Hi Krish, Please make a complete video of bedrock llama2 chat further steps for providing the output as API to the front-end or to check on postman by passing inputs and inference parameters using fastapi or Django.

I love your videos. Before starting the setup, could you make sure that your code is future proof by sharing the Python/Conda version you are using? Preferably, start with the `pyenv install` command. Could you also commit the `requirements.txt` file with the version number you used? Thank you 🙏

Hi sir I am getting error "cannot import name 'VectorStoreIndex' from 'llama_index' (unknown location)". Can you help me with this?

same issues with me @@user-jx3wy6fe4s

Can you use OCR model also to read images in pdf.

As per my knowledge, for Indexing it doesn't use OpenAI. For retrieval it does. Correct me if I am wrong

@@vivekshindeVivekShinde we are using indexing with the API because by default we are utilizing OpenAI's text embedding. Indexing involves embedding the text into respective folders. However, you have the freedom to change the embedding method from OpenAI to any other open-source option available for this purpose.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

Facing similar issue. Let me know if you find something. It ll be helpful

@@vivekshindeVivekShinde sure....you found any solution?

Did you try to ask LLM to translate the response to Portuguese?

Which location is to be added in method_location?

also where exactly did you use open ai I am not able to understand it

No. I think Llamaindex is not using thr OpenAI api key. Also he didn't use it anywhere in the project. Like he said in future we will create more complex conversational bots, maybe at that time he ll use it. He just added that OpenAI part for sake of maintaining the future flow. I might be wrong. Feel free to correct me.

@@vivekshindeVivekShinde 'VectorStoreIndex' is using openai internally for generating embeddings.

@@subhamjyoti4189 I dont think VectorStoreIndex uses OpenAI's embeddings.

For response synthesis. Once the relevant nodes are retrieved, it is passed as a context to LLM(openAI) model and then LLM provided the answer in much better way to the users query.

As far as I understand, It is RAG is based on embedding for similarity search. LLAMA index is just at frame work to build application on top of it.

Use this technique it will work

@@krishnaik06 Sure sir

@@krishnaik06 Thank you soo much sir for helping lot of students.... You are Amazing😍 Waiting for more projects. And also one request from my side sir... Please share some project ideas to us as assignments. It will help us to do it on our own Please sir... Please share some application ideas

​@@chinnibngrm272 omg stop begging.

Yes I got it The correct code should be like this: from llama_index.core import VectorStoreIndex,SimpleDirectoryReader documents=SimpleDirectoryReader("data").load_data()

@@Decoder_Sami That little tidbit took me about 3 hours to figure out. Thanks for posting!

Same here Did you find a workaround?

Toh gemini use karle mere bhai

Either create a new account and get free but limited access for 30 days or use gemini pro

did you find any alternative? i am stuck here as well

@@shravaninevagi5729 in source file .venv\Lib\site-packages\llama_index\core\embeddings\utils.py change below for GooglePalmEmbedding which is working in my case Install llama-index-embeddings-google by command "pip install llama-index-embeddings-google" """Embedding utils for LlamaIndex.""" import os from typing import TYPE_CHECKING, List, Optional, Union if TYPE_CHECKING: from llama_index.core.bridge.langchain import Embeddings as LCEmbeddings from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.callbacks import CallbackManager from llama_index.core.embeddings.mock_embed_model import MockEmbedding from llama_index.core.utils import get_cache_dir from llama_index.embeddings.google import GooglePaLMEmbedding EmbedType = Union[BaseEmbedding, "LCEmbeddings", str] def save_embedding(embedding: List[float], file_path: str) -> None: """Save embedding to file.""" with open(file_path, "w") as f: f.write(",".join([str(x) for x in embedding])) def load_embedding(file_path: str) -> List[float]: """Load embedding from file. Will only return first embedding in file.""" with open(file_path) as f: for line in f: embedding = [float(x) for x in line.strip().split(",")] break return embedding def resolve_embed_model( embed_model: Optional[EmbedType] = None, callback_manager: Optional[CallbackManager] = None, ) -> BaseEmbedding: """Resolve embed model.""" from llama_index.core.settings import Settings try: from llama_index.core.bridge.langchain import Embeddings as LCEmbeddings except ImportError: LCEmbeddings = None # Check if embed_model is 'default' or not specified if embed_model == "default" or embed_model is None: # Initialize Google PaLM embedding google_palm_embedding = GooglePaLMEmbedding() embed_model = google_palm_embedding return embed_model

thanks