inference running locally. use neo4j next

.gitignore

@@ -4,4 +4,5 @@ dickens/
 book.txt
 lightrag-dev/
 .idea/
-dist/
+dist/
+env/

lightrag/kg/__init__.py

@@ -1,5 +1,27 @@
-from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
+print ("init package vars here. ......")
+from .neo4j import GraphStorage as Neo4JStorage
+
+
+# import sys
+# import importlib
+# # Specify the path to the directory containing the module
+# # Add the directory to the system path
+# module_dir = '/Users/kenwiltshire/documents/dev/LightRag/lightrag/kg'
+# sys.path.append(module_dir)
+# # Specify the module name
+# module_name = 'neo4j'
+# # Import the module
+# spec = importlib.util.spec_from_file_location(module_name, f'{module_dir}/{module_name}.py')
+
+# Neo4JStorage = importlib.util.module_from_spec(spec)
+# spec.loader.exec_module(Neo4JStorage)
+
+
+
+# Relative imports are still possible by adding a leading period to the module name when using the from ... import form:
+
+# # Import names from pkg.string
+# from .string import name1, name2
+# # Import pkg.string
+# from . import string
 
-__version__ = "0.0.7"
-__author__ = "Zirui Guo"
-__url__ = "https://github.com/HKUDS/LightRAG"

lightrag/kg/neo4j.py

@@ -3,11 +3,15 @@ import html
 import os
 from dataclasses import dataclass
 from typing import Any, Union, cast
-import networkx as nx
 import numpy as np
 from nano_vectordb import NanoVectorDB
 
-from .utils import load_json, logger, write_json
+
+
+#  import package.common.utils as utils
+
+
+from lightrag.utils import load_json, logger, write_json
 from ..base import (
     BaseGraphStorage
 )
@@ -22,10 +26,10 @@ PASSWORD = "your_password"
 @dataclass
 class GraphStorage(BaseGraphStorage):
     @staticmethod
-    def load_nx_graph(file_name) -> nx.Graph:
-        if os.path.exists(file_name):
-            return nx.read_graphml(file_name)
-        return None
+    # def load_nx_graph(file_name) -> nx.Graph:
+    #     if os.path.exists(file_name):
+    #         return nx.read_graphml(file_name)
+    #     return None
 
     def __post_init__(self):
         # self._graph = preloaded_graph or nx.Graph()
@@ -102,7 +106,7 @@ class GraphStorage(BaseGraphStorage):
             result = session.run(
                 """MATCH (n1:{node_label1})-[r]-(n2:{node_label2})
                 RETURN count(r) AS degree"""
-                .format(node_label1=node_label1, node_label2=node_label2)
+                .format(entity_name__label_source=entity_name__label_source, entity_name_label_target=entity_name_label_target)
             )        
             record = result.single()        
             return record["degree"]
@@ -263,7 +267,7 @@ class GraphStorage(BaseGraphStorage):
         with self._driver.session()  as session:
             #Define the Cypher query
             options = self.global_config["node2vec_params"]
-            query = f"""CALL gds.node2vec.stream('myGraph', {**options})
+            query = f"""CALL gds.node2vec.stream('myGraph', {options})  # **options
                     YIELD nodeId, embedding 
                     RETURN nodeId, embedding"""
             # Run the query and process the results

lightrag/lightrag.py

@@ -1,5 +1,6 @@
 import asyncio
 import os
+import importlib
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
 from functools import partial
@@ -23,6 +24,11 @@ from .storage import (
     NanoVectorDBStorage,
     NetworkXStorage,
 )
+ 
+from .kg.neo4j import (
+    GraphStorage as Neo4JStorage
+)
+
 from .utils import (
     EmbeddingFunc,
     compute_mdhash_id,
@@ -93,7 +99,14 @@ class LightRAG:
     key_string_value_json_storage_cls: Type[BaseKVStorage] = JsonKVStorage
     vector_db_storage_cls: Type[BaseVectorStorage] = NanoVectorDBStorage
     vector_db_storage_cls_kwargs: dict = field(default_factory=dict)
-    graph_storage_cls: Type[BaseGraphStorage] = NetworkXStorage
+
+    # module = importlib.import_module('kg.neo4j')
+    # Neo4JStorage = getattr(module, 'GraphStorage')
+
+    if True==False:
+        graph_storage_cls: Type[BaseGraphStorage] = Neo4JStorage
+    else:
+        graph_storage_cls: Type[BaseGraphStorage] = NetworkXStorage
     enable_llm_cache: bool = True
 
