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import os |
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from typing import Optional |
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from pydantic import Field, BaseModel |
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from omegaconf import OmegaConf |
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from llama_index.core.utilities.sql_wrapper import SQLDatabase |
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from sqlalchemy import create_engine |
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from dotenv import load_dotenv |
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load_dotenv(override=True) |
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from vectara_agentic.agent import Agent |
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from vectara_agentic.tools import ToolsFactory, VectaraToolFactory |
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def create_assistant_tools(cfg): |
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class QueryCFPBComplaints(BaseModel): |
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query: str = Field(description="The user query.") |
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Company: Optional[str] = Field( |
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default=None, |
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description="The company that the complaint is about.", |
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examples=['CAPITAL ONE FINANCIAL CORPORATION', 'BANK OF AMERICA, NATIONAL ASSOCIATION', 'CITIBANK, N.A.', 'WELLS FARGO & COMPANY', 'JPMORGAN CHASE & CO.'] |
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) |
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State: Optional[str] = Field( |
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default=None, |
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descripition="The two-character state code where the consumer lives.", |
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examples=['CA', 'FL', 'NY', 'TX', 'GA'] |
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) |
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vec_factory = VectaraToolFactory( |
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vectara_api_key=cfg.api_keys, |
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vectara_customer_id=cfg.customer_id, |
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vectara_corpus_id=cfg.corpus_ids |
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) |
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summarizer = 'vectara-experimental-summary-ext-2023-12-11-med-omni' |
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ask_complaints = vec_factory.create_rag_tool( |
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tool_name = "ask_complaints", |
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tool_description = """ |
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Given a user query, |
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returns a response to a user question about customer complaints for bank services. |
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""", |
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tool_args_schema = QueryCFPBComplaints, |
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reranker = "multilingual_reranker_v1", rerank_k = 100, |
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n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005, |
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summary_num_results = 30, |
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mmr_diversity_bias = 0.4, |
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vectara_summarizer = summarizer, |
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include_citations = True, |
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) |
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tools_factory = ToolsFactory() |
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db_tools = tools_factory.database_tools( |
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tool_name_prefix = "cfpb", |
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content_description = 'Customer complaints about five banks (Bank of America, Wells Fargo, Capital One, Chase, and CITI Bank)', |
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sql_database = SQLDatabase(create_engine('sqlite:///cfpb_database.db')), |
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) |
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return (tools_factory.standard_tools() + |
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tools_factory.guardrail_tools() + |
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db_tools + |
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[ask_complaints] |
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) |
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def initialize_agent(_cfg, agent_progress_callback=None): |
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cfpb_complaints_bot_instructions = """ |
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- You are a helpful research assistant, |
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with expertise in finance and complaints from the CFPB (Consumer Financial Protection Bureau), |
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in conversation with a user. |
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- For analytical/numeric questions, try to use the cfpb_load_data and other database tools. |
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- Never discuss politics, and always respond politely. |
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""" |
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agent = Agent( |
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tools=create_assistant_tools(_cfg), |
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topic="Customer complaints from the Consumer Financial Protection Bureau (CFPB)", |
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custom_instructions=cfpb_complaints_bot_instructions, |
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agent_progress_callback=agent_progress_callback |
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) |
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agent.report() |
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return agent |
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def get_agent_config() -> OmegaConf: |
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cfg = OmegaConf.create({ |
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'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']), |
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'corpus_ids': str(os.environ['VECTARA_CORPUS_IDS']), |
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'api_keys': str(os.environ['VECTARA_API_KEYS']), |
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'examples': os.environ.get('QUERY_EXAMPLES', None), |
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'demo_name': "cfpb-assistant", |
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'demo_welcome': "Welcome to the CFPB Customer Complaints demo.", |
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'demo_description': "This assistant can help you gain insights into customer complaints to banks recorded by the Consumer Financial Protection Bureau.", |
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}) |
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return cfg |
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