Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Knowledge Graph RAG: fix setup #148

Open
wants to merge 4 commits into
base: main
Choose a base branch
from
Open

Knowledge Graph RAG: fix setup #148

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
c6cf94a
doc: install external dependencies
gschup Jul 12, 2024
a576897
fix: Requests version conflict
gschup Jul 12, 2024
03c2a5f
fix: use full module path for import
gschup Jul 12, 2024
708d378
fix: download NLTK tagger
gschup Jul 12, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 9 additions & 4 deletions experimental/knowledge_graph_rag/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -85,25 +85,30 @@ python3 -m virtualenv venv
source venv/bin/activate
```

### 4. Install the required packages
### 4. Install external dependencies
```bash
sudo apt install poppler-utils ffmpeg libsm6 libxext6 tesseract-ocr libtesseract-dev
```

### 5. Install the required packages

```bash
pip install -r requirements.txt
```

### 5. Setup a hosted Milvus vector database
### 6. Setup a hosted Milvus vector database

Follow the instructions [here](https://milvus.io/docs/install_standalone-docker.md) to deploy a hosted Milvus instance for the vector database backend. Note that it must be Milvus 2.4 or better to support [hybrid search](https://milvus.io/docs/multi-vector-search.md). We do not support disabling this feature for previous versions of Milvus as of now.

### 5. Launch the Streamlit frontend
### 7. Launch the Streamlit frontend

```bash
streamlit run app.py
```

Open the URL in your browser to access the UI and chatbot!

### 6. Upload Docs and Train Model
### 8. Upload Docs and Train Model

Upload your own documents to a folder, or use an existing folder for the knowledge graph creation. Note that the implementation currently focuses on text from PDFs only. It can be extended to other text file formats using the Unstructured.io data loader in LangChain.

Expand Down
3 changes: 3 additions & 0 deletions experimental/knowledge_graph_rag/app.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,9 @@
from vectorstore.search import SearchHandler
from langchain_nvidia_ai_endpoints import ChatNVIDIA

import nltk
nltk.download('averaged_perceptron_tagger')

def load_data(input_dir, num_workers):
reader = SimpleDirectoryReader(input_dir=input_dir)
documents = reader.load_data(num_workers=num_workers)
Expand Down
4 changes: 2 additions & 2 deletions experimental/knowledge_graph_rag/requirements.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,8 @@ llama_index==0.10.50
networkx==3.2.1
numpy==1.24.1
pandas==2.2.2
pymilvus==2.4.3
Requests==2.32.3
pymilvus[model]==2.4.3
Requests==2.31.0
streamlit==1.30.0
unstructured[all-docs]
tqdm==4.66.1
2 changes: 1 addition & 1 deletion experimental/knowledge_graph_rag/utils/lc_graph.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@

from langchain_nvidia_ai_endpoints import ChatNVIDIA
import concurrent.futures
from preprocessor import extract_triples
from utils.preprocessor import extract_triples
from tqdm import tqdm
from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
Expand Down