From be72c825d2214af7810618fc0fa42f60a3a4f303 Mon Sep 17 00:00:00 2001
From: LarFii <834462287@qq.com>
Date: Wed, 4 Dec 2024 16:01:19 +0800
Subject: [PATCH 1/4] fix entity extract
---
lightrag/operate.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/lightrag/operate.py b/lightrag/operate.py
index a40af9e0..09585e50 100644
--- a/lightrag/operate.py
+++ b/lightrag/operate.py
@@ -296,7 +296,9 @@ async def extract_entities(
chunk_key = chunk_key_dp[0]
chunk_dp = chunk_key_dp[1]
content = chunk_dp["content"]
- hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
+ # hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
+ hint_prompt = entity_extract_prompt.format(**context_base, input_text="{input_text}").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):
From 44d441a951e259e3f5bffb729cb1dfea6c2cdd8f Mon Sep 17 00:00:00 2001
From: LarFii <834462287@qq.com>
Date: Wed, 4 Dec 2024 19:44:04 +0800
Subject: [PATCH 2/4] update insert custom kg
---
Dockerfile | 56 -------------------------
README.md | 70 ++++++++------------------------
examples/insert_custom_kg.py | 34 ++++++++--------
examples/lightrag_nvidia_demo.py | 59 +++++++++++++++------------
lightrag/__init__.py | 2 +-
lightrag/lightrag.py | 38 ++++++++++++++++-
lightrag/llm.py | 18 +++++---
lightrag/operate.py | 5 ++-
8 files changed, 119 insertions(+), 163 deletions(-)
delete mode 100644 Dockerfile
diff --git a/Dockerfile b/Dockerfile
deleted file mode 100644
index 787816fe..00000000
--- a/Dockerfile
+++ /dev/null
@@ -1,56 +0,0 @@
-FROM debian:bullseye-slim
-ENV JAVA_HOME=/opt/java/openjdk
-COPY --from=eclipse-temurin:17 $JAVA_HOME $JAVA_HOME
-ENV PATH="${JAVA_HOME}/bin:${PATH}" \
- NEO4J_SHA256=7ce97bd9a4348af14df442f00b3dc5085b5983d6f03da643744838c7a1bc8ba7 \
- NEO4J_TARBALL=neo4j-enterprise-5.24.2-unix.tar.gz \
- NEO4J_EDITION=enterprise \
- NEO4J_HOME="/var/lib/neo4j" \
- LANG=C.UTF-8
-ARG NEO4J_URI=https://dist.neo4j.org/neo4j-enterprise-5.24.2-unix.tar.gz
-
-RUN addgroup --gid 7474 --system neo4j && adduser --uid 7474 --system --no-create-home --home "${NEO4J_HOME}" --ingroup neo4j neo4j
-
-COPY ./local-package/* /startup/
-
-RUN apt update \
- && apt-get install -y curl gcc git jq make procps tini wget \
- && curl --fail --silent --show-error --location --remote-name ${NEO4J_URI} \
- && echo "${NEO4J_SHA256} ${NEO4J_TARBALL}" | sha256sum -c --strict --quiet \
- && tar --extract --file ${NEO4J_TARBALL} --directory /var/lib \
- && mv /var/lib/neo4j-* "${NEO4J_HOME}" \
- && rm ${NEO4J_TARBALL} \
- && sed -i 's/Package Type:.*/Package Type: docker bullseye/' $NEO4J_HOME/packaging_info \
- && mv /startup/neo4j-admin-report.sh "${NEO4J_HOME}"/bin/neo4j-admin-report \
- && mv "${NEO4J_HOME}"/data /data \
- && mv "${NEO4J_HOME}"/logs /logs \
- && chown -R neo4j:neo4j /data \
- && chmod -R 777 /data \
- && chown -R neo4j:neo4j /logs \
- && chmod -R 777 /logs \
- && chown -R neo4j:neo4j "${NEO4J_HOME}" \
- && chmod -R 777 "${NEO4J_HOME}" \
- && chmod -R 755 "${NEO4J_HOME}/bin" \
- && ln -s /data "${NEO4J_HOME}"/data \
- && ln -s /logs "${NEO4J_HOME}"/logs \
- && git clone https://github.com/ncopa/su-exec.git \
- && cd su-exec \
- && git checkout 4c3bb42b093f14da70d8ab924b487ccfbb1397af \
- && echo d6c40440609a23483f12eb6295b5191e94baf08298a856bab6e15b10c3b82891 su-exec.c | sha256sum -c \
- && echo 2a87af245eb125aca9305a0b1025525ac80825590800f047419dc57bba36b334 Makefile | sha256sum -c \
