Merge pull request #325 from jin38324/main

Enhance Query Logic and Add Configurable Features

.gitignore

@@ -12,3 +12,5 @@ ignore_this.txt
 .venv/
 *.ignore.*
 .ruff_cache/
+gui/
+*.log

README.md

@@ -555,6 +555,35 @@ if __name__ == "__main__":
 
 </details>
 
+### LightRAG init parameters
+
+| **Parameter** | **Type** | **Explanation** | **Default** |
+| --- | --- | --- | --- |
+| **working\_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
+| **kv\_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
+| **vector\_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
+| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
+| **log\_level** |     | Log level for application runtime | `logging.DEBUG` |
+| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
+| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
+| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
+| **entity\_extract\_max\_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
+| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
+| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
+| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
+| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embedding` |
+| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
+| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
+| **llm\_model\_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` |
+| **llm\_model\_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` |
+| **llm\_model\_max\_token\_size** | `int` | Maximum token size for LLM generation (affects entity relation summaries) | `32768` |
+| **llm\_model\_max\_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `16` |
+| **llm\_model\_kwargs** | `dict` | Additional parameters for LLM generation |     |
+| **vector\_db\_storage\_cls\_kwargs** | `dict` | Additional parameters for vector database (currently not used) |     |
+| **enable\_llm\_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
+| **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese"}`: sets example limit and output language | `example_number: all examples, language: English` |
+| **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
+
 ## API Server Implementation
 
 LightRAG also provides a FastAPI-based server implementation for RESTful API access to RAG operations. This allows you to run LightRAG as a service and interact with it through HTTP requests.

examples/insert_custom_kg.py

@@ -1,5 +1,5 @@
 import os
-from lightrag import LightRAG, QueryParam
+from lightrag import LightRAG
 from lightrag.llm import gpt_4o_mini_complete
 #########
 # Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
@@ -24,50 +24,50 @@ custom_kg = {
             "entity_name": "CompanyA",
             "entity_type": "Organization",
             "description": "A major technology company",
-            "source_id": "Source1"
+            "source_id": "Source1",
         },
         {
             "entity_name": "ProductX",
             "entity_type": "Product",
             "description": "A popular product developed by CompanyA",
-            "source_id": "Source1"
+            "source_id": "Source1",
         },
         {
             "entity_name": "PersonA",
             "entity_type": "Person",
             "description": "A renowned researcher in AI",
-            "source_id": "Source2"
+            "source_id": "Source2",
         },
         {
             "entity_name": "UniversityB",
             "entity_type": "Organization",
             "description": "A leading university specializing in technology and sciences",
-            "source_id": "Source2"
+            "source_id": "Source2",
         },
         {
             "entity_name": "CityC",
             "entity_type": "Location",
             "description": "A large metropolitan city known for its culture and economy",
-            "source_id": "Source3"
+            "source_id": "Source3",
         },
         {
             "entity_name": "EventY",
             "entity_type": "Event",
             "description": "An annual technology conference held in CityC",
-            "source_id": "Source3"
+            "source_id": "Source3",
         },
         {
             "entity_name": "CompanyD",
             "entity_type": "Organization",
             "description": "A financial services company specializing in insurance",
-            "source_id": "Source4"
+            "source_id": "Source4",
         },
         {
             "entity_name": "ServiceZ",
             "entity_type": "Service",
             "description": "An insurance product offered by CompanyD",
-            "source_id": "Source4"
+            "source_id": "Source4",
-        }
+        },
     ],
     "relationships": [
         {
@@ -76,7 +76,7 @@ custom_kg = {
             "description": "CompanyA develops ProductX",
             "keywords": "develop, produce",
             "weight": 1.0,
-            "source_id": "Source1"
+            "source_id": "Source1",
         },
         {
             "src_id": "PersonA",
@@ -84,7 +84,7 @@ custom_kg = {
             "description": "PersonA works at UniversityB",
             "keywords": "employment, affiliation",
             "weight": 0.9,
-            "source_id": "Source2"
+            "source_id": "Source2",
         },
         {
             "src_id": "CityC",
@@ -92,7 +92,7 @@ custom_kg = {
             "description": "EventY is hosted in CityC",
             "keywords": "host, location",
             "weight": 0.8,
-            "source_id": "Source3"
+            "source_id": "Source3",
         },
         {
             "src_id": "CompanyD",
@@ -100,9 +100,9 @@ custom_kg = {
             "description": "CompanyD provides ServiceZ",
             "keywords": "provide, offer",
             "weight": 1.0,
-            "source_id": "Source4"
+            "source_id": "Source4",
-        }
+        },
-    ]
+    ],
 }
 
 rag.insert_custom_kg(custom_kg)

examples/lightrag_api_oracle_demo..py

@@ -1,10 +1,13 @@
 from fastapi import FastAPI, HTTPException, File, UploadFile
+from fastapi import Query
 from contextlib import asynccontextmanager
 from pydantic import BaseModel
-from typing import Optional
+from typing import Optional, Any
 
 import sys
 import os
+
+
 from pathlib import Path
 
 import asyncio
@@ -16,9 +19,7 @@ import numpy as np
 
 from lightrag.kg.oracle_impl import OracleDB
 
-
 print(os.getcwd())
-
 script_directory = Path(__file__).resolve().parent.parent
 sys.path.append(os.path.abspath(script_directory))
 
