Response synthesizer

[d3buggerdan]

@dosu
'''
from llama_index.core import PromptTemplate
text_qa_template_str = (
"Say hi at the start. Given the context provided, answer the following question with as much detail and relevant information as possible. "
"Ensure to include explanations and any necessary background information to make the response informative and comprehensive.\n"
"Context: {context}\n"
"Question: {question}\n"
"Answer:\n"
)
text_qa_template = PromptTemplate(text_qa_template_str)
refine_template_str = (
"Say hi at the start. Given the new context, refine the original answer to better answer the question. "
"If the context isn't useful, output the original answer again. Ensure the refined answer is detailed and includes relevant explanations.\n"
"Original Answer: {original_answer}\n"
"New Context: {new_context}\n"
"Refined Answer:\n"
)

refine_template = PromptTemplate(refine_template_str)

response_synthesizer = get_response_synthesizer(
response_mode="tree_summarize",
streaming=True,
structured_answer_filtering= True,
)
query_engine = RetrieverQueryEngine(
retriever=default_retriever,
response_synthesizer=response_synthesizer,
)
prompts_dict = query_engine.get_prompts()
for k, p in prompts_dict.items():
print(f"Prompt Key: {k}\nText:\n{p.get_template()}\n")
Prompt Key: response_synthesizer:summary_template
Text:
Context information from multiple sources is below.

{context_str}

Given the information from multiple sources and not prior knowledge, answer the query.
Query: {query_str}
Answer:
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": text_qa_template}
)
prompts_dict = query_engine.get_prompts()
for k, p in prompts_dict.items():
print(f"Prompt Key: {k}\nText:\n{p.get_template()}\n")
Prompt Key: response_synthesizer:summary_template
Text:
Context information from multiple sources is below.

{context_str}

Given the information from multiple sources and not prior knowledge, answer the query.
Query: {query_str}
Answer:

'''

why is it not updating?

[dosubot[bot]]

To ensure that your custom prompt templates are correctly updated in the RetrieverQueryEngine, you should use the update_prompts method properly. Here is a step-by-step guide to help you resolve the issue:

1. Define the Custom Prompt Templates:
   Ensure that your custom prompt templates are defined correctly using the PromptTemplate class.

2. Initialize the RetrieverQueryEngine:
   Make sure you initialize the RetrieverQueryEngine with the appropriate parameters, including the response_synthesizer.

3. Update the Prompts:
   Use the update_prompts method to update the prompts in the RetrieverQueryEngine.

4. Verify the Updated Prompts:
   Check if the prompts have been updated correctly by retrieving and printing them.

Here is the corrected code snippet:

from llama_index.core import PromptTemplate
from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.core.response_synthesizers.factory import get_response_synthesizer

# Define custom prompt templates
text_qa_template_str = (
    "Say hi at the start. Given the context provided, answer the following question with as much detail and relevant information as possible. "
    "Ensure to include explanations and any necessary background information to make the response informative and comprehensive.\n"
    "Context: {context}\n"
    "Question: {question}\n"
    "Answer:\n"
)
text_qa_template = PromptTemplate(text_qa_template_str)

refine_template_str = (
    "Say hi at the start. Given the new context, refine the original answer to better answer the question. "
    "If the context isn't useful, output the original answer again. Ensure the refined answer is detailed and includes relevant explanations.\n"
    "Original Answer: {original_answer}\n"
    "New Context: {new_context}\n"
    "Refined Answer:\n"
)
refine_template = PromptTemplate(refine_template_str)

# Initialize the response synthesizer
response_synthesizer = get_response_synthesizer(
    response_mode="tree_summarize",
    streaming=True,
    structured_answer_filtering=True,
)

# Initialize the RetrieverQueryEngine
query_engine = RetrieverQueryEngine(
    retriever=default_retriever,
    response_synthesizer=response_synthesizer,
)

# Update the query engine with the custom prompt template
query_engine.update_prompts(
    {"response_synthesizer:text_qa_template": text_qa_template}
)

# Verify the updated prompts
prompts_dict = query_engine.get_prompts()
for k, p in prompts_dict.items():
    print(f"Prompt Key: {k}\nText:\n{p.get_template()}\n")

This code should correctly update the text_qa_template in the RetrieverQueryEngine. If the prompt is still not updating, ensure that the response_synthesizer and query_engine are correctly initialized and that the update_prompts method is called with the correct key-value pairs ^[1].

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

To set up the chat engine and perform chat interactions using the index.as_chat_engine() method in the llama_index library, follow these steps:

1. Initialize the Index: Create an index from your documents.
2. Convert to Chat Engine: Use the as_chat_engine method to convert the index to a chat engine.

Here's an example code snippet demonstrating these steps:

from llama_index.core.indices.base import BaseIndex
from llama_index.core.schema import Document

# Step 1: Create an index from documents
documents = [Document(text="Your document text here")]
index = BaseIndex.from_documents(documents)

# Step 2: Convert the index to a chat engine
chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)

# Now you can use the chat_engine to perform chat interactions
response = chat_engine.chat("Your question here")
print(response)

In this example:

• BaseIndex.from_documents(documents) creates an index from the provided documents.
• index.as_chat_engine(chat_mode="condense_question", verbose=True) converts the index to a chat engine using the specified chat mode.
• chat_engine.chat("Your question here") performs a chat interaction with the chat engine.

The chat_mode parameter can be set to different modes such as best, condense_question, context, condense_plus_context, simple, react, and openai, depending on your requirements ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[d3buggerdan]

@dosu lets say i already have a query engine set up how can i set up a chatbot on this instead of using a index.as_chat_engine?

[dosubot[bot]]

To set up a chatbot using your existing query engine instead of using index.as_chat_engine, you can use the low-level composition API. Here is an example:

from llama_index.core import PromptTemplate
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core.chat_engine import CondenseQuestionChatEngine

# Define a custom prompt template
custom_prompt = PromptTemplate(
    """\
Given a conversation (between Human and Assistant) and a follow up message from Human, \
rewrite the message to be a standalone question that captures all relevant context \
from the conversation.

<Chat History>
{chat_history}

<Follow Up Message>
{question}

<Standalone question>
"""
)

# Initialize custom chat history
custom_chat_history = [
    ChatMessage(
        role=MessageRole.USER,
        content="Hello assistant, we are having an insightful discussion about Paul Graham today.",
    ),
    ChatMessage(role=MessageRole.ASSISTANT, content="Okay, sounds good."),
]

# Use your existing query engine
query_engine = your_existing_query_engine  # Replace with your actual query engine

# Create the chat engine using the low-level API
chat_engine = CondenseQuestionChatEngine.from_defaults(
    query_engine=query_engine,
    condense_question_prompt=custom_prompt,
    chat_history=custom_chat_history,
    verbose=True,
)

# Example of a chat interaction
response = chat_engine.chat("Can you tell me more about his early life?")
print(response)

This code snippet demonstrates how to configure a CondenseQuestionChatEngine using your existing query engine ^[1].

To continue talking to Dosu, mention @dosu.

[d3buggerdan]

@dosu how can i print or see the chat history

[dosubot[bot]]

To print or see the chat history in the CondenseQuestionChatEngine, you can access the chat_history attribute of the chat engine. Here's how you can do it:

# Example of a chat interaction
response = chat_engine.chat("Can you tell me more about his early life?")
print(response)

# Print the chat history
for message in chat_engine.chat_history:
    print(f"Role: {message.role}, Content: {message.content}")

This will print each message in the chat history, showing the role (USER or ASSISTANT) and the content of the message.

To continue talking to Dosu, mention @dosu.