diff --git a/.gitignore b/.gitignore index 3e5a50e..6da28ba 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,9 @@ ncm_text.json #chromadb #csv tipi ncm_chapter_85.csv +app/libs/extract_pdf/extract_new/vectorstore2_invoice +vectorstore2_invoice +/vectorstore2_invoice #collection chromadb @@ -200,6 +203,7 @@ env-embedding INVOICE TECSYS.pdf exemplo_pdf_entrada.pdf Pedido compras_Mouser.pdf +Invoice Mouser_.pdf # mkdocs documentation /site diff --git a/app/libs/extract_pdf/extract_new/embeddings.py b/app/libs/extract_pdf/extract_new/embeddings.py new file mode 100644 index 0000000..2949597 --- /dev/null +++ b/app/libs/extract_pdf/extract_new/embeddings.py @@ -0,0 +1,121 @@ +import uuid +import requests +from typing import List +from langchain_community.document_loaders.pdf import PyPDFLoader +from langchain_text_splitters import RecursiveCharacterTextSplitter +from langchain.embeddings.base import Embeddings +from langchain_community.vectorstores import Chroma +import streamlit as st +import pandas as pd +from app.log.logger import logger + +VECTORSTORE_PATH = "app/libs/extract_pdf/extract_new/vectorstore2_invoice" +EMBEDDING_MODEL = "nomic-embed-text" +OLLAMA_URL = "http://localhost:11434" +TIMEOUT = 120 +PDF_PATH = "./app/libs/extract_pdf/extract_new/INVOICE TECSYS.pdf" + + +class CustomOllamaEmbeddingFunction(Embeddings): + def __init__(self, model_name: str, url: str = OLLAMA_URL, timeout: int = TIMEOUT): + self.model_name = model_name + self.url = url + self.timeout = timeout + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + return [self._request_embedding(text) for text in texts] + + def embed_query(self, text: str) -> List[float]: + return self._request_embedding(text) + + def _request_embedding(self, text: str) -> List[float]: + try: + response = requests.post( + f"{self.url}/api/embeddings", + json={"model": self.model_name, "prompt": text}, + timeout=self.timeout + ) + response.raise_for_status() + return response.json().get("embedding", []) + except Exception as e: + logger.error(f"[EMBEDDING] Error: {e}") + return [] + + +class InvoiceChromaDBManager: + def __init__(self): + logger.info("[CHROMADB-PDF] Initializing") + self.embedding_function = CustomOllamaEmbeddingFunction(EMBEDDING_MODEL) + self.vectorstore = self._load_or_create_vectorstore() + self.retriever = self.vectorstore.as_retriever( + search_type="similarity", + search_kwargs={"k": 20} + ) + logger.info("[CHROMADB-PDF] Ready") + + def _load_or_create_vectorstore(self): + try: + store = Chroma( + persist_directory=VECTORSTORE_PATH, + embedding_function=self.embedding_function + ) + if store._collection.count() > 0: + logger.info(f"[CHROMADB-PDF] Loaded with {store._collection.count()} documents") + return store + except Exception as e: + logger.warning(f"[CHROMADB-PDF] Failed to load: {e}") + + return self._create_vectorstore_from_pdf(PDF_PATH) + + def _create_vectorstore_from_pdf(self, path: str): + logger.info("[CHROMADB-PDF] Creating new store") + + pages = PyPDFLoader(path).load() + logger.info(f"[CHROMADB-PDF] Loaded {len(pages)} pages") + + splitter = RecursiveCharacterTextSplitter( + chunk_size=150, + chunk_overlap=50, + length_function=len, + separators=["\n", "PN:", " - ", ":", " ", " ", ""] + ) + chunks = splitter.split_documents(pages) + + logger.info(f"[CHROMADB-PDF] Produced {len(chunks)} chunks") + + unique = {} + for doc in chunks: + uid = str(uuid.uuid5(uuid.NAMESPACE_DNS, doc.page_content)) + if uid not in unique: + unique[uid] = doc + + logger.info(f"[CHROMADB-PDF] Deduplicated to {len(unique)} chunks") + + store = Chroma.from_documents( + documents=list(unique.values()), + ids=list(unique.keys()), + embedding=self.embedding_function, + persist_directory=VECTORSTORE_PATH + ) + store.persist() + logger.info("[CHROMADB-PDF] Store created") + + return store + + def search_parts(self, query: str = "PN:") -> List[str]: + try: + logger.info(f"[CHROMADB-PDF] Searching: {query}") + docs = self.retriever.invoke(query) + return [doc.page_content for doc in docs] + except Exception as e: + logger.error(f"[CHROMADB-PDF] Search error: {e}") + return [] + + +invoice_chroma_manager = InvoiceChromaDBManager() + +if __name__ == "__main__": + results = invoice_chroma_manager.search_parts("PN:") + print("Relevant Parts Found:") + for i, part in enumerate(results, 1): + print(f"{i}. {part}") diff --git a/app/libs/extract_pdf/extract_new/extract_pn_from_text.py b/app/libs/extract_pdf/extract_new/extract_pn_from_text.py new file mode 100644 index 0000000..073d297 --- /dev/null +++ b/app/libs/extract_pdf/extract_new/extract_pn_from_text.py @@ -0,0 +1,200 @@ +import re +from typing import List + + +def extract_part_numbers(text: str) -> List[str]: + """ + Extrai possíveis Part Numbers (PNs) de um texto cru. + + Estratégia (v3.0): + - Regex bem genérico para pegar tokens "candidatos" (letras/dígitos + [-_.]). + - Vários filtros de EXCLUSÃO (telefone, CEP, datas, HS Code, medidas, pacotes SMD, etc.). + - Regras específicas para alguns fornecedores (Mouser, Tecsys). + - Só depois aplicamos regras de ACEITAÇÃO para decidir se um token é PN. + - No final, removemos duplicados mantendo a ordem original. + """ + + # Normalizamos uma cópia em maiúsculas para detecção de fornecedor + upper_text = text.upper() + + is_mouser = ( + "MOUSER ELECTRONICS" in upper_text + or "WWW.MOUSER.COM" in upper_text + or "MOUSER BRASIL" in upper_text + ) + is_tecsys = ( + "TECSYS DO BRASIL" in upper_text + or "XWORKSOLUTIONS.COM" in upper_text + ) + + # Regex "genérica" de candidatos a PN: + # - começa com letra/dígito + # - depois 3+ caracteres de [letra, dígito, -, _, .] + pn_regex = r"\b([A-Za-z0-9][A-Za-z0-9\-_.]{3,})\b" + candidates = re.findall(pn_regex, text) + + # Listas de apoio + generic_blacklist = { + # termos de invoice / administrativos + "EX-WORKS", "WORKS", "INVOICE", "TOTAL", "SUBTOTAL", + "INFORMATION", "DECLARE", "CONTAINED", "CORRECT", + "ITEM", "DESC", "MFR", "SCHEDULE", "LOT", "COO", "PAGE", + "CUSTOMER", "NUMBER", "PAYMENT", "TERMS", "SHIP", "INCOTERM", + "INVOICE#", "INVOICENO", + + # pesos / medidas gerais + "N.WT", "G.WT", "NWT", "GWT", + + # termos administrativos mais chatos + "ANTI-CORRUPTION", "END-USER", "ENDUSER", + "ENVIE-NOS", "ENVIENOS", "END-USE", + + # descrições genéricas + "RETURN", "LOSS", "GAIN", "MOUNT", "SOURCE", + "HARDWARE", "EMBEDDED", "COMPUTER", "LINUX", + "SINGLE", "BOARD", + + # coisas de CPU / SoC + "ARM926J", + } + + # pacotes/encapsulamentos comuns (não queremos como PN) + package_tokens = { + "DFN-12", "DFN12", + "SMA", "DO-214AC", "SOT-23", "SOT-223", "SOT-523", "SOT-723", + "QFN", "BGA", "SOIC", + "TO-220", "TO-252-3", "TO-252", "TO252", "TO-92", "LGA", "LCC", + } + + # resultado final + final: List[str] = [] + + for tok in candidates: + t = tok.strip() + if not t: + continue + + upper = t.upper() + + # --------------------------- + # 1) EXCLUSÕES UNIVERSAIS + # --------------------------- + + # a) telefones: 0000-0000, 12-3456-7890, etc. + if re.match(r"^\d{4}-\d{4}$", t): + continue + if re.match(r"^\d{2,3}-\d{3,4}-\d{3,4}$", t): + continue + + # b) CEP: 12345-678 ou 12.345-678 + if re.match(r"^\d{5}-\d{3}$", t): + continue + if re.match(r"^\d{2}\.\d{3}-\d{3}$", t): + continue + + # c) datas / períodos: 3-2025, 03-2025, 3/5/2025 etc. + if re.match(r"^\d{1,2}[-/]\d{1,2}[-/]\d{2,4}$", t): + continue + if re.match(r"^\d{1,2}[-/]\d{1,4}$", t): + continue + + # d) só letras → não é PN + if t.isalpha(): + continue + + # e) faixa numérica tipo 1700-2700 (frequência) + if re.match(r"^\d{3,4}-\d{3,4}$", t): + continue + + # f) domínio/host sem dígitos (ex: "xworksolutions.com") + if "." in t and not any(c.isdigit() for c in t): + continue + + # g) palavras compostas por letras e hífen, mas sem dígitos (UP-RIGHT, ITEM-CLIENTE) + if re.match(r"^[A-Za-z\-]+$", t) and not any(c.isdigit() for c in t): + continue + + # h) blacklist forte (palavras que NUNCA queremos como PN) + if upper in generic_blacklist: + continue + + # i) tokens de pacote/encapsulamento (SOT-23, DFN-12, etc.) + if upper in package_tokens: + continue + + # j) terminações técnicas de unidade (medida, capacitância, etc.) + if upper.endswith(("MM", "CM", "IN")): + continue + if upper.endswith(("UF", "NF", "PF", "MF")): + continue + if upper.endswith(("VAC", "VDC", "AC", "X1", "X2")): + continue + if upper.endswith(("HZ", "KHZ", "MHZ", "GHZ")): + continue + + # k) tamanhos tipo 12.5X25 ou 6X12 + if re.match(r"^\d+(\.\d+)?x\d+(\.\d+)?$", t, re.IGNORECASE): + continue + + # --------------------------- + # 2) REGRAS ESPECÍFICAS POR FORNECEDOR + # --------------------------- + + if is_mouser: + # Códigos internos Mouser tipo 61-1520598 (prefixo numérico + hífen + dígitos) + if re.match(r"^\d{2,4}-\d{5,}$", t): + # se NÃO estiver marcado explicitamente como PN:, ignoramos + if not re.search(rf"PN[:\s]+{re.escape(t)}\b", text, flags=re.IGNORECASE): + continue + + if is_tecsys: + # Códigos internos da Tecsys (ex: 00020020069), numéricos longos + # Se forem só números 9–12 dígitos e NÃO estiverem em "PN: 00020020069", ignoramos + if t.isdigit() and 9 <= len(t) <= 12: + if not re.search(rf"PN[:\s]+{re.escape(t)}\b", text, flags=re.IGNORECASE): + continue + + # --------------------------- + # 3) REGRAS ESPECIAIS PARA NÚMEROS PUROS + # --------------------------- + + if t.isdigit(): + # HS CODE / SCHEDULE B: 10 dígitos (3926909989, 8536694030, etc.) + if len(t) == 10: + continue + + # Só aceitaremos números longos como PN se aparecerem explicitamente após "PN:" + if re.search(rf"PN[:\s]+{re.escape(t)}\b", text, flags=re.IGNORECASE): + if t not in final: + final.append(t) + # Se não estiver próximo de "PN:", descartamos + continue + + # --------------------------- + # 4) ACEITAÇÕES GERAIS (ALFA + DÍGITO) + # --------------------------- + + has_alpha = any(c.isalpha() for c in t) + has_digit = any(c.isdigit() for c in t) + + # a) PNs