@@ -37,14 +38,13 @@ APIKEY = "ocigenerativeai"
 # Configure working directory
 WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
 print(f"WORKING_DIR: {WORKING_DIR}")
-LLM_MODEL = os.environ.get("LLM_MODEL", "cohere.command-r-plus")
+LLM_MODEL = os.environ.get("LLM_MODEL", "cohere.command-r-plus-08-2024")
 print(f"LLM_MODEL: {LLM_MODEL}")
 EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "cohere.embed-multilingual-v3.0")
 print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
 EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 512))
 print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
 
-
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
@@ -94,10 +94,10 @@ async def init():
             "user": "",
             "password": "",
             "dsn": "",
-            "config_dir": "",
+            "config_dir": "path_to_config_dir",
-            "wallet_location": "",
+            "wallet_location": "path_to_wallet_location",
-            "wallet_password": "",
+            "wallet_password": "wallet_password",
-            "workspace": "",
+            "workspace": "company",
         }  # specify which docs you want to store and query
     )
 
@@ -105,6 +105,7 @@ async def init():
     await oracle_db.check_tables()
     # Initialize LightRAG
     # We use Oracle DB as the KV/vector/graph storage
+    # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
     rag = LightRAG(
         enable_llm_cache=False,
         working_dir=WORKING_DIR,
@@ -128,6 +129,17 @@ async def init():
     return rag
 
 
+# Extract and Insert into LightRAG storage
+# with open("./dickens/book.txt", "r", encoding="utf-8") as f:
+#   await rag.ainsert(f.read())
+
+# # Perform search in different modes
+# modes = ["naive", "local", "global", "hybrid"]
+# for mode in modes:
+#     print("="*20, mode, "="*20)
+#     print(await rag.aquery("这篇文档是关于什么内容的?", param=QueryParam(mode=mode)))
+#     print("-"*100, "\n")
+
 # Data models
 
 
@@ -135,6 +147,11 @@ class QueryRequest(BaseModel):
     query: str
     mode: str = "hybrid"
     only_need_context: bool = False
+    only_need_prompt: bool = False
+
+
+class DataRequest(BaseModel):
+    limit: int = 100
 
 
 class InsertRequest(BaseModel):
@@ -143,7 +160,7 @@ class InsertRequest(BaseModel):
 
 class Response(BaseModel):
     status: str
-    data: Optional[str] = None
+    data: Optional[Any] = None
     message: Optional[str] = None
 
 
@@ -167,17 +184,35 @@ app = FastAPI(
 
 @app.post("/query", response_model=Response)
 async def query_endpoint(request: QueryRequest):
-    try:
+    # try:
     # loop = asyncio.get_event_loop()
+    if request.mode == "naive":
+        top_k = 3
+    else:
+        top_k = 60
     result = await rag.aquery(
         request.query,
         param=QueryParam(
-                mode=request.mode, only_need_context=request.only_need_context
+            mode=request.mode,
+            only_need_context=request.only_need_context,
+            only_need_prompt=request.only_need_prompt,
+            top_k=top_k,
         ),
     )
     return Response(status="success", data=result)
-    except Exception as e:
+    # except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    #     raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/data", response_model=Response)
+async def query_all_nodes(type: str = Query("nodes"), limit: int = Query(100)):
+    if type == "nodes":
+        result = await rag.chunk_entity_relation_graph.get_all_nodes(limit=limit)
+    elif type == "edges":
+        result = await rag.chunk_entity_relation_graph.get_all_edges(limit=limit)
+    elif type == "statistics":
+        result = await rag.chunk_entity_relation_graph.get_statistics()
+    return Response(status="success", data=result)
 
 
 @app.post("/insert", response_model=Response)
@@ -220,7 +255,7 @@ async def health_check():
 if __name__ == "__main__":
     import uvicorn
 
-    uvicorn.run(app, host="0.0.0.0", port=8020)
+    uvicorn.run(app, host="127.0.0.1", port=8020)
 
 # Usage example
 # To run the server, use the following command in your terminal:

examples/lightrag_oracle_demo.py

@@ -84,6 +84,7 @@ async def main():
 
         # Initialize LightRAG
         # We use Oracle DB as the KV/vector/graph storage
+        # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
         rag = LightRAG(
             enable_llm_cache=False,
             working_dir=WORKING_DIR,

lightrag/base.py

@@ -17,9 +17,12 @@ T = TypeVar("T")
 class QueryParam:
     mode: Literal["local", "global", "hybrid", "naive"] = "global"
     only_need_context: bool = False
+    only_need_prompt: bool = False
     response_type: str = "Multiple Paragraphs"
     # Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.
     top_k: int = 60
+    # Number of document chunks to retrieve.
+    # top_n: int = 10
     # Number of tokens for the original chunks.
     max_token_for_text_unit: int = 4000
     # Number of tokens for the relationship descriptions

lightrag/kg/oracle_impl.py

@@ -545,6 +545,13 @@ class OracleGraphStorage(BaseGraphStorage):
         if res:
             return res
 
+    async def get_statistics(self):
+        SQL = SQL_TEMPLATES["get_statistics"]
+        params = {"workspace": self.db.workspace}
+        res = await self.db.query(sql=SQL, params=params, multirows=True)
+        if res:
+            return res
+
 
