Facing issues to get the correct response even after generating a proper context related to the user query and then passing it in system prompt , and then using the simple chat engine to get the answers

[pratikanand13]

I initialised a context chat engine to get the context relevent to the question and then used a simple chat engine to get the final repsonse related to the query

code

`retriever = index.as_retriever()
context_engine = ContextChatEngine.from_defaults(
retriever=retriever,
llm=llm,
memory=memory,
context_template=get_context_prompt_template(),
)
context_str, _ = await context_engine._agenerate_context(last_message_content)

    system_prompt = get_system_prompt_template().format(
        facts_list=formatted_facts,
        question=last_message_content,
        context_str=context_str,
    )
    logger.info("Prompts and context generated successfully.")
    chat_engine = SimpleChatEngine.from_defaults(
        prefix_messages=[ChatMessage(content=system_prompt, role="system")]
    response = chat_engine.achat(last_message_content,message_history)

    )`

when i logged the system prompt , i has the full info inside with the context user question and every other details and instruction which i wanted to be followed in my response but even after so i failed to get the repsonse .

[pratikanand13]

also if someone can guide me through aboout how to write proper and accurate prompts according my use-case

[dosubot[bot]]

Hey @pratikanand13, I'm here to help you with any bugs or questions you have. I can also guide you on becoming a contributor. Let me know how I can assist you!

To address the issue of not getting the correct response from the chat engine, even after generating a proper context and system prompt, you can consider the following steps:

1. Ensure Proper Context Retrieval: Verify that the retriever is correctly fetching relevant context for the input message. The _get_nodes method in the ContextChatEngine is responsible for retrieving and postprocessing nodes (context information). Ensure that the retriever and any postprocessors are configured correctly to provide useful context.

2. Check Context and Refine Templates: Make sure that the context_template and context_refine_template are correctly set and tailored to your use case. Customize these templates to better fit the type of responses you expect.

3. Utilize Dynamic Prompts: Ensure that the get_response_synthesizer method is correctly synthesizing responses using the context and refine messages. This involves checking the qa_messages and refine_messages generated by get_prefix_messages_with_context.

4. Review System Prompt and Prefix Messages: If you are using a system prompt, ensure it is not conflicting with prefix messages. The from_defaults method checks for this and raises an error if both are specified.

5. Memory Management: Ensure that the memory is correctly storing and retrieving chat history, as this can affect the context provided to the LLM.

6. Testing and Validation: Utilize tests to validate the behavior of your chat engine. The provided test cases in the llama-index-core/tests/chat_engine/test_context.py file demonstrate how to test the chat engine's response to ensure it includes the system prompt and handles chat history correctly ^[1][2].

For writing accurate prompts, consider the following:

• Clarity and Specificity: Ensure your prompts are clear and specific to guide the model effectively.
• Contextual Relevance: Include relevant context in your prompts to help the model understand the background and nuances of the query.
• Iterative Testing: Experiment with different prompt structures and iterate based on the responses you receive to refine your approach.

Additionally, there are similar discussions that might provide further insights:

• Help needed with chat engine(condense or context) HELP!!! ^[3]
• How to avoid query engine is giving out of context response ^[4]
• [Question]: How can I improve responses? ^[5]

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[pratikanand13]

if i directly use llm.achat passing in the system prompt (the system prompt has context in it,which can be a very large text but not more than around 4k words) will it cause an issue? @dosu

[pratikanand13]

i am using llama3.2 3b model , can you provide its token count and few example to Advanced Prompts: Include variable mappings, functions, partials, and few-shot examples. @dosu

[dosubot[bot]]

The Llama3.2 3b model in the LlamaIndex framework has a token limit of 8192 tokens ^[1].

