dosubot[bot]
commented
11 months ago 🤖
Hello,
The error you're encountering is due to the allowed_operators attribute of the QdrantTranslator object being None. This is causing an issue when the from_llm method of the SelfQueryRetriever class tries to join this None value, leading to a TypeError: can only join an iterable.
In the context shared, the QdrantTranslator class does not have an allowed_operators attribute. Instead, it has an allowed_comparators attribute. This could be the source of the confusion and the error.
If allowed_operators is a required attribute for the QdrantTranslator object, you should add it to the class definition and initialize it as an iterable. Here's a possible modification:
class QdrantTranslator(Visitor):
"""Translate `Qdrant` internal query language elements to valid filters."""
allowed_comparators = (
Comparator.EQ,
Comparator.LT,
Comparator.LTE,
Comparator.GT,
Comparator.GTE,
)
"""Subset of allowed logical comparators."""
allowed_operators = [] # Add this line
def __init__(self, metadata_key: str):
self.metadata_key = metadata_key
# ...If allowed_operators is supposed to be None in some cases, you should modify the from_llm method of the SelfQueryRetriever class to handle this case. Here's a possible modification:
if "allowed_operators" not in chain_kwargs:
allowed_operators = structured_query_translator.allowed_operators
if allowed_operators is None:
allowed_operators = [] # or some other default value
chain_kwargs["allowed_operators"] = allowed_operatorsThis code checks if allowed_operators is None and, if it is, sets it to an empty list (or some other default value) before assigning it to chain_kwargs["allowed_operators"]. This ensures that chain_kwargs["allowed_operators"] is always an iterable, preventing the TypeError.
Please try these modifications and let me know if the issue persists.
Regarding the purpose of the allowed_operators attribute in the QdrantTranslator object within the LangChain codebase, I wasn't able to find an answer within the repository. It's possible that this attribute is used in some other part of the codebase that wasn't included in the context provided.
I hope this helps! If you have any other questions or need further clarification, feel free to ask.
Best, Dosu
Sources
- TypeError: 'NoneType' object is not callable in SelfQueryRetriever.from_llm
- Problem with SelfQueryRetriever
- libs/langchain/langchain/retrievers/self_query/base.py
- libs/langchain/langchain/retrievers/self_query/qdrant.py
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xieqihui
System Info
accelerate==0.23.0 aiohttp==3.8.6 aiosignal==1.3.1 altair==5.1.2 annotated-types==0.6.0 anyio==3.7.1 appdirs==1.4.4 asgiref==3.7.2 asttokens==2.4.0 async-timeout==4.0.3 attrs==23.1.0 auto-gptq==0.4.2 backcall==0.2.0 bentoml==1.1.7 bitsandbytes==0.41.1 blinker==1.6.3 build==1.0.3 cachetools==5.3.1 cattrs==23.1.2 certifi==2023.7.22 cffi==1.16.0 chardet==5.2.0 charset-normalizer==3.3.0 circus==0.18.0 click==8.1.7 click-option-group==0.5.6 cloudpickle==3.0.0 cmake==3.27.7 colorama==0.4.6 coloredlogs==15.0.1 comm==0.1.4 contextlib2==21.6.0 contourpy==1.1.1 cryptography==41.0.4 cssselect==1.2.0 cuda-python==12.2.0 cycler==0.12.1 Cython==3.0.4 dataclasses-json==0.6.1 datasets==2.14.5 debugpy==1.8.0 decorator==5.1.1 deepmerge==1.1.0 Deprecated==1.2.14 dill==0.3.7 distro==1.8.0 et-xmlfile==1.1.0 executing==2.0.0 fastcore==1.5.29 filelock==3.12.4 filetype==1.2.0 