题意：类型错误：期望字符串或缓冲区 - Langchain，OpenAI Embeddings
问题背景：
I am trying to create RAG using the product manuals in pdf which are splitted, indexed and stored in Chroma persisted on a disk. When I try the function that classifies the reviews using the documents context, below is the error I get:
我正在尝试使用 PDF 格式的产品手册创建 RAG，这些手册被拆分、索引并存储在硬盘上的 Chroma 中。当我尝试使用文档上下文对评论进行分类的函数时，出现了以下错误：
from langchain import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain.embeddings import AzureOpenAIEmbeddings
from langchain.chat_models import AzureChatOpenAI
from langchain.vectorstores import Chromallm = AzureChatOpenAI(azure_deployment="ChatGPT-16K",openai_api_version="2023-05-15",azure_endpoint=endpoint,api_key=result["access_token"],temperature=0,seed = 100)embedding_model = AzureOpenAIEmbeddings(api_version="2023-05-15",azure_endpoint=endpoint,api_key=result["access_token"],azure_deployment="ada002",
)vectordb = Chroma(persist_directory=vector_db_path,embedding_function=embedding_model,collection_name="product_manuals",
)def format_docs(docs):return "\n\n".join(doc.page_content for doc in docs)def classify (review_title, review_text, product_num):template = """You are a customer service AI Assistant that handles responses to negative product reviews. Use the context below and categorize {review_title} and {review_text} into defect, misuse or poor quality categories based only on provided context. If you don't know, say that you do not know, don't try to make up an answer. Respond back with an answer in the following format:poor qualitymisusedefect{context}Category: """rag_prompt = PromptTemplate.from_template(template)retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={'filter': {'product_num': product_num}})retrieval_chain = ({"context": retriever | format_docs, "review_title: RunnablePassthrough(), "review_text": RunnablePassthrough()}| rag_prompt| llm| StrOutputParser())return retrieval_chain.invoke({"review_title": review_title, "review_text": review_text})classify(review_title="Terrible", review_text ="This baking sheet is terrible. It stains so easily and i've tried everything to get it clean", product_num ="8888999")Error stack: 错误信息：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File <command-3066972537097411>, line 1
----> 1 issue_recommendation(2 review_title="Terrible",3 review_text="This baking sheet is terrible. It stains so easily and i've tried everything to get it clean. I've maybe used it 5 times and it looks like it's 20 years old. The side of the pan also hold water, so when you pick it up off the drying rack, water runs out. I would never purchase these again.",4 product_num="8888999"5 6 )File <command-3066972537097410>, line 44, in issue_recommendation(review_title, review_text, product_num)36 retriever = vectordb.as_retriever(search_type="similarity", search_kwargs={'filter': {'product_num': product_num}})38 retrieval_chain = (39 {"context": retriever | format_docs, "review_text": RunnablePassthrough()}40 | rag_prompt41 | llm42 | StrOutputParser()43 )
---> 44 return retrieval_chain.invoke({"review_title":review_title, "review_text": review_text})File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:1762, in RunnableSequence.invoke(self, input, config)1760 try:1761 for i, step in enumerate(self.steps):
-> 1762 input = step.invoke(1763 input,1764 # mark each step as a child run1765 patch_config(1766 config, callbacks=run_manager.get_child(f"seq:step:{i+1}")1767 ),1768 )1769 # finish the root run1770 except BaseException as e:File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:2327, in RunnableParallel.invoke(self, input, config)2314 with get_executor_for_config(config) as executor:2315 futures = [2316 executor.submit(2317 step.invoke,(...)2325 for key, step in steps.items()2326 ]
-> 2327 output = {key: future.result() for key, future in zip(steps, futures)}2328 # finish the root run2329 except BaseException as e:File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:2327, in <dictcomp>(.0)2314 with get_executor_for_config(config) as executor:2315 futures = [2316 executor.submit(2317 step.invoke,(...)2325 for key, step in steps.items()2326 ]
-> 2327 output = {key: future.result() for key, future in zip(steps, futures)}2328 # finish the root run2329 except BaseException as e:File /usr/lib/python3.10/concurrent/futures/_base.py:451, in Future.result(self, timeout)449 raise CancelledError()450 elif self._state == FINISHED:
--> 451 return self.__get_result()453 self._condition.wait(timeout)455 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:File /usr/lib/python3.10/concurrent/futures/_base.py:403, in Future.__get_result(self)401 if self._exception:402 try:
--> 403 raise self._exception404 finally:405 # Break a reference cycle with the exception in self._exception406 self = NoneFile /usr/lib/python3.10/concurrent/futures/thread.py:58, in _WorkItem.run(self)55 return57 try:
---> 58 result = self.fn(*self.args, **self.kwargs)59 except BaseException as exc:60 self.future.set_exception(exc)File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/runnables/base.py:1762, in RunnableSequence.invoke(self, input, config)1760 try:1761 for i, step in enumerate(self.steps):
-> 1762 input = step.invoke(1763 input,1764 # mark each step as a child run1765 patch_config(1766 config, callbacks=run_manager.get_child(f"seq:step:{i+1}")1767 ),1768 )1769 # finish the root run1770 except BaseException as e:File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/retrievers.py:121, in BaseRetriever.invoke(self, input, config)117 def invoke(118 self, input: str, config: Optional[RunnableConfig] = None119 ) -> List[Document]:120 config = ensure_config(config)
