what is llama_chat

what is llama_chat_agent

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Please stand by @cK5150 as I search the knowledge base. We are experiencing especially high traffic right now and might take longer than usual. :wave:

kkapa.ai

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llama_chat_agent is an instance of an agent executor created using the create_llama_chat_agent function from the GPT Index Langchain Integrations. It is a chatbot agent that has access to the LlamaToolkit and the specified language model (LLM). The agent can be used to interact with the chatbot and answer queries using the provided tools and data.

Here's an example of how to create a llama_chat_agent:

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memory = ConversationBufferMemory(memory_key="chat_history")
llm = OpenAI(temperature=0)
agent_chain = create_llama_chat_agent(
    toolkit,
    llm,
    memory=memory,
    verbose=True
)

For more information, refer to this source (https://gpt-index.readthedocs.io/en/latest/guides/tutorials/building_a_chatbot.html).
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ccK5150

@kapa.ai how do I see the prompt the llm isBeingSent

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To see the prompt being sent to the LLM, you can use the LLMPredictor class from GPT Index. When you call the predict or apredict method on an instance of LLMPredictor, it returns a tuple containing the predicted answer and the formatted prompt. You can access the formatted prompt by indexing the returned tuple.

Here's an example of how to use LLMPredictor:

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from gpt_index.service_context.llm_predictor import LLMPredictor
from gpt_index.prompts import Prompt

# Create an instance of LLMPredictor
llm_predictor = LLMPredictor()

# Create a prompt
prompt = Prompt("Your prompt text here")

# Call the predict method and get the answer and formatted prompt
answer, formatted_prompt = llm_predictor.predict(prompt)

# Print the formatted prompt
print(formatted_prompt)

Keep in mind that this example assumes you have already set up the necessary components for GPT Index, such as the LLM and the service context. For more information on setting up GPT Index, refer to the documentation (https://gpt-index.readthedocs.io/en/latest).
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ccK5150

@kapa.ai

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

Sorry @cK5150, I seem to be having a bit of trouble. Don't worry though, I've pinged my supprt and they will try to fix me up! :technologist:

Perhaps try again in 15mins?

ccK5150

@kapa.ai create a custom prompt and then print to the consoleAfter index.send(query,prompt

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To create a custom prompt and print it to the console after sending a query to the index, you can follow these steps:

Create a custom prompt using the Prompt class or a derived class like QuestionAnswerPrompt.
Send the query to the index along with the custom prompt.
Print the formatted prompt to the console.

Here's an example using the GPTSimpleVectorIndex and a custom QuestionAnswerPrompt:

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from llama_index import QuestionAnswerPrompt, GPTSimpleVectorIndex, SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader('data').load_data()

# Define custom QuestionAnswerPrompt
query_str = "What did the author do growing up?"
QA_PROMPT_TMPL = (
    "We have provided context information below. \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "Given this information, please answer the question: {query_str}\n"
)
QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)

# Build GPTSimpleVectorIndex
index = GPTSimpleVectorIndex.from_documents(documents)

# Send the query with the custom prompt
response = index.query(query_str, text_qa_template=QA_PROMPT)

# Print the response and the formatted prompt
print("Response:", response)
print("Formatted Prompt:", QA_PROMPT.format(query_str=query_str))

This example assumes you have already set up the necessary components for GPT Index, such as the LLM and the service context. For more information on setting up GPT Index, refer to the documentation (https://gpt-index.readthedocs.io/en/latest).
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ccK5150

@kapa.ai were does context_str come from

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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The context_str is a variable used in the custom prompt template to represent the context information that will be provided to the LLM when querying the index. In the example provided earlier, the context_str is a placeholder within the QA_PROMPT_TMPL string, which is later used to create a QuestionAnswerPrompt instance.

When you send a query to the index using the index.query() method, the GPT Index library automatically fills in the context_str with the relevant context information from the documents in the index. This context information is then used by the LLM to generate a response to the query.

Here's the relevant part of the example code:

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# Define custom QuestionAnswerPrompt
query_str = "What did the author do growing up?"
QA_PROMPT_TMPL = (
    "We have provided context information below. \n"
    "---------------------\n"
    "{{context_str}}"
    "\n---------------------\n"
    "Given this information, please answer the question: {{query_str}}\n"
)
QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)

In this example, the {{context_str}} placeholder within the QA_PROMPT_TMPL string will be replaced with the actual context information when the query is sent to the index.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ccK5150

@kapa.ai "C:\Users\peace\AppData\Local\Programs\Python\Python310\lib\string.py", line 227, in get_value
return kwargs[key]
KeyError: 'context_str'

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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I apologize for the confusion earlier. It seems there was a mistake in the example code. The correct way to use placeholders in the custom prompt template is to use {} instead of {{}}. Here's the corrected example code:

Plain Text

# Define custom QuestionAnswerPrompt
query_str = "What did the author do growing up?"
QA_PROMPT_TMPL = (
    "We have provided context information below. \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "Given this information, please answer the question: {query_str}\n"
)
QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)

Now, when you send a query to the index using the index.query() method, the GPT Index library should automatically fill in the context_str with the relevant context information from the documents in the index.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

ccK5150

@kapa.ai How do I make a custom prompt

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

@cK5150 That's a great question, but unfortunately I am only capable of responding to a maximum of three questions per thread. Don't hesitate to create a new thread for any further questions you have though. I'm here to help!

ccK5150

@kapa.ai does GPTSimpleVectorIndex have a context_str it sets

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