fonttools==4.43.1 frozenlist==1.4.0 fs==2.4.16 fsspec==2023.6.0 ghapi==1.0.4 gitdb==4.0.10 GitPython==3.1.40 greenlet==3.0.0 grpcio==1.59.0 grpcio-health-checking==1.59.0 grpcio-tools==1.59.0 h11==0.14.0 h2==4.1.0 hpack==4.0.0 httpcore==0.18.0 httpx==0.25.0 huggingface-hub==0.17.3 humanfriendly==10.0 hyperframe==6.0.1 idna==3.4 importlib-metadata==6.8.0 inflection==0.5.1 InstructorEmbedding==1.0.1 ipykernel==6.25.2 ipython==8.16.1 ipywidgets==8.1.1 jedi==0.19.1 Jinja2==3.1.2 joblib==1.3.2 JPype1==1.4.1 jsonpatch==1.33 jsonpointer==2.4 jsonschema==4.19.1 jsonschema-specifications==2023.7.1 jupyter_client==8.4.0 jupyter_core==5.4.0 jupyterlab-widgets==3.0.9 kiwisolver==1.4.5 langchain==0.0.318 langsmith==0.0.47 lark==1.1.7 lxml==4.9.3 markdown-it-py==3.0.0 MarkupSafe==2.1.3 marshmallow==3.20.1 matplotlib==3.8.0 matplotlib-inline==0.1.6 mdurl==0.1.2 mpmath==1.3.0 multidict==6.0.4 multiprocess==0.70.15 mypy-extensions==1.0.0 nest-asyncio==1.5.8 networkx==3.2 ninja==1.11.1.1 nltk==3.8.1 numexpr==2.8.7 numpy==1.26.1 openai==0.28.1 openapi-schema-pydantic==1.2.4 openllm==0.3.9 openllm-client==0.3.9 openllm-core==0.3.9 openpyxl==3.1.2 opentelemetry-api==1.20.0 opentelemetry-instrumentation==0.41b0 opentelemetry-instrumentation-aiohttp-client==0.41b0 opentelemetry-instrumentation-asgi==0.41b0 opentelemetry-instrumentation-grpc==0.41b0 opentelemetry-sdk==1.20.0 opentelemetry-semantic-conventions==0.41b0 opentelemetry-util-http==0.41b0 optimum==1.13.2 orjson==3.9.9 packaging==23.2 pandas==2.1.1 parso==0.8.3 pathspec==0.11.2 pdfminer.six==20221105 pdfquery==0.4.3 peft==0.5.0 pickleshare==0.7.5 Pillow==10.1.0 pip-autoremove==0.10.0 pip-requirements-parser==32.0.1 pip-review==1.3.0 pip-tools==7.3.0 platformdirs==3.11.0 portalocker==2.8.2 prometheus-client==0.17.1 prompt-toolkit==3.0.39 protobuf==4.24.4 psutil==5.9.6 pure-eval==0.2.2 pyarrow==13.0.0 pycparser==2.21 pycryptodome==3.19.0 pydantic==2.4.2 pydantic_core==2.10.1 pydeck==0.8.0 Pygments==2.16.1 Pympler==1.0.1 PyMuPDF==1.23.5 pymupdf-fonts==1.0.5 PyMuPDFb==1.23.5 pynvml==11.5.0 pyparsing==3.1.1 pyproject_hooks==1.0.0 pyquery==2.0.0 pyreadline3==3.4.1 python-dateutil==2.8.2 python-dotenv==1.0.0 python-json-logger==2.0.7 python-multipart==0.0.6 pytz==2023.3.post1 pytz-deprecation-shim==0.1.0.post0 pywin32==306 PyYAML==6.0.1 pyzmq==25.1.1 qdrant-client==1.6.3 referencing==0.30.2 regex==2023.10.3 requests==2.31.0 rich==13.6.0 roman==4.1 rouge==1.0.1 rpds-py==0.10.6 safetensors==0.4.0 schema==0.7.5 scikit-learn==1.3.1 scipy==1.11.3 sentence-transformers==2.2.2 sentencepiece==0.1.99 sigfig==1.3.3 simple-di==0.1.5 six==1.16.0 smmap==5.0.1 sniffio==1.3.0 sortedcontainers==2.4.0 spyder-kernels==2.4.4 SQLAlchemy==2.0.22 stack-data==0.6.3 starlette==0.31.1 streamlit==1.27.2 streamlit-chat==0.1.1 sympy==1.12 tabula-py==2.8.2 tabulate==0.9.0 tenacity==8.2.3 threadpoolctl==3.2.0 tiktoken==0.5.1 tokenizers==0.14.1 toml==0.10.2 toolz==0.12.0 torch==2.1.0 torchaudio==2.1.0 torchvision==0.16.0 tornado==6.3.3 tqdm==4.66.1 traitlets==5.11.2 transformers @ git+https://github.com/huggingface/transformers@43bfd093e1817c0333a1e10fcbdd54f1032baad0 typing-inspect==0.9.0 typing_extensions==4.8.0 tzdata==2023.3 tzlocal==5.1 urllib3==1.26.18 uvicorn==0.23.2 validators==0.22.0 watchdog==3.0.0 watchfiles==0.21.0 wcwidth==0.2.8 widgetsnbextension==4.0.9 wrapt==1.15.0 xformers==0.0.22.post4 xlrd==2.0.1 xxhash==3.4.1 yarl==1.9.2 zipp==3.17.0
using python 3.11
Who can help?