- && make \
- && mv /su-exec/su-exec /usr/bin/su-exec \
- && apt-get -y purge --auto-remove curl gcc git make \
- && rm -rf /var/lib/apt/lists/* /su-exec
-
-
-ENV PATH "${NEO4J_HOME}"/bin:$PATH
-
-WORKDIR "${NEO4J_HOME}"
-
-VOLUME /data /logs
-
-EXPOSE 7474 7473 7687
-
-ENTRYPOINT ["tini", "-g", "--", "/startup/docker-entrypoint.sh"]
-CMD ["neo4j"]
diff --git a/README.md b/README.md
index 02ecbe4e..40908c3d 100644
--- a/README.md
+++ b/README.md
@@ -42,9 +42,9 @@ This repository hosts the code of LightRAG. The structure of this code is based
## Algorithm Flowchart
-*Figure 1: LightRAG Indexing Flowchart*
+*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
-*Figure 2: LightRAG Retrieval and Querying Flowchart*
+*Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
## Install
@@ -364,7 +364,21 @@ custom_kg = {
"weight": 1.0,
"source_id": "Source1"
}
- ]
+ ],
+ "chunks": [
+ {
+ "content": "ProductX, developed by CompanyA, has revolutionized the market with its cutting-edge features.",
+ "source_id": "Source1",
+ },
+ {
+ "content": "PersonA is a prominent researcher at UniversityB, focusing on artificial intelligence and machine learning.",
+ "source_id": "Source2",
+ },
+ {
+ "content": "None",
+ "source_id": "UNKNOWN",
+ },
+ ],
}
rag.insert_custom_kg(custom_kg)
@@ -947,56 +961,6 @@ def extract_queries(file_path):
```
-## Code Structure
-
-```python
-.
-├── examples
-│ ├── batch_eval.py
-│ ├── generate_query.py
-│ ├── graph_visual_with_html.py
-│ ├── graph_visual_with_neo4j.py
-│ ├── lightrag_api_openai_compatible_demo.py
-│ ├── lightrag_azure_openai_demo.py
-│ ├── lightrag_bedrock_demo.py
-│ ├── lightrag_hf_demo.py
-│ ├── lightrag_lmdeploy_demo.py
-│ ├── lightrag_ollama_demo.py
-│ ├── lightrag_openai_compatible_demo.py
-│ ├── lightrag_openai_demo.py
-│ ├── lightrag_siliconcloud_demo.py
-│ └── vram_management_demo.py
-├── lightrag
-│ ├── kg
-│ │ ├── __init__.py
-│ │ └── neo4j_impl.py
-│ ├── __init__.py
-│ ├── base.py
-│ ├── lightrag.py
-│ ├── llm.py
-│ ├── operate.py
-│ ├── prompt.py
-│ ├── storage.py
-│ └── utils.py
-├── reproduce
-│ ├── Step_0.py
-│ ├── Step_1_openai_compatible.py
-│ ├── Step_1.py
-│ ├── Step_2.py
-│ ├── Step_3_openai_compatible.py
-│ └── Step_3.py
-├── .gitignore
-├── .pre-commit-config.yaml
-├── Dockerfile
-├── get_all_edges_nx.py
-├── LICENSE
-├── README.md
-├── requirements.txt
-├── setup.py
-├── test_neo4j.py
-└── test.py
-```
-
## Star History
diff --git a/examples/insert_custom_kg.py b/examples/insert_custom_kg.py
index 19da0f29..1c02ea25 100644
--- a/examples/insert_custom_kg.py
+++ b/examples/insert_custom_kg.py
@@ -56,18 +56,6 @@ custom_kg = {
"description": "An annual technology conference held in CityC",
"source_id": "Source3",
},
- {
- "entity_name": "CompanyD",
- "entity_type": "Organization",
- "description": "A financial services company specializing in insurance",
- "source_id": "Source4",
- },
- {
- "entity_name": "ServiceZ",
- "entity_type": "Service",
- "description": "An insurance product offered by CompanyD",
- "source_id": "Source4",
- },
],
"relationships": [
{
@@ -94,13 +82,23 @@ custom_kg = {
"weight": 0.8,
"source_id": "Source3",
},
+ ],
+ "chunks": [
{
- "src_id": "CompanyD",
- "tgt_id": "ServiceZ",
- "description": "CompanyD provides ServiceZ",
- "keywords": "provide, offer",
- "weight": 1.0,
- "source_id": "Source4",