For advanced prompts, LlamaIndex offers several features:

1. Variable Mappings: You can map expected template variables to different names in your custom string template. This is useful for adapting existing templates without changing the original variable names.

   template_var_mappings = {"context_str": "my_context", "query_str": "my_query"}
   prompt_tmpl = PromptTemplate(
       qa_prompt_tmpl_str, template_var_mappings=template_var_mappings
   )

2. Functions: You can use functions as template variables for dynamic formatting. This allows for operations like reformatting the context_str.

   def format_context_fn(**kwargs):
       context_list = kwargs["context_str"].split("\n\n")
       fmtted_context = "\n\n".join([f"- {c}" for c in context_list])
       return fmtted_context
   
   prompt_tmpl = PromptTemplate(
       qa_prompt_tmpl_str, function_mappings={"context_str": format_context_fn}
   )

3. Partials: Partially format a prompt by filling in some variables while leaving others for later. This maintains flexibility in prompt construction.

   prompt_tmpl_str = "{foo} {bar}"
   prompt_tmpl = PromptTemplate(prompt_tmpl_str)
   partial_prompt_tmpl = prompt_tmpl.partial_format(foo="abc")
   fmt_str = partial_prompt_tmpl.format(bar="def")

4. Few-Shot Examples: Structure few-shot examples by embedding and indexing pre-generated question/answer pairs. These are dynamically loaded to guide the model in producing structured outputs.

   def few_shot_examples_fn(**kwargs):
       query_str = kwargs["query_str"]
       retrieved_nodes = few_shot_retriever.retrieve(query_str)
       result_strs = []
       for n in retrieved_nodes:
           raw_dict = json.loads(n.get_content())
           query = raw_dict["query"]
           response_dict = json.loads(raw_dict["response"])
           result_str = f"Query: {query}\nResponse: {response_dict}"
           result_strs.append(result_str)
       return "\n\n".join(result_strs)
   
   qa_prompt_tmpl = PromptTemplate(
       qa_prompt_tmpl_str,
       function_mappings={"few_shot_examples": few_shot_examples_fn},
   )

These features enable more flexible and dynamic prompt construction, allowing for custom and expressive prompts that can be reused and adapted efficiently ^[2][3][4].

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[pratikanand13]

how can this be useful ? Utilize Dynamic Prompts: Ensure that the get_response_synthesizer method is correctly synthesizing responses using the context and refine messages. This involves checking the qa_messages and refine_messages generated by get_prefix_messages_with_context.

Review System Prompt and Prefix Messages: If you are using a system prompt, ensure it is not conflicting with prefix messages. The from_defaults method checks for this and raises an error if both are specified. @dosu

[dosubot[bot]]

Utilizing dynamic prompts and reviewing system prompt and prefix messages can significantly enhance the response synthesis process in the get_response_synthesizer method:

1. Dynamic Prompts: By using the get_prefix_messages_with_context method, you can generate contextually relevant messages for both QA and refine prompts. This ensures that the language model receives the most pertinent information and instructions based on the current chat history and context, leading to more accurate and context-aware responses ^[1].

2. System Prompt and Prefix Messages: It's crucial to ensure that the system prompt does not conflict with prefix messages. The from_defaults method in the ContextChatEngine class handles potential conflicts by raising a ValueError if both system_prompt and prefix_messages are specified. This ensures that only one is used, maintaining consistency and relevance in the conversation flow ^[1].

By leveraging these techniques, you can improve the coherence and contextual appropriateness of the responses generated by the language model.

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[dosubot[bot]]

Here's how you can integrate the get_prefix_messages_with_context method and the use of PromptTemplate with variable mappings, functions, and partials in your flow:

1. Use Case for get_prefix_messages_with_context:

   • This method is used to prepare a list of ChatMessage objects that include context, system prompts, prefix messages, and chat history. It ensures that the context and system prompt are part of the conversation history, which is crucial for generating contextually relevant responses.

2. Integration of few_shot_examples_fn:

   • The few_shot_examples_fn function dynamically loads few-shot examples during prompt formatting. This is useful for guiding the model with structured examples based on the current query.

