseLoader]) -> VectorStoreIndexWrapper: | |
| """Create a vectorstore index from loaders.""" | |
| docs = [] | |
| for loader in loaders: | |
| async for doc in loader.alazy_load(): | |
| docs.append(doc) | |
| return await self.afrom_documents(docs) | |
| def from_documents(self, documents: List[Document]) -> VectorStoreIndexWrapper: | |
| """Create a vectorstore index from documents.""" | |
| sub_docs = self.text_splitter.split_documents(documents) | |
| vectorstore = self.vectorstore_cls.from_documents( | |
| sub_docs, self.embedding, **self.vectorstore_kwargs | |
| ) | |
| return VectorStoreIndexWrapper(vectorstore=vectorstore) | |
| async def afrom_documents( | |
| self, documents: List[Document] | |
| ) -> VectorStoreIndexWrapper: | |
| """Create a vectorstore index from documents.""" | |
| sub_docs = self.text_splitter.split_documents(documents) | |
| vectorstore = await self.vectorstore_cls.afrom_documents( | |
| sub_docs, self.embedding, **self.vectorstore_kwargs | |
| ) | |
| return VectorStoreIndexWrapper(vectorstore=vectorstore) | |