+ "content": "ProductX, developed by CompanyA, has revolutionized the market with its cutting-edge features.",
+ "source_id": "Source1",
+ },
+ {
+ "content": "PersonA is a prominent researcher at UniversityB, focusing on artificial intelligence and machine learning.",
+ "source_id": "Source2",
+ },
+ {
+ "content": "EventY, held in CityC, attracts technology enthusiasts and companies from around the globe.",
+ "source_id": "Source3",
+ },
+ {
+ "content": "None",
+ "source_id": "UNKNOWN",
},
],
}
diff --git a/examples/lightrag_nvidia_demo.py b/examples/lightrag_nvidia_demo.py
index 10d43c42..5af562b0 100644
--- a/examples/lightrag_nvidia_demo.py
+++ b/examples/lightrag_nvidia_demo.py
@@ -1,11 +1,14 @@
import os
import asyncio
from lightrag import LightRAG, QueryParam
-from lightrag.llm import openai_complete_if_cache, nvidia_openai_embedding, nvidia_openai_complete
+from lightrag.llm import (
+ openai_complete_if_cache,
+ nvidia_openai_embedding,
+)
from lightrag.utils import EmbeddingFunc
import numpy as np
-#for custom llm_model_func
+# for custom llm_model_func
from lightrag.utils import locate_json_string_body_from_string
WORKING_DIR = "./dickens"
@@ -13,14 +16,15 @@ WORKING_DIR = "./dickens"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
-#some method to use your API key (choose one)
+# some method to use your API key (choose one)
# NVIDIA_OPENAI_API_KEY = os.getenv("NVIDIA_OPENAI_API_KEY")
-NVIDIA_OPENAI_API_KEY = "nvapi-xxxx" #your api key
+NVIDIA_OPENAI_API_KEY = "nvapi-xxxx" # your api key
# using pre-defined function for nvidia LLM API. OpenAI compatible
# llm_model_func = nvidia_openai_complete
-#If you trying to make custom llm_model_func to use llm model on NVIDIA API like other example:
+
+# If you trying to make custom llm_model_func to use llm model on NVIDIA API like other example:
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
@@ -37,36 +41,41 @@ async def llm_model_func(
return locate_json_string_body_from_string(result)
return result
-#custom embedding
+
+# custom embedding
nvidia_embed_model = "nvidia/nv-embedqa-e5-v5"
+
+
async def indexing_embedding_func(texts: list[str]) -> np.ndarray:
return await nvidia_openai_embedding(
texts,
- model = nvidia_embed_model, #maximum 512 token
+ model=nvidia_embed_model, # maximum 512 token
# model="nvidia/llama-3.2-nv-embedqa-1b-v1",
api_key=NVIDIA_OPENAI_API_KEY,
base_url="https://integrate.api.nvidia.com/v1",
- input_type = "passage",
- trunc = "END", #handling on server side if input token is longer than maximum token
- encode = "float"
+ input_type="passage",
+ trunc="END", # handling on server side if input token is longer than maximum token
+ encode="float",
)
+
async def query_embedding_func(texts: list[str]) -> np.ndarray:
return await nvidia_openai_embedding(
texts,
- model = nvidia_embed_model, #maximum 512 token
+ model=nvidia_embed_model, # maximum 512 token
# model="nvidia/llama-3.2-nv-embedqa-1b-v1",
api_key=NVIDIA_OPENAI_API_KEY,
base_url="https://integrate.api.nvidia.com/v1",
- input_type = "query",
- trunc = "END", #handling on server side if input token is longer than maximum token
- encode = "float"
+ input_type="query",
+ trunc="END", # handling on server side if input token is longer than maximum token
+ encode="float",
)
-#dimension are same
+
+# dimension are same
async def get_embedding_dim():
test_text = ["This is a test sentence."]