alfanuméricos "clássicos" (sem depender de hífen) + # Ex: LTC3625EDE, AHEF1000, CL10C330JB8NNNC, GRM1885C1H180JA01D + if len(t) >= 6 and has_alpha and has_digit: + if t not in final: + final.append(t) + continue + + # b) híbridos com hífen e comprimento razoável + # Ex: STPS5H100B-TR, ESD7C3.3DT5G, ECS-3225Q-33-260-BS-TR + if "-" in t and has_digit and len(t) >= 8: + if t not in final: + final.append(t) + continue + + # c) fallback: tokens longos com letras (casos exóticos) + if len(t) >= 12 and has_alpha: + if t not in final: + final.append(t) + continue + + return final diff --git a/app/libs/extract_pdf/extract_new/extract_supplier.py b/app/libs/extract_pdf/extract_new/extract_supplier.py new file mode 100644 index 0000000..610563e --- /dev/null +++ b/app/libs/extract_pdf/extract_new/extract_supplier.py @@ -0,0 +1,414 @@ +import re +from dataclasses import dataclass +from typing import List, Optional, Tuple + + +@dataclass +class SupplierInfo: + supplier_name: Optional[str] = None + supplier_address: Optional[str] = None + supplier_email: Optional[str] = None + supplier_phone: Optional[str] = None + + +# ------------------------ +# Normalização básica +# ------------------------ +def _normalize_text(text: str) -> Tuple[str, List[str]]: + # Normaliza quebras de linha e espaços + text = text.replace("\r\n", "\n").replace("\r", "\n") + lines = [re.sub(r"\s+", " ", line).strip() for line in text.split("\n")] + # remove linhas totalmente vazias + lines = [ln for ln in lines if ln] + return text, lines + + +# ------------------------ +# Detecção de "perfil" do fornecedor +# ------------------------ +def _detect_supplier_profile(text: str) -> str: + up = text.upper() + + # Mouser + if ( + "MOUSER ELECTRONICS" in up + or "MOUSER.COM" in up + or "AMERICAS.REMIT@MOUSER.COM" in up + or "1000 NORTH MAIN STREET" in up and "MANSFIELD" in up + ): + return "mouser" + + # Avnet + if "AVNET" in up: + return "avnet" + + # XWORK Solutions + if "XWORK SOLUTIONS" in up or "XWORKSOLUTIONS.COM" in up: + return "xwork" + + # genérico + return "generic" + + +# ------------------------ +# Extração genérica de e-mail +# ------------------------ +def _extract_email(text: str, preferred_domains: Optional[List[str]] = None) -> Optional[str]: + emails = re.findall(r"[\w\.-]+@[\w\.-]+\.\w+", text) + if not emails: + return None + + if preferred_domains: + for dom in preferred_domains: + for em in emails: + if dom.lower() in em.lower(): + return em + + # fallback: primeiro e-mail + return emails[0] + + +# ------------------------ +# Extração genérica de telefone +# (somente números com separadores, pra não confundir com PN) +# ------------------------ +PHONE_PATTERN = re.compile( + r"(?:\+?\d{1,3}[\s\-\.])?(?:\(?\d{2,4}\)?[\s\-\.])?\d{3,5}[\s\-\.]\d{3,5}" +) + + +def _extract_phone(lines: List[str]) -> Optional[str]: + # 1) Tenta primeiro linhas com palavras-chave + key_words = ("PHONE", "TEL", "TOLL FREE", "PH:") + for line in lines: + up = line.upper() + if any(k in up for k in key_words): + m = PHONE_PATTERN.search(line) + if m and not re.search(r"[A-Za-z]", m.group()): + return m.group().strip() + + # 2) Fallback: varrer tudo + for line in lines: + m = PHONE_PATTERN.search(line) + if m and not re.search(r"[A-Za-z]", m.group()): + return