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Delete src/backend/langflow/components/retrievers/VectaraSelfQueryRetriever.py

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Atharva Jadhav 2023-12-18 04:52:25 +05:30 • committed by GitHub
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src/backend/langflow/components/retrievers/VectaraSelfQueryRetriever.py
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from typing import Optional, List
from langflow import CustomComponent
import json
from langchain.schema import BaseRetriever
from langchain.schema.vectorstore import VectorStore
from langchain.base_language import BaseLanguageModel
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
class VectaraComponent(CustomComponent):
display_name: str = "Vectara Self Query Retriever for Vectara Vector Store"
description: str = "Implementation of Vectara Self Query Retriever"
documentation = (
"https://python.langchain.com/docs/integrations/vectorstores/vectara"
)
beta = True
field_config = {
"code": {"show": False},
"vectorstore": {
"display_name": "Vectara Vector Store",
"info": "Input Vectara Vectore Store"
},
"llm": {
"display_name": "LLM",
"info": "For self query retriever"
},
"document_content_description":{
"display_name": "Document Content Description",
"info": "For self query retriever",
},
"metadata_field_info": {
"display_name": "Metadata Field Info",
"info": "Check dictionary format in documentation for self query retriever",
"info": "Each metadata field is a string in the form of json containing additional search metadata.\nExample input: {\"name\":\"speech\",\"description\":\"what name of the speech\",\"type\":\"string or list[string]\"}.\nThe keys should remain constant",
},
}
def build(
self,
vectorstore: VectorStore = None,
document_content_description: str = None,
llm: BaseLanguageModel = None,
metadata_field_info: List[str] = None,
) -> BaseRetriever:
metadata_field_obj = []
for meta in metadata_field_info:
meta_obj = json.loads(meta)
if 'name' not in meta_obj or 'description' not in meta_obj or 'type' not in meta_obj :
raise Exception('Incorrect metadata field info format.')
attribute_info = AttributeInfo(
name = meta_obj['name'],
description = meta_obj['description'],
type = meta_obj['type'],
)
metadata_field_obj.append(attribute_info)
return SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_obj,
verbose=True
)