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updated docs

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Yannick Stephan
2025-02-19 09:49:35 +01:00
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README.md
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@@ -34,8 +34,14 @@ This repository hosts the code of LightRAG. The structure of this code is based
<img src="https://i-blog.csdnimg.cn/direct/b2aaf634151b4706892693ffb43d9093.png" width="800" alt="LightRAG Diagram">
</div>
</div>
</br>
<details>
<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
🎉 News
</summary>
## 🎉 News
- [x] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
- [x] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
@@ -54,12 +60,20 @@ This repository hosts the code of LightRAG. The structure of this code is based
- [x] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
- [x] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
## Algorithm Flowchart
</details>
<details>
<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
Algorithm Flowchart
</summary>
![LightRAG Indexing Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-VectorDB-Json-KV-Store-Indexing-Flowchart-scaled.jpg)
*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
![LightRAG Retrieval and Querying Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-Querying-Flowchart-Dual-Level-Retrieval-Generation-Knowledge-Graphs-scaled.jpg)
*Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
</details>
## Install
* Install from source (Recommend)
@@ -81,6 +95,9 @@ pip install lightrag-hku
```bash
curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
```
## Query
Use the below Python snippet (in a script) to initialize LightRAG and perform queries:
```python
@@ -88,124 +105,67 @@ import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
#########
# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
# import nest_asyncio
# nest_asyncio.apply()
#########
WORKING_DIR = "./dickens"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
rag = LightRAG(
working_dir=WORKING_DIR,
working_dir="your/path",
embedding_func=openai_embed,
llm_model_func=gpt_4o_mini_complete # Use gpt_4o_mini_complete LLM model
# llm_model_func=gpt_4o_complete # Optionally, use a stronger model
llm_model_func=gpt_4o_mini_complete
)
with open("./book.txt") as f:
rag.insert(f.read())
# Insert text
rag.insert("Your text")
# Perform naive search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
mode="naive"
# Perform local search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
mode="local"
# Perform global search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
mode="global"
# Perform hybrid search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))
mode="hybrid"
# Mix mode Integrates knowledge graph and vector retrieval.
mode="mix"
# Perform mix search (Knowledge Graph + Vector Retrieval)
# Mix mode combines knowledge graph and vector search:
# - Uses both structured (KG) and unstructured (vector) information
# - Provides comprehensive answers by analyzing relationships and context
# - Supports image content through HTML img tags
# - Allows control over retrieval depth via top_k parameter
print(rag.query("What are the top themes in this story?", param=QueryParam(
mode="mix")))
rag.query(
"What are the top themes in this story?",
param=QueryParam(mode=mode)
)
```
### Conversation History Support
LightRAG now supports multi-turn dialogue through the conversation history feature. Here's how to use it:
### Query Param
```python
from lightrag import LightRAG, QueryParam
# Initialize LightRAG
rag = LightRAG(working_dir=WORKING_DIR)
# Create conversation history
conversation_history = [
{"role": "user", "content": "What is the main character's attitude towards Christmas?"},
{"role": "assistant", "content": "At the beginning of the story, Ebenezer Scrooge has a very negative attitude towards Christmas..."},
{"role": "user", "content": "How does his attitude change?"}
]
# Create query parameters with conversation history
query_param = QueryParam(
mode="mix", # or any other mode: "local", "global", "hybrid"
conversation_history=conversation_history, # Add the conversation history
history_turns=3 # Number of recent conversation turns to consider
)
# Make a query that takes into account the conversation history
response = rag.query(
"What causes this change in his character?",
param=query_param
)
```
### Custom Prompt Support
LightRAG now supports custom prompts for fine-tuned control over the system's behavior. Here's how to use it:
```python
from lightrag import LightRAG, QueryParam
# Initialize LightRAG
rag = LightRAG(working_dir=WORKING_DIR)
# Create query parameters
query_param = QueryParam(
mode="hybrid", # or other mode: "local", "global", "hybrid", "mix" and "naive"
)
# Example 1: Using the default system prompt
response_default = rag.query(
"What are the primary benefits of renewable energy?",
param=query_param
)
print(response_default)
# Example 2: Using a custom prompt
custom_prompt = """
You are an expert assistant in environmental science. Provide detailed and structured answers with examples.