 N_T = {
     "full_docs": "LIGHTRAG_DOC_FULL",
@@ -717,13 +724,22 @@ SQL_TEMPLATES = {
                 WHEN NOT MATCHED THEN
                     INSERT(workspace,source_name,target_name,weight,keywords,description,source_chunk_id,content,content_vector)
                     values (:workspace,:source_name,:target_name,:weight,:keywords,:description,:source_chunk_id,:content,:content_vector) """,
-    "get_all_nodes": """SELECT t1.name as id,t1.entity_type as label,t1.DESCRIPTION,t2.content
+    "get_all_nodes": """WITH t0 AS (
-                FROM LIGHTRAG_GRAPH_NODES t1
+                        SELECT name AS id, entity_type AS label, entity_type, description,
-                LEFT JOIN LIGHTRAG_DOC_CHUNKS t2 on t1.source_chunk_id=t2.id
+                            '["' || replace(source_chunk_id, '<SEP>', '","') || '"]'     source_chunk_ids
-                WHERE t1.workspace=:workspace
+                        FROM lightrag_graph_nodes
-                order by t1.CREATETIME DESC
+                        WHERE workspace = :workspace
-                fetch first :limit rows only
+                        ORDER BY createtime DESC fetch first :limit rows only
-                """,
+                    ), t1 AS (
+                        SELECT t0.id, source_chunk_id
+                        FROM t0, JSON_TABLE ( source_chunk_ids, '$[*]' COLUMNS ( source_chunk_id PATH '$' ) )
+                    ), t2 AS (
+                        SELECT t1.id, LISTAGG(t2.content, '\n') content
+                        FROM t1 LEFT JOIN lightrag_doc_chunks t2 ON t1.source_chunk_id = t2.id
+                        GROUP BY t1.id
+                    )
+                    SELECT t0.id, label, entity_type, description, t2.content
+                    FROM t0 LEFT JOIN t2 ON t0.id = t2.id""",
     "get_all_edges": """SELECT t1.id,t1.keywords as label,t1.keywords, t1.source_name as source, t1.target_name as target,
                 t1.weight,t1.DESCRIPTION,t2.content
                 FROM LIGHTRAG_GRAPH_EDGES t1
@@ -731,4 +747,13 @@ SQL_TEMPLATES = {
                 WHERE t1.workspace=:workspace
                 order by t1.CREATETIME DESC
                 fetch first :limit rows only""",
+    "get_statistics": """select  count(distinct CASE WHEN type='node' THEN id END) as nodes_count,
+                count(distinct CASE WHEN type='edge' THEN id END) as edges_count
+                FROM (
+                select 'node' as type, id FROM GRAPH_TABLE (lightrag_graph
+                    MATCH (a) WHERE a.workspace=:workspace columns(a.name as id))
+                UNION
+                select 'edge' as type, TO_CHAR(id) id FROM GRAPH_TABLE (lightrag_graph
+                    MATCH (a)-[e]->(b) WHERE e.workspace=:workspace columns(e.id))
+                )""",
 }

lightrag/lightrag.py

@@ -13,9 +13,8 @@ from .llm import (
 from .operate import (
     chunking_by_token_size,
     extract_entities,
-    local_query,
+    # local_query,global_query,hybrid_query,
-    global_query,
+    kg_query,
-    hybrid_query,
     naive_query,
 )
 
@@ -415,28 +414,8 @@ class LightRAG:
         return loop.run_until_complete(self.aquery(query, param))
 
     async def aquery(self, query: str, param: QueryParam = QueryParam()):
-        if param.mode == "local":
+        if param.mode in ["local", "global", "hybrid"]:
-            response = await local_query(
+            response = await kg_query(
-                query,
-                self.chunk_entity_relation_graph,
-                self.entities_vdb,
-                self.relationships_vdb,
-                self.text_chunks,
-                param,
-                asdict(self),
-            )
-        elif param.mode == "global":
-            response = await global_query(
-                query,
-                self.chunk_entity_relation_graph,
-                self.entities_vdb,
-                self.relationships_vdb,
-                self.text_chunks,
-                param,
-                asdict(self),
-            )
-        elif param.mode == "hybrid":
-            response = await hybrid_query(
                 query,
                 self.chunk_entity_relation_graph,
                 self.entities_vdb,

lightrag/llm.py

@@ -69,12 +69,15 @@ async def openai_complete_if_cache(
     response = await openai_async_client.chat.completions.create(
         model=model, messages=messages, **kwargs
     )
-
+    content = response.choices[0].message.content
+    if r"\u" in content:
+        content = content.encode("utf-8").decode("unicode_escape")
+    # print(content)
     if hashing_kv is not None:
         await hashing_kv.upsert(
             {args_hash: {"return": response.choices[0].message.content, "model": model}}
         )
-    return response.choices[0].message.content
+    return content
 
 
 @retry(

lightrag/operate.py

@@ -249,6 +249,17 @@ async def extract_entities(
     entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"]
 
     ordered_chunks = list(chunks.items())
+    # add language and example number params to prompt
+    language = global_config["addon_params"].get(
+        "language", PROMPTS["DEFAULT_LANGUAGE"]
+    )
+    example_number = global_config["addon_params"].get("example_number", None)
+    if example_number and example_number < len(PROMPTS["entity_extraction_examples"]):
+        examples = "\n".join(
+            PROMPTS["entity_extraction_examples"][: int(example_number)]
+        )
+    else:
+        examples = "\n".join(PROMPTS["entity_extraction_examples"])
 
     entity_extract_prompt = PROMPTS["entity_extraction"]
     context_base = dict(
@@ -256,7 +267,10 @@ async def extract_entities(
         record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"],
         completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"],
         entity_types=",".join(PROMPTS["DEFAULT_ENTITY_TYPES"]),
+        examples=examples,
+        language=language,
     )
+
     continue_prompt = PROMPTS["entiti_continue_extraction"]
     if_loop_prompt = PROMPTS["entiti_if_loop_extraction"]
 
