feat: add Neo4j graph backend (#61)

* refactor: make _storage a folder

* feat: add neo4j backend

* fix: remove test coverage for neo4j

* refactor: dspy extraction

* docs: update neo4j

* fix: neo4j return clusters in node_data

* tests: fix test wrong with clusters node data

* improve coverage of llm

.coveragerc

@@ -5,4 +5,7 @@ exclude_lines =
 
     # Don't complain if tests don't hit defensive assertion code:
     raise NotImplementedError
-    logger.
+    logger.
+omit =
+    # Don't have a nice github action for neo4j now, so skip this file:
+    nano_graphrag/_storage/gdb_neo4j.py

.github/workflows/test.yml

@@ -42,6 +42,8 @@ jobs:
         run: |
           flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
       - name: Build and Test
+        env:
+          NANO_GRAPHRAG_TEST_IGNORE_NEO4J: true
         run: |
           python -m pytest -o log_cli=true -o log_cli_level="INFO" --cov=nano_graphrag --cov-report=xml -v ./
       - name: Check codecov file

.gitignore

@@ -1,8 +1,7 @@
 # Created by https://www.toptal.com/developers/gitignore/api/python
 # Edit at https://www.toptal.com/developers/gitignore?templates=python
 test_cache.json
-run_test.py
-run_test_zh.py
+run_test*.py
 nano_graphrag_cache*/
 *.txt
 examples/benchmarks/fixtures/

docs/use_neo4j_for_graphrag.md

@@ -0,0 +1,27 @@
+1. Install [Neo4j](https://neo4j.com/docs/operations-manual/current/installation/)
+2. Install Neo4j GDS (graph data science) [plugin](https://neo4j.com/docs/graph-data-science/current/installation/neo4j-server/)
+3. Start neo4j server
+4. Get the `NEO4J_URL`,  `NEO4J_USER` and `NEO4J_PASSWORD`
+   - By default, `NEO4J_URL` is `neo4j://localhost:7687` ,  `NEO4J_USER` is `neo4j` and `NEO4J_PASSWORD` is `neo4j`
+
+Pass your neo4j instance to `GraphRAG`:
+
+```python
+from nano_graphrag import GraphRAG
+from nano_graphrag._storage import Neo4jStorage
+
+neo4j_config = {
+  "neo4j_url": os.environ.get("NEO4J_URL", "neo4j://localhost:7687"),
+  "neo4j_auth": (
+      os.environ.get("NEO4J_USER", "neo4j"),
+      os.environ.get("NEO4J_PASSWORD", "neo4j"),
+  )
+}
+GraphRAG(
+  graph_storage_cls=Neo4jStorage,
+  addon_params=neo4j_config,
+)
+```
+
+
+

examples/no_openai_key_at_all.py

@@ -34,6 +34,7 @@ async def ollama_model_if_cache(
 ) -> str:
     # remove kwargs that are not supported by ollama
     kwargs.pop("max_tokens", None)
+    kwargs.pop("response_format", None)
 
     ollama_client = ollama.AsyncClient()
     messages = []

examples/using_ollama_as_llm.py

@@ -18,6 +18,7 @@ async def ollama_model_if_cache(
 ) -> str:
     # remove kwargs that are not supported by ollama
     kwargs.pop("max_tokens", None)
+    kwargs.pop("response_format", None)
 
     ollama_client = ollama.AsyncClient()
     messages = []

examples/using_ollama_as_llm_and_embedding.py

@@ -20,11 +20,13 @@
 EMBEDDING_MODEL_DIM = 768
 EMBEDDING_MODEL_MAX_TOKENS = 8192
 
+
 async def ollama_model_if_cache(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
     # remove kwargs that are not supported by ollama
     kwargs.pop("max_tokens", None)
+    kwargs.pop("response_format", None)
 
     ollama_client = ollama.AsyncClient()
     messages = []
@@ -98,20 +100,21 @@ def insert():
     # rag = GraphRAG(working_dir=WORKING_DIR, enable_llm_cache=True)
     # rag.insert(FAKE_TEXT[half_len:])
 