3. Using PromptTemplate with Partials:

   • The PromptTemplate class allows for partial formatting, which is useful when you want to fill in some variables while leaving others for later. This flexibility is beneficial in scenarios where not all information is available upfront.

Here's how you can incorporate these elements into your flow:

from llama_index import ContextChatEngine, GuidelineEvaluator, ChatMessage, LLM, BaseRetriever, BaseMemory, PromptTemplate
import json

# Initialize components
retriever = BaseRetriever()  # Replace with your actual retriever
llm = LLM()  # Replace with your actual language model
memory = BaseMemory()  # Replace with your actual memory implementation

# Define context and refine templates
context_template = PromptTemplate("Use the context: {context_str}")
context_refine_template = PromptTemplate("Refine the context: {context_str}")

# Initialize ContextChatEngine
context_engine = ContextChatEngine(
    retriever=retriever,
    llm=llm,
    memory=memory,
    prefix_messages=[],
    context_template=context_template,
    context_refine_template=context_refine_template
)

# Generate context
async def generate_context(message):
    context_str, _ = await context_engine._agenerate_context(message)
    return context_str

# Initialize GuidelineEvaluator
guidelines = [
    "The response should fully answer the query.",
    "The response should avoid being vague or ambiguous.",
    "The response should be specific and use statistics or numbers when possible."
]
evaluator = GuidelineEvaluator(llm=llm, guidelines=guidelines)

# Evaluate response
async def evaluate_response(query, response):
    evaluation_result = await evaluator.evaluate(query, response)
    return evaluation_result

# Few-shot examples function
def few_shot_examples_fn(**kwargs):
    query_str = kwargs["query_str"]
    retrieved_nodes = few_shot_retriever.retrieve(query_str)
    result_strs = []
    for n in retrieved_nodes:
        raw_dict = json.loads(n.get_content())
        query = raw_dict["query"]
        response_dict = json.loads(raw_dict["response"])
        result_str = f"Query: {query}\nResponse: {response_dict}"
        result_strs.append(result_str)
    return "\n\n".join(result_strs)

# Use generated context in system prompt
async def get_refined_answer(message):
    context_str = await generate_context(message)
    system_prompt = f"System prompt with context: {context_str}"
    chat_engine = SimpleChatEngine.from_defaults(
        prefix_messages=[ChatMessage(content=system_prompt, role="system")]
    )
    response = await chat_engine.achat(message)
    evaluation_result = await evaluate_response(message, response)
    return response, evaluation_result

# Example usage
message = "Your user query here"
response, evaluation_result = await get_refined_answer(message)
print("Response:", response)
print("Evaluation Result:", evaluation_result)

# Example of using PromptTemplate with partials
prompt_tmpl_str = "{foo} {bar}"
prompt_tmpl = PromptTemplate(prompt_tmpl_str)
partial_prompt_tmpl = prompt_tmpl.partial_format(foo="abc")
fmt_str = partial_prompt_tmpl.format(bar="def")
print("Formatted String:", fmt_str)

This setup demonstrates how to use get_prefix_messages_with_context for context preparation, integrate few-shot examples dynamically, and utilize PromptTemplate for flexible prompt construction ^[1][2].

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[pratikanand13]

if the token size is limited , how can we pas the large contexts in system propmts, which will actually increase the token limit , how can we encounter this and also get accurate responses from the chat_engine @dosu

[dosubot[bot]]

To handle large contexts in system prompts while staying within the token size limit and still getting accurate responses from the chat engine, you can use the CondensePlusContextChatEngine class from the LlamaIndex framework. This class helps manage token size effectively by condensing conversation history and focusing on relevant context. Here's how it works:

1. Condense the Question: The _condense_question method reduces the conversation history and the latest user message into a standalone question, minimizing the context size.

   def _condense_question(self, chat_history: List[ChatMessage], latest_message: str) -> str:
       if self._skip_condense or len(chat_history) == 0:
           return latest_message
   
       chat_history_str = messages_to_history_str(chat_history)
       llm_input = self._condense_prompt_template.format(
           chat_history=chat_history_str, question=latest_message
       )
   
       return str(self._llm.complete(llm_input))

