Traceback (most recent call last): File "/home/cdhd/PythonCode/pythonProject/Knowledge/Neo4j/neo4j_vectore.py", line 80, in <module> graph_documents = llm_transformer.convert_to_graph_documents(document) File "/home/cdhd/PythonCode/pythonProject/.venv/lib/python3.10/site-packages/langchain_experimental/graph_transformers/llm.py", line 762, in convert_to_graph_documents return [self.process_response(document) for document in documents] File "/home/cdhd/PythonCode/pythonProject/.venv/lib/python3.10/site-packages/langchain_experimental/graph_transformers/llm.py", line 762, in <listcomp> return [self.process_response(document) for document in documents] File "/home/cdhd/PythonCode/pythonProject/.venv/lib/python3.10/site-packages/langchain_experimental/graph_transformers/llm.py", line 714, in process_response nodes_set.add((rel["head"], rel["head_type"])) TypeError: list indices must be integers or slices, not str

[li-hhhh]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
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Example Code

documents = SimpleDirectoryReader(input_files=["./TestFile/公司主要人员架构类/hhh.txt"]).load_data()
print("documents",documents[0].text)
document = [Document(page_content=documents[0].text)]
llm_lang = HuggingFacePipeline.from_model_id(
    model_id="/home/cdhd/PythonCode/pythonProject/model/qwen2-7b",
    task="text-generation",
    pipeline_kwargs={"max_new_tokens": 10},
    device = 0,
)
graph_store = Neo4jGraphStore(
    username="neo4j",
    password="12345678",
    url="bolt://localhost:7687",
    database="neo4j"

)

# 清理数据集
graph_store.query(
    """
MATCH (n) DETACH DELETE n
"""
)
llm_transformer = LLMGraphTransformer(llm=llm_lang)
graph_documents = llm_transformer.convert_to_graph_documents(document)

Description

我在执行上面的代码时，显示该错误，官方案例运行之后也发生了同样的错误，这是因为什么原因造成的呢？可以怎么取解决？

System Info

Package Version

---

accelerate 0.31.0
aiohttp 3.9.5
aiosignal 1.3.1
annotated-types 0.7.0
anyio 4.4.0
argon2-cffi 23.1.0
argon2-cffi-bindings 21.2.0
arrow 1.3.0
asgiref 3.8.1
asttokens 2.4.1
async-lru 2.0.4
async-timeout 4.0.3
attrs 23.2.0
Babel 2.15.0
backoff 2.2.1
bcrypt 4.1.3
beautifulsoup4 4.12.3
bleach 6.1.0
boto3 1.34.136
botocore 1.34.136
build 1.2.1
bz2file 0.98
cachetools 5.3.3
certifi 2024.6.2
cffi 1.16.0
chardet 5.2.0
charset-normalizer 3.3.2
chroma-hnswlib 0.7.3
chromadb 0.5.3
ci-info 0.3.0
click 8.1.7
cohere 5.5.8
colorama 0.4.6
coloredlogs 15.0.1
comm 0.2.2
configobj 5.0.8
configparser 7.0.0
contourpy 1.2.1
cycler 0.12.1
dataclasses-json 0.6.7
datasets 2.20.0
debugpy 1.8.2
decorator 5.1.1
deepdiff 7.0.1
defusedxml 0.7.1
Deprecated 1.2.14
dill 0.3.8
dirtyjson 1.0.8
distro 1.9.0
dnspython 2.6.1
docx 0.2.4
docx2txt 0.8
email_validator 2.2.0
emoji 2.12.1
et-xmlfile 1.1.0
etelemetry 0.3.1
exceptiongroup 1.2.1
executing 2.0.1
fastapi 0.110.3
fastapi-cli 0.0.4
fastavro 1.9.4
fastjsonschema 2.20.0
filelock 3.15.4
filetype 1.2.0
fitz 0.0.1.dev2
FlagEmbedding 1.2.10
flatbuffers 24.3.25
fonttools 4.53.1
fqdn 1.5.1
frozenlist 1.4.1
fsspec 2024.5.0
gitdb 4.0.11
GitPython 3.1.43
google-auth 2.31.0
googleapis-common-protos 1.63.2
greenlet 3.0.3
grpcio 1.64.1
h11 0.14.0
html2text 2024.2.26
httpcore 1.0.5
httplib2 0.22.0
httptools 0.6.1
httpx 0.27.0
httpx-sse 0.4.0
huggingface-hub 0.23.4
humanfriendly 10.0
idna 3.7
importlib_metadata 7.1.0
importlib_resources 6.4.0
interchange 2021.0.4
ipykernel 6.29.5
ipython 8.26.0
isodate 0.6.1
isoduration 20.11.0
jedi 0.19.1
Jinja2 3.1.4
jmespath 1.0.1
joblib 1.4.2
json_repair 0.25.2
json5 0.9.25
jsonpatch 1.33
jsonpath-python 1.0.6
jsonpickle 3.2.2
jsonpointer 3.0.0
jsonschema 4.22.0
jsonschema-specifications 2023.12.1
jupyter_client 8.6.2
jupyter_core 5.7.2
jupyter-events 0.10.0
jupyter-lsp 2.2.5
jupyter_server 2.14.1
jupyter_server_terminals 0.5.3
jupyterlab 4.2.3
jupyterlab_pygments 0.3.0
jupyterlab_server 2.27.2
kaleido 0.2.1
kiwisolver 1.4.5
kubernetes 30.1.0
langchain 0.2.7
langchain-cli 0.0.25
langchain-community 0.2.7
langchain-core 0.2.12
langchain-experimental 0.0.62
langchain-huggingface 0.0.3
langchain-openai 0.1.14
langchain-text-splitters 0.2.2