+
 # We're using Ollama to generate embeddings for the BGE model
 @wrap_embedding_func_with_attrs(
-    embedding_dim= EMBEDDING_MODEL_DIM,
-    max_token_size= EMBEDDING_MODEL_MAX_TOKENS,
+    embedding_dim=EMBEDDING_MODEL_DIM,
+    max_token_size=EMBEDDING_MODEL_MAX_TOKENS,
 )
-
-async def ollama_embedding(texts :list[str]) -> np.ndarray:
+async def ollama_embedding(texts: list[str]) -> np.ndarray:
     embed_text = []
     for text in texts:
-      data = ollama.embeddings(model=EMBEDDING_MODEL, prompt=text)
-      embed_text.append(data["embedding"])
-    
+        data = ollama.embeddings(model=EMBEDDING_MODEL, prompt=text)
+        embed_text.append(data["embedding"])
+
     return embed_text
 
+
 if __name__ == "__main__":
     insert()
     query()

nano_graphrag/_llm.py

@@ -13,6 +13,23 @@
 from ._utils import compute_args_hash, wrap_embedding_func_with_attrs
 from .base import BaseKVStorage
 
+global_openai_async_client = None
+global_azure_openai_async_client = None
+
+
+def get_openai_async_client_instance():
+    global global_openai_async_client
+    if global_openai_async_client is None:
+        global_openai_async_client = AsyncOpenAI()
+    return global_openai_async_client
+
+
+def get_azure_openai_async_client_instance():
+    global global_azure_openai_async_client
+    if global_azure_openai_async_client is None:
+        global_azure_openai_async_client = AsyncAzureOpenAI()
+    return global_azure_openai_async_client
+
 
 @retry(
     stop=stop_after_attempt(5),
@@ -22,7 +39,7 @@
 async def openai_complete_if_cache(
     model, prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
-    openai_async_client = AsyncOpenAI()
+    openai_async_client = get_openai_async_client_instance()
     hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages = []
     if system_prompt:
@@ -78,7 +95,7 @@ async def gpt_4o_mini_complete(
     retry=retry_if_exception_type((RateLimitError, APIConnectionError)),
 )
 async def openai_embedding(texts: list[str]) -> np.ndarray:
-    openai_async_client = AsyncOpenAI()
+    openai_async_client = get_openai_async_client_instance()
     response = await openai_async_client.embeddings.create(
         model="text-embedding-3-small", input=texts, encoding_format="float"
     )
@@ -93,7 +110,7 @@ async def openai_embedding(texts: list[str]) -> np.ndarray:
 async def azure_openai_complete_if_cache(
     deployment_name, prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
-    azure_openai_client = AsyncAzureOpenAI()
+    azure_openai_client = get_azure_openai_async_client_instance()
     hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages = []
     if system_prompt:
@@ -154,11 +171,7 @@ async def azure_gpt_4o_mini_complete(
     retry=retry_if_exception_type((RateLimitError, APIConnectionError)),
 )
 async def azure_openai_embedding(texts: list[str]) -> np.ndarray:
-    azure_openai_client = AsyncAzureOpenAI(
-        api_key=os.environ.get("API_KEY_EMB"),
-        api_version=os.environ.get("API_VERSION_EMB"),
-        azure_endpoint=os.environ.get("AZURE_ENDPOINT_EMB"),
-    )
+    azure_openai_client = get_azure_openai_async_client_instance()
     response = await azure_openai_client.embeddings.create(
         model="text-embedding-3-small", input=texts, encoding_format="float"
     )

nano_graphrag/_storage/__init__.py

@@ -0,0 +1,5 @@
+from .gdb_networkx import NetworkXStorage
+from .gdb_neo4j import Neo4jStorage
+from .vdb_hnswlib import HNSWVectorStorage
+from .vdb_nanovectordb import NanoVectorDBStorage
+from .kv_json import JsonKVStorage