     # extension

lightrag/llm.py

@@ -72,7 +72,9 @@ async def openai_complete_if_cache(
 
 @retry(
     stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
+    #kw_
+    wait=wait_exponential(multiplier=1, min=4, max=60),
+    # wait=wait_exponential(multiplier=1, min=4, max=10),
     retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
 )
 async def azure_openai_complete_if_cache(model,

lightrag/storage.py

@@ -6,8 +6,6 @@ from typing import Any, Union, cast
 import networkx as nx
 import numpy as np
 from nano_vectordb import NanoVectorDB
-from kg.neo4j import GraphStorage
-
 
 from .utils import load_json, logger, write_json
 from .base import (
@@ -99,66 +97,14 @@ class NanoVectorDBStorage(BaseVectorStorage):
             d["__vector__"] = embeddings[i]
         results = self._client.upsert(datas=list_data)
         return results
-    
-
-@dataclass
-class PineConeVectorDBStorage(BaseVectorStorage):
-    cosine_better_than_threshold: float = 0.2
-
-    def __post_init__(self):
-        self._client_file_name = os.path.join(
-            self.global_config["working_dir"], f"vdb_{self.namespace}.json"
-        )
-        self._max_batch_size = self.global_config["embedding_batch_num"]
-        self._client = NanoVectorDB(
-            self.embedding_func.embedding_dim, storage_file=self._client_file_name
-        )
-        import os
-        from pinecone import Pinecone
-
-        pc = Pinecone() #api_key=os.environ.get('PINECONE_API_KEY'))
-        # From here on, everything is identical to the REST-based SDK.
-        self._client = pc.Index(host=self._client_pinecone_host)#'my-index-8833ca1.svc.us-east1-gcp.pinecone.io')
-
-        self.cosine_better_than_threshold = self.global_config.get(
-            "cosine_better_than_threshold", self.cosine_better_than_threshold
-        )
-
-    async def upsert(self, data: dict[str, dict]):
-        logger.info(f"Inserting {len(data)} vectors to {self.namespace}")
-        if not len(data):
-            logger.warning("You insert an empty data to vector DB")
-            return []
-        list_data = [
-            {
-                "__id__": k,
-                **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields},
-            }
-            for k, v in data.items()
-        ]
-        contents = [v["content"] for v in data.values()]
-        batches = [
-            contents[i : i + self._max_batch_size]
-            for i in range(0, len(contents), self._max_batch_size)
-        ]
-        embeddings_list = await asyncio.gather(
-            *[self.embedding_func(batch) for batch in batches]
-        )
-        embeddings = np.concatenate(embeddings_list)
-        for i, d in enumerate(list_data):
-            d["__vector__"] = embeddings[i]
-            # self._client.upsert(vectors=[]) pinecone
-        results = self._client.upsert(datas=list_data)
-        return results
 
     async def query(self, query: str, top_k=5):
         embedding = await self.embedding_func([query])
         embedding = embedding[0]
-        # self._client.query(vector=[...], top_key=10) pinecone
         results = self._client.query(
-            vector=embedding,
+            query=embedding,
             top_k=top_k,
-            better_than_threshold=self.cosine_better_than_threshold, ???
+            better_than_threshold=self.cosine_better_than_threshold,
         )
         results = [
             {**dp, "id": dp["__id__"], "distance": dp["__metrics__"]} for dp in results
@@ -166,8 +112,7 @@ class PineConeVectorDBStorage(BaseVectorStorage):
         return results
 
     async def index_done_callback(self):
-        print("self._client.save()")
-        # self._client.save()
+        self._client.save()
 
 
 @dataclass
@@ -298,5 +243,3 @@ class NetworkXStorage(BaseGraphStorage):
 
         nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes]
         return embeddings, nodes_ids
-
-

requirements.txt

@@ -12,4 +12,5 @@ torch
 transformers
 xxhash
 pyvis
-aiohttp
+aiohttp
+neo4j

test.py

@@ -0,0 +1,36 @@
+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
+
+#########
+# 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,
+    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
+)
+
+with open("./book.txt") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+
+# Perform local search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
+
+# Perform global search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
+
+# Perform hybrid search
+print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))