[dosubot[bot]]

The error TypeError: list indices must be integers or slices, not str occurs because the code is trying to access a list element using a string key, which is not allowed in Python. This issue typically arises when the structure of the data being processed does not match the expected format.

In the process_response method of the LLMGraphTransformer class, the rel dictionary is expected to have a specific structure. Ensure that the rel dictionary in your response has the following structure:

{
    "head": "entity_name",
    "head_type": "entity_type",
    "relation": "relationship_type",
    "tail": "entity_name",
    "tail_type": "entity_type"
}

Here's a snippet from the process_response method that shows how the rel dictionary is accessed:

for rel in parsed_json:
    nodes_set.add((rel["head"], rel["head_type"]))
    nodes_set.add((rel["tail"], rel["tail_type"]))

    source_node = Node(id=rel["head"], type=rel["head_type"])
    target_node = Node(id=rel["tail"], type=rel["tail_type"])
    relationships.append(
        Relationship(
            source=source_node, target=target_node, type=rel["relation"]
        )
    )

To resolve the issue, ensure that the data being passed to process_response is correctly formatted. You can add debugging statements to check the structure of the data before it is processed.

Additionally, make sure that your documents list contains Document objects and that you are not using string indices to access list elements. Here is an example of how to correctly use the convert_to_graph_documents method:

from langchain_community.graphs.index_creator import GraphIndexCreator
from langchain_core.documents import Document

# Assuming you have a list of documents
documents = [
    Document(page_content="Adam is a software engineer in Microsoft since 2009."),
    Document(page_content="Microsoft Word is a lightweight app that is accessible offline.")
]

# Create an instance of GraphIndexCreator
graph_index_creator = GraphIndexCreator()

# Convert documents into graph documents
graph_documents = graph_index_creator.convert_to_graph_documents(documents)

Ensure that your Document objects are correctly formatted and that you are using the appropriate methods from the LangChain library ^[1][2].

To continue talking to Dosu, mention @dosu.

[li-hhhh]

The error TypeError: list indices must be integers or slices, not str occurs because the code is trying to access a list element using a string key, which is not allowed in Python. This issue typically arises when the structure of the data being processed does not match the expected format.

