py-vectara-agentic

A python library for creating AI assistants with Vectara, using Agentic RAG

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The `vectara-agentic` Python library is designed for developing powerful AI assistants using Vectara and Agentic-RAG. It supports various agent types, includes pre-built tools for domains like finance and legal, and enables easy creation of custom AI assistants and agents. The library provides tools for summarizing text, rephrasing text, legal tasks like summarizing legal text and critiquing as a judge, financial tasks like analyzing balance sheets and income statements, and database tools for inspecting and querying databases. It also supports observability via LlamaIndex and Arize Phoenix integration.

README:

vectara-agentic

Documentation · Examples · Discord

✨ Overview

vectara-agentic is a Python library for developing powerful AI assistants and agents using Vectara and Agentic-RAG. It leverages the LlamaIndex Agent framework and provides helper functions to quickly create tools that connect to Vectara corpora.

Key Features

Rapid Tool Creation:
Build Vectara RAG tools or search tools with a single line of code.
Agent Flexibility:
Supports multiple agent types including ReAct, OpenAIAgent, LATS, and LLMCompiler.
Pre-Built Domain Tools:
Tools tailored for finance, legal, and other verticals.
Multi-LLM Integration:
Seamless integration with OpenAI, Anthropic, Gemini, GROQ, Together.AI, Cohere, Bedrock, and Fireworks.
Observability:
Built-in support with Arize Phoenix for monitoring and feedback.
Workflow Support:
Extend your agent’s capabilities by defining custom workflows using the run() method.

📚 Example AI Assistants

Check out our example AI assistants:

Prerequisites

Vectara account
A Vectara corpus with an API key
Python 3.10 or higher
OpenAI API key (or API keys for Anthropic, TOGETHER.AI, Fireworks AI, Bedrock, Cohere, GEMINI or GROQ, if you choose to use them)

Installation

pip install vectara-agentic

🚀 Quick Start

1. Initialize the Vectara tool factory

import os
from vectara_agentic.tools import VectaraToolFactory

vec_factory = VectaraToolFactory(
    vectara_api_key=os.environ['VECTARA_API_KEY'],
    vectara_customer_id=os.environ['VECTARA_CUSTOMER_ID'],
    vectara_corpus_key=os.environ['VECTARA_CORPUS_KEY']
)

2. Create a Vectara RAG Tool

A RAG tool calls the full Vectara RAG pipeline to provide summarized responses to queries grounded in data.

from pydantic import BaseModel, Field

years = list(range(2020, 2024))
tickers = {
    "AAPL": "Apple Computer",
    "GOOG": "Google",
    "AMZN": "Amazon",
    "SNOW": "Snowflake",
}

class QueryFinancialReportsArgs(BaseModel):
    query: str = Field(..., description="The user query.")
    year: int | str = Field(..., description=f"The year this query relates to. An integer between {min(years)} and {max(years)} or a string specifying a condition on the year (example: '>2020').")
    ticker: str = Field(..., description=f"The company ticker. Must be a valid ticket symbol from the list {tickers.keys()}.")

query_financial_reports_tool = vec_factory.create_rag_tool(
    tool_name="query_financial_reports",
    tool_description="Query financial reports for a company and year",
    tool_args_schema=QueryFinancialReportsArgs,
    lambda_val=0.005,
    summary_num_results=7, 
    # Additional arguments
)

See the docs for additional arguments to customize your Vectara RAG tool.

3. Create other tools (optional)

In addition to RAG tools, you can generate a lot of other types of tools the agent can use. These could be mathematical tools, tools that call other APIs to get more information, or any other type of tool.

See Agent Tools for more information.