- embedding = await indexing_embedding_func(test_text)
+ embedding = await indexing_embedding_func(test_text)
embedding_dim = embedding.shape[1]
return embedding_dim
@@ -88,29 +97,29 @@ async def main():
embedding_dimension = await get_embedding_dim()
print(f"Detected embedding dimension: {embedding_dimension}")
- #lightRAG class during indexing
+ # lightRAG class during indexing
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
- # llm_model_name="meta/llama3-70b-instruct", #un comment if
+ # llm_model_name="meta/llama3-70b-instruct", #un comment if
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
- max_token_size=512, #maximum token size, somehow it's still exceed maximum number of token
- #so truncate (trunc) parameter on embedding_func will handle it and try to examine the tokenizer used in LightRAG
- #so you can adjust to be able to fit the NVIDIA model (future work)
+ max_token_size=512, # maximum token size, somehow it's still exceed maximum number of token
+ # so truncate (trunc) parameter on embedding_func will handle it and try to examine the tokenizer used in LightRAG
+ # so you can adjust to be able to fit the NVIDIA model (future work)
func=indexing_embedding_func,
),
)
-
- #reading file
+
+ # reading file
with open("./book.txt", "r", encoding="utf-8") as f:
await rag.ainsert(f.read())
- #redefine rag to change embedding into query type
+ # redefine rag to change embedding into query type
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
- # llm_model_name="meta/llama3-70b-instruct", #un comment if
+ # llm_model_name="meta/llama3-70b-instruct", #un comment if
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=512,
diff --git a/lightrag/__init__.py b/lightrag/__init__.py
index a8b60e55..ea579af2 100644
--- a/lightrag/__init__.py
+++ b/lightrag/__init__.py
@@ -1,5 +1,5 @@
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
-__version__ = "1.0.2"
+__version__ = "1.0.3"
__author__ = "Zirui Guo"
__url__ = "https://github.com/HKUDS/LightRAG"
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index 97b2f256..f2f8d07a 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -329,13 +329,39 @@ class LightRAG:
async def ainsert_custom_kg(self, custom_kg: dict):
update_storage = False
try:
+ # Insert chunks into vector storage
+ all_chunks_data = {}
+ chunk_to_source_map = {}
+ for chunk_data in custom_kg.get("chunks", []):
+ chunk_content = chunk_data["content"]
+ source_id = chunk_data["source_id"]
+ chunk_id = compute_mdhash_id(chunk_content.strip(), prefix="chunk-")
+
+ chunk_entry = {"content": chunk_content.strip(), "source_id": source_id}
+ all_chunks_data[chunk_id] = chunk_entry
+ chunk_to_source_map[source_id] = chunk_id
+ update_storage = True
+
+ if self.chunks_vdb is not None and all_chunks_data:
+ await self.chunks_vdb.upsert(all_chunks_data)
+ if self.text_chunks is not None and all_chunks_data:
+ await self.text_chunks.upsert(all_chunks_data)
+
# Insert entities into knowledge graph
all_entities_data = []
for entity_data in custom_kg.get("entities", []):
entity_name = f'"{entity_data["entity_name"].upper()}"'
entity_type = entity_data.get("entity_type", "UNKNOWN")
description = entity_data.get("description", "No description provided")
- source_id = entity_data["source_id"]
+ # source_id = entity_data["source_id"]
+ source_chunk_id = entity_data.get("source_id", "UNKNOWN")
+ source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")
+
+ # Log if source_id is UNKNOWN
+ if source_id == "UNKNOWN":
+ logger.warning(
+ f"Entity '{entity_name}' has an UNKNOWN source_id. Please check the source mapping."
