Se hace una depuración inicial del proyecto
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Mongar28
parent
f397b2d446
commit
61da645cf9
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@ -133,7 +133,6 @@ def search_sale_order(order_id: int):
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return response_sale
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return response_sale
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@tool
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@tool
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def add_lines_to_order(
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def add_lines_to_order(
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order_id: int, product: str, unit: str, quantity: str, unitprice: str
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order_id: int, product: str, unit: str, quantity: str, unitprice: str
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@ -210,36 +209,3 @@ tools = [
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add_lines_to_order,
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add_lines_to_order,
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search_associate_party_to_contact_mechanism,
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search_associate_party_to_contact_mechanism,
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]
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]
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# if __name__ == "__main__":
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# # Crear una orden de venta
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# party = 2573
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# pickup_location = "at_home"
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# order_id = create_sale_order(party=party, pickup_location=pickup_location)
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# print(f"\nOrden creada con ID: {order_id}")
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# # Agregar líneas a la orden
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# product = "1"
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# unit = "1"
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# quantity = "3"
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# unitprice = "15"
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# add_line_response = add_lines_to_order(order_id, product, unit, quantity, unitprice)
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# print(f"\nRespuesta al agregar línea: {add_line_response.text}")
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# # Verificar la orden actualizada
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# updated_order = search_sale_order(order_id)
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# print(f"\nOrden actualizada: {updated_order.text}")
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# # Agregar otra línea a la orden
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# product = "2"
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# unit = "1"
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# quantity = "3"
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# unitprice = "15"
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# add_line_response = add_lines_to_order(order_id, product, unit, quantity, unitprice)
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# print(f"\nRespuesta al agregar línea: {add_line_response.text}")
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# # Verificar la orden actualizada
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# updated_order = search_sale_order(order_id)
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# print(f"\nOrden actualizada: {updated_order.text}")
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@ -1,82 +0,0 @@
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from seller.catalog_tools import CatalogTrytonTools
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import json
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# result = json.dumps(response, indent=4)
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url = "http://192.168.0.25:8000"
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key = "9a9ffc430146447d81e6698240199a4be2b0e774cb18474999d0f60e33b5b1eb1cfff9d9141346a98844879b5a9e787489c891ddc8fb45cc903b7244cab64fb1"
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db = "tryton"
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application_name = "sale_don_confiao"
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catalog = CatalogTrytonTools(url, application_name, key, db)
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# Lista de los productos
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print("=== Productos ===")
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response = catalog.list_products()
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products: str = ""
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for product in response:
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id = product["id"]
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name = product["name"]
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id_unit_measurement = product["default_uom."]["id"]
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products += f"- id: {id} - Nombre: {name} - ID Unidad de Medida: {id_unit_measurement}\n"
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print(products)
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# Precio del los productos
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print("=== Precios de los productos ===")
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response = catalog.search_products("Arroz")
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precios: str = ""
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for product in response:
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id = product["id"]
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name = product["name"]
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precio = product["template."]["list_price"]["decimal"]
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id_unit_measurement = product["default_uom."]["id"]
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precios += f"- id: {id} - Nombre: {name}: ${precio} - ID Unidad de Medida: {id_unit_measurement}\n"
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print(precios)
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# Revisar disponibilidad de los productos
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# print("=== Disponibilidad de los productos ===")
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# response = catalog.search_products("Papa")
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# disponibilidad: str = ""
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# for product in response:
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# id = product["id"]
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# name = product["name"]
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# stock = product["quantity"]
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# disponibilidad += f"- id: {id} - Nombre: {name} - Stock: {stock}\n"
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# print(disponibilidad)
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# from langgraph_tools.tools.catalog.catalog_tools import (
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# list_products,
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# search_products,
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# check_price,
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# )
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# def test_catalog_functions():
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# print("=== Probando lista de productos ===")
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# print(list_products())
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# print("\n")
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# print("=== Probando búsqueda de productos ===")
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# print(search_products("Arroz"))
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# print("\n")
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# print("=== Probando verificación de precio ===")
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# print(check_price("Arroz"))
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# print("\n")
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# if __name__ == "__main__":
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# test_catalog_functions()
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@ -1,20 +0,0 @@
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# from langchain_tools.llm import load_llm_openai
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# import yaml
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#
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# with open("langgraph_tools/prompts.yaml", "r") as f:
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# PROMPTS = yaml.safe_load(f)
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#
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#
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# llm = load_llm_openai()
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# query = "Necesito informacion sobre la tienda."
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# prompt = PROMPTS["classifier"]["system"].format(query=query)
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#
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# response: dict = llm.invoke(prompt)
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#
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# print(response.content)
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from langgraph_tools.tools.general_info import get_link_page
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link = get_link_page()
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print(link)
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@ -1,10 +0,0 @@
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from dotenv import load_dotenv
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from langchain_openai import OpenAIEmbeddings
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def load_embeddins():
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load_dotenv()
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# model = "text-embedding-ada-002"
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model = "text-embedding-3-small"
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return OpenAIEmbeddings(model=model)
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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def load_llm_openai():
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load_dotenv()
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# model = "gpt-3.5-turbo-0125"
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# model = "gpt-4o"
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model = "gpt-4o-mini"
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llm = ChatOpenAI(
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model=model,
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temperature=0.1,
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max_tokens=2000,
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)
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return llm
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from langchain.chains import create_history_aware_retriever
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.chains import create_retrieval_chain
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from langchain.chains.combine_documents import create_stuff_documents_chain
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def create_rag_chain(llm, retriever):
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contextualize_q_system_prompt = """
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Given a chat history and the latest user question \
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which might reference context in the chat history,
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formulate a standalone question \
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which can be understood without the chat history.