4. Create your agent

from vectara_agentic import Agent

agent = Agent(
    tools=[query_financial_reports_tool],
    topic="10-K financial reports",
    custom_instructions="""
        - You are a helpful financial assistant in conversation with a user. Use your financial expertise when crafting a query to the tool, to ensure you get the most accurate information.
        - You can answer questions, provide insights, or summarize any information from financial reports.
        - A user may refer to a company's ticker instead of its full name - consider those the same when a user is asking about a company.
        - When calculating a financial metric, make sure you have all the information from tools to complete the calculation.
        - In many cases you may need to query tools on each sub-metric separately before computing the final metric.
        - When using a tool to obtain financial data, consider the fact that information for a certain year may be reported in the following year's report.
        - Report financial data in a consistent manner. For example if you report revenue in thousands, always report revenue in thousands.
    """
)

See the docs for additional arguments, including agent_progress_callback and query_logging_callback.

5. Run a chat interaction

res = agent.chat("What was the revenue for Apple in 2021?")
print(res.response)

Note that:

vectara-agentic also supports achat() and two streaming variants stream_chat() and astream_chat().
The response types from chat() and achat() are of type AgentResponse. If you just need the actual string response it's available as the response variable, or just use str(). For advanced use-cases you can look at other AgentResponse variables such as sources.

Advanced Usage: Workflows

In addition to standard chat interactions, vectara-agentic supports custom workflows via the run() method. Workflows allow you to structure multi-step interactions where inputs and outputs are validated using Pydantic models. To learn more about workflows read the documentation

Defining a Custom Workflow

Create a workflow by subclassing llama_index.core.workflow.Workflow and defining the input/output models:

from pydantic import BaseModel
from llama_index.core.workflow import (
    StartEvent,StopEvent, Workflow, step,
)

class MyWorkflow(Workflow):
    class InputsModel(BaseModel):
        query: str

    class OutputsModel(BaseModel):
        answer: str

    @step
    async def my_step(self, ev: StartEvent) -> StopEvent:
        # do something here
        return StopEvent(result="Hello, world!")

When the run() method in vectara-agentic is invoked, it calls the workflow with the following variables in the StartEvent:

agent: the agent object used to call run() (self)
tools: the tools provided to the agent. Those can be used as needed in the flow.
llm: a pointer to a LlamaIndex llm, so it can be used in the workflow. For example, one of the steps may call llm.acomplete(prompt)
verbose: controls whether extra debug information is displayed
inputs: this is the actual inputs to the workflow provided by the call to run() and must be of type InputsModel

Using the Workflow with Your Agent

When initializing your agent, pass the workflow class using the workflow_cls parameter:

agent = Agent(
    tools=[query_financial_reports_tool],
    topic="10-K financial reports",
    custom_instructions="You are a helpful financial assistant.",
    workflow_cls=MyWorkflow,       # Provide your custom workflow here
    workflow_timeout=120           # Optional: Set a timeout (default is 120 seconds)
)

Running the Workflow

Prepare the inputs using your workflow’s InputsModel and execute the workflow using run():

# Create an instance of the workflow's input model
inputs = MyWorkflow.InputsModel(query="What is Vectara?", extra_param=42)

# Run the workflow (ensure you're in an async context or use asyncio.run)
workflow_result = asyncio.run(agent.run(inputs))

# Access the output from the workflow's OutputsModel
print(workflow_result.answer)

Using SubQuestionQueryWorkflow

vectara-agentic already includes one useful workflow you can use right away (it is also useful as an advanced example) This workflow is called SubQuestionQueryWorkflow and it works by breaking a complex query into sub-queries and then executing each sub-query with the agent until it reaches a good response.

🧰 Vectara tools

vectara-agentic provides two helper functions to connect with Vectara RAG

create_rag_tool() to create an agent tool that connects with a Vectara corpus for querying.
create_search_tool() to create a tool to search a Vectara corpus and return a list of matching documents.

See the documentation for the full list of arguments for create_rag_tool() and create_search_tool(), to understand how to configure Vectara query performed by those tools.

Creating a Vectara RAG tool

A Vectara RAG tool is often the main workhorse for any Agentic RAG application, and enables the agent to query one or more Vectara RAG corpora.

