Getting ValueError: Missing some input keys: {'query'} while using RetrievalQA

[saiful1984]

My code for RetrievalQA is below

class RAGQueryHandler:
def init(self, retriever, llm):
self.retriever = retriever
self.llm = llm

def query_rag(self, query):
    template = """Use the following context to answer the question. If you don't know, say "I don't know."

    {context}

    Question: {query}
    Answer:"""

    prompt = PromptTemplate(template=template, input_variables=["context", "query"])
    qa_chain = RetrievalQA.from_chain_type(
        llm=self.llm,
        chain_type="stuff",
        retriever=self.retriever,
        chain_type_kwargs={"prompt": prompt},
    )
    #return qa_chain
    # Retrieve sources
    docs = self.retriever.get_relevant_documents(query)
    sources = list(set(doc.metadata["source"] for doc in docs if "source" in doc.metadata))
    context = "\n".join([doc.page_content for doc in docs])

    print(f"docs: {docs}")
    # print(f"sources {sources}")
    # print(f"context: {context}")
    # Run the query and get the answer
    #answer = qa_chain.run(query)
    #answer = qa_chain.invoke({"query": query})
    answer = qa_chain.invoke({"query": query, "context": docs})
    #answer = qa_chain.invoke({"input": query})
    #answer = qa.run(query=query)


    #return {"answer": answer, "sources": sources}
    return {"answer": answer["result"], "sources": sources}
    
    However when I run the below code
    rag_handler = RAGQueryHandler(retriever, llm)
    query = "What is the capital of England?"
  
  # Use the query_rag method instead of get_answer
  answer = rag_handler.query_rag(query)
  
  
  I am getting 
  
  ValueError                                Traceback (most recent call last)

Cell In[59], line 5
2 query = "What is the capital of England?"
4 # Use the query_rag method instead of get_answer
----> 5 answer = rag_handler.query_rag(query)
6 #result = rag_handler.query_rag({"query": query})
7 print(f"Question: {query}")

Cell In[57], line 34, in RAGQueryHandler.query_rag(self, query)
28 print(f"docs: {docs}")
29 # print(f"sources {sources}")
30 # print(f"context: {context}")
31 # Run the query and get the answer
32 #answer = qa_chain.run(query)
33 #answer = qa_chain.invoke({"query": query})
---> 34 answer = qa_chain.invoke({"query": query, "context": docs})
35 #answer = qa_chain.invoke({"input": query})
36 #answer = qa.run(query=query)
37
38
39 #return {"answer": answer, "sources": sources}
40 return {"answer": answer["result"], "sources": sources}

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:170, in Chain.invoke(self, input, config, **kwargs)
168 except BaseException as e:
169 run_manager.on_chain_error(e)
--> 170 raise e
171 run_manager.on_chain_end(outputs)
173 if include_run_info:

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:160, in Chain.invoke(self, input, config, **kwargs)
157 try:
158 self._validate_inputs(inputs)
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
165 final_outputs: Dict[str, Any] = self.prep_outputs(
166 inputs, outputs, return_only_outputs
167 )
168 except BaseException as e:

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/retrieval_qa/base.py:154, in BaseRetrievalQA._call(self, inputs, run_manager)
152 else:
153 docs = self._get_docs(question) # type: ignore[call-arg]
--> 154 answer = self.combine_documents_chain.run(
155 input_documents=docs, question=question, callbacks=_run_manager.get_child()
156 )
158 if self.return_source_documents:
159 return {self.output_key: answer, "source_documents": docs}

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:181, in deprecated..deprecate..warning_emitting_wrapper(*args, **kwargs)
179 warned = True
180 emit_warning()
--> 181 return wrapped(*args, **kwargs)

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:611, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
606 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
607 _output_key
608 ]
610 if kwargs and not args:
--> 611 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
612 _output_key
613 ]
615 if not kwargs and not args:
616 raise ValueError(
617 "run supported with either positional arguments or keyword arguments,"
618 " but none were provided."
619 )

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:181, in deprecated..deprecate..warning_emitting_wrapper(*args, **kwargs)
179 warned = True
180 emit_warning()
--> 181 return wrapped(*args, **kwargs)

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:389, in Chain.call(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
357 """Execute the chain.
358
359 Args:
(...)
380 Chain.output_keys.
381 """
382 config = {
383 "callbacks": callbacks,
384 "tags": tags,
385 "metadata": metadata,
386 "run_name": run_name,
387 }
--> 389 return self.invoke(
390 inputs,
391 cast(RunnableConfig, {k: v for k, v in config.items() if v is not None}),
392 return_only_outputs=return_only_outputs,
393 include_run_info=include_run_info,
394 )