---Conversation History---
{history}
---Knowledge Base---
{context_data}
---Response Rules---
- Target format and length: {response_type}
"""
response_custom = rag.query(
"What are the primary benefits of renewable energy?",
param=query_param,
system_prompt=custom_prompt # Pass the custom prompt
)
print(response_custom)
class QueryParam:
mode: Literal["local", "global", "hybrid", "naive", "mix"] = "global"
"""Specifies the retrieval mode:
- "local": Focuses on context-dependent information.
- "global": Utilizes global knowledge.
- "hybrid": Combines local and global retrieval methods.
- "naive": Performs a basic search without advanced techniques.
- "mix": Integrates knowledge graph and vector retrieval. Mix mode combines knowledge graph and vector search:
- Uses both structured (KG) and unstructured (vector) information
- Provides comprehensive answers by analyzing relationships and context
- Supports image content through HTML img tags
- Allows control over retrieval depth via top_k parameter
"""
only_need_context: bool = False
"""If True, only returns the retrieved context without generating a response."""
response_type: str = "Multiple Paragraphs"
"""Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'."""
top_k: int = 60
"""Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
max_token_for_text_unit: int = 4000
"""Maximum number of tokens allowed for each retrieved text chunk."""
max_token_for_global_context: int = 4000
"""Maximum number of tokens allocated for relationship descriptions in global retrieval."""
max_token_for_local_context: int = 4000
"""Maximum number of tokens allocated for entity descriptions in local retrieval."""
...
```
> default value of Top_k can be change by environment variables TOP_K.
<details>
<summary> Using Open AI-like APIs </summary>
<summary> <b>Using Open AI-like APIs</b> </summary>
* LightRAG also supports Open AI-like chat/embeddings APIs:
```python
@@ -243,7 +203,7 @@ rag = LightRAG(
</details>
<details>
<summary> Using Hugging Face Models </summary>
<summary> <b>Using Hugging Face Models</b> </summary>
* If you want to use Hugging Face models, you only need to set LightRAG as follows:
@@ -274,7 +234,7 @@ rag = LightRAG(
</details>
<details>
<summary> Using Ollama Models </summary>
<summary> <b>Using Ollama Models</b> </summary>
### Overview
If you want to use Ollama models, you need to pull model you plan to use and embedding model, for example `nomic-embed-text`.
@@ -353,90 +313,89 @@ In order to run this experiment on low RAM GPU you should select small model and
</details>
### Query Param
<details>
<summary> <b>Conversation History Support</b> </summary>
LightRAG now supports multi-turn dialogue through the conversation history feature. Here's how to use it:
```python
class QueryParam:
mode: Literal["local", "global", "hybrid", "naive", "mix"] = "global"
"""Specifies the retrieval mode:
- "local": Focuses on context-dependent information.
- "global": Utilizes global knowledge.
- "hybrid": Combines local and global retrieval methods.
- "naive": Performs a basic search without advanced techniques.
- "mix": Integrates knowledge graph and vector retrieval.
"""
only_need_context: bool = False
"""If True, only returns the retrieved context without generating a response."""
response_type: str = "Multiple Paragraphs"
"""Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'."""
top_k: int = 60
"""Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
max_token_for_text_unit: int = 4000
"""Maximum number of tokens allowed for each retrieved text chunk."""
max_token_for_global_context: int = 4000
"""Maximum number of tokens allocated for relationship descriptions in global retrieval."""
max_token_for_local_context: int = 4000
"""Maximum number of tokens allocated for entity descriptions in local retrieval."""
...
```
from lightrag import LightRAG, QueryParam
> default value of Top_k can be change by environment variables TOP_K.
# Initialize LightRAG
rag = LightRAG(working_dir=WORKING_DIR)
### Batch Insert
# Create conversation history
conversation_history = [
{"role": "user", "content": "What is the main character's attitude towards Christmas?"},
{"role": "assistant", "content": "At the beginning of the story, Ebenezer Scrooge has a very negative attitude towards Christmas..."},
{"role": "user", "content": "How does his attitude change?"}
]
```python
# Basic Batch Insert: Insert multiple texts at once
rag.insert(["TEXT1", "TEXT2",...])