The tool generated always includes the query argument, followed by 1 or more optional arguments used for metadata filtering, defined by tool_args_schema.

For example, in the quickstart example the schema is:

class QueryFinancialReportsArgs(BaseModel):
    query: str = Field(..., description="The user query.")
    year: int | str = Field(..., description=f"The year this query relates to. An integer between {min(years)} and {max(years)} or a string specifying a condition on the year (example: '>2020').")
    ticker: str = Field(..., description=f"The company ticker. Must be a valid ticket symbol from the list {tickers.keys()}.")

The query is required and is always the query string. The other arguments are optional and will be interpreted as Vectara metadata filters.

For example, in the example above, the agent may call the query_financial_reports_tool tool with query='what is the revenue?', year=2022 and ticker='AAPL'. Subsequently the RAG tool will issue a Vectara RAG query with the same query, but with metadata filtering (doc.year=2022 and doc.ticker='AAPL').

There are also additional cool features supported here:

An argument can be a condition, for example year='>2022' translates to the correct metadata filtering condition doc.year>2022
if fixed_filter is defined in the RAG tool, it provides a constant metadata filtering that is always applied. For example, if fixed_filter=doc.filing_type='10K' then a query with query='what is the reveue', year=2022 and ticker='AAPL' would translate into query='what is the revenue' with metadata filtering condition of "doc.year=2022 AND doc.ticker='AAPL' and doc.filing_type='10K'"

Note that tool_args_type is an optional dictionary that indicates the level at which metadata filtering is applied for each argument (doc or part)

Creating a Vectara search tool

The Vectara search tool allows the agent to list documents that match a query. This can be helpful to the agent to answer queries like "how many documents discuss the iPhone?" or other similar queries that require a response in terms of a list of matching documents.

🛠️ Agent Tools at a Glance

vectara-agentic provides a few tools out of the box (see ToolsCatalog for details):

Standard tools:

summarize_text: a tool to summarize a long text into a shorter summary (uses LLM)
rephrase_text: a tool to rephrase a given text, given a set of rephrase instructions (uses LLM) These tools use an LLM and so would use the Tools LLM specified in your AgentConfig. To instantiate them:

from vectara_agentic.tools_catalog import ToolsCatalog
summarize_text = ToolsCatalog(agent_config).summarize_text

This ensures the summarize_text tool is configured with the proper LLM provider and model as specified in the Agent configuration.

Legal tools: a set of tools for the legal vertical, such as:

summarize_legal_text: summarize legal text with a certain point of view
critique_as_judge: critique a legal text as a judge, providing their perspective

Financial tools: based on tools from Yahoo! Finance:

tools to understand the financials of a public company like: balance_sheet, income_statement, cash_flow
stock_news: provides news about a company
stock_analyst_recommendations: provides stock analyst recommendations for a company.

Database tools: providing tools to inspect and query a database

list_tables: list all tables in the database
describe_tables: describe the schema of tables in the database
load_data: returns data based on a SQL query
load_sample_data: returns the first 25 rows of a table
load_unique_values: returns the top unique values for a given column

In addition, we include various other tools from LlamaIndex ToolSpecs:

Tavily search and EXA.AI
arxiv
neo4j & Kuzu for Graph DB integration
Google tools (including gmail, calendar, and search)
Slack

Note that some of these tools may require API keys as environment variables

You can create your own tool directly from a Python function using the create_tool() method of the ToolsFactory class:

def mult_func(x, y):
    return x * y

mult_tool = ToolsFactory().create_tool(mult_func)

Note: When you define your own Python functions as tools, implement them at the top module level, and not as nested functions. Nested functions are not supported if you use serialization (dumps/loads or from_dict/to_dict).

