cjber commited on
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c0faedc
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1 Parent(s): e7ff63b

fix installation instructions

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  1. README.md +4 -4
README.md CHANGED
@@ -20,9 +20,9 @@ Planning AI is a sophisticated tool designed to process and analyze responses to
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  To set up the project, ensure you have Python 3.8 or higher installed. Then, clone the repository and install the required dependencies:
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  ```bash
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- git clone <repository-url>
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  cd planning_ai
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- pip install -r requirements.txt
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  ```
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  ## Usage
@@ -34,7 +34,7 @@ pip install -r requirements.txt
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  python planning_ai/preprocessing/web_comments.py
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  ```
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- 2. **Run Analysis**: Execute the main script to process the documents and generate summaries.
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  ```bash
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  python planning_ai/main.py
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  ```
@@ -44,7 +44,7 @@ pip install -r requirements.txt
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  1. **Data Loading**: Documents are loaded from the staging directory using the `DirectoryLoader`.
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  2. **Text Splitting**: Documents are split into manageable chunks using `CharacterTextSplitter`.
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  3. **Graph Processing**: The `StateGraph` orchestrates the flow of data through various nodes, including mapping and reducing summaries.
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- 4. **Summarization**: The `map_chain` and `reduce_chain` are used to generate and refine summaries.
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  5. **Output**: Final summaries and thematic breakdowns are printed and can be exported for further analysis.
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  ## Configuration
 
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  To set up the project, ensure you have Python 3.8 or higher installed. Then, clone the repository and install the required dependencies:
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  ```bash
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+ git clone https://github.com/cjber/planning-ai.git
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  cd planning_ai
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+ pip install . (or uv sync)
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  ```
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  ## Usage
 
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  python planning_ai/preprocessing/web_comments.py
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  ```
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+ 2. **Run Graph**: Execute the main script to process the documents and generate a Quarto summary document.
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  ```bash
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  python planning_ai/main.py
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  ```
 
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  1. **Data Loading**: Documents are loaded from the staging directory using the `DirectoryLoader`.
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  2. **Text Splitting**: Documents are split into manageable chunks using `CharacterTextSplitter`.
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  3. **Graph Processing**: The `StateGraph` orchestrates the flow of data through various nodes, including mapping and reducing summaries.
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+ 4. **Summarisation**: The `map_chain` and `reduce_chain` are used to generate and refine summaries.
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  5. **Output**: Final summaries and thematic breakdowns are printed and can be exported for further analysis.
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  ## Configuration