Trying to Create ReACT Agent using Mistral LLM using HuggingFace Inference API but getting error

HuggingFaceAPI

import os
from getpass import getpass
from huggingface_hub import login

HuggingFace API

HF_TOKEN = getpass()

login(token=HF_TOKEN)

Loading LLM endpoint

from llama_index.llms.huggingface import HuggingFaceInferenceAPI
llm = HuggingFaceInferenceAPI(model_name="mistralai/Mistral-7B-Instruct-v0.2", token=HF_TOKEN, num_output=512, is_function_calling_model=True, is_chat_model=True)

Simple Calculator

from llama_index.core.agent import ReActAgent
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool

Define Tool

def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b

def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b

def subtract(a: int, b: int) -> int:
"""Subtract two integers and returns the result integer"""
return a - b

multiply_tool = FunctionTool.from_defaults(fn=multiply)
add_tool = FunctionTool.from_defaults(fn=add)
subtract_tool = FunctionTool.from_defaults(fn=subtract)

Mistral

agent = ReActAgent.from_tools([multiply_tool, add_tool, subtract_tool], llm=llm, verbose=True)
response = agent.chat("What is 20+(2*4)? Calculate step by step.")


Error -
NotImplementedError: Messages passed in must be of odd length.

Seeking Advice on Optimizing Index Creation Time for Ingestion Pipeline

I'm developing an ingestion pipeline using llama_index to process Paul Graham's essays. Despite successful data loading and node creation, the indexing phase with VectorStoreIndex is extremely slow, taking over two hours and still running.

Setup Overview:

1. Data Loading:

from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader(input_files=["data/paul_graham_essays.txt"])
docs = reader.load_data()

1. Embedding Configuration:
2. LLM: llama3:instruct from Ollama.
3. Embedding: BAAI/bge-small-en-v1.5 with batch size 50.

from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
Settings.llm = Ollama(model="llama3:instruct", request_timeout=90.0)
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5", embed_batch_size=50)

1. Ingestion Pipeline:
2. Features: Sentence splitting and title extraction.

import nest_asyncio
nest_asyncio.apply()
from llama_index.core import IngestionPipeline, Document
pipeline = IngestionPipeline(transformations=[SentenceSplitter(chunk_size=25), TitleExtractor()])
nodes = pipeline.run(docs)

1. Index Creation:

from llama_index.storage import VectorStoreIndex
index = VectorStoreIndex(nodes)

Does anyone have insights on optimizing this indexing process to reduce time? Any advice or experiences with similar challenges would be greatly appreciated.