c6dd01e820
From pipeline > config > UI. Provide example project for promptui - Pipeline to config: `kotaemon.contribs.promptui.config.export_pipeline_to_config`. The config follows schema specified in this document: https://cinnamon-ai.atlassian.net/wiki/spaces/ATM/pages/2748711193/Technical+Detail. Note: this implementation exclude the logs, which will be handled in AUR-408. - Config to UI: `kotaemon.contribs.promptui.build_from_yaml` - Example project is located at `examples/promptui/`
99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
from abc import abstractmethod
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from typing import List, Optional
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from theflow import Node, Param
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from ..base import BaseComponent
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from ..docstores import BaseDocumentStore
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from ..documents.base import Document, RetrievedDocument
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from ..embeddings import BaseEmbeddings
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from ..vectorstores import BaseVectorStore
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class BaseRetrieval(BaseComponent):
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"""Define the base interface of a retrieval pipeline"""
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@abstractmethod
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def run_raw(self, text: str, top_k: int = 1) -> List[RetrievedDocument]:
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...
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@abstractmethod
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def run_batch_raw(
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self, text: List[str], top_k: int = 1
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) -> List[List[RetrievedDocument]]:
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...
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@abstractmethod
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def run_document(self, text: Document, top_k: int = 1) -> List[RetrievedDocument]:
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...
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@abstractmethod
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def run_batch_document(
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self, text: List[Document], top_k: int = 1
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) -> List[List[RetrievedDocument]]:
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...
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class RetrieveDocumentFromVectorStorePipeline(BaseRetrieval):
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"""Retrieve list of documents from vector store"""
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vector_store: Param[BaseVectorStore] = Param()
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doc_store: Optional[BaseDocumentStore] = None
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embedding: Node[BaseEmbeddings] = Node()
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# TODO: refer to llama_index's storage as well
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def run_raw(self, text: str, top_k: int = 1) -> List[RetrievedDocument]:
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return self.run_batch_raw([text], top_k=top_k)[0]
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def run_batch_raw(
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self, text: List[str], top_k: int = 1
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) -> List[List[RetrievedDocument]]:
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if self.doc_store is None:
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raise ValueError(
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"doc_store is not provided. Please provide a doc_store to "
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"retrieve the documents"
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)
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result = []
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for each_text in text:
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emb = self.embedding(each_text)
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_, scores, ids = self.vector_store.query(embedding=emb, top_k=top_k)
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docs = self.doc_store.get(ids)
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each_result = [
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RetrievedDocument(**doc.to_dict(), score=score)
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for doc, score in zip(docs, scores)
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]
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result.append(each_result)
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return result
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def run_document(self, text: Document, top_k: int = 1) -> List[RetrievedDocument]:
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return self.run_raw(text.text, top_k)
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def run_batch_document(
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self, text: List[Document], top_k: int = 1
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) -> List[List[RetrievedDocument]]:
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return self.run_batch_raw(text=[t.text for t in text], top_k=top_k)
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def is_document(self, text, *args, **kwargs) -> bool:
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if isinstance(text, Document):
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return True
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elif isinstance(text, List) and isinstance(text[0], Document):
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return True
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return False
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def is_batch(self, text, *args, **kwargs) -> bool:
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if isinstance(text, list):
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return True
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return False
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def persist(self, path: str):
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"""Save the whole state of the indexing pipeline vector store and all
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necessary information to disk
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Args:
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path (str): path to save the state
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"""
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def load(self, path: str):
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"""Load all information from disk to an object"""
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