# Batch Insert with custom batch size configuration
rag = LightRAG(
working_dir=WORKING_DIR,
addon_params={
"insert_batch_size": 20 # Process 20 documents per batch
}
)
rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in batches of 20
```
The `insert_batch_size` parameter in `addon_params` controls how many documents are processed in each batch during insertion. This is useful for:
- Managing memory usage with large document collections
- Optimizing processing speed
- Providing better progress tracking
- Default value is 10 if not specified
### Incremental Insert
```python
# Incremental Insert: Insert new documents into an existing LightRAG instance
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=8192,
func=embedding_func,
),
# Create query parameters with conversation history
query_param = QueryParam(
mode="mix", # or any other mode: "local", "global", "hybrid"
conversation_history=conversation_history, # Add the conversation history
history_turns=3 # Number of recent conversation turns to consider
)
with open("./newText.txt") as f:
rag.insert(f.read())
# Make a query that takes into account the conversation history
response = rag.query(
"What causes this change in his character?",
param=query_param
)
```
### Insert using Pipeline
The `apipeline_enqueue_documents` and `apipeline_process_enqueue_documents` functions allow you to perform incremental insertion of documents into the graph.
</details>
This is useful for scenarios where you want to process documents in the background while still allowing the main thread to continue executing.
<details>
<summary> <b>Custom Prompt Support</b> </summary>
And using a routine to process news documents.
LightRAG now supports custom prompts for fine-tuned control over the system's behavior. Here's how to use it:
```python
rag = LightRAG(..)
await rag.apipeline_enqueue_documents(input)
# Your routine in loop
await rag.apipeline_process_enqueue_documents(input)
from lightrag import LightRAG, QueryParam
# Initialize LightRAG
rag = LightRAG(working_dir=WORKING_DIR)
# Create query parameters
query_param = QueryParam(
mode="hybrid", # or other mode: "local", "global", "hybrid", "mix" and "naive"
)
# Example 1: Using the default system prompt
response_default = rag.query(
"What are the primary benefits of renewable energy?",
param=query_param
)
print(response_default)
# Example 2: Using a custom prompt
custom_prompt = """
You are an expert assistant in environmental science. Provide detailed and structured answers with examples.
---Conversation History---
{history}
---Knowledge Base---
{context_data}
---Response Rules---
- Target format and length: {response_type}
"""
response_custom = rag.query(
"What are the primary benefits of renewable energy?",
param=query_param,
system_prompt=custom_prompt # Pass the custom prompt
)
print(response_custom)
```
### Separate Keyword Extraction
</details>
<details>
<summary> <b>Separate Keyword Extraction</b> </summary>
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.
##### How It Works?
@@ -457,110 +416,10 @@ rag.query_with_separate_keyword_extraction(
)
```
### Using Neo4J for Storage
</details>
* For production level scenarios you will most likely want to leverage an enterprise solution
* for KG storage. Running Neo4J in Docker is recommended for seamless local testing.
* See: https://hub.docker.com/_/neo4j
```python
export NEO4J_URI="neo4j://localhost:7687"
export NEO4J_USERNAME="neo4j"
export NEO4J_PASSWORD="password"
# When you launch the project be sure to override the default KG: NetworkX
# by specifying kg="Neo4JStorage".
# Note: Default settings use NetworkX
# Initialize LightRAG with Neo4J implementation.
WORKING_DIR = "./local_neo4jWorkDir"
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model
graph_storage="Neo4JStorage", #<-----------override KG default
log_level="DEBUG" #<-----------override log_level default
)
```
see test_neo4j.py for a working example.
### Using PostgreSQL for Storage
For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE).
* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
* Create index for AGE example: (Change below `dickens` to your graph name if necessary)
```sql
load 'age';
SET search_path = ag_catalog, "$user", public;
CREATE INDEX CONCURRENTLY entity_p_idx ON dickens."Entity" (id);
CREATE INDEX CONCURRENTLY vertex_p_idx ON dickens."_ag_label_vertex" (id);
CREATE INDEX CONCURRENTLY directed_p_idx ON dickens."DIRECTED" (id);
CREATE INDEX CONCURRENTLY directed_eid_idx ON dickens."DIRECTED" (end_id);
CREATE INDEX CONCURRENTLY directed_sid_idx ON dickens."DIRECTED" (start_id);
CREATE INDEX CONCURRENTLY directed_seid_idx ON dickens."DIRECTED" (start_id,end_id);
CREATE INDEX CONCURRENTLY edge_p_idx ON dickens."_ag_label_edge" (id);
CREATE INDEX CONCURRENTLY edge_sid_idx ON dickens."_ag_label_edge" (start_id);
CREATE INDEX CONCURRENTLY edge_eid_idx ON dickens."_ag_label_edge" (end_id);
CREATE INDEX CONCURRENTLY edge_seid_idx ON dickens."_ag_label_edge" (start_id,end_id);
create INDEX CONCURRENTLY vertex_idx_node_id ON dickens."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
-- drop if necessary
drop INDEX entity_p_idx;
drop INDEX vertex_p_idx;
drop INDEX directed_p_idx;
drop INDEX directed_eid_idx;
drop INDEX directed_sid_idx;
drop INDEX directed_seid_idx;
drop INDEX edge_p_idx;
drop INDEX edge_sid_idx;
drop INDEX edge_eid_idx;
drop INDEX edge_seid_idx;
drop INDEX vertex_idx_node_id;
drop INDEX entity_idx_node_id;
drop INDEX entity_node_id_gin_idx;
```
* Known issue of the Apache AGE: The released versions got below issue:
> You might find that the properties of the nodes/edges are empty.