+ )
# Prepare node data
node_data = {
@@ -359,7 +385,15 @@ class LightRAG:
description = relationship_data["description"]
keywords = relationship_data["keywords"]
weight = relationship_data.get("weight", 1.0)
- source_id = relationship_data["source_id"]
+ # source_id = relationship_data["source_id"]
+ source_chunk_id = relationship_data.get("source_id", "UNKNOWN")
+ source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")
+
+ # Log if source_id is UNKNOWN
+ if source_id == "UNKNOWN":
+ logger.warning(
+ f"Relationship from '{src_id}' to '{tgt_id}' has an UNKNOWN source_id. Please check the source mapping."
+ )
# Check if nodes exist in the knowledge graph
for need_insert_id in [src_id, tgt_id]:
diff --git a/lightrag/llm.py b/lightrag/llm.py
index e247699b..e670c6ce 100644
--- a/lightrag/llm.py
+++ b/lightrag/llm.py
@@ -502,11 +502,12 @@ async def gpt_4o_mini_complete(
**kwargs,
)
+
async def nvidia_openai_complete(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
result = await openai_complete_if_cache(
- "nvidia/llama-3.1-nemotron-70b-instruct", #context length 128k
+ "nvidia/llama-3.1-nemotron-70b-instruct", # context length 128k
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
@@ -517,6 +518,7 @@ async def nvidia_openai_complete(
return locate_json_string_body_from_string(result)
return result
+
async def azure_openai_complete(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
@@ -610,12 +612,12 @@ async def openai_embedding(
)
async def nvidia_openai_embedding(
texts: list[str],
- model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", #refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
+ model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", # refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
base_url: str = "https://integrate.api.nvidia.com/v1",
api_key: str = None,
- input_type: str = "passage", #query for retrieval, passage for embedding
- trunc: str = "NONE", #NONE or START or END
- encode: str = "float" #float or base64
+ input_type: str = "passage", # query for retrieval, passage for embedding
+ trunc: str = "NONE", # NONE or START or END
+ encode: str = "float", # float or base64
) -> np.ndarray:
if api_key:
os.environ["OPENAI_API_KEY"] = api_key
@@ -624,10 +626,14 @@ async def nvidia_openai_embedding(
AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
)
response = await openai_async_client.embeddings.create(
- model=model, input=texts, encoding_format=encode, extra_body={"input_type": input_type, "truncate": trunc}
+ model=model,
+ input=texts,
+ encoding_format=encode,
+ extra_body={"input_type": input_type, "truncate": trunc},
)
return np.array([dp.embedding for dp in response.data])
+
@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8191)
@retry(
stop=stop_after_attempt(3),
diff --git a/lightrag/operate.py b/lightrag/operate.py
index 09585e50..5f653d27 100644
--- a/lightrag/operate.py
+++ b/lightrag/operate.py
@@ -297,7 +297,9 @@ async def extract_entities(
chunk_dp = chunk_key_dp[1]
content = chunk_dp["content"]
# hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
- hint_prompt = entity_extract_prompt.format(**context_base, input_text="{input_text}").format(**context_base, input_text=content)
+ hint_prompt = entity_extract_prompt.format(
+ **context_base, input_text="{input_text}"
+ ).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)
@@ -949,7 +951,6 @@ async def _find_related_text_unit_from_relationships(
split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])
for dp in edge_datas
]
-
all_text_units_lookup = {}
for index, unit_list in enumerate(text_units):
From 1fdaf40b6e2381ad272c47299b1cd3fec32b9e9a Mon Sep 17 00:00:00 2001
From: LarFii <834462287@qq.com>
Date: Wed, 4 Dec 2024 19:58:16 +0800
Subject: [PATCH 3/4] Update Code Structure
---
README.md | 58 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 58 insertions(+)
diff --git a/README.md b/README.md
index 40908c3d..457554f6 100644
--- a/README.md
+++ b/README.md
@@ -961,6 +961,64 @@ def extract_queries(file_path):
```
+## Code Structure
+
+```python
+.
