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Update README.md
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README.md
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README.md
@ -441,11 +441,15 @@ if __name__ == "__main__":
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- [Direct OpenAI Example](examples/lightrag_llamaindex_direct_demo.py)
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- [LiteLLM Proxy Example](examples/lightrag_llamaindex_litellm_demo.py)
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</details>
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### Conversation History Support
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LightRAG now supports multi-turn dialogue through the conversation history feature. Here's how to use it:
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<details>
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```python
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# Create conversation history
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conversation_history = [
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@ -506,6 +510,8 @@ response_custom = rag.query(
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print(response_custom)
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```
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</details>
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### Separate Keyword Extraction
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We've introduced a new function `query_with_separate_keyword_extraction` to enhance the keyword extraction capabilities. This function separates the keyword extraction process from the user's prompt, focusing solely on the query to improve the relevance of extracted keywords.
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@ -519,7 +525,8 @@ The function operates by dividing the input into two parts:
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It then performs keyword extraction exclusively on the `user query`. This separation ensures that the extraction process is focused and relevant, unaffected by any additional language in the `prompt`. It also allows the `prompt` to serve purely for response formatting, maintaining the intent and clarity of the user's original question.
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**Usage Example**
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<details>
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<summary> <b> Usage Example </b></summary>
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This `example` shows how to tailor the function for educational content, focusing on detailed explanations for older students.
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@ -531,67 +538,6 @@ rag.query_with_separate_keyword_extraction(
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)
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```
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### Insert Custom KG
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```python
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custom_kg = {
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"chunks": [
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{
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"content": "Alice and Bob are collaborating on quantum computing research.",
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"source_id": "doc-1"
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}
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],
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"entities": [
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{
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"entity_name": "Alice",
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"entity_type": "person",
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"description": "Alice is a researcher specializing in quantum physics.",
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"source_id": "doc-1"
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},
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{
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"entity_name": "Bob",
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"entity_type": "person",
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"description": "Bob is a mathematician.",
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"source_id": "doc-1"
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},
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{
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"entity_name": "Quantum Computing",
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"entity_type": "technology",
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"description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
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"source_id": "doc-1"
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}
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],
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"relationships": [
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{
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"src_id": "Alice",
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"tgt_id": "Bob",
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"description": "Alice and Bob are research partners.",
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"keywords": "collaboration research",
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"weight": 1.0,
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"source_id": "doc-1"
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},
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{
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"src_id": "Alice",
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"tgt_id": "Quantum Computing",
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"description": "Alice conducts research on quantum computing.",
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"keywords": "research expertise",
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"weight": 1.0,
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"source_id": "doc-1"
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},
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{
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"src_id": "Bob",
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"tgt_id": "Quantum Computing",
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"description": "Bob researches quantum computing.",
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"keywords": "research application",
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"weight": 1.0,
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"source_id": "doc-1"
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}
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]
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}
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rag.insert_custom_kg(custom_kg)
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```
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</details>
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## Insert
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@ -683,6 +629,70 @@ rag.insert(text_content.decode('utf-8'))
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</details>
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<details>
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<summary> <b> Insert Custom KG </b></summary>
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```python
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custom_kg = {
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"chunks": [
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{
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"content": "Alice and Bob are collaborating on quantum computing research.",
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"source_id": "doc-1"
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}
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],
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"entities": [
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{
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"entity_name": "Alice",
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"entity_type": "person",
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"description": "Alice is a researcher specializing in quantum physics.",
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"source_id": "doc-1"
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},
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{
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"entity_name": "Bob",
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"entity_type": "person",
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"description": "Bob is a mathematician.",
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"source_id": "doc-1"
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},
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{
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"entity_name": "Quantum Computing",
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"entity_type": "technology",
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"description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
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"source_id": "doc-1"
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}
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],
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"relationships": [
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{
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"src_id": "Alice",
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"tgt_id": "Bob",
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"description": "Alice and Bob are research partners.",
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"keywords": "collaboration research",
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"weight": 1.0,
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"source_id": "doc-1"
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},
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{
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"src_id": "Alice",
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"tgt_id": "Quantum Computing",
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"description": "Alice conducts research on quantum computing.",
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"keywords": "research expertise",
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"weight": 1.0,
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"source_id": "doc-1"
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},
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{
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"src_id": "Bob",
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"tgt_id": "Quantum Computing",
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"description": "Bob researches quantum computing.",
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"keywords": "research application",
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"weight": 1.0,
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"source_id": "doc-1"
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}
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]
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}
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rag.insert_custom_kg(custom_kg)
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```
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</details>
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<details>
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<summary><b>Citation Functionality</b></summary>
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@ -842,7 +852,8 @@ rag.delete_by_doc_id("doc_id")
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LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph.
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### Create Entities and Relations
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<details>
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<summary> <b> Create Entities and Relations </b></summary>
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```python
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# Create new entity
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@ -865,7 +876,10 @@ relation = rag.create_relation("Google", "Gmail", {
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})
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```
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### Edit Entities and Relations
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</details>
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<details>
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<summary> <b> Edit Entities and Relations </b></summary>
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```python
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# Edit an existing entity
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@ -902,6 +916,8 @@ All operations are available in both synchronous and asynchronous versions. The
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These operations maintain data consistency across both the graph database and vector database components, ensuring your knowledge graph remains coherent.
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</details>
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## Data Export Functions
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### Overview
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@ -910,7 +926,8 @@ LightRAG allows you to export your knowledge graph data in various formats for a
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### Export Functions
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#### Basic Usage
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<details>
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<summary> <b> Basic Usage </b></summary>
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```python
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# Basic CSV export (default format)
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@ -920,7 +937,10 @@ rag.export_data("knowledge_graph.csv")
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rag.export_data("output.xlsx", file_format="excel")
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```
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#### Different File Formats supported
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</details>
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<details>
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<summary> <b> Different File Formats supported </b></summary>
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```python
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#Export data in CSV format
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@ -935,13 +955,18 @@ rag.export_data("graph_data.md", file_format="md")
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# Export data in Text
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rag.export_data("graph_data.txt", file_format="txt")
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```
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#### Additional Options
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</details>
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<details>
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<summary> <b> Additional Options </b></summary>
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Include vector embeddings in the export (optional):
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```python
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rag.export_data("complete_data.csv", include_vector_data=True)
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```
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</details>
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### Data Included in Export
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All exports include:
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