No response
Information
Related Components
Reproduction
from dotenv import load_dotenv import os from langchain.chat_models import ChatOpenAI from qdrant_client import QdrantClient as qcqc from langchain.embeddings import HuggingFaceInstructEmbeddings from langchain.vectorstores import Qdrant from langchain.retrievers.self_query.base import SelfQueryRetriever from langchain.chains.query_constructor.base import AttributeInfo
load_dotenv() openai_key = os.getenv('OPENAI_API_KEY') db_path = os.getenv('vectordb_local_path') key = openai_key llm = ChatOpenAI( temperature = 0, model = 'gpt-3.5-turbo', streaming = True)
text_metadata = [AttributeInfo(name = 'book name', description = "name of the book.", type = "string"), AttributeInfo(name = 'author', description = 'Author of the book', type = 'string'), AttributeInfo(name = 'creation data', description = 'the date the book was written', type = 'list[int]'), AttributeInfo(name = 'page', description = "page number.", type = "int"), AttributeInfo(name = 'images', description = "dictionary whoes keys are name and description of images on the page,\ and whoes contents are image references on pdfs", type = "dict{string:string}"), AttributeInfo(name = 'tables', description = 'list of tables from the page', type = 'list[dataframe]') ]
def retreive_conversation_construct(store,store_content_description, metadata_format=text_metadata,verbose=False): ''' this is the first part of this function, and is the first problem i ran into ''' retriever = SelfQueryRetriever.from_llm(llm = llm, vectorstore=store, document_contents = store_content_description, metadata_field_info = metadata_format, enable_limit=True, fix_invalid = True, verbose=verbose) return retriever client = qcqc(path= db_path) model_name = "hkunlp/instructor-xl" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} load_dotenv() path = os.getenv('instructor_local_dir') os.environ['CURL_CA_BUNDLE'] = '' embed_instruction ='Represent the document for retrieval: ' embeddings = HuggingFaceInstructEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, cache_folder = path, embed_instruction = embed_instruction) vector_store = Qdrant(client= client, collection_name= 'my cluster', embeddings= embeddings) store_content_description = 'this is a paper about generating training data for large language models.' retreive_conversation_construct(vector_store,store_content_description)
Expected behavior
retriever should get generated.
I found in self_query.py, the .from_llm() method eventually leads to _get_builtin_translator getting called, which returns QdrantTranslator(metadata_key=vectorstore.metadata_payload_key) as structured_query_translator.
but later when calling structured_query_translator.allowed_operators from qdrant.py, the QdrantTranslator doesn't have allowed_operators, thus returns a None object.
this results in the following error:
File d:\ai_dev\research_assistant\testing.py:74 retreive_conversation_construct(vector_store,store_content_description)
File d:\ai_dev\research_assistant\testing.py:50 in retreive_conversation_construct retriever = SelfQueryRetriever.from_llm(llm = llm,
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\retrievers\self_query\base.py:214 in from_llm query_constructor = load_query_constructor_runnable(
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\query_constructor\base.py:317 in load_query_constructor_runnable prompt = get_query_constructor_prompt(
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\query_constructor\base.py:203 in get_query_constructor_prompt allowed_operators=" | ".join(allowed_operators),
TypeError: can only join an iterable