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Merge branch 'main' of https://github.com/arc53/DocsGPT

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This commit is contained in:
GH Action - Upstream Sync
2025-03-21 01:26:21 +00:00
parent b09386d102 4095b2b674
commit 718a86ecda
4 changed files with 25 additions and 77 deletions
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13
application/retriever/brave_search.py
View File

@@ -1,15 +1,16 @@
import json
from application.retriever.base import BaseRetriever
from langchain_community.tools import BraveSearch
from application.core.settings import settings
from application.llm.llm_creator import LLMCreator
from langchain_community.tools import BraveSearch
from application.retriever.base import BaseRetriever
class BraveRetSearch(BaseRetriever):
def __init__(
self,
question,
source,
chat_history,
prompt,
@@ -19,7 +20,7 @@ class BraveRetSearch(BaseRetriever):
user_api_key=None,
decoded_token=None,
):
self.question = question
self.question = ""
self.source = source
self.chat_history = chat_history
self.prompt = prompt
@@ -93,7 +94,9 @@ class BraveRetSearch(BaseRetriever):
for line in completion:
yield {"answer": str(line)}
def search(self):
def search(self, query: str = ""):
if query:
self.question = query
return self._get_data()
def get_params(self):

47
application/retriever/duckduck_search.py
View File

@@ -1,15 +1,15 @@
from application.retriever.base import BaseRetriever
from application.core.settings import settings
from application.llm.llm_creator import LLMCreator
from langchain_community.tools import DuckDuckGoSearchResults
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from application.core.settings import settings
from application.llm.llm_creator import LLMCreator
from application.retriever.base import BaseRetriever
class DuckDuckSearch(BaseRetriever):
def __init__(
self,
question,
source,
chat_history,
prompt,
@@ -19,7 +19,7 @@ class DuckDuckSearch(BaseRetriever):
user_api_key=None,
decoded_token=None,
):
self.question = question
self.question = ""
self.source = source
self.chat_history = chat_history
self.prompt = prompt
@@ -38,41 +38,24 @@ class DuckDuckSearch(BaseRetriever):
self.user_api_key = user_api_key
self.decoded_token = decoded_token
def _parse_lang_string(self, input_string):
result = []
current_item = ""
inside_brackets = False
for char in input_string:
if char == "[":
inside_brackets = True
elif char == "]":
inside_brackets = False
result.append(current_item)
current_item = ""
elif inside_brackets:
current_item += char
if inside_brackets:
result.append(current_item)
return result
def _get_data(self):
if self.chunks == 0:
docs = []
else:
wrapper = DuckDuckGoSearchAPIWrapper(max_results=self.chunks)
search = DuckDuckGoSearchResults(api_wrapper=wrapper)
search = DuckDuckGoSearchResults(api_wrapper=wrapper, output_format="list")
results = search.run(self.question)
results = self._parse_lang_string(results)
docs = []
for i in results:
try:
text = i.split("title:")[0]
title = i.split("title:")[1].split("link:")[0]
link = i.split("link:")[1]
docs.append({"text": text, "title": title, "link": link})
docs.append(
{
"text": i.get("snippet", "").strip(),
"title": i.get("title", "").strip(),
"link": i.get("link", "").strip(),
}
)
except IndexError:
pass
if settings.LLM_NAME == "llama.cpp":
@@ -110,7 +93,9 @@ class DuckDuckSearch(BaseRetriever):
for line in completion:
yield {"answer": str(line)}
def search(self):
def search(self, query: str = ""):
if query:
self.question = query
return self._get_data()
def get_params(self):

1
frontend/src/upload/Upload.tsx
View File

@@ -471,6 +471,7 @@ function Upload({
}, 3000);
};
xhr.open('POST', `${apiHost}/api/remote`);
xhr.setRequestHeader('Authorization', `Bearer ${token}`);
xhr.send(formData);
};
const { getRootProps, getInputProps, isDragActive } = useDropzone({

41
tests/test_vector_store.py
View File

@@ -1,41 +0,0 @@
"""
Tests regarding the vector store class, including checking
compatibility between different transformers and local vector
stores (index.faiss)
"""
import pytest
from application.vectorstore.faiss import FaissStore
from application.core.settings import settings
def test_init_local_faiss_store_huggingface():
"""
Test that asserts that initializing a FaissStore with
the huggingface sentence transformer below together with the
index.faiss file in the application/ folder results in a
dimension mismatch error.
"""
import os
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
from langchain_community.vectorstores import FAISS
# Ensure application directory exists
index_path = os.path.join("application")
os.makedirs(index_path, exist_ok=True)
# Create an index.faiss with a different embeddings dimension
# Use a different embedding model with a smaller dimension
other_embedding_model = "sentence-transformers/all-MiniLM-L6-v2" # Dimension 384
other_embeddings = HuggingFaceEmbeddings(model_name=other_embedding_model)
# Create some dummy documents
docs = [Document(page_content="Test document")]
# Create index using the other embeddings
other_docsearch = FAISS.from_documents(docs, other_embeddings)
# Save index to application/
other_docsearch.save_local(index_path)
# Now set the EMBEDDINGS_NAME to the one with a different dimension
settings.EMBEDDINGS_NAME = "huggingface_sentence-transformers/all-mpnet-base-v2" # Dimension 768
with pytest.raises(ValueError) as exc_info:
FaissStore("", None)
assert "Embedding dimension mismatch" in str(exc_info.value)