davidmezzetti

davidmezzetti

from txtai import Embeddings

# In-memory data
data = [{"name":"John", "age": 16}, {"name":"Jon", "age": 45},{"name":"Sarah", "age": 18}]

# Vector embeddings index with content storage
embeddings = Embeddings(content=True, columns={"text": "name"})
embeddings.index(data)

# Vector similarity

davidmezzetti

🤖 Agent Team 🤖

This example shows how to build an Agent having multiple agents as tools. This "Agent Team" works together to answer questions.

A more basic example can be found here

Install

pip install git+https://github.com/neuml/txtai

davidmezzetti

🚀 Agents with txtai

We're thrilled to share a preview version of txtai agents. Inspired by the simplicity of frameworks like OpenAI Swarm, txtai agents are built on top of the Transformers Agent framework. This supports all LLMs txtai supports (Hugging Face, llama.cpp, OpenAI + Claude + AWS Bedrock via LiteLLM).

The following example shows how to create an agent with txtai. Agents will be available in the upcoming txtai 8.0 release (available now in the txtai GitHub repo now - follow #804 - feedback welcome).

Install

davidmezzetti

from txtai import Embeddings

embeddings = Embeddings(
  method="model2vec",
  path="minishlab/M2V_base_output"
)

embeddings.index(["test"])
embeddings.search("test")
# [(0, 1.0)]

davidmezzetti

from txtai import Embeddings
embeddings = Embeddings(
  method="sentence-transformers",
  vectors={
    "backend": "onnx", 
    "model_kwargs": {"file_name": "model_qint8_avx512.onnx"}
  }
)
embeddings.index(["test"])
embeddings.search("test")

davidmezzetti

from txtai import Embeddings, RAG

# Load embeddings index
embeddings = Embeddings()
embeddings.load(provider="huggingface-hub", container="neuml/txtai-wikipedia")

template = """
Answer the following question using the provided context.

Question:

davidmezzetti

import re

from txtai import Embeddings, LLM

# Prompt courtesy of the following link: https://github.com/codelion/optillm/blob/main/cot_reflection.py
def cot(system, user):
    system = f"""
        {system}

        You are an AI assistant that uses a Chain of Thought (CoT) approach with reflection to answer queries. Follow these steps:

davidmezzetti

import os

from txtai import Embeddings, RAG

# For demo only. Set via environment.
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
os.environ["ANN_URL"] = "postgresql+psycopg2://postgres:pass@aws.dbhost/postgres"
os.environ["CLIENT_URL"] = "postgresql+psycopg2://postgres:pass@aws.dbhost/postgres"

davidmezzetti

import json
import re

import yfinance as yf

from txtai import Embeddings
from txtai.pipeline import Textractor

def djia():
    """