HI Team, how to achieve the following

At a glance

The post is about how to achieve the following LangChain code in LlamaIndex: final_prompt = Chatprompt.from_template(context_prompt) chain = prompt | MODEL.llm | StrOutputParser() response = chain.run({ "input": question, "history": "\n".join(memory)}, config = {"callbacks": [MODEL.callback]}). The comments provide examples of using PromptTemplate and ChatPromptTemplate with OpenAI's LLM, and discuss how to manage memory and use other LLM providers like Ollama. There is no explicitly marked answer, but the community members provide guidance on how to adapt the code for different use cases.

Useful resources

MMKhere

HI Team, how to achieve the following langchain code in to llamaindex - final_prompt = Chatprompt.from_template(context_prompt)

chain = prompt | MODEL.llm | StrOutputParser()

response = chain.run({
"input": question,
"history": "\n".join(memory)},
config = {"callbacks": [MODEL.callback]})

7 comments

LLogan M

For just a string prompt

Plain Text

from llama_index.core.prompts import PromptTemplate
from llama_index.llms.openai import OpenAI

prompt = PromptTemplate("Some topic {topic}")

formatted_prompt = prompt.format(topic="ghosts")

llm = OpenAI(model="gpt-4o-mini")
response = llm.complete(formatted_prompt)
print(str(response))

For a prompt with chat messages

Plain Text

from llama_index.core.prompts import ChatPromptTemplate
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI

prompt = ChatPromptTemplate.from_messages([
  ChatMessage(role="system", content="Talk like a pirate."),
  ChatMessage(role="user", content="Tell me a joke about {topic}.")
])

formatted_messages = prompt.format_messages(topic="Dogs")

llm = OpenAI(model="gpt-4o-mini")
response = llm.chat(formatted_messages)
print(response.message.content)

MMKhere

Thanks @Logan M , however how to pass the question like below along with history and callack, response = chain.run({
"input": question,
"history": "\n".join(memory)},
config = {"callbacks": [MODEL.callback]})

LLogan M

I don't know what your callback is doing

If you want memory, you'll have to manage that

Plain Text

from llama_index.core.memory import ChatMemoryBuffer

memory = ChatMemoryBuffer.from_defaults(llm=llm)
memory.put(ChatMessage(role='user', content='some_message'))

# get latest buffer
messages = memory.get()

# get all
messages = memory.get_all()

# combine memory with your prompt
new_message = ChatMessage(role="user", content=formatted_prompt)
messages = memory.get()

response = llm.chat(messages)

# add messages to memory
memory.put(new_message)
memory.put(response.message)

MMKhere

@Logan M - okay got it thanks again, how about the following if i want to change it for other providers other than openAI , llm = OpenAI(model="gpt-4o-mini")

LLogan M

Then you can install and use any other llm

For example

LLogan M

pip install llama-index-llms-ollama

Plain Text

from llama_index.llms.ollama import Ollama

llm = Ollama(model="llama3.1:latest", request_timeout=120)

We should have a notebook in our docs for every LLM we support

LLogan M

https://docs.llamaindex.ai/en/stable/examples/llm/ollama/

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HI Team, how to achieve the following