update insert custom kg

Dockerfile

@@ -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"]

README.md

@@ -42,9 +42,9 @@ This repository hosts the code of LightRAG. The structure of this code is based
 ## Algorithm Flowchart
 
 ![LightRAG Indexing Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-VectorDB-Json-KV-Store-Indexing-Flowchart-scaled.jpg)
-*Figure 1: LightRAG Indexing Flowchart*
+*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
 ![LightRAG Retrieval and Querying Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-Querying-Flowchart-Dual-Level-Retrieval-Generation-Knowledge-Graphs-scaled.jpg)
-*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):
 ```
 </details>
 
-## 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
 
 <a href="https://star-history.com/#HKUDS/LightRAG&Date">

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",
+            "content": "ProductX, developed by CompanyA, has revolutionized the market with its cutting-edge features.",
-            "tgt_id": "ServiceZ",
+            "source_id": "Source1",
-            "description": "CompanyD provides ServiceZ",
+        },
-            "keywords": "provide, offer",
+        {
-            "weight": 1.0,
+            "content": "PersonA is a prominent researcher at UniversityB, focusing on artificial intelligence and machine learning.",
-            "source_id": "Source4",
+            "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",
         },
     ],
 }

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",
+        input_type="passage",
-        trunc = "END", #handling on server side if input token is longer than maximum token
+        trunc="END",  # handling on server side if input token is longer than maximum token
-        encode = "float"
+        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",
+        input_type="query",
-        trunc = "END", #handling on server side if input token is longer than maximum token
+        trunc="END",  # handling on server side if input token is longer than maximum token
-        encode = "float"
+        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 
+                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 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)
+                # 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,

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"

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]:

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
+    input_type: str = "passage",  # query for retrieval, passage for embedding
-    trunc: str = "NONE", #NONE or START or END
+    trunc: str = "NONE",  # NONE or START or END
-    encode: str = "float" #float or base64
+    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),

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):