New asset for analyzing and talking to financial documents with graph…

ai/generative-ai-service/financial-analysis-chatbot/README.md

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+# 📊 Document Analysis with Graphs  
+A Streamlit-based application for extracting insights from financial documents by combining text and visual (chart/image) content using Oracle Generative AI.
+
+This tool enables semantic search, summarization, and financial Q&A by leveraging OCI GenAI services — providing rich context-aware answers grounded in both OCR-extracted text and chart images.
+
+Author: **Ali Ottoman**
+
+---
+
+## 🔧 Features
+
+### Multimodal Financial Document Processing
+- Upload PDFs or images of corporate financial documents.
+- Extract both **textual data** and **visual elements** (charts, tables, graphs).
+
+### Oracle GenAI-Powered Search & QA
+- Embed documents using **Cohere Embed v4.0** via OCI Generative AI.
+- Use **Llama 4 Maverick** to answer questions with visual + textual reasoning.
+- "Super Searcher" mode rewrites your query with **Command A** for enhanced semantic search.
+
+### Semantic Memory & Chat Interface
+- Context-aware responses based on prior conversation.
+- Semantic search across vectorized chunks using Qdrant.
+- Responses grounded in document context + image evidence.
+
+### Summary & Analytics View
+- Summarizes uploaded financial reports into key highlights.
+- Understand KPIs, trends, and performance across firm sizes and time periods.
+
+---
+
+## 👥 Who Can Use This
+
+**Finance & Strategy Teams**  
+→ Analyze trends, ratios, and balance sheet insights across time with chart references.
+
+**Business Analysts**  
+→ Automate exploration of complex PDF documents and balance sheets.
+
+**Developers & AI Engineers**  
+→ Explore multimodal document Q&A using OCI’s latest GenAI capabilities.
+
+**Anyone using OCI AI Services**  
+→ Seamlessly integrate this workflow into larger OCI-based analytics pipelines.
+
+---
+
+## 🗂️ Files & Structure
+
+```
+.  
+├── doc_analysis_with_graphs.py  # Main Streamlit app  
+├── config.py                    # OCI config & model IDs (user-provided)  
+├── requirements.txt            # Python dependencies  
+└── README.md                   # You're reading it
+```
+
+---
+
+## ⚙️ Setup & Installation
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/your-username/your-repo.git
+cd your-repo
+```
+
+### 2. Configure OCI Credentials
+
+Fill out the `config.py` file:
+
+```python
+# config.py
+COMPARTMENT_ID = "<your OCI Compartment OCID>"
+```
+
+Ensure you also have an OCI config file (usually at `~/.oci/config`) with proper credentials.
+
+### 3. Install Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## 🚀 Run the App
+
+```bash
+streamlit run doc_analysis_with_graphs.py
+```
+
+---
+
+## 📝 How to Use
+
+### 1. Upload your documents  
+→ PDFs or images containing **financial reports, charts, balance sheets**
+
+### 2. Ask your question  
+→ Examples:
+- “What is the change in ROA from 1990 to 2000?”
+- “Summarize the key liquidity trends in small firms.”
+- “Explain the data in Chart 2a on debt ratios.”
+
+### 3. View responses  
+→ AI replies with:
+- Financially-grounded insights  
+- Visual chart references (axes, values)  
+- Source document images  
+- NULL if data is unavailable
+
+---
+
+## 🛠️ Customization
+
+- **Enable/disable Super Searcher** to use Command-A for rephrased queries.
+- **Change model temperature or token limits** in `ChatOCIGenAI` constructor.
+- **Add custom logic** to extend analysis for ratios, ROE, gearing, sector comparison, etc.
+
+---
+
+## 🧠 Example Chat
+
+> **You**: What is the debt-to-assets ratio trend from 1990 to 2000?  
+>  
+> **AI**:
+> - Debt-to-assets ratio declined from **47% in 1990** to **39% in 2000**.  
+> - As per **Chart 1a**, small firms saw the sharpest drop post-1997.  
+> - The Y-axis shows the ratio (%) and the X-axis is the year.
+
+---
+
+## 🔧 OCI Services Used
+
+### 1. **OCI Generative AI – Embeddings**
+- Used for vector search on document content.
+```python
+from langchain_community.embeddings.oci_generative_ai import OCIGenAIEmbeddings
+```
+
+### 2. **OCI Generative AI – LLM (Llama 4 Maverick)**
+- Used to extract structured insights from text + images.
+```python
+from langchain_community.chat_models.oci_generative_ai import ChatOCIGenAI
+```
+
+---
+
+## 🔗 Docs & References
+
+- 📘 [OCI Generative AI Overview](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)  
+- 📘 [OCI Document Understanding](https://docs.oracle.com/en-us/iaas/Content/document-understanding/using/home.htm)  
+
+---
+
+## 📄 License
+
+MIT License — see [LICENSE](LICENSE) for details.

ai/generative-ai-service/financial-analysis-chatbot/config.py

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+"""
+config file
+
+- Add the OCID of your compartment
+"""
+
+COMPARTMENT_ID = ""
+
+OTHER_MODELS = [
+    "meta.llama-4-maverick-17b-128e-instruct-fp8",
+    "meta.llama-4-scout-17b-16e-instruct"]
+
+MODEL_ID = "cohere.command-a-03-2025"