Replies: 1 comment 6 replies
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The "ValueError: Single '}' encountered in format string" error typically occurs when there are unescaped curly braces in your format string. In Python, you need to escape curly braces by doubling them ( In your code, the error might be occurring in the Here's a revised version of your code with the necessary adjustments: def retreival_qa_chain(chat_history_session, prompt, not_uuid):
embedding = OpenAIEmbeddings()
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.5)
vector_store = PGVector(
connection_string=CONNECTION_STRING,
collection_name=not_uuid,
embedding_function=embedding,
)
retriever = vector_store.as_retriever(search_kwargs={"k": 8})
chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={"prompt": prompt},
)
return chain
custom_message = generate_prompt(not_uuid)
qa_chains = retreival_qa_chain(
custom_message, not_uuid
)
chat_response = chat_langchain(qa_chains, query, not_uuid)Additionally, ensure that the prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{{context}}
Question: {{question}}
Helpful Answer:"""Make sure that any usage of |
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The error you're encountering indicates that the To resolve this, ensure that the
Here's a revised version of your code: from langchain_core.prompts import PromptTemplate
from langchain.chains import RetrievalQA, StuffDocumentsChain
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import PGVector
def retreival_qa_chain(chat_history_session, prompt, not_uuid):
embedding = OpenAIEmbeddings()
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.5)
vector_store = PGVector(
connection_string=CONNECTION_STRING,
collection_name=not_uuid,
embedding_function=embedding,
)
retriever = vector_store.as_retriever(search_kwargs={"k": 8})
# Define the prompt template with the required input variables
prompt_template = PromptTemplate(
template="""Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:""",
input_variables=["context", "question"]
)
# Create the LLM chain with the prompt template
llm_chain = StuffDocumentsChain(
llm_chain=llm,
document_prompt=prompt_template,
document_variable_name="context"
)
chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={"llm_chain": llm_chain}
)
return chain
custom_message = generate_prompt(not_uuid)
qa_chains = retreival_qa_chain(
custom_message, not_uuid
)
chat_response = chat_langchain(qa_chains, query, not_uuid)Ensure that the |
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@dosu Traceback (most recent call last): |
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The error you're encountering indicates that the Here's a revised version of your code to correctly instantiate and use the from langchain_core.prompts import PromptTemplate
from langchain.chains import RetrievalQA, StuffDocumentsChain
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import PGVector
def retreival_qa_chain(chat_history_session, prompt, not_uuid):
embedding = OpenAIEmbeddings()
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.5)
vector_store = PGVector(
connection_string=CONNECTION_STRING,
collection_name=not_uuid,
embedding_function=embedding,
)
retriever = vector_store.as_retriever(search_kwargs={"k": 8})
# Define the prompt template with the required input variables
prompt_template = PromptTemplate(
template="""Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:""",
input_variables=["context", "question"]
)
# Create the LLM chain with the prompt template
llm_chain = StuffDocumentsChain(
llm_chain=llm,
document_prompt=prompt_template,
document_variable_name="context"
)
chain = RetrievalQA.from_chain_type(
llm=llm_chain,
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={"llm_chain": llm_chain}
)
return chain
custom_message = generate_prompt(not_uuid)
qa_chains = retreival_qa_chain(
custom_message, not_uuid
)
chat_response = chat_langchain(qa_chains, query, not_uuid)Ensure that the |
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@dosu |
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To resolve the Here's the corrected code snippet: import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
import weaviate
# Check env vars
if os.environ.get("WEAVIATE_API_KEY", None) is None:
raise Exception("Missing `WEAVIATE_API_KEY` environment variable.")
if os.environ.get("WEAVIATE_ENVIRONMENT", None) is None:
raise Exception("Missing `WEAVIATE_ENVIRONMENT` environment variable.")
if os.environ.get("WEAVIATE_URL", None) is None:
raise Exception("Missing `WEAVIATE_URL` environment variable.")
if os.environ.get("OPENAI_API_KEY", None) is None:
raise Exception("Missing `OPENAI_API_KEY` environment variable.")
# Initialize the retriever
WEAVIATE_INDEX_NAME = os.environ.get("WEAVIATE_INDEX", "langchain-test")
WEAVIATE_URL = os.getenv("WEAVIATE_URL")
auth_client_secret = (weaviate.AuthApiKey(api_key=os.getenv("WEAVIATE_API_KEY")),)
client = weaviate.Client(
url=WEAVIATE_URL,
additional_headers={
"X-Openai-Api-Key": os.getenv("OPENAI_API_KEY"),
},
)
retriever = WeaviateHybridSearchRetriever(
client=client,
index_name=WEAVIATE_INDEX_NAME,
text_key="text",
attributes=[],
create_schema_if_missing=True,
)
# RAG prompt
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# RAG
model = ChatOpenAI()
chain = (
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
| prompt
| model
| StrOutputParser()
)In this corrected code, the |
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Example Code
Description
I am getting below error while running the above code
Internal Server Error: /get-response/
Traceback (most recent call last):
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/django/core/handlers/exception.py", line 55, in inner
response = get_response(request)
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/django/core/handlers/base.py", line 197, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/django/views/decorators/csrf.py", line 56, in wrapper_view
return view_func(*args, **kwargs)
File "/home/ubuntu/app/CustomBot/chatbot/views.py", line 559, in GetChatResponse
chat_response = chat_langchain(qa_chains, query, not_uuid)
File "/home/ubuntu/app/CustomBot/accounts/common_langcain_qa.py", line 511, in chat_langchain
agent = initialize_agent(
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py", line 170, in warning_emitting_wrapper
return wrapped(*args, **kwargs)
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain/agents/initialize.py", line 75, in initialize_agent
agent_obj = agent_cls.from_llm_and_tools(
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain/agents/mrkl/base.py", line 134, in from_llm_and_tools
prompt = cls.create_prompt(
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain/agents/mrkl/base.py", line 104, in create_prompt
return PromptTemplate.from_template(template)
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain_core/prompts/prompt.py", line 282, in from_template
input_variables = get_template_variables(template, template_format)
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain_core/prompts/string.py", line 249, in get_template_variables
input_variables = {
File "/home/ubuntu/app/CustomBot/cenv/lib/python3.10/site-packages/langchain_core/prompts/string.py", line 249, in
input_variables = {
ValueError: Single '}' encountered in format string
System Info
langchain==0.2.11
langchain-anthropic==0.1.22
langchain-community==0.2.10
langchain-core==0.2.26
langchain-openai==0.0.5
langchain-qdrant==0.1.3
langchain-text-splitters==0.2.2
langchain-weaviate==0.0.2
langchainhub==0.1.14
Linux
Python 3.12.4
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