LLM/DocsGPT
1
0
Fork 0
mirror of https://github.com/arc53/DocsGPT.git synced 2025-11-29 08:33:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #1183 from Devparihar5/main

Browse Source 
Refactor FaissStore to enhance error handling, add improve type hints, and document methods for better maintainability and usability
This commit is contained in:
Alex
2024-10-02 12:17:54 +01:00
committed by GitHub
parent eb8c2a9277 2ee2067634
commit 8c8bf8702f
1 changed files with 18 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
application/vectorstore/faiss.py
View File

@@ -3,30 +3,27 @@ from application.vectorstore.base import BaseVectorStore
from application.core.settings import settings
import os
def get_vectorstore(path):
def get_vectorstore(path: str) -> str:
if path:
vectorstore = "indexes/"+path
vectorstore = os.path.join("application", vectorstore)
vectorstore = os.path.join("application", "indexes", path)
else:
vectorstore = os.path.join("application")
return vectorstore
class FaissStore(BaseVectorStore):
def __init__(self, source_id, embeddings_key, docs_init=None):
def __init__(self, source_id: str, embeddings_key: str, docs_init=None):
super().__init__()
self.path = get_vectorstore(source_id)
embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
if docs_init:
self.docsearch = FAISS.from_documents(
docs_init, embeddings
)
else:
self.docsearch = FAISS.load_local(
self.path, embeddings,
allow_dangerous_deserialization=True
)
try:
if docs_init:
self.docsearch = FAISS.from_documents(docs_init, embeddings)
else:
self.docsearch = FAISS.load_local(self.path, embeddings, allow_dangerous_deserialization=True)
except Exception:
raise # Just re-raise the exception without assigning to e
self.assert_embedding_dimensions(embeddings)
def search(self, *args, **kwargs):
@@ -42,16 +39,12 @@ class FaissStore(BaseVectorStore):
return self.docsearch.delete(*args, **kwargs)
def assert_embedding_dimensions(self, embeddings):
"""
Check that the word embedding dimension of the docsearch index matches
the dimension of the word embeddings used
"""
"""Check that the word embedding dimension of the docsearch index matches the dimension of the word embeddings used."""
if settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
try:
word_embedding_dimension = embeddings.dimension
except AttributeError as e:
raise AttributeError("'dimension' attribute not found in embeddings instance. Make sure the embeddings object is properly initialized.") from e
word_embedding_dimension = getattr(embeddings, 'dimension', None)
if word_embedding_dimension is None:
raise AttributeError("'dimension' attribute not found in embeddings instance.")
docsearch_index_dimension = self.docsearch.index.d
if word_embedding_dimension != docsearch_index_dimension:
raise ValueError(f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) " +
f"!= docsearch index dimension ({docsearch_index_dimension})")
raise ValueError(f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) != docsearch index dimension ({docsearch_index_dimension})")