> It is a known issue of the release version: https://github.com/apache/age/pull/1721
>
> You can Compile the AGE from source code and fix it.
### Using Faiss for Storage
- Install the required dependencies:
```
pip install faiss-cpu
```
You can also install `faiss-gpu` if you have GPU support.
- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
```
async def embedding_func(texts: list[str]) -> np.ndarray:
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(texts, convert_to_numpy=True)
return embeddings
# Initialize LightRAG with the LLM model function and embedding function
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=384,
max_token_size=8192,
func=embedding_func,
),
vector_storage="FaissVectorDBStorage",
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": 0.3 # Your desired threshold
}
)
```
### Insert Custom KG
<details>
<summary> <b>Insert Custom KG</b> </summary>
```python
rag = LightRAG(
@@ -616,6 +475,213 @@ custom_kg = {
rag.insert_custom_kg(custom_kg)
```
</details>
## Insert
#### Basic Insert
```python
# Basic Insert
rag.insert("Text")
```
<details>
<summary> <b> Batch Insert </b></summary>
```python
# Basic Batch Insert: Insert multiple texts at once
rag.insert(["TEXT1", "TEXT2",...])
# Batch Insert with custom batch size configuration
rag = LightRAG(
working_dir=WORKING_DIR,
addon_params={
"insert_batch_size": 20 # Process 20 documents per batch
}
)
rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in batches of 20
```
The `insert_batch_size` parameter in `addon_params` controls how many documents are processed in each batch during insertion. This is useful for:
- Managing memory usage with large document collections
- Optimizing processing speed
- Providing better progress tracking
- Default value is 10 if not specified
</details>
<details>
<summary><b>Incremental Insert</b></summary>
```python
# Incremental Insert: Insert new documents into an existing LightRAG instance
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=8192,
func=embedding_func,
),
)
with open("./newText.txt") as f:
rag.insert(f.read())
```
</details>
<details>
<summary><b>Insert using Pipeline</b></summary>
The `apipeline_enqueue_documents` and `apipeline_process_enqueue_documents` functions allow you to perform incremental insertion of documents into the graph.
This is useful for scenarios where you want to process documents in the background while still allowing the main thread to continue executing.
And using a routine to process news documents.
```python
rag = LightRAG(..)
await rag.apipeline_enqueue_documents(input)
# Your routine in loop
await rag.apipeline_process_enqueue_documents(input)
```
</details>
<details>
<summary><b>Insert Multi-file Type Support</b></summary>
The `textract` supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF.
```python
import textract
file_path = 'TEXT.pdf'
text_content = textract.process(file_path)
rag.insert(text_content.decode('utf-8'))
```
</details>
### Storage
<details>
<summary> <b>Using Neo4J for Storage</b> </summary>
* For production level scenarios you will most likely want to leverage an enterprise solution
* for KG storage. Running Neo4J in Docker is recommended for seamless local testing.
* See: https://hub.docker.com/_/neo4j
```python
export NEO4J_URI="neo4j://localhost:7687"
export NEO4J_USERNAME="neo4j"
export NEO4J_PASSWORD="password"
# When you launch the project be sure to override the default KG: NetworkX
# by specifying kg="Neo4JStorage".
# Note: Default settings use NetworkX
# Initialize LightRAG with Neo4J implementation.
WORKING_DIR = "./local_neo4jWorkDir"
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model
graph_storage="Neo4JStorage", #<-----------override KG default
log_level="DEBUG" #<-----------override log_level default
)
```
see test_neo4j.py for a working example.