2. Retrieve Context Nodes: The _get_nodes method retrieves relevant context nodes using a retriever, ensuring only the most pertinent information is included.

   def _get_nodes(self, message: str) -> List[NodeWithScore]:
       nodes = self._retriever.retrieve(message)
       for postprocessor in self._node_postprocessors:
           nodes = postprocessor.postprocess_nodes(
               nodes, query_bundle=QueryBundle(message)
           )
       return nodes

3. Synthesize Response: The _get_response_synthesizer method uses the condensed question and retrieved context to generate a response, ensuring accuracy and relevance.

   def _get_response_synthesizer(self, chat_history: List[ChatMessage], streaming: bool = False) -> CompactAndRefine:
       system_prompt = self._system_prompt or ""
       qa_messages = get_prefix_messages_with_context(
           self._context_prompt_template,
           system_prompt,
           [],
           chat_history,
           self._llm.metadata.system_role,
       )
       refine_messages = get_prefix_messages_with_context(
           self._context_refine_prompt_template,
           system_prompt,
           [],
           chat_history,
           self._llm.metadata.system_role,
       )
   
       return get_response_synthesizer(
           self._llm,
           self.callback_manager,
           qa_messages,
           refine_messages,
           streaming,
           qa_function_mappings=self._context_prompt_template.function_mappings,
           refine_function_mappings=self._context_refine_prompt_template.function_mappings,
       )

Additionally, the PromptHelper class can be used to manage token limitations by calculating available context size and repacking or truncating text chunks to fit within the context window. This helps maximize the use of the available context window and reduce the number of LLM calls needed ^[1][2].

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[pratikanand13]

can you help me provide a full flow of CondensePlusContextChatEngine with PromptHelper , also consider that i have to provide in some other temp vars such as language and also want to hide some details from the response ( like hide some info or instruct the model to not provide the info about something which is in my index ) . Please provide me code keeping in mind the above instructions . @dosu

[dosubot[bot]]

To implement a full flow using CondensePlusContextChatEngine with PromptHelper, while incorporating additional template variables like language and hiding certain details from the response, you can follow this approach:

1. Initialize Components: Set up the necessary components such as the retriever, language model (LLM), and memory.

2. Define Custom Prompts: Use PromptTemplate to define custom prompts that include additional variables and functions to hide specific details.

3. Use CondensePlusContextChatEngine: Integrate the engine to condense questions, retrieve context, and generate responses.

4. Manage Token Limitations: Utilize PromptHelper to handle token limitations effectively.

Here's a code example demonstrating this flow:

from llama_index import (
    CondensePlusContextChatEngine,
    PromptTemplate,
    ChatMessage,
    LLM,
    BaseRetriever,
    BaseMemory,
    PromptHelper
)

# Initialize components
retriever = BaseRetriever()  # Replace with your actual retriever
llm = LLM()  # Replace with your actual language model
memory = BaseMemory()  # Replace with your actual memory implementation

# Define custom prompt templates
def hide_info_fn(**kwargs):
    context = kwargs["context_str"]
    # Example: Remove sensitive information
    hidden_context = context.replace("sensitive_info", "[REDACTED]")
    return hidden_context

context_template = PromptTemplate(
    "Use the context: {context_str} in {language}",
    function_mappings={"context_str": hide_info_fn}
)

context_refine_template = PromptTemplate(
    "Refine the context: {context_str} in {language}",
    function_mappings={"context_str": hide_info_fn}
)

# Initialize CondensePlusContextChatEngine
context_engine = CondensePlusContextChatEngine(
    retriever=retriever,
    llm=llm,
    memory=memory,
    context_prompt=context_template,
    context_refine_prompt=context_refine_template
)

# Manage token limitations with PromptHelper
prompt_helper = PromptHelper(max_tokens=4096)  # Adjust max_tokens as needed