m.group().strip() + + return None + + +# ------------------------ +# Heurísticas genéricas para NOME e ENDEREÇO +# ------------------------ +_SUPPLIER_NAME_KEYWORDS = { + "ELECTRONICS", + "ELECTRONIC", + "INDUSTRIAL", + "MARKETING", + "CORP", + "CORPORATION", + "LIMITED", + "LTD", + "LTDA", + "S.A.", + "GMBH", + "SOLUTIONS", + "TECHNOLOGIES", + "TECNOLOGIA", + "COMPANY", + "CO.", + "INC", +} + + +def _looks_like_supplier_name(line: str) -> bool: + up = line.upper() + + # Ignorar TECSYS (cliente) + if "TECSYS" in up: + return False + + # Não pode ser linha de item (começando com número + data + etc) + if re.match(r"^\d{1,3}\s+\d{2}/\d{2}/\d{2,4}", line): + return False + + # Tem alguma palavra "típica" de razão social? + if any(k in up for k in _SUPPLIER_NAME_KEYWORDS): + return True + + # Ou é uma linha toda em maiúsculas razoável, sem ser texto genérico + if up == line and 5 <= len(line) <= 80 and "INVOICE" not in up and "BILL TO" not in up: + return True + + return False + + +def _extract_name_generic(lines: List[str]) -> Optional[str]: + # 1) prioriza linhas com "Company:" + for line in lines: + if "COMPANY:" in line.upper(): + # pega o texto após "Company:" + parts = line.split(":", 1) + if len(parts) == 2: + cand = parts[1].strip() + if cand and "TECSYS" not in cand.upper(): + return cand + + # 2) usa heurística de "parece razão social" + for line in lines: + if _looks_like_supplier_name(line): + return line.strip() + + return None + + +_ADDRESS_HINT_WORDS = ( + "STREET", + "ST.", + "AVENUE", + "AVE", + "ROAD", + "RD", + "DRIVE", + "DR", + "BOULEVARD", + "BLVD", + "LANE", + "LN", + "WAY", + "HIGHWAY", + "HWY", + "RUA", + "AV.", + "AV ", + "AVENIDA", + "ROD.", + "ESTRADA", + "ZIP", + "CEP", + "TX", + "SP", + "USA", + "BRASIL", + "BRAZIL", +) + + +def _line_looks_like_address(line: str) -> bool: + up = line.upper() + + # tem pelo menos um dígito + if not re.search(r"\d", line): + return False + + # precisa ter alguma palavra típica de endereço + if not any(w in up for w in _ADDRESS_HINT_WORDS): + return False + + # não pode conter "PN:" (linha de item) + if "PN:" in up: + return False + + # não pode ser linha com muitos campos numéricos tipo item + if re.match(r"^\d{1,3}\s+\d{2}/\d{2}/\d{2,4}", line): + return False + + return True + + +def _extract_address_generic(lines: List[str], name_idx: Optional[int]) -> Optional[str]: + # Se soubermos a linha do nome, começamos a procurar endereço mais abaixo + start = 0 + if name_idx is not None: + start = name_idx + 1 + + best = [] + + # procura até algumas linhas abaixo + for idx in range(start, min(len(lines), start + 10)): + line = lines[idx] + if _line_looks_like_address(line): + best.append(line) + # tenta incluir 1–2 linhas logo abaixo se forem parecidas + for j in range(idx + 1, min(len(lines), idx + 3)): + if _line_looks_like_address(lines[j]): + best.append(lines[j]) + break + + if not best: + return None + + addr = " ".join(best) + # corta qualquer coisa depois de " * Número de Referência", se existir + addr = re.split(r"\*\s*N[úu]mero de Refer[êe]ncia", addr, flags=re.IGNORECASE)[0].strip() + return addr or None + + +# ------------------------ +# Perfis específicos +# ------------------------ +def _extract_mouser(lines: List[str], text: str) -> SupplierInfo: + up = text.upper() + name = "Mouser Electronics" + + # tenta pegar da linha "Company: ..." + for line in lines: + if "COMPANY:" in line.upper() and "MOUSER" in line.upper(): + parts = line.split(":", 1) + if len(parts) == 2: + cand = parts[1].strip() + if cand: + name = cand + break + + # endereço preferencial: o da sede de Mansfield, TX + addr = None + for line in lines: + up_line = line.upper() + if "1000 NORTH MAIN STREET" in up_line: + addr = line.strip() + # tenta pegar a próxima linha se tiver cidade/estado + idx = lines.index(line) + if idx + 1 < len(lines): + nxt = lines[idx + 1].strip() + if "MANSFIELD" in nxt.upper() or "TX" in nxt.upper(): + addr = addr + " " + nxt + break + + # se não achar por esse caminho, usa heurística genérica perto do nome + if addr is None: + name_idx = None + for i, line in enumerate(lines): + if "MOUSER" in line.upper(): + name_idx = i + break + addr = _extract_address_generic(lines, name_idx) + + email = _extract_email(text, preferred_domains=["mouser.com"]) + + phone = _extract_phone(lines) + + return SupplierInfo( + supplier_name=name, + supplier_address=addr, + supplier_email=email, + supplier_phone=phone, + ) + + +def _extract_avnet(lines: List[str], text: str) -> SupplierInfo: + name = None + for line in lines: + if "AVNET" in line.upper(): + # remove sufixos tipo (000034/02) + name = re.sub(r"\s*\(.*?\)\s*$", "", line).strip() + break + + # se ainda não tiver nome, usa genérico + if not name: + name = _extract_name_generic(lines) + + # tenta achar endereço em alguma linha logo abaixo do nome + name_idx = None + if name: + for i, line in enumerate(lines): + if name in line: + name_idx = i + break + + addr = _extract_address_generic(lines, name_idx) + email = _extract_email(text, preferred_domains=["avnet.com"]) + phone = _extract_phone(lines) + + return SupplierInfo( + supplier_name=name, + supplier_address=addr, + supplier_email=email, + supplier_phone=phone, + ) + + +def _extract_xwork(lines: List[str], text: str) -> SupplierInfo: + name = None + for line in lines: + if "XWORK SOLUTIONS" in line.upper(): + name = line.strip() + break + if not name: + name = "XWORK SOLUTIONS CORP" + + # endereço: linha que contenha "MIAMI" ou "FL - 33176" + addr = None + for line in lines: + up = line.upper() + if "MIAMI" in up or "FL - 33176" in up: + addr = line.strip() + break + + email = _extract_email(text, preferred_domains=["xworksolutions.com"]) + + # telefone: linha com "PH:" preferencialmente + phone = None + for line in lines: + if "PH:" in line.upper(): + m = PHONE_PATTERN.search(line) + if m and not re.search(r"[A-Za-z]", m.group()): + phone = m.group().strip() + break + + if not phone: + phone = _extract_phone(lines) + + return SupplierInfo( + supplier_name=name, + supplier_address=addr, + supplier_email=email, + supplier_phone=phone, + ) + + +def _extract_generic(lines: List[str], text: str) -> SupplierInfo: + name = _extract_name_generic(lines) + + name_idx = None + if name: + for i, line in enumerate(lines): + if name in line: + name_idx = i + break + + addr = _extract_address_generic(lines, name_idx) + email = _extract_email(text) + phone = _extract_phone(lines) + + return SupplierInfo( + supplier_name=name, + supplier_address=addr, + supplier_email=email, + supplier_phone=phone, + ) + + +# ------------------------ +# Função principal pública +# ------------------------ +def extract_supplier(text: str) -> SupplierInfo: + """ + Extrai informações do fornecedor a partir do texto completo do PDF. + + Estratégia AB: + 1) Detecta 'perfil' do fornecedor (Mouser, Avnet, XWork, genérico) + 2) Usa regras específicas se for um perfil conhecido + 3) Cai na heurística genérica caso contrário + """ + norm_text, lines = _normalize_text(text) + profile = _detect_supplier_profile(norm_text) + + if profile == "mouser": + return _extract_mouser(lines, norm_text) + if profile == "avnet": + return _extract_avnet(lines, norm_text) + if profile == "xwork": + return _extract_xwork(lines, norm_text) + + # fallback genérico + return _extract_generic(lines, norm_text) diff --git a/app/libs/extract_pdf/extract_new/extractor_pn.py b/app/libs/extract_pdf/extract_new/extractor_pn.py new file mode 100644 index 0000000..fbd81e7 --- /dev/null +++ b/app/libs/extract_pdf/extract_new/extractor_pn.py @@ -0,0 +1,176 @@ +import uuid +import requests +import re +from typing import List +from pathlib import Path + +from langchain_community.document_loaders.pdf import PyPDFLoader #type: ignore +from langchain_text_splitters import RecursiveCharacterTextSplitter #type: ignore +from langchain.embeddings.base import Embeddings #type: ignore +from langchain_community.vectorstores import Chroma #type: ignore + +from app.log.logger import logger # mantém o seu logger +from app.libs.extract_pdf.extract_new.extract_pn_from_text import extract_part_numbers + +# Caminhos base +BASE_DIR = Path(__file__).resolve().parent +PDF_PATH = BASE_DIR / "INVOICE TECSYS.pdf" +VECTORSTORE_PATH = BASE_DIR / "vectorstore2_invoice" + +# Modelo de embedding do Ollama +EMBEDDING_MODEL = "qwen3-embedding:0.6b" +OLLAMA_URL = "http://localhost:11434" +TIMEOUT = 120 + + +class CustomOllamaEmbeddingFunction(Embeddings): + def __init__(self, model_name: str, url: str = OLLAMA_URL, timeout: int = TIMEOUT): + self.model_name = model_name + self.url = url + self.timeout = timeout + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + vectors: List[List[float]] = [] + for text in texts: + if not text or not text.strip(): + # fallback seguro para textos vazios + vectors.append([0.0] * 768) + continue + + emb = self._request_embedding(text) + if not emb: + # fallback seguro caso o Ollama falhe + emb = [0.0] * 768 + + vectors.append(emb) + + return vectors + + def embed_query(self, text: str) -> List[float]: + return self._request_embedding(text) + + def _request_embedding(self, text: str) -> List[float]: + try: + response = requests.post( + f"{self.url}/api/embeddings", + json={"model": self.model_name, "prompt": text}, + timeout=self.timeout, + ) + response.raise_for_status() + emb = response.json().get("embedding", []) + if not emb: + logger.error(f"[EMBEDDING] Empty embedding returned for text: {text[:80]!r}") + return emb + except Exception as e: + logger.error(f"[EMBEDDING] Error: {e}") + return [] + + +class InvoiceChromaDBManager: + def __init__(self): + logger.info("[CHROMADB-PDF] Initializing") + self.embedding_function = CustomOllamaEmbeddingFunction(EMBEDDING_MODEL) + self.vectorstore = self._load_or_create_vectorstore() + self.retriever = self.vectorstore.as_retriever( + search_type="similarity", + search_kwargs={"k": 