update reproduce

README.md

@@ -149,7 +149,6 @@ Output your evaluation in the following JSON format:
 }}
 ```
 ### Overall Performance Table
-### Overall Performance Table
 |                      | **Agriculture**             |                       | **CS**                    |                       | **Legal**                 |                       | **Mix**                   |                       |
 |----------------------|-------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
 |                      | NaiveRAG                | **LightRAG**           | NaiveRAG              | **LightRAG**           | NaiveRAG              | **LightRAG**           | NaiveRAG              | **LightRAG**           |
@@ -173,6 +172,114 @@ Output your evaluation in the following JSON format:
 | **Empowerment**       | 36.69%                  | **63.31%**             | 45.09%                | **54.91%**             | 42.81%                | **57.19%**             | **52.94%**            | 47.06%                |
 | **Overall**           | 43.62%                  | **56.38%**             | 45.98%                | **54.02%**             | 45.70%                | **54.30%**             | **51.86%**            | 48.14%                |
 
+## Reproduce 
+All the code can be found in the `./reproduce` directory.
+### Step-0 Extract Unique Contexts
+First, we need to extract unique contexts in the datasets.
+```python
+def extract_unique_contexts(input_directory, output_directory):
+
+    os.makedirs(output_directory, exist_ok=True)
+
+    jsonl_files = glob.glob(os.path.join(input_directory, '*.jsonl'))
+    print(f"Found {len(jsonl_files)} JSONL files.")
+
+    for file_path in jsonl_files:
+        filename = os.path.basename(file_path)
+        name, ext = os.path.splitext(filename)
+        output_filename = f"{name}_unique_contexts.json"
+        output_path = os.path.join(output_directory, output_filename)
+
+        unique_contexts_dict = {}
+
+        print(f"Processing file: {filename}")
+
+        try:
+            with open(file_path, 'r', encoding='utf-8') as infile:
+                for line_number, line in enumerate(infile, start=1):
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        json_obj = json.loads(line)
+                        context = json_obj.get('context')
+                        if context and context not in unique_contexts_dict:
+                            unique_contexts_dict[context] = None
+                    except json.JSONDecodeError as e:
+                        print(f"JSON decoding error in file {filename} at line {line_number}: {e}")
+        except FileNotFoundError:
+            print(f"File not found: {filename}")
+            continue
+        except Exception as e:
+            print(f"An error occurred while processing file {filename}: {e}")
+            continue
+
+        unique_contexts_list = list(unique_contexts_dict.keys())
+        print(f"There are {len(unique_contexts_list)} unique `context` entries in the file {filename}.")
+
+        try:
+            with open(output_path, 'w', encoding='utf-8') as outfile:
+                json.dump(unique_contexts_list, outfile, ensure_ascii=False, indent=4)
+            print(f"Unique `context` entries have been saved to: {output_filename}")
+        except Exception as e:
+            print(f"An error occurred while saving to the file {output_filename}: {e}")
+
+    print("All files have been processed.")
+
+```
+### Step-1 Insert Contexts
+For the extracted contexts, we insert them into the LightRAG system.
+
+```python
+def insert_text(rag, file_path):
+    with open(file_path, mode='r') as f:
+        unique_contexts = json.load(f)
+    
+    retries = 0
+    max_retries = 3
+    while retries < max_retries:
+        try:
+            rag.insert(unique_contexts)
+            break
+        except Exception as e:
+            retries += 1
+            print(f"Insertion failed, retrying ({retries}/{max_retries}), error: {e}")
+            time.sleep(10)
+    if retries == max_retries:
+        print("Insertion failed after exceeding the maximum number of retries")
+```
+### Step-2 Generate Queries
+
+We extract tokens from both the first half and the second half of each context in the dataset, then combine them to generate queries for dataset descriptions.
+```python
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+
+def get_summary(context, tot_tokens=2000):
+    tokens = tokenizer.tokenize(context)
+    half_tokens = tot_tokens // 2
+
+    start_tokens = tokens[1000:1000 + half_tokens]
+    end_tokens = tokens[-(1000 + half_tokens):1000]
+
+    summary_tokens = start_tokens + end_tokens
+    summary = tokenizer.convert_tokens_to_string(summary_tokens)
+    
+    return summary
+```
+
+### Step-3 Query
+For the queries generated in Step-2, we will extract them and query LightRAG.
+```python
+def extract_queries(file_path):
+    with open(file_path, 'r') as f:
+        data = f.read()
+    
+    data = data.replace('**', '')
+
+    queries = re.findall(r'- Question \d+: (.+)', data)
+
+    return queries
+```
 ## Code Structure
 
 ```python
@@ -191,6 +298,11 @@ Output your evaluation in the following JSON format:
 │   ├── prompt.py
 │   ├── storage.py
 │   └── utils.jpeg
+├── reproduce
+│   ├── Step_0.py
+│   ├── Step_1.py
+│   ├── Step_2.py
+│   └── Step_3.py
 ├── LICENSE
 ├── README.md
 ├── requirements.txt