# Generate context and response
async def get_condensed_response(message, language):
    # Condense question and retrieve context
    condensed_question = context_engine._condense_question([], message)
    context_nodes = context_engine._get_nodes(condensed_question)

    # Format context with language and hidden info
    context_str = "\n".join(node.get_content() for node in context_nodes)
    formatted_context = context_template.format(context_str=context_str, language=language)

    # Prepare system prompt
    system_prompt = f"System prompt with context: {formatted_context}"
    chat_engine = SimpleChatEngine.from_defaults(
        prefix_messages=[ChatMessage(content=system_prompt, role="system")]
    )

    # Generate response
    response = await chat_engine.achat(message)
    return response

# Example usage
message = "Your user query here"
language = "English"
response = await get_condensed_response(message, language)
print("Response:", response)

This code demonstrates how to set up a flow using CondensePlusContextChatEngine with PromptHelper, incorporating additional template variables and hiding specific details from the response. Adjust the components and templates according to your specific use case ^[1].

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[pratikanand13]

getting this error "2025-01-10 11:26:28 - app.api.services.chat_service - ERROR - Error in chat engine processing
Traceback (most recent call last):
File "/Users/pratikanand/Desktop/Internship/avyaa-ml/app/api/services/chat_service.py", line 182, in process_chat_astream
response = await llm.astream_chat([chat_message])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pratikanand/Desktop/Internship/venv/lib/python3.11/site-packages/llama_index/core/instrumentation/dispatcher.py", line 255, in async_wrapper
result = await func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pratikanand/Desktop/Internship/venv/lib/python3.11/site-packages/llama_index/core/llms/callbacks.py", line 76, in wrapped_async_llm_chat
f_return_val = await f(_self, messages, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pratikanand/Desktop/Internship/venv/lib/python3.11/site-packages/llama_index/llms/ollama/base.py", line 325, in astream_chat
ollama_messages = self._convert_to_ollama_messages(messages)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pratikanand/Desktop/Internship/venv/lib/python3.11/site-packages/llama_index/llms/ollama/base.py", line 179, in _convert_to_ollama_messages
return [
^
File "/Users/pratikanand/Desktop/Internship/venv/lib/python3.11/site-packages/llama_index/llms/ollama/base.py", line 181, in
"role": message.role.value,
^^^^^^^^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'value'
An error occurred while processing the chat: 500: Error in chat engine: 'str' object has no attribute 'value'
INFO: 127.0.0.1:59010 - "POST /api/chat/operator HTTP/1.1" 500 Internal Server Error

" , while using this `chat_message = ChatMessage(
role=MessageRole.USER,
content=system_prompt # Ensure system_prompt is properly formatted
)

    # Pass message_history separately if required by `astream_chat`
    response = await llm.astream_chat([chat_message])

`

@dosu @dosu

[dosubot[bot]]

The error you're encountering, 'str' object has no attribute 'value', typically occurs when the role attribute of a ChatMessage is expected to be an instance of the MessageRole class, but a string is being used instead. In the LlamaIndex framework, MessageRole is an enumeration with attributes like USER, ASSISTANT, etc., and these should be used instead of plain strings.

To resolve this issue, ensure that the role attribute of your ChatMessage is set using the MessageRole enum, like so:

from llama_index import ChatMessage, MessageRole

chat_message = ChatMessage(
    role=MessageRole.USER,  # Use MessageRole.USER instead of "user"
    content=system_prompt  # Ensure system_prompt is properly formatted
)

# Pass message_history separately if required by `astream_chat`
response = await llm.astream_chat([chat_message])

Make sure that MessageRole.USER is used instead of a string like "user". This should fix the AttributeError you're encountering ^[1].

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[pratikanand13]

iam doing the same as in example , but still not getting result @dosu

[dosubot[bot]]

It looks like this issue needs further investigation, @logan-markewich could you please take a look and provide your insights?

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