@@ -271,7 +285,6 @@ async def extract_entities(
         content = chunk_dp["content"]
         hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
         final_result = await use_llm_func(hint_prompt)
-
         history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
         for now_glean_index in range(entity_extract_max_gleaning):
             glean_result = await use_llm_func(continue_prompt, history_messages=history)
@@ -414,7 +427,7 @@ async def extract_entities(
     return knowledge_graph_inst
 
 
-async def local_query(
+async def kg_query(
     query,
     knowledge_graph_inst: BaseGraphStorage,
     entities_vdb: BaseVectorStorage,
@@ -424,42 +437,63 @@ async def local_query(
     global_config: dict,
 ) -> str:
     context = None
-    use_model_func = global_config["llm_model_func"]
+    example_number = global_config["addon_params"].get("example_number", None)
-
+    if example_number and example_number < len(PROMPTS["keywords_extraction_examples"]):
-    kw_prompt_temp = PROMPTS["keywords_extraction"]
+        examples = "\n".join(
-    kw_prompt = kw_prompt_temp.format(query=query)
+            PROMPTS["keywords_extraction_examples"][: int(example_number)]
-    result = await use_model_func(kw_prompt)
-    json_text = locate_json_string_body_from_string(result)
-
-    try:
-        keywords_data = json.loads(json_text)
-        keywords = keywords_data.get("low_level_keywords", [])
-        keywords = ", ".join(keywords)
-    except json.JSONDecodeError:
-        try:
-            result = (
-                result.replace(kw_prompt[:-1], "")
-                .replace("user", "")
-                .replace("model", "")
-                .strip()
         )
-            result = "{" + result.split("{")[-1].split("}")[0] + "}"
+    else:
+        examples = "\n".join(PROMPTS["keywords_extraction_examples"])
+
+    # Set mode
+    if query_param.mode not in ["local", "global", "hybrid"]:
+        logger.error(f"Unknown mode {query_param.mode} in kg_query")
+        return PROMPTS["fail_response"]
+
+    # LLM generate keywords
+    use_model_func = global_config["llm_model_func"]
+    kw_prompt_temp = PROMPTS["keywords_extraction"]
+    kw_prompt = kw_prompt_temp.format(query=query, examples=examples)
+    result = await use_model_func(kw_prompt)
+    logger.info("kw_prompt result:")
+    print(result)
+    try:
+        json_text = locate_json_string_body_from_string(result)
+        keywords_data = json.loads(json_text)
+        hl_keywords = keywords_data.get("high_level_keywords", [])
+        ll_keywords = keywords_data.get("low_level_keywords", [])
 
-            keywords_data = json.loads(result)
-            keywords = keywords_data.get("low_level_keywords", [])
-            keywords = ", ".join(keywords)
     # Handle parsing error
     except json.JSONDecodeError as e:
-            print(f"JSON parsing error: {e}")
+        print(f"JSON parsing error: {e} {result}")
         return PROMPTS["fail_response"]
-    if keywords:
+
-        context = await _build_local_query_context(
+    # Handdle keywords missing
+    if hl_keywords == [] and ll_keywords == []:
+        logger.warning("low_level_keywords and high_level_keywords is empty")
+        return PROMPTS["fail_response"]
+    if ll_keywords == [] and query_param.mode in ["local", "hybrid"]:
+        logger.warning("low_level_keywords is empty")
+        return PROMPTS["fail_response"]
+    else:
+        ll_keywords = ", ".join(ll_keywords)
+    if hl_keywords == [] and query_param.mode in ["global", "hybrid"]:
+        logger.warning("high_level_keywords is empty")
+        return PROMPTS["fail_response"]
+    else:
+        hl_keywords = ", ".join(hl_keywords)
+
+    # Build context
+    keywords = [ll_keywords, hl_keywords]
+    context = await _build_query_context(
         keywords,
         knowledge_graph_inst,
         entities_vdb,
+        relationships_vdb,
         text_chunks_db,
         query_param,
     )
+
     if query_param.only_need_context:
         return context
     if context is None:
@@ -468,6 +502,8 @@ async def local_query(
     sys_prompt = sys_prompt_temp.format(
         context_data=context, response_type=query_param.response_type
     )
+    if query_param.only_need_prompt:
+        return sys_prompt
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
@@ -486,22 +522,114 @@ async def local_query(
     return response
 
