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import pandas as pd |
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import requests |
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from pydantic import Field, BaseModel |
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from omegaconf import OmegaConf |
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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 QueryDocsArgs(BaseModel): |
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query: str = Field(..., description="The user query, always in the form of a question", |
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examples=["Based on uploaded documents, what are the top four challenges of the Fintech sector in Saudi Arabia? list them in bullet points."]) |
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vec_factory = VectaraToolFactory(vectara_api_key=cfg.api_key, |
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vectara_corpus_key=cfg.corpus_key) |
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summarizer = 'mockingbird-1.0-2024-07-16' |
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ask_docs = vec_factory.create_rag_tool( |
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tool_name = "ask_docs", |
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tool_description = """ |
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Responds to an user question about a particular analysis, based on the documentation provide. |
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""", |
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tool_args_schema = QueryDocsArgs, |
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reranker = "chain", rerank_k = 100, |
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rerank_chain = [ |
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{ |
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"type": "multilingual_reranker_v1", |
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}, |
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{ |
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"type": "mmr", |
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"diversity_bias": 0.2, |
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"limit": 50 |
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} |
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], |
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n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005, |
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summary_num_results = 15, |
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vectara_summarizer = summarizer, |
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include_citations = True, |
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save_history = True, |
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verbose=False |
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) |
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tools_factory = ToolsFactory() |
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return ( |
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tools_factory.standard_tools() + |
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[ask_docs] |
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) |
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def initialize_agent(_cfg, agent_progress_callback=None): |
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stc_bank_bot_instructions = """ |
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- Call the the ask_docs tool to retrieve the information to answer the user query. |
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- If the question has an 'Excel' or 'excel' word only fetch for the documents with 'type_file' equals to 'excel'. |
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- Always print the title of the References |
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""" |
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agent = Agent( |
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tools=create_assistant_tools(_cfg), |
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topic="STC Bank questions", |
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custom_instructions=stc_bank_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 |