--> 121 return self.get_relevant_documents(122 input,123 callbacks=config.get("callbacks"),124 tags=config.get("tags"),125 metadata=config.get("metadata"),126 run_name=config.get("run_name"),127 )File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/retrievers.py:223, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)221 except Exception as e:222 run_manager.on_retriever_error(e)
--> 223 raise e224 else:225 run_manager.on_retriever_end(226 result,227 **kwargs,228 )File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/retrievers.py:216, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)214 _kwargs = kwargs if self._expects_other_args else {}215 if self._new_arg_supported:
--> 216 result = self._get_relevant_documents(217 query, run_manager=run_manager, **_kwargs218 )219 else:220 result = self._get_relevant_documents(query, **_kwargs)File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_core/vectorstores.py:654, in VectorStoreRetriever._get_relevant_documents(self, query, run_manager)650 def _get_relevant_documents(651 self, query: str, *, run_manager: CallbackManagerForRetrieverRun652 ) -> List[Document]:653 if self.search_type == "similarity":
--> 654 docs = self.vectorstore.similarity_search(query, **self.search_kwargs)655 elif self.search_type == "similarity_score_threshold":656 docs_and_similarities = (657 self.vectorstore.similarity_search_with_relevance_scores(658 query, **self.search_kwargs659 )660 )File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:348, in Chroma.similarity_search(self, query, k, filter, **kwargs)331 def similarity_search(332 self,333 query: str,(...)336 **kwargs: Any,337 ) -> List[Document]:338 """Run similarity search with Chroma.339 340 Args:(...)346 List[Document]: List of documents most similar to the query text.347 """
--> 348 docs_and_scores = self.similarity_search_with_score(349 query, k, filter=filter, **kwargs350 )351 return [doc for doc, _ in docs_and_scores]File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/vectorstores/chroma.py:437, in Chroma.similarity_search_with_score(self, query, k, filter, where_document, **kwargs)429 results = self.__query_collection(430 query_texts=[query],431 n_results=k,(...)434 **kwargs,435 )436 else:
--> 437 query_embedding = self._embedding_function.embed_query(query)438 results = self.__query_collection(439 query_embeddings=[query_embedding],440 n_results=k,(...)443 **kwargs,444 )446 return _results_to_docs_and_scores(results)File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/embeddings/openai.py:691, in OpenAIEmbeddings.embed_query(self, text)682 def embed_query(self, text: str) -> List[float]:683 """Call out to OpenAI's embedding endpoint for embedding query text.684 685 Args:(...)689 Embedding for the text.690 """
--> 691 return self.embed_documents([text])[0]File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/embeddings/openai.py:662, in OpenAIEmbeddings.embed_documents(self, texts, chunk_size)659 # NOTE: to keep things simple, we assume the list may contain texts longer660 # than the maximum context and use length-safe embedding function.661 engine = cast(str, self.deployment)
--> 662 return self._get_len_safe_embeddings(texts, engine=engine)File /local_disk0/.ephemeral_nfs/envs/pythonEnv-65a09d8c-062d-4f4f-9c52-1bf534f6511e/lib/python3.10/site-packages/langchain_community/embeddings/openai.py:465, in OpenAIEmbeddings._get_len_safe_embeddings(self, texts, engine, chunk_size)459 if self.model.endswith("001"):460 # See: https://github.com/openai/openai-python/461 # issues/418#issuecomment-1525939500462 # replace newlines, which can negatively affect performance.463 text = text.replace("\n", " ")
--> 465 token = encoding.encode(466 text=text,467 allowed_special=self.allowed_special,468 disallowed_special=self.disallowed_special,469 )471 # Split tokens into chunks respecting the embedding_ctx_length472 for j in range(0, len(token), self.embedding_ctx_length):File /databricks/python/lib/python3.10/site-packages/tiktoken/core.py:116, in Encoding.encode(self, text, allowed_special, disallowed_special)114 if not isinstance(disallowed_special, frozenset):115 disallowed_special = frozenset(disallowed_special)
--> 116 if match := _special_token_regex(disallowed_special).search(text):117 raise_disallowed_special_token(match.group())119 try:TypeError: expected string or bufferEmbeddings seems to work fine when I test. It also works fine when I remove the context and retriever from the chain. It seems to be related to embeddings. Examples on Langchain website instantiates retriver from Chroma.from_documents() whereas I load Chroma vector store from a persisted path. I also tried invoking with review_text only (instead of review title and review text) but the error persists. Not sure why this is happening. These are the package versions I work:
当我测试时，Embeddings 似乎工作正常。当我从链中移除上下文和检索器时，它也能正常工作。问题似乎与 Embeddings 有关。Langchain 网站上的示例是通过 Chroma.from_documents() 实例化检索器，而我是从已保存的路径加载 Chroma 向量存储。我也尝试仅使用 review_text（而不是 review title 和 review text），但错误仍然存在。不确定为什么会这样。这是我使用的包版本：
Name: openai Version: 1.6.1
Name: langchain Version: 0.0.354
问题解决：
I've come across the same issue, and turned out that langchain pass a key-value pair as an input to the encoding.code() while it requires str type. A work around is by using itemgetter() to get the direct string input. It might be something like this
我也遇到了同样的问题，发现是由于 langchain 将一个键值对作为输入传递给 encoding.code()，而它需要的是 str 类型。一个解决方法是使用 itemgetter() 来获取直接的字符串输入。可能是这样的：
retrieval_chain = ({"document": itemgetter("question") | self.retriever,"question": itemgetter("question"),}| prompt| model| StrOutputParser())
You can find the reference here
你可以在这里找到参考资料。
