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paco-ravenpack merged 1 commit into from
Dec 18, 2025
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Adapt readme to version 1.0.0 #32

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29 changes: 15 additions & 14 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -79,20 +79,17 @@ pip install bigdata-research-tools
Install additional packages for specific features:

```bash
# For Excel export functionality
pip install bigdata-research-tools[excel]

# For visualization features
pip install bigdata-research-tools[plotly]

# For OpenAI integration, also via Azure
# For OpenAI integration
pip install bigdata-research-tools[openai]

# For Azure OpenAI integration
pip install bigdata-research-tools[azure]

# For AWS Bedrock integration
pip install bigdata-research-tools[bedrock]

# For all optional features
pip install bigdata-research-tools[excel,plotly,openai]
pip install bigdata-research-tools[azure,bedrock,openai]
```

---
Expand All @@ -104,6 +101,8 @@ pip install bigdata-research-tools[excel,plotly,openai]
Set up your credentials using environment variables:

```bash
export BIGDATA_API_KEY="your_api_key"
# or
export BIGDATA_USERNAME="your_username"
export BIGDATA_PASSWORD="your_password"
```
Expand All @@ -113,6 +112,8 @@ export BIGDATA_PASSWORD="your_password"
Create a `.env` file in your project directory:

```bash
BIGDATA_API_KEY="your_api_key"
# or
BIGDATA_USERNAME="your_username"
BIGDATA_PASSWORD="your_password"
```
Expand Down Expand Up @@ -170,7 +171,7 @@ watchlist = bigdata.watchlists.get("watchlist_id")
companies = bigdata.knowledge_graph.get_entities(watchlist.items)

screener = ThematicScreener(
llm_model="openai::gpt-4o-mini",
llm_model_config="openai::gpt-4o-mini",
main_theme="Electric Vehicles",
companies=companies,
start_date="2024-01-01",
Expand All @@ -191,7 +192,7 @@ Parameters to initialize the `ThematicScreener` class.

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `llm_model` | `str` | ✅ | LLM model identifier |
| `llm_model_config` | `str` | ✅ | LLM model identifier |
| `main_theme` | `str` | ✅ | Main theme to analyze |
| `companies` | `List[Company]` | ✅ | List of companies to screen (see [Company Objects](#company-objects)) |
| `start_date` | `str` | ✅ | Start date (YYYY-MM-DD) |
Expand Down Expand Up @@ -243,7 +244,7 @@ watchlist = bigdata.watchlists.get("watchlist_id")
companies = bigdata.knowledge_graph.get_entities(watchlist.items)

analyzer = RiskAnalyzer(
llm_model="openai::gpt-4o-mini",
llm_model_config="openai::gpt-4o-mini",
main_theme="Supply Chain Disruption",
companies=companies,
start_date="2024-01-01",
Expand All @@ -265,7 +266,7 @@ Parameters to initialize the `RiskAnalyzer` class.

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `llm_model` | `str` | ✅ | LLM model identifier |
| `llm_model_config` | `str` | ✅ | LLM model identifier |
| `main_theme` | `str` | ✅ | Main risk theme |
| `companies` | `List[Company]` | ✅ | Companies to analyze (see [Company Objects](#company-objects)) |
| `start_date` | `str` | ✅ | Analysis start date |
Expand Down Expand Up @@ -318,7 +319,7 @@ narrative_miner = NarrativeMiner(
"Machine Learning Innovation",
"Data Privacy Concerns"
],
llm_model="openai::gpt-4o-mini",
llm_model_config="openai::gpt-4o-mini",
start_date="2024-01-01",
end_date="2024-12-31",
fiscal_year=2024,
Expand All @@ -341,7 +342,7 @@ Parameters to initialize the `NarrativeMiner` class.
| `narrative_sentences` | `List[str]` | ✅ | List of narrative sentences to track |
| `start_date` | `str` | ✅ | Start date in YYYY-MM-DD format |
| `end_date` | `str` | ✅ | End date in YYYY-MM-DD format |
| `llm_model` | `str` | ✅ | LLM model in format "provider::model" |
| `llm_model_config` | `str` | ✅ | LLM model in format "provider::model" |
| `document_type` | `DocumentType` | ✅ | Document scope (see [Document Types](#document-types))|
| `fiscal_year` | `int` | ❌ | Fiscal year for transcripts/filings. Set to `None` for news |
| `sources` | `List[str]` | ❌ | Filter by specific news sources |
Expand Down