🛠️ Configuration

Configuring Vectara-agentic

The main way to control the behavior of vectara-agentic is by passing an AgentConfig object to your Agent when creating it. For example:

agent_config = AgentConfig(
    agent_type = AgentType.REACT,
    main_llm_provider = ModelProvider.ANTHROPIC,
    main_llm_model_name = 'claude-3-5-sonnet-20241022',
    tool_llm_provider = ModelProvider.TOGETHER,
    tool_llm_model_name = 'meta-llama/Llama-3.3-70B-Instruct-Turbo'
)

agent = Agent(
    tools=[query_financial_reports_tool],
    topic="10-K financial reports",
    custom_instructions="You are a helpful financial assistant in conversation with a user.",
    agent_config=agent_config
)

The AgentConfig object may include the following items:

agent_type: the agent type. Valid values are REACT, LLMCOMPILER, LATS or OPENAI (default: OPENAI).
main_llm_provider and tool_llm_provider: the LLM provider for main agent and for the tools. Valid values are OPENAI, ANTHROPIC, TOGETHER, GROQ, COHERE, BEDROCK, GEMINI or FIREWORKS (default: OPENAI).
main_llm_model_name and tool_llm_model_name: agent model name for agent and tools (default depends on provider).
observer: the observer type; should be ARIZE_PHOENIX or if undefined no observation framework will be used.
endpoint_api_key: a secret key if using the API endpoint option (defaults to dev-api-key)
max_reasoning_steps: the maximum number of reasoning steps (iterations for React and function calls for OpenAI agent, respectively). Defaults to 50.

If any of these are not provided, AgentConfig first tries to read the values from the OS environment.

Configuring Vectara tools: rag_tool, or search_tool

When creating a VectaraToolFactory, you can pass in a vectara_api_key, and vectara_corpus_key to the factory.

If not passed in, it will be taken from the environment variables (VECTARA_API_KEY and VECTARA_CORPUS_KEY). Note that VECTARA_CORPUS_KEY can be a single KEY or a comma-separated list of KEYs (if you want to query multiple corpora).

These values will be used as credentials when creating Vectara tools - in create_rag_tool() and create_search_tool().

Setting up a privately hosted LLM

If you want to setup vectara-agentic to use your own self-hosted LLM endpoint, follow the example below

        config = AgentConfig(
            agent_type=AgentType.REACT,
            main_llm_provider=ModelProvider.PRIVATE,
            main_llm_model_name="meta-llama/Meta-Llama-3.1-8B-Instruct",
            private_llm_api_base="http://vllm-server.company.com/v1",
            private_llm_api_key="TEST_API_KEY",
        )
        agent = Agent(agent_config=config, tools=tools, topic=topic,
                      custom_instructions=custom_instructions)

In this case we specify the Main LLM provider to be privately hosted with Llama-3.1-8B as the model.

The ModelProvider.PRIVATE specifies a privately hosted LLM.
The private_llm_api_base specifies the api endpoint to use, and the private_llm_api_key specifies the private API key requires to use this service.

ℹ️ Additional Information

About Custom Instructions for your Agent

The custom instructions you provide to the agent guide its behavior. Here are some guidelines when creating your instructions:

Write precise and clear instructions, without overcomplicating.
Consider edge cases and unusual or atypical scenarios.
Be cautious to not over-specify behavior based on your primary use-case, as it may limit the agent's ability to behave properly in others.

Diagnostics

The Agent class defines a few helpful methods to help you understand the internals of your application.

The report() method prints out the agent object’s type, the tools, and the LLMs used for the main agent and tool calling.
The token_counts() method tells you how many tokens you have used in the current session for both the main agent and tool calling LLMs. This can be helpful if you want to track spend by token.

Serialization

The Agent class supports serialization. Use the dumps() to serialize and loads() to read back from a serialized stream.

Note: due to cloudpickle limitations, if a tool contains Python weakref objects, serialization won't work and an exception will be raised.

Observability

vectara-agentic supports observability via the existing integration of LlamaIndex and Arize Phoenix. First, set VECTARA_AGENTIC_OBSERVER_TYPE to ARIZE_PHOENIX in AgentConfig (or env variable).

