dosubot[bot]
commented
4 months ago To refine your approach and address the issues you're encountering, let's break down the process into clearer steps, focusing on separating the retrieval of nodes based on your initial question from processing those nodes with a more complex instruction for the LLM.
Step 1: Retrieve Nodes Based on a Question
First, you need to retrieve nodes relevant to your initial question. This involves querying your vector database with the question's embedding and retrieving a set of nodes. The code snippet provided in the previous message outlines how to perform this retrieval, but let's ensure it's integrated correctly in your workflow. You'll need to generate an embedding for your initial question and use that embedding to query the vector database:
# Assuming `question` is your initial question
query_embedding = embed_model.embed([question])[0] # Embed the question
# Create a query for the vector store
query = VectorStoreQuery(
query_embedding=query_embedding, # Use the embedding of your initial question
similarity_top_k=10, # Adjust based on how many nodes you want to retrieve
mode=VectorStoreQueryMode.DEFAULT
)
# Retrieve nodes
nodes = retriever.retrieve(query)Ensure you have the correct setup for embedding your question and that retriever.retrieve(query) is correctly implemented to handle the query and return the desired nodes.
Step 2: Process Retrieved Nodes with a Complex Instruction
After retrieving the nodes, you'll want to process them with a separate, more complex instruction for the LLM. This step is crucial for applying your specific logic or instructions to the data obtained from the nodes. The processing might involve filtering the nodes, extracting specific information, or preparing a new query for the LLM based on the nodes' data:
# Example function to process nodes
def process_retrieved_nodes(nodes):
# Your logic here to process the nodes, apply further queries, etc.
# This could involve generating a new query for the LLM based on the nodes' data
pass
final_results = process_retrieved_nodes(nodes)This function is a placeholder for your logic. Depending on your application, you might generate a new query for the LLM or perform other data processing tasks based on the nodes you've retrieved.
Troubleshooting and Further Steps
- If you encounter errors or issues, please ensure that all dependencies are correctly installed and that your environment variables (e.g.,
PINECONE_API_KEY,PINECONE_NAME) are correctly set. - Verify that the embedding model and the LLM are correctly initialized and that you're using their functionalities as intended.
- For any errors related to the vector database queries or LLM processing, check the error messages for clues on what might be going wrong. These messages can often point you towards missing arguments, incorrect data types, or other common issues.
By following these steps and ensuring each part of your code is correctly implemented, you should be able to achieve your goal of querying a vector database with one question to retrieve nodes and then querying those nodes with another question. If specific errors or challenges arise, sharing those details can help in providing more targeted advice.
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Jez77
logan-markewich
Question Validation
Question
I am very new to programming and Llama_index, so please bear with me.
I wanted to query a vector database on the basis of one question and return a set of nodes, then provide those nodes with the query plus some further info to an llm to provide a final answer. I am having real trouble understanding how to do this and everything i try ends in errors or fails.
I am not entirely sure whether this needs to be a two step process: (1) retrieve the nodes; (2) run the final query against the retrieved nodes. Or whether I could achieve the same end result with a separate query and system prompt. I don't really mind, I just want to retrieve a list of nodes on the basis of a question, then run a separate query against that node list.
the instruction to answer in spanish is an example, and i have a much more complex instruction i want to give the LLM. So i don't not want the query to the vector database to include the "answer in spanish" aspect.
This is the code I have so far. I would be very grateful for help.
from dotenv import load_dotenv import os
from llama_index.embeddings.openai import OpenAIEmbedding from pinecone import Pinecone, ServerlessSpec from llama_index.vector_stores.pinecone import PineconeVectorStore from llama_index.core import VectorStoreIndex, get_response_synthesizer, ServiceContext, StorageContext from llama_index.core.retrievers import VectorIndexRetriever from llama_index.core.query_engine import RetrieverQueryEngine from llama_index.llms.anthropic import Anthropic from llama_index.core import Settings import streamlit as st
os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1" load_dotenv()
pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
llm = Anthropic(model="claude-3-sonnet-20240229", max_tokens=2048, temperature=0.2)
embed_model = OpenAIEmbedding(model="text-embedding-3-large", embed_batch_size=100)
Settings.llm = llm Settings.embed_model = embed_model
pinecone_index = pc.Index(name=os.environ['PINECONE_NAME']) vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
Settings.llm = llm index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
retriever = VectorIndexRetriever( index=index, similarity_top_k=1, )
response_synthesizer = get_response_synthesizer()
query_engine = RetrieverQueryEngine( retriever=retriever, response_synthesizer=response_synthesizer, )
st.title("Hi, i'm a bot, try me")
question = st.text_input("Enter your question:")
if st.button("Get Answer"): response = query_engine.query( f"""
Answer in spanish. Question: {question} """ )