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Do NOT answer the question, \
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just reformulate it if needed and otherwise return it as is.
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"""
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contextualize_q_prompt = ChatPromptTemplate.from_messages(
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[
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("system", contextualize_q_system_prompt),
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MessagesPlaceholder("chat_history"),
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("human", "{input}"),
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]
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)
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history_aware_retriever = create_history_aware_retriever(
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llm, retriever, contextualize_q_prompt
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)
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# ___________________Chain con el chat history_______________________-
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qa_system_prompt = """
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You are an assistant for question-answering tasks. \
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Use the following pieces of retrieved context to answer the question. \
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If you don't know the answer, just say that you don't know. \
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The length of the answer should be sufficient to address
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what is being asked, \
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meaning don't limit yourself in length.\
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{context}"""
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qa_prompt = ChatPromptTemplate.from_messages(
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[
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("system", qa_system_prompt),
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MessagesPlaceholder("chat_history"),
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("human", "{input}"),
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]
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)
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question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
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return create_retrieval_chain(
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history_aware_retriever, question_answer_chain)
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from langchain_chroma import Chroma
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def create_retriever(embeddings, persist_directory: str):
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# Cargamos la vectorstore
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# vectordb = Chroma.from_documents(
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# persist_directory=st.session_state.persist_directory,
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# Este es el directorio del la vs del docuemnto del usuario
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# que se encuentra cargado en la session_state.
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# embedding_function=embeddings,
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# )
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vectordb = Chroma(
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persist_directory=persist_directory,
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embedding_function=embeddings,
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)
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# Creamos el retriver para que retorne los fragmentos mas relevantes.
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return vectordb.as_retriever(search_kwargs={"k": 6})
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from langchain_community.document_loaders.pdf import PyPDFLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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import os
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# def load_split_docs(file_name: str) -> list:
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# file_path: str = os.path.join("documents", "pdfs", file_name)
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# loader = PyPDFLoader(file_path)
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# docs: list = loader.load()
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# chunk_size: int = 2000
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# chunk_overlap: int = 300
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#
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# splitter = RecursiveCharacterTextSplitter(
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# chunk_size=chunk_size, chunk_overlap=chunk_overlap
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# )
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# docs_split: list = splitter.split_documents(docs)
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#
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# return docs_split
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def load_split_docs(file_name: str) -> list:
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# Obtener el directorio base del proyecto
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base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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# Construir la ruta absoluta al PDF
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file_path = os.path.join(base_dir, "documents", "pdfs", file_name)
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# Verificar si el archivo existe
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if not os.path.exists(file_path):
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print(f"Archivo no encontrado en: {file_path}")
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raise FileNotFoundError(f"No se encontró el archivo en: {file_path}")
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loader = PyPDFLoader(file_path)
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docs: list = loader.load()
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chunk_size: int = 2000
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chunk_overlap: int = 300
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splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size, chunk_overlap=chunk_overlap
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)
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docs_split: list = splitter.split_documents(docs)
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return docs_split
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from langchain_chroma import Chroma
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import os
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#
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# def create_vectorstore(docs_split: list, embeddings, file_name: str):
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# db_name: str = file_name.replace(".pdf", "").replace(" ", "_").lower()
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# persist_directory: str = f"embeddings/{db_name}"
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#
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# # Crear el directorio si no existe
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# os.makedirs(persist_directory, exist_ok=True)
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#
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# # Siempre crear/actualizar el vectorstore
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# vectordb = Chroma.from_documents(
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# persist_directory=persist_directory,
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# documents=docs_split,
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# embedding=embeddings,
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# )
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#
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# return vectordb
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def create_vectorstore(docs_split: list, embeddings, file_name: str):
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# Obtener el directorio base del proyecto
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base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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# Crear el nombre de la base de datos
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db_name: str = file_name.replace(".pdf", "").replace(" ", "_").lower()
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# Construir la ruta absoluta para los embeddings
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persist_directory: str = os.path.join(base_dir, "embeddings", db_name)
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# Crear el directorio si no existe
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os.makedirs(persist_directory, exist_ok=True)
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# Debug log
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print(f"Creando vectorstore en: {persist_directory}")
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try:
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# Crear/actualizar el vectorstore
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vectordb = Chroma.from_documents(
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persist_directory=persist_directory,
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documents=docs_split,
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embedding=embeddings,
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)
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return vectordb
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except Exception as e:
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print(f"Error al crear vectorstore: {e}")
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raise
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#!/usr/bin/env python3
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from app.langgraph_tools.tools.orders.order_tools_2 import create_party
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import json
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# Prueba de la función create_party
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def test_create_party():
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# Parámetros de ejemplo
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party_full_name = "Cristian Garces"
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contact_method_type = "email"
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contact_method_value = "cristian.garces@example.com"
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# Llamar a la función
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response = create_party(
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party_full_name=party_full_name,
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contact_method_type=contact_method_type,
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contact_method_value=contact_method_value
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)
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# Imprimir la respuesta
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print("Status Code:", response.status_code)
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try:
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print("Response:", json.loads(response.text))
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except json.JSONDecodeError:
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print("Raw Response:", response.text)
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if __name__ == "__main__":
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test_create_party()
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