 
-async def _build_local_query_context(
+async def _build_query_context(
+    query: list,
+    knowledge_graph_inst: BaseGraphStorage,
+    entities_vdb: BaseVectorStorage,
+    relationships_vdb: BaseVectorStorage,
+    text_chunks_db: BaseKVStorage[TextChunkSchema],
+    query_param: QueryParam,
+):
+    ll_kewwords, hl_keywrds = query[0], query[1]
+    if query_param.mode in ["local", "hybrid"]:
+        if ll_kewwords == "":
+            ll_entities_context, ll_relations_context, ll_text_units_context = (
+                "",
+                "",
+                "",
+            )
+            warnings.warn(
+                "Low Level context is None. Return empty Low entity/relationship/source"
+            )
+            query_param.mode = "global"
+        else:
+            (
+                ll_entities_context,
+                ll_relations_context,
+                ll_text_units_context,
+            ) = await _get_node_data(
+                ll_kewwords,
+                knowledge_graph_inst,
+                entities_vdb,
+                text_chunks_db,
+                query_param,
+            )
+    if query_param.mode in ["global", "hybrid"]:
+        if hl_keywrds == "":
+            hl_entities_context, hl_relations_context, hl_text_units_context = (
+                "",
+                "",
+                "",
+            )
+            warnings.warn(
+                "High Level context is None. Return empty High entity/relationship/source"
+            )
+            query_param.mode = "local"
+        else:
+            (
+                hl_entities_context,
+                hl_relations_context,
+                hl_text_units_context,
+            ) = await _get_edge_data(
+                hl_keywrds,
+                knowledge_graph_inst,
+                relationships_vdb,
+                text_chunks_db,
+                query_param,
+            )
+    if query_param.mode == "hybrid":
+        entities_context, relations_context, text_units_context = combine_contexts(
+            [hl_entities_context, ll_entities_context],
+            [hl_relations_context, ll_relations_context],
+            [hl_text_units_context, ll_text_units_context],
+        )
+    elif query_param.mode == "local":
+        entities_context, relations_context, text_units_context = (
+            ll_entities_context,
+            ll_relations_context,
+            ll_text_units_context,
+        )
+    elif query_param.mode == "global":
+        entities_context, relations_context, text_units_context = (
+            hl_entities_context,
+            hl_relations_context,
+            hl_text_units_context,
+        )
+    return f"""
+-----Entities-----
+```csv
+{entities_context}
+```
+-----Relationships-----
+```csv
+{relations_context}
+```
+-----Sources-----
+```csv
+{text_units_context}
+```
+"""
+
+
+async def _get_node_data(
     query,
     knowledge_graph_inst: BaseGraphStorage,
     entities_vdb: BaseVectorStorage,
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
 ):
+    # get similar entities
     results = await entities_vdb.query(query, top_k=query_param.top_k)
-
     if not len(results):
         return None
+    # get entity information
     node_datas = await asyncio.gather(
         *[knowledge_graph_inst.get_node(r["entity_name"]) for r in results]
     )
     if not all([n is not None for n in node_datas]):
         logger.warning("Some nodes are missing, maybe the storage is damaged")
+
+    # get entity degree
     node_degrees = await asyncio.gather(
         *[knowledge_graph_inst.node_degree(r["entity_name"]) for r in results]
     )
@@ -510,15 +638,19 @@ async def _build_local_query_context(
         for k, n, d in zip(results, node_datas, node_degrees)
         if n is not None
     ]  # what is this text_chunks_db doing.  dont remember it in airvx.  check the diagram.
+    # get entitytext chunk
     use_text_units = await _find_most_related_text_unit_from_entities(
         node_datas, query_param, text_chunks_db, knowledge_graph_inst
     )
+    # get relate edges
     use_relations = await _find_most_related_edges_from_entities(
         node_datas, query_param, knowledge_graph_inst
     )
     logger.info(
         f"Local query uses {len(node_datas)} entites, {len(use_relations)} relations, {len(use_text_units)} text units"
     )
+
+    # build prompt
     entites_section_list = [["id", "entity", "type", "description", "rank"]]
     for i, n in enumerate(node_datas):
         entites_section_list.append(
@@ -553,20 +685,7 @@ async def _build_local_query_context(
     for i, t in enumerate(use_text_units):
         text_units_section_list.append([i, t["content"]])
     text_units_context = list_of_list_to_csv(text_units_section_list)
-    return f"""
+    return entities_context, relations_context, text_units_context
------Entities-----
-```csv
-{entities_context}
-```
------Relationships-----
-```csv
-{relations_context}
-```
------Sources-----
-```csv
-{text_units_context}
-```
-"""
 
 
 async def _find_most_related_text_unit_from_entities(
@@ -683,86 +802,9 @@ async def _find_most_related_edges_from_entities(
     return all_edges_data
 
