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82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
from typing import Any, List, Optional, Type
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from pydantic import BaseModel, Extra, Field
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from langchain.base_language import BaseLanguageModel
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from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
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from langchain.chains.retrieval_qa.base import RetrievalQA
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from langchain.document_loaders.base import BaseLoader
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from langchain.embeddings.base import Embeddings
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.llms.openai import OpenAI
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from langchain.schema import Document
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from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.chroma import Chroma
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def _get_default_text_splitter() -> TextSplitter:
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return RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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class VectorStoreIndexWrapper(BaseModel):
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"""Wrapper around a vectorstore for easy access."""
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vectorstore: VectorStore
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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def query(
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self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
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) -> str:
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"""Query the vectorstore."""
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llm = llm or OpenAI(temperature=0)
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chain = RetrievalQA.from_chain_type(
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llm, retriever=self.vectorstore.as_retriever(), **kwargs
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)
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return chain.run(question)
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def query_with_sources(
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self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
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) -> dict:
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"""Query the vectorstore and get back sources."""
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llm = llm or OpenAI(temperature=0)
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chain = RetrievalQAWithSourcesChain.from_chain_type(
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llm, retriever=self.vectorstore.as_retriever(), **kwargs
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)
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return chain({chain.question_key: question})
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class VectorstoreIndexCreator(BaseModel):
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"""Logic for creating indexes."""
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vectorstore_cls: Type[VectorStore] = Chroma
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embedding: Embeddings = Field(default_factory=OpenAIEmbeddings)
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text_splitter: TextSplitter = Field(default_factory=_get_default_text_splitter)
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vectorstore_kwargs: dict = Field(default_factory=dict)
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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def from_loaders(self, loaders: List[BaseLoader]) -> VectorStoreIndexWrapper:
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"""Create a vectorstore index from loaders."""
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docs = []
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for loader in loaders:
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docs.extend(loader.load())
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return self.from_documents(docs)
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def from_documents(self, documents: List[Document]) -> VectorStoreIndexWrapper:
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"""Create a vectorstore index from documents."""
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sub_docs = self.text_splitter.split_documents(documents)
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vectorstore = self.vectorstore_cls.from_documents(
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sub_docs, self.embedding, **self.vectorstore_kwargs
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)
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return VectorStoreIndexWrapper(vectorstore=vectorstore)
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