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:170, in Chain.invoke(self, input, config, **kwargs)
168 except BaseException as e:
169 run_manager.on_chain_error(e)
--> 170 raise e
171 run_manager.on_chain_end(outputs)
173 if include_run_info:

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:158, in Chain.invoke(self, input, config, **kwargs)
151 run_manager = callback_manager.on_chain_start(
152 None,
153 inputs,
154 run_id,
155 name=run_name,
156 )
157 try:
--> 158 self._validate_inputs(inputs)
159 outputs = (
160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
165 final_outputs: Dict[str, Any] = self.prep_outputs(
166 inputs, outputs, return_only_outputs
167 )

File ~/Documents/dc/miniconda3/envs/agentDemo/lib/python3.11/site-packages/langchain/chains/base.py:290, in Chain._validate_inputs(self, inputs)
288 missing_keys = set(self.input_keys).difference(inputs)
289 if missing_keys:
--> 290 raise ValueError(f"Missing some input keys: {missing_keys}")

ValueError: Missing some input keys: {'query'}

How can i fix this

[dosubot[bot]]

Hey @saiful1984! I'm here to help you out with any bugs, questions, or contributing help you might need. Let's squash those bugs together! 🤖

To fix the ValueError: Missing some input keys: {'query'} in your RAGQueryHandler class, ensure that the input dictionary you pass to the invoke method matches the expected input keys. In your case, the RetrievalQA chain expects a key named 'query'.

Here's how you can adjust your code:

1. Ensure that the input dictionary you pass to qa_chain.invoke includes the 'query' key. You are already doing this correctly in the line:

   answer = qa_chain.invoke({"query": query, "context": docs})

2. If the error persists, verify that the RetrievalQA class is correctly set up to accept the 'query' key. The RetrievalQA class is deprecated, and it is recommended to use the create_retrieval_chain function instead. This might help avoid such issues.

Here's an example of how you can use create_retrieval_chain:

from langchain.chains import create_retrieval_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

# Assuming retriever and llm are already set up
system_prompt = (
    "Use the given context to answer the question. "
    "If you don't know the answer, say you don't know. "
    "Use three sentence maximum and keep the answer concise. "
    "Context: {context}"
)
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{input}"),
    ]
)
question_answer_chain = create_stuff_documents_chain(llm, prompt)
chain = create_retrieval_chain(retriever, question_answer_chain)

result = chain.invoke({"input": query})

Switching to create_retrieval_chain can help align with the latest practices and avoid deprecated features ^[1][2].

To continue talking to Dosu, mention @dosu.

---

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[saiful1984]

@dosu How did u got create_stuff_documents_chain()
Also I like to implement in ollama
Can u change the code

[dosubot[bot]]

The create_stuff_documents_chain function is part of LangChain and is used to create a chain that processes a list of documents by combining them into a single context window. This function is particularly useful for its enhanced support for streaming and batch processing ^[1][2].

To implement this with Ollama, you can use the langchain-ollama package. Here's how you can modify the code to integrate with Ollama:

1. Install the langchain-ollama package if you haven't already:

   pip install langchain-ollama

2. Modify the code to use Ollama's language model:

from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_ollama.chat_models import ChatOllama

# Set up the Ollama language model
llm = ChatOllama(model="llama3.1", temperature=0)

# Define the system prompt
system_prompt = (
    "Use the given context to answer the question. "
    "If you don't know the answer, say you don't know. "
    "Use three sentence maximum and keep the answer concise. "
    "Context: {context}"
)

# Create the prompt template
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{input}"),
    ]
)

# Create the question-answer chain
question_answer_chain = create_stuff_documents_chain(llm, prompt)

# Create the retrieval chain
chain = create_retrieval_chain(retriever, question_answer_chain)

# Invoke the chain with the query
result = chain.invoke({"input": query})

print(result)

This setup uses the ChatOllama class to access Ollama's language model and integrates it into the retrieval chain. Make sure your Ollama server is running locally and that you have the necessary models downloaded ^[3][4].

To continue talking to Dosu, mention @dosu.

---

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[saiful1984]

@dosu In your code how does context gets fitted in "Context: {context}"

[dosubot[bot]]

In the code, the context is dynamically filled into the "Context: {context}" part of the system prompt through the use of the ChatPromptTemplate. When the retrieval chain is invoked with a query, the relevant documents are retrieved by the retriever, and their content is combined into a single context string. This context string is then passed to the prompt template, replacing the {context} placeholder with the actual content of the documents. This allows the language model to use the provided context to generate an informed response to the query.

To continue talking to Dosu, mention @dosu.

---

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[saiful1984]

The issue is resolved now