 
-async def global_query(
+async def _get_edge_data(
-    query,
-    knowledge_graph_inst: BaseGraphStorage,
-    entities_vdb: BaseVectorStorage,
-    relationships_vdb: BaseVectorStorage,
-    text_chunks_db: BaseKVStorage[TextChunkSchema],
-    query_param: QueryParam,
-    global_config: dict,
-) -> str:
-    context = None
-    use_model_func = global_config["llm_model_func"]
-
-    kw_prompt_temp = PROMPTS["keywords_extraction"]
-    kw_prompt = kw_prompt_temp.format(query=query)
-    result = await use_model_func(kw_prompt)
-    json_text = locate_json_string_body_from_string(result)
-
-    try:
-        keywords_data = json.loads(json_text)
-        keywords = keywords_data.get("high_level_keywords", [])
-        keywords = ", ".join(keywords)
-    except json.JSONDecodeError:
-        try:
-            result = (
-                result.replace(kw_prompt[:-1], "")
-                .replace("user", "")
-                .replace("model", "")
-                .strip()
-            )
-            result = "{" + result.split("{")[-1].split("}")[0] + "}"
-
-            keywords_data = json.loads(result)
-            keywords = keywords_data.get("high_level_keywords", [])
-            keywords = ", ".join(keywords)
-
-        except json.JSONDecodeError as e:
-            # Handle parsing error
-            print(f"JSON parsing error: {e}")
-            return PROMPTS["fail_response"]
-    if keywords:
-        context = await _build_global_query_context(
-            keywords,
-            knowledge_graph_inst,
-            entities_vdb,
-            relationships_vdb,
-            text_chunks_db,
-            query_param,
-        )
-
-    if query_param.only_need_context:
-        return context
-    if context is None:
-        return PROMPTS["fail_response"]
-
-    sys_prompt_temp = PROMPTS["rag_response"]
-    sys_prompt = sys_prompt_temp.format(
-        context_data=context, response_type=query_param.response_type
-    )
-    response = await use_model_func(
-        query,
-        system_prompt=sys_prompt,
-    )
-    if len(response) > len(sys_prompt):
-        response = (
-            response.replace(sys_prompt, "")
-            .replace("user", "")
-            .replace("model", "")
-            .replace(query, "")
-            .replace("<system>", "")
-            .replace("</system>", "")
-            .strip()
-        )
-
-    return response
-
-
-async def _build_global_query_context(
     keywords,
     knowledge_graph_inst: BaseGraphStorage,
-    entities_vdb: BaseVectorStorage,
     relationships_vdb: BaseVectorStorage,
     text_chunks_db: BaseKVStorage[TextChunkSchema],
     query_param: QueryParam,
@@ -804,6 +846,7 @@ async def _build_global_query_context(
     logger.info(
         f"Global query uses {len(use_entities)} entites, {len(edge_datas)} relations, {len(use_text_units)} text units"
     )
+
     relations_section_list = [
         ["id", "source", "target", "description", "keywords", "weight", "rank"]
     ]
@@ -838,21 +881,7 @@ async def _build_global_query_context(
     for i, t in enumerate(use_text_units):
         text_units_section_list.append([i, t["content"]])
     text_units_context = list_of_list_to_csv(text_units_section_list)
-
+    return entities_context, relations_context, text_units_context
-    return f"""
------Entities-----
-```csv
-{entities_context}
-```
------Relationships-----
-```csv
-{relations_context}
-```
------Sources-----
-```csv
-{text_units_context}
-```
-"""
 
 
 async def _find_most_related_entities_from_relationships(
@@ -929,134 +958,11 @@ async def _find_related_text_unit_from_relationships(
     return all_text_units
 
 
-async def hybrid_query(
+def combine_contexts(entities, relationships, sources):
-    query,
-    knowledge_graph_inst: BaseGraphStorage,
-    entities_vdb: BaseVectorStorage,
-    relationships_vdb: BaseVectorStorage,
-    text_chunks_db: BaseKVStorage[TextChunkSchema],
-    query_param: QueryParam,
-    global_config: dict,
-) -> str:
-    low_level_context = None
-    high_level_context = None
-    use_model_func = global_config["llm_model_func"]
-
-    kw_prompt_temp = PROMPTS["keywords_extraction"]
-    kw_prompt = kw_prompt_temp.format(query=query)
-
-    result = await use_model_func(kw_prompt)
-    json_text = locate_json_string_body_from_string(result)
-    try:
-        keywords_data = json.loads(json_text)
-        hl_keywords = keywords_data.get("high_level_keywords", [])
-        ll_keywords = keywords_data.get("low_level_keywords", [])
-        hl_keywords = ", ".join(hl_keywords)
-        ll_keywords = ", ".join(ll_keywords)
-    except json.JSONDecodeError:
-        try:
-            result = (
-                result.replace(kw_prompt[:-1], "")
-                .replace("user", "")
-                .replace("model", "")
-                .strip()
-            )
-            result = "{" + result.split("{")[-1].split("}")[0] + "}"
-            keywords_data = json.loads(result)
-            hl_keywords = keywords_data.get("high_level_keywords", [])
-            ll_keywords = keywords_data.get("low_level_keywords", [])
-            hl_keywords = ", ".join(hl_keywords)
-            ll_keywords = ", ".join(ll_keywords)
-        # Handle parsing error
-        except json.JSONDecodeError as e:
-            print(f"JSON parsing error: {e}")
-            return PROMPTS["fail_response"]
-
-    if ll_keywords:
-        low_level_context = await _build_local_query_context(
-            ll_keywords,
-            knowledge_graph_inst,
-            entities_vdb,
-            text_chunks_db,
-            query_param,
-        )
-
-    if hl_keywords:
-        high_level_context = await _build_global_query_context(
-            hl_keywords,
-            knowledge_graph_inst,
-            entities_vdb,
-            relationships_vdb,
-            text_chunks_db,
-            query_param,
-        )
-
-    context = combine_contexts(high_level_context, low_level_context)
-
-    if query_param.only_need_context:
-        return context
-    if context is None:
-        return PROMPTS["fail_response"]
-
-    sys_prompt_temp = PROMPTS["rag_response"]
-    sys_prompt = sys_prompt_temp.format(
-        context_data=context, response_type=query_param.response_type
-    )
-    response = await use_model_func(
-        query,
-        system_prompt=sys_prompt,
-    )
-    if len(response) > len(sys_prompt):
-        response = (
-            response.replace(sys_prompt, "")
-            .replace("user", "")
-            .replace("model", "")
-            .replace(query, "")
-            .replace("<system>", "")
-            .replace("</system>", "")
-            .strip()
-        )
-    return response
-
-
-def combine_contexts(high_level_context, low_level_context):
     # Function to extract entities, relationships, and sources from context strings
-
+    hl_entities, ll_entities = entities[0], entities[1]
-    def extract_sections(context):
+    hl_relationships, ll_relationships = relationships[0], relationships[1]
-        entities_match = re.search(
+    hl_sources, ll_sources = sources[0], sources[1]
-            r"-----Entities-----\s*```csv\s*(.*?)\s*```", context, re.DOTALL
-        )
-        relationships_match = re.search(
-            r"-----Relationships-----\s*```csv\s*(.*?)\s*```", context, re.DOTALL
-        )
-        sources_match = re.search(
-            r"-----Sources-----\s*```csv\s*(.*?)\s*```", context, re.DOTALL
-        )
-
-        entities = entities_match.group(1) if entities_match else ""
-        relationships = relationships_match.group(1) if relationships_match else ""
-        sources = sources_match.group(1) if sources_match else ""
-
-        return entities, relationships, sources
-
-    # Extract sections from both contexts
-
-    if high_level_context is None:
-        warnings.warn(
-            "High Level context is None. Return empty High entity/relationship/source"
-        )
-        hl_entities, hl_relationships, hl_sources = "", "", ""
-    else:
-        hl_entities, hl_relationships, hl_sources = extract_sections(high_level_context)
-
-    if low_level_context is None:
-        warnings.warn(
-            "Low Level context is None. Return empty Low entity/relationship/source"
-        )
-        ll_entities, ll_relationships, ll_sources = "", "", ""
-    else:
-        ll_entities, ll_relationships, ll_sources = extract_sections(low_level_context)
-
     # Combine and deduplicate the entities
     combined_entities = process_combine_contexts(hl_entities, ll_entities)
 