</details>
<details>
<summary> <b>Using PostgreSQL for Storage</b> </summary>
For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE).
* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
* Create index for AGE example: (Change below `dickens` to your graph name if necessary)
```sql
load 'age';
SET search_path = ag_catalog, "$user", public;
CREATE INDEX CONCURRENTLY entity_p_idx ON dickens."Entity" (id);
CREATE INDEX CONCURRENTLY vertex_p_idx ON dickens."_ag_label_vertex" (id);
CREATE INDEX CONCURRENTLY directed_p_idx ON dickens."DIRECTED" (id);
CREATE INDEX CONCURRENTLY directed_eid_idx ON dickens."DIRECTED" (end_id);
CREATE INDEX CONCURRENTLY directed_sid_idx ON dickens."DIRECTED" (start_id);
CREATE INDEX CONCURRENTLY directed_seid_idx ON dickens."DIRECTED" (start_id,end_id);
CREATE INDEX CONCURRENTLY edge_p_idx ON dickens."_ag_label_edge" (id);
CREATE INDEX CONCURRENTLY edge_sid_idx ON dickens."_ag_label_edge" (start_id);
CREATE INDEX CONCURRENTLY edge_eid_idx ON dickens."_ag_label_edge" (end_id);
CREATE INDEX CONCURRENTLY edge_seid_idx ON dickens."_ag_label_edge" (start_id,end_id);
create INDEX CONCURRENTLY vertex_idx_node_id ON dickens."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
-- drop if necessary
drop INDEX entity_p_idx;
drop INDEX vertex_p_idx;
drop INDEX directed_p_idx;
drop INDEX directed_eid_idx;
drop INDEX directed_sid_idx;
drop INDEX directed_seid_idx;
drop INDEX edge_p_idx;
drop INDEX edge_sid_idx;
drop INDEX edge_eid_idx;
drop INDEX edge_seid_idx;
drop INDEX vertex_idx_node_id;
drop INDEX entity_idx_node_id;
drop INDEX entity_node_id_gin_idx;
```
* Known issue of the Apache AGE: The released versions got below issue:
> You might find that the properties of the nodes/edges are empty.
> It is a known issue of the release version: https://github.com/apache/age/pull/1721
>
> You can Compile the AGE from source code and fix it.
</details>
<details>
<summary> <b>Using Faiss for Storage</b> </summary>
- Install the required dependencies:
```
pip install faiss-cpu
```
You can also install `faiss-gpu` if you have GPU support.
- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
```
async def embedding_func(texts: list[str]) -> np.ndarray:
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(texts, convert_to_numpy=True)
return embeddings
# Initialize LightRAG with the LLM model function and embedding function
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=384,
max_token_size=8192,
func=embedding_func,
),
vector_storage="FaissVectorDBStorage",
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": 0.3 # Your desired threshold
}
)
```
</details>
### Delete
```python
@@ -637,18 +703,6 @@ rag.delete_by_entity("Project Gutenberg")
rag.delete_by_doc_id("doc_id")
```
### Multi-file Type Support
The `textract` supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF.
```python
import textract
file_path = 'TEXT.pdf'
text_content = textract.process(file_path)
rag.insert(text_content.decode('utf-8'))
```
### Graph Visualization
@@ -806,6 +860,9 @@ if __name__ == "__main__":
### LightRAG init parameters
<details>
<summary> Parameters </summary>
| **Parameter** | **Type** | **Explanation** | **Default** |
|----------------------------------------------| --- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
| **working\_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
@@ -836,6 +893,8 @@ if __name__ == "__main__":
| **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
|**log\_dir** | `str` | Directory to store logs. | `./` |
</details>
### Error Handling
<details>

12
contributor-README.md
View File

@@ -1,12 +0,0 @@
# Handy Tips for Developers Who Want to Contribute to the Project
## Pre-commit Hooks
Please ensure you have run pre-commit hooks before committing your changes.
### Guides
1. **Installing Pre-commit Hooks**:
- Install pre-commit using pip: `pip install pre-commit`
- Initialize pre-commit in your repository: `pre-commit install`
- Run pre-commit hooks: `pre-commit run --all-files`
2. **Pre-commit Hooks Configuration**:
- Create a `.pre-commit-config.yaml` file in the root of your repository.
- Add your hooks to the `.pre-commit-config.yaml`file.