In the process_response method of the LLMGraphTransformer class, the rel dictionary is expected to have a specific structure. Ensure that the rel dictionary in your response has the following structure:

{
    "head": "entity_name",
    "head_type": "entity_type",
    "relation": "relationship_type",
    "tail": "entity_name",
    "tail_type": "entity_type"
}

Here's a snippet from the process_response method that shows how the rel dictionary is accessed:

for rel in parsed_json:
    nodes_set.add((rel["head"], rel["head_type"]))
    nodes_set.add((rel["tail"], rel["tail_type"]))

    source_node = Node(id=rel["head"], type=rel["head_type"])
    target_node = Node(id=rel["tail"], type=rel["tail_type"])
    relationships.append(
        Relationship(
            source=source_node, target=target_node, type=rel["relation"]
        )
    )

To resolve the issue, ensure that the data being passed to process_response is correctly formatted. You can add debugging statements to check the structure of the data before it is processed.

Additionally, make sure that your documents list contains Document objects and that you are not using string indices to access list elements. Here is an example of how to correctly use the convert_to_graph_documents method:

from langchain_community.graphs.index_creator import GraphIndexCreator
from langchain_core.documents import Document

# Assuming you have a list of documents
documents = [
    Document(page_content="Adam is a software engineer in Microsoft since 2009."),
    Document(page_content="Microsoft Word is a lightweight app that is accessible offline.")
]

# Create an instance of GraphIndexCreator
graph_index_creator = GraphIndexCreator()

# Convert documents into graph documents
graph_documents = graph_index_creator.convert_to_graph_documents(documents)

Ensure that your Document objects are correctly formatted and that you are using the appropriate methods from the LangChain library [1][2].

To continue talking to Dosu, mention @dosu.

这个方式并没有解决问题，请问有其他的解决方法吗

[dosubot[bot]]

确保 parsed_json 变量被正确解析为字典列表。以下是 process_response 方法的相关代码和潜在修复：

def process_response(self, document: Document) -> GraphDocument:
    text = document.page_content
    raw_schema = self.chain.invoke({"input": text})
    if self._function_call:
        raw_schema = cast(Dict[Any, Any], raw_schema)
        nodes, relationships = _convert_to_graph_document(raw_schema)
    else:
        nodes_set = set()
        relationships = []
        if not isinstance(raw_schema, str):
            raw_schema = raw_schema.content
        parsed_json = self.json_repair.loads(raw_schema)
        
        # 确保 parsed_json 是字典列表
        if isinstance(parsed_json, dict):
            parsed_json = [parsed_json]
        
        for rel in parsed_json:
            nodes_set.add((rel["head"], rel["head_type"]))
            nodes_set.add((rel["tail"], rel["tail_type"]))

            source_node = Node(id=rel["head"], type=rel["head_type"])
            target_node = Node(id=rel["tail"], type=rel["tail_type"])
            relationships.append(
                Relationship(
                    source=source_node, target=target_node, type=rel["relation"]
                )
            )
        nodes = [Node(id=el[0], type=el[1]) for el in list(nodes_set)]

    if self.strict_mode and (self.allowed_nodes or self.allowed_relationships):
        if self.allowed_nodes:
            lower_allowed_nodes = [el.lower() for el in self.allowed_nodes]
            nodes = [
                node for node in nodes if node.type.lower() in lower_allowed_nodes
            ]
            relationships = [
                rel
                for rel in relationships
                if rel.source.type.lower() in lower_allowed_nodes
                and rel.target.type.lower() in lower_allowed_nodes
            ]
        if self.allowed_relationships:
            relationships = [
                rel
                for rel in relationships
                if rel.type.lower()
                in [el.lower() for el in self.allowed_relationships]
            ]

    return GraphDocument(nodes=nodes, relationships=relationships, source=document)

这个修复确保 parsed_json 始终是字典列表，从而防止 TypeError 发生 ^[1].

To continue talking to Dosu, mention @dosu.