@@ -1068,21 +974,7 @@ def combine_contexts(high_level_context, low_level_context):
     # Combine and deduplicate the sources
     combined_sources = process_combine_contexts(hl_sources, ll_sources)
 
-    # Format the combined context
+    return combined_entities, combined_relationships, combined_sources
-    return f"""
------Entities-----
-```csv
-{combined_entities}
-```
------Relationships-----
-```csv
-{combined_relationships}
-```
------Sources-----
-```csv
-{combined_sources}
-```
-"""
 
 
 async def naive_query(
@@ -1105,13 +997,15 @@ async def naive_query(
         max_token_size=query_param.max_token_for_text_unit,
     )
     logger.info(f"Truncate {len(chunks)} to {len(maybe_trun_chunks)} chunks")
-    section = "--New Chunk--\n".join([c["content"] for c in maybe_trun_chunks])
+    section = "\n--New Chunk--\n".join([c["content"] for c in maybe_trun_chunks])
     if query_param.only_need_context:
         return section
     sys_prompt_temp = PROMPTS["naive_rag_response"]
     sys_prompt = sys_prompt_temp.format(
         content_data=section, response_type=query_param.response_type
     )
+    if query_param.only_need_prompt:
+        return sys_prompt
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,

lightrag/prompt.py

@@ -2,6 +2,7 @@ GRAPH_FIELD_SEP = "<SEP>"
 
 PROMPTS = {}
 
+PROMPTS["DEFAULT_LANGUAGE"] = "English"
 PROMPTS["DEFAULT_TUPLE_DELIMITER"] = "<|>"
 PROMPTS["DEFAULT_RECORD_DELIMITER"] = "##"
 PROMPTS["DEFAULT_COMPLETION_DELIMITER"] = "<|COMPLETE|>"
@@ -11,6 +12,7 @@ PROMPTS["DEFAULT_ENTITY_TYPES"] = ["organization", "person", "geo", "event"]
 
 PROMPTS["entity_extraction"] = """-Goal-
 Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.
+Use {language} as output language.
 
 -Steps-
 1. Identify all entities. For each identified entity, extract the following information:
@@ -38,7 +40,19 @@ Format the content-level key words as ("content_keywords"{tuple_delimiter}<high_
 ######################
 -Examples-
 ######################
-Example 1:
+{examples}
+
+#############################
+-Real Data-
+######################
+Entity_types: {entity_types}
+Text: {input_text}
+######################
+Output:
+"""
+
+PROMPTS["entity_extraction_examples"] = [
+    """Example 1:
 
 Entity_types: [person, technology, mission, organization, location]
 Text:
@@ -62,8 +76,8 @@ Output:
 ("relationship"{tuple_delimiter}"Jordan"{tuple_delimiter}"Cruz"{tuple_delimiter}"Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order."{tuple_delimiter}"ideological conflict, rebellion"{tuple_delimiter}5){record_delimiter}
 ("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"The Device"{tuple_delimiter}"Taylor shows reverence towards the device, indicating its importance and potential impact."{tuple_delimiter}"reverence, technological significance"{tuple_delimiter}9){record_delimiter}
 ("content_keywords"{tuple_delimiter}"power dynamics, ideological conflict, discovery, rebellion"){completion_delimiter}
-#############################
+#############################""",
-Example 2:
+    """Example 2:
 