+├── .github
+│ ├── workflows
+│ │ └── linting.yaml
+├── examples/
+│ ├── batch_eval.py
+│ ├── generate_query.py
+│ ├── graph_visual_with_html.py
+│ ├── graph_visual_with_neo4j.py
+│ ├── insert_custom_kg.py
+│ ├── lightrag_api_openai_compatible_demo.py
+│ ├── lightrag_api_oracle_demo..py
+│ ├── lightrag_azure_openai_demo.py
+│ ├── lightrag_bedrock_demo.py
+│ ├── lightrag_hf_demo.py
+│ ├── lightrag_lmdeploy_demo.py
+│ ├── lightrag_nvidia_demo.py
+│ ├── lightrag_ollama_demo.py
+│ ├── lightrag_openai_compatible_demo.py
+│ ├── lightrag_openai_demo.py
+│ ├── lightrag_oracle_demo.py
+│ ├── lightrag_siliconcloud_demo.py
+│ └── vram_management_demo.py
+├── lightrag
+│ ├── kg
+│ │ ├── __init__.py
+│ │ ├── oracle_impl.py
+│ │ └── neo4j_impl.py
+│ ├── __init__.py
+│ ├── base.py
+│ ├── lightrag.py
+│ ├── llm.py
+│ ├── operate.py
+│ ├── prompt.py
+│ ├── storage.py
+│ └── utils.py
+├── reproduce
+│ ├── Step_0.py
+│ ├── Step_1_openai_compatible.py
+│ ├── Step_1.py
+│ ├── Step_2.py
+│ ├── Step_3_openai_compatible.py
+│ └── Step_3.py
+├── .gitignore
+├── .pre-commit-config.yaml
+├── get_all_edges_nx.py
+├── LICENSE
+├── README.md
+├── requirements.txt
+├── setup.py
+├── test_neo4j.py
+└── test.py
+```
+
+
## Star History
From dff3fe18e9f87a5bee329f2699898b17011f507c Mon Sep 17 00:00:00 2001
From: LarFii <834462287@qq.com>
Date: Wed, 4 Dec 2024 19:59:50 +0800
Subject: [PATCH 4/4] Update Code Structure
---
README.md | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 457554f6..4b483e1b 100644
--- a/README.md
+++ b/README.md
@@ -965,8 +965,8 @@ def extract_queries(file_path):
```python
.
-├── .github
-│ ├── workflows
+├── .github/
+│ ├── workflows/
│ │ └── linting.yaml
├── examples/
│ ├── batch_eval.py
@@ -987,8 +987,8 @@ def extract_queries(file_path):
│ ├── lightrag_oracle_demo.py
│ ├── lightrag_siliconcloud_demo.py
│ └── vram_management_demo.py
-├── lightrag
-│ ├── kg
+├── lightrag/
+│ ├── kg/
│ │ ├── __init__.py
│ │ ├── oracle_impl.py
│ │ └── neo4j_impl.py
@@ -1000,7 +1000,7 @@ def extract_queries(file_path):
│ ├── prompt.py
│ ├── storage.py
│ └── utils.py
-├── reproduce
+├── reproduce/
│ ├── Step_0.py
│ ├── Step_1_openai_compatible.py
│ ├── Step_1.py