Then you can use Arize Phoenix in three ways:

Locally.
1. If you have a local phoenix server that you've run using e.g. python -m phoenix.server.main serve, vectara-agentic will send all traces to it.
2. If not, vectara-agentic will run a local instance during the agent's lifecycle, and will close it when finished.
3. In both cases, traces will be sent to the local instance, and you can see the dashboard at http://localhost:6006
Hosted Instance. In this case the traces are sent to the Phoenix instances hosted on Arize.
1. Go to https://app.phoenix.arize.com, setup an account if you don't have one.
2. create an API key and put it in the PHOENIX_API_KEY environment variable - this indicates you want to use the hosted version.
3. To view the traces go to https://app.phoenix.arize.com.

Now when you run your agent, all call traces are sent to Phoenix and recorded. In addition, vectara-agentic also records FCS (factual consistency score, aka HHEM) values into Arize for every Vectara RAG call. You can see those results in the Feedback column of the arize UI.

🌐 API Endpoint

vectara-agentic can be easily hosted locally or on a remote machine behind an API endpoint, by following theses steps:

Step 1: Setup your API key

Ensure that you have your API key set up as an environment variable:

export VECTARA_AGENTIC_API_KEY=<YOUR-ENDPOINT-API-KEY>

if you don't specify an Endpoint API key it uses the default "dev-api-key".

Step 2: Start the API Server

Initialize the agent and start the FastAPI server by following this example:

from vectara_agentic.agent import Agent
from vectara_agentic.agent_endpoint import start_app
agent = Agent(...)            # Initialize your agent with appropriate parameters
start_app(agent)

You can customize the host and port by passing them as arguments to start_app():

Default: host="0.0.0.0" and port=8000. For example:

start_app(agent, host="0.0.0.0", port=8000)

Step 3: Access the API Endpoint

Once the server is running, you can interact with it using curl or any HTTP client. For example:

curl -G "http://<remote-server-ip>:8000/chat" \
--data-urlencode "message=What is Vectara?" \
-H "X-API-Key: <YOUR-ENDPOINT-API-KEY>"

🤝 Contributing

We welcome contributions! Please see our contributing guide for more information.

📝 License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

📞 Contact

Website: vectara.com
Twitter: @vectara
GitHub: @vectara
LinkedIn: @vectara
Discord: Join our community

For Tasks:

Click tags to check more tools for each tasks

summarize text rephrase text analyze financials query database develop custom ai assistants

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This repository provides a hands-on workshop for developers and solution builders to build a real-life serverless LLM application using foundation models (FMs) through Amazon Bedrock and advanced design patterns such as Reason and Act (ReAct) Agent, text-to-SQL, and Retrieval Augmented Generation (RAG). It guides users through labs to explore common and advanced LLM application design patterns, helping them build a complex Agentic LLM assistant capable of answering retrieval and analytical questions on internal knowledge bases. The repository includes labs on IaC with AWS CDK, building serverless LLM assistants with AWS Lambda and Amazon Bedrock, refactoring LLM assistants into custom agents, extending agents with semantic retrieval, and querying SQL databases. Users need to set up AWS Cloud9, configure model access on Amazon Bedrock, and use Amazon SageMaker Studio environment to run data-pipelines notebooks.

github

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llamabot

LlamaBot is a Pythonic bot interface to Large Language Models (LLMs), providing an easy way to experiment with LLMs in Jupyter notebooks and build Python apps utilizing LLMs. It supports all models available in LiteLLM. Users can access LLMs either through local models with Ollama or by using API providers like OpenAI and Mistral. LlamaBot offers different bot interfaces like SimpleBot, ChatBot, QueryBot, and ImageBot for various tasks such as rephrasing text, maintaining chat history, querying documents, and generating images. The tool also includes CLI demos showcasing its capabilities and supports contributions for new features and bug reports from the community.

github

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**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

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deepeval

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

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MegaDetector

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

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leapfrogai

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

github

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llava-docker

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

github

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carrot

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

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TrustLLM

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

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AI-YinMei

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.

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