mirror of https://github.com/Shubhamsaboo/awesome-llm-apps.git synced 2026-10-31 03:28:04 +08:00

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sailikhithk 6e1e315c02 fix: remove deprecated pathlib backport and migrate PyPDF2 to pypdf (#1029 )

Remove pathlib==1.0.1 from ai_tic_tac_toe_agent/requirements.txt. This is the
abandoned 2012 backport of the stdlib module that can shadow and break Python's
built-in pathlib on modern Python. The code already uses `from pathlib import
Path` (stdlib), so the package is unnecessary.

Migrate PyPDF2 to pypdf in two projects (ai_recruitment_agent_team and
rag_chain). PyPDF2 is deprecated and had a ReDoS vulnerability (CVE-2023-36810).
The maintained successor is pypdf, which the rest of the repo already uses in
10+ other projects. The PdfReader API is identical:
  - import PyPDF2 -> import pypdf
  - PyPDF2.PdfReader -> pypdf.PdfReader

Files changed:
- ai_tic_tac_toe_agent/requirements.txt: removed pathlib==1.0.1
- ai_recruitment_agent_team/requirements.txt: PyPDF2==3.0.1 -> pypdf>=4.0.0
- ai_recruitment_agent_team/ai_recruitment_agent_team.py: import and usage
- ai_recruitment_agent_team/README.md: updated tech stack mention
- rag_chain/requirements.txt: PyPDF2 -> pypdf>=4.0.0

2026-08-15 17:35:13 -05:00

app.py

Ambrogio: Code improvements

2025-02-13 21:59:11 +01:00

README.md

Updated streamlit UI

2025-01-14 20:28:18 +05:30

requirements.txt

fix: remove deprecated pathlib backport and migrate PyPDF2 to pypdf (#1029 )

2026-08-15 17:35:13 -05:00

README.md

PharmaQuery

Overview

PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain.

Demo

https://github.com/user-attachments/assets/c12ee305-86fe-4f71-9219-57c7f438f291

Features

Natural Language Querying: Ask complex questions about the pharmaceutical industry and get concise, accurate answers.
Custom Database: Upload your own research documents to enhance the retrieval system's knowledge base.
Similarity Search: Retrieves the most relevant documents for your query using AI embeddings.
Streamlit Interface: User-friendly interface for queries and document uploads.

Technologies Used

Programming Language: Python 3.10+
Framework: LangChain
Database: ChromaDB
Models:
- Embeddings: Google Gemini API (embedding-001)
- Chat: Google Gemini API (gemini-1.5-pro)
PDF Processing: PyPDFLoader
Document Splitter: SentenceTransformersTokenTextSplitter

Requirements

Install Dependencies:
```
pip install -r requirements.txt
```
Run the Application:
```
streamlit run app.py
```
Use the Application:
- Paste your Google API Key in the sidebar.
- Enter your query in the main interface.
- Optionally, upload research papers in the sidebar to enhance the database.

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