 Entity_types: [person, technology, mission, organization, location]
 Text:
@@ -80,8 +94,8 @@ Output:
 ("relationship"{tuple_delimiter}"The team"{tuple_delimiter}"Washington"{tuple_delimiter}"The team receives communications from Washington, which influences their decision-making process."{tuple_delimiter}"decision-making, external influence"{tuple_delimiter}7){record_delimiter}
 ("relationship"{tuple_delimiter}"The team"{tuple_delimiter}"Operation: Dulce"{tuple_delimiter}"The team is directly involved in Operation: Dulce, executing its evolved objectives and activities."{tuple_delimiter}"mission evolution, active participation"{tuple_delimiter}9){completion_delimiter}
 ("content_keywords"{tuple_delimiter}"mission evolution, decision-making, active participation, cosmic significance"){completion_delimiter}
-#############################
+#############################""",
-Example 3:
+    """Example 3:
 
 Entity_types: [person, role, technology, organization, event, location, concept]
 Text:
@@ -107,14 +121,8 @@ Output:
 ("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Humanity's Response"{tuple_delimiter}"Alex and his team are the key figures in Humanity's Response to the unknown intelligence."{tuple_delimiter}"collective action, cosmic significance"{tuple_delimiter}8){record_delimiter}
 ("relationship"{tuple_delimiter}"Control"{tuple_delimiter}"Intelligence"{tuple_delimiter}"The concept of Control is challenged by the Intelligence that writes its own rules."{tuple_delimiter}"power dynamics, autonomy"{tuple_delimiter}7){record_delimiter}
 ("content_keywords"{tuple_delimiter}"first contact, control, communication, cosmic significance"){completion_delimiter}
-#############################
+#############################""",
--Real Data-
+]
-######################
-Entity_types: {entity_types}
-Text: {input_text}
-######################
-Output:
-"""
 
 PROMPTS[
     "summarize_entity_descriptions"
@@ -123,6 +131,7 @@ Given one or two entities, and a list of descriptions, all related to the same e
 Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
 If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary.
 Make sure it is written in third person, and include the entity names so we the have full context.
+Use Chinese as output language.
 
 #######
 -Data-
@@ -169,6 +178,7 @@ Add sections and commentary to the response as appropriate for the length and fo
 PROMPTS["keywords_extraction"] = """---Role---
 
 You are a helpful assistant tasked with identifying both high-level and low-level keywords in the user's query.
+Use Chinese as output language.
 
 ---Goal---
 
@@ -184,7 +194,20 @@ Given the query, list both high-level and low-level keywords. High-level keyword
 ######################
 -Examples-
 ######################
-Example 1:
+{examples}
+
+#############################
+-Real Data-
+######################
+Query: {query}
+######################
+The `Output` should be human text, not unicode characters. Keep the same language as `Query`.
+Output:
+
+"""
+
+PROMPTS["keywords_extraction_examples"] = [
+    """Example 1:
 
 Query: "How does international trade influence global economic stability?"
 ################
@@ -193,8 +216,8 @@ Output:
   "high_level_keywords": ["International trade", "Global economic stability", "Economic impact"],
   "low_level_keywords": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"]
 }}
-#############################
+#############################""",
-Example 2:
+    """Example 2:
 
 Query: "What are the environmental consequences of deforestation on biodiversity?"
 ################
@@ -203,8 +226,8 @@ Output:
   "high_level_keywords": ["Environmental consequences", "Deforestation", "Biodiversity loss"],
   "low_level_keywords": ["Species extinction", "Habitat destruction", "Carbon emissions", "Rainforest", "Ecosystem"]
 }}
-#############################
+#############################""",
-Example 3:
+    """Example 3:
 
 Query: "What is the role of education in reducing poverty?"
 ################
@@ -213,14 +236,9 @@ Output:
   "high_level_keywords": ["Education", "Poverty reduction", "Socioeconomic development"],
   "low_level_keywords": ["School access", "Literacy rates", "Job training", "Income inequality"]
 }}
-#############################
+#############################""",
--Real Data-
+]
-######################
-Query: {query}
-######################
-Output:
 
-"""
 
 PROMPTS["naive_rag_response"] = """---Role---
 

lightrag/utils.py

@@ -47,10 +47,27 @@ class EmbeddingFunc:
 
 def locate_json_string_body_from_string(content: str) -> Union[str, None]:
     """Locate the JSON string body from a string"""
+    try:
         maybe_json_str = re.search(r"{.*}", content, re.DOTALL)
         if maybe_json_str is not None:
-        return maybe_json_str.group(0)
+            maybe_json_str = maybe_json_str.group(0)
-    else:
+            maybe_json_str = maybe_json_str.replace("\\n", "")
+            maybe_json_str = maybe_json_str.replace("\n", "")
+            maybe_json_str = maybe_json_str.replace("'", '"')
+            json.loads(maybe_json_str)
+            return maybe_json_str
+    except Exception:
+        pass
+        # try:
+        #     content = (
+        #         content.replace(kw_prompt[:-1], "")
+        #         .replace("user", "")
+        #         .replace("model", "")
+        #         .strip()
+        #     )
+        #     maybe_json_str = "{" + content.split("{")[1].split("}")[0] + "}"
+        #     json.loads(maybe_json_str)
+
         return None