Master your supply chain, untangling every challenge.
- Install quarto from Quarto
quarto install tinytex
quarto render docs --format pdf
Install python from Python Install node from Node Install mongodb from MongoDB Install mysql from MySQL
Check and update .env file with the correct database connection strings and urls.
### backend requirements
```bash
python -m venv .venv
#windows
.venv\Scripts\activate
# linux
source .venv/bin/activate
python -m pip install -r requirements.txt
# create the mysql database
python create_db.py
cd tsfrontend
npm install
python backend/main.py
graph TD
Input --> llm
llm --> response
response --> llm2
llm2 --> llm
llm2 --NO--> llm3
llm2 --Yes--> llm4
graph LR
subgraph Collect
PDF[PDF\nannual reports] --> Store
Crawling[
Crawling:\nWeb Site\nBrand Site\nProduct Pages
] --> Store
SEC[
US SEC\nFilings\n10-K\n10-Q
] --> Store
Wikipedia[Wiki\nCompany Pages\nIngrediens\nChemicals] --> Store
DuckDuckGo[Search\nDuckDuckGo] --> Store
Store --> Ingestion['Processing\nIngestion']
end
subgraph Ingest
Ingestion --> Transform <--> Extract[Extract:\n Nodes, Edges]
Transform <--> Structurize[Structurize:\n CSV, JSON, Markdown]
Transform --> Embeddings
Structurize --> Knowledge
end
subgraph Knowledge
Transform --> LLM
Extract <--> IndexGraph
Extract <--> IndexFact
LLM --> Structurize
end
RAG --> Retrievers
subgraph GraphDatabase
IndexGraph[Graph\nNodes\nLinks] --> Neo4j
RAG --> Neo4j
end
subgraph FactDatabase
IndexFact --> Sqlite
IndexFact --> Markdown
Markdown --> RAG
Sqlite --> RAG
end
subgraph VectorStores
Embeddings --> Faiss
Embeddings --> Chroma
Faiss --> RAG
Chroma --> RAG
end
subgraph UI
UIAI --> Query --> Retrievers --> Results --> Visualize
Query --> Visualize
end
Current Status
graph LR
style Collect fill:#ff6347,stroke:#333,stroke-width:2px
style Ingest fill:#ffa500,stroke:#333,stroke-width:2px
style Knowledge fill:#ffff00,stroke:#333,stroke-width:2px
style GraphDatabase fill:#008000,stroke:#333,stroke-width:2px
style FactDatabase fill:#0000ff,stroke:#333,stroke-width:2px
style VectorStores fill:#40e0d0,stroke:#333,stroke-width:2px
style UI fill:#1e90ff,stroke:#333,stroke-width:2px
subgraph Collect
PDF[PDF\nannual reports] --> Store
Crawling[
Crawling:\nWeb Site\nBrand Site\nProduct Pages
] --> Store
SEC[
US SEC\nFilings\n10-K\n10-Q
] --> Store
Wikipedia[Wiki\nCompany Pages\nIngrediens\nChemicals] --> Store
DuckDuckGo[Search\nDuckDuckGo] --> Store
Store --> Ingestion['Processing\nIngestion']
end
subgraph Ingest
Ingestion --> Transform <--> Extract[Extract:\n Nodes, Edges]
Transform <--> Structurize[Structurize:\n CSV, JSON, Markdown]
Transform --> Embeddings
Structurize --> Knowledge
end
subgraph Knowledge
Transform --> LLM
Extract <--> IndexGraph
Extract <--> IndexFact
LLM --> Structurize
end
RAG --> Retrievers
subgraph GraphDatabase
IndexGraph[Graph\nNodes\nLinks] --> Neo4j
RAG --> Neo4j
end
subgraph FactDatabase
IndexFact --> Sqlite
IndexFact --> Markdown
Markdown --> RAG
Sqlite --> RAG
end
subgraph VectorStores
Embeddings --> Faiss
Embeddings --> Chroma
Faiss --> RAG
Chroma --> RAG
end
subgraph UI
UIAI --> Query --> Retrievers --> Results --> Visualize
Query --> Visualize
end