Receiving attribute error that the type object 'Pinecone' has no attribute 'from_existing_index'

Question

I'm following a git repo and learning how to make a chatbot using pinecone and llama-2, but I'm getting an attribute error while using the Pinecone module, even though I've copied the same code as given in the repo:

These are the dependencies:

from langchain import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Pinecone
import pinecone
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers
from tqdm import tqdm
import os
from pinecone import Pinecone, ServerlessSpec

The code where error occurs:

docsearch=Pinecone.from_existing_index(index_name, embeddings)

query = "What are Allergies"

docs=docsearch.similarity_search(query, k=3)

print("Result", docs)

Getting the following error:

AttributeError Traceback (most recent call last) <ipython-input-52-de2ccf18d9e8> in <cell line: 1>() ----> 1 docsearch=Pinecone.from_existing_index(index_name, embeddings) 2 3 query = "What are Allergies" 4 5 docs=docsearch.similarity_search(query, k=3)

AttributeError: type object 'Pinecone' has no attribute 'from_existing_index'

There's a thread on another website that said that the langchain_pinecone lib and the OG pinecone lib are being confused somehow but I don't know how to resolve it. I'd appreciate it if I can get a solution this.

Olsi Hoxha · Accepted Answer · 2024-12-19 17:19:58Z

Just checked the docs and the code would look like below to do your queries

from pinecone.grpc import PineconeGRPC as Pinecone

# Initialize a Pinecone client with your API key
pc = Pinecone(api_key="YOUR_API_KEY")

query = "Tell me about the tech company known as Apple."

# Convert the query into a numerical vector that Pinecone can search with
query_embedding = pc.inference.embed(
    model="multilingual-e5-large",
    inputs=[query],
    parameters={
        "input_type": "query"
    }
)

# Search the index for the three most similar vectors
results = index.query(
    namespace="example-namespace",
    vector=query_embedding[0].values,
    top_k=3,
    include_values=False,
    include_metadata=True
)

print(results)

The docs below are really clear:

Just use pinecone

Langchain Pinecone

you can create the embeddings in your own way as well just adapt to Pinecone format here is how I use it for example (with OpenAI)

res = client.embeddings.create(
        input=text_input, model=OPENAI_EMBEDDING_MODEL)

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