20}, + ) + + # também deixo o texto bruto disponível para varredura direta + self.pages = PyPDFLoader(str(PDF_PATH)).load() + self.full_text = "\n\n".join(doc.page_content for doc in self.pages) + + logger.info("[CHROMADB-PDF] Ready") + + def _load_or_create_vectorstore(self): + try: + store = Chroma( + persist_directory=str(VECTORSTORE_PATH), + embedding_function=self.embedding_function, + ) + if store._collection.count() > 0: + logger.info(f"[CHROMADB-PDF] Loaded with {store._collection.count()} documents") + return store + except Exception as e: + logger.warning(f"[CHROMADB-PDF] Failed to load: {e}") + + return self._create_vectorstore_from_pdf(PDF_PATH) + + def _create_vectorstore_from_pdf(self, path: Path): + logger.info(f"[CHROMADB-PDF] Creating new store from: {path}") + + pages = PyPDFLoader(str(path)).load() + logger.info(f"[CHROMADB-PDF] Loaded {len(pages)} pages") + + splitter = RecursiveCharacterTextSplitter( + chunk_size=500, + chunk_overlap=80, + length_function=len, + separators=["\n\n", "\n", "\t"], + ) + + chunks = splitter.split_documents(pages) + logger.info(f"[CHROMADB-PDF] Produced {len(chunks)} chunks") + + # Deduplicar chunks pelo conteúdo + unique = {} + for doc in chunks: + uid = str(uuid.uuid5(uuid.NAMESPACE_DNS, doc.page_content)) + unique[uid] = doc.page_content + + ids = list(unique.keys()) + texts = list(unique.values()) + + logger.info(f"[CHROMADB-PDF] Deduplicated to {len(texts)} chunks") + + # Criar banco vazio com embedder customizado + store = Chroma( + persist_directory=str(VECTORSTORE_PATH), + embedding_function=self.embedding_function, + ) + + # Inserir textos + embeddings + store.add_texts(texts=texts, ids=ids) + + logger.info("[CHROMADB-PDF] Store created and populated") + return store + + def search_parts(self, query: str = "PN:") -> List[str]: + """Busca chunks similares à query (para debug / inspeção).""" + try: + logger.info(f"[CHROMADB-PDF] Searching: {query}") + docs = self.retriever.invoke(query) + return [doc.page_content for doc in docs] + except Exception as e: + logger.error(f"[CHROMADB-PDF] Search error: {e}") + return [] + + def _is_valid_pn(self, pn: str) -> bool: + """Heurística simples para filtrar lixo.""" + if len(pn) < 4: + return False + + blacklist = { + "DESC", "ITEM", "MFR", "SCHEDULE", "COO", + "LOT", "DATE", "PAGE", "TOTAL", "NWT", "GWT", + "BOX", "NET", "EXWORKS", "WORKS", + } + + if pn.upper() in blacklist: + return False + + return True + +if __name__ == "__main__": + manager = InvoiceChromaDBManager() + + print("=== CHUNKS RELEVANTES (contendo 'PN:') ===") + parts = manager.search_parts("PN:") + for i, part in enumerate(parts, 1): + print(f"\n[{i}] ----------------------------") + print(part) + + print("\n=== PART NUMBERS EXTRAÍDOS ===") + pns = extract_part_numbers(manager.full_text) + for pn in pns: + print("-", pn) diff --git a/app/libs/extract_pdf/extract_new/extractor_supplier.py b/app/libs/extract_pdf/extract_new/extractor_supplier.py new file mode 100644 index 0000000..d3a3299 --- /dev/null +++ b/app/libs/extract_pdf/extract_new/extractor_supplier.py @@ -0,0 +1,169 @@ +import uuid +from typing import List +from pathlib import Path + +from langchain_community.document_loaders.pdf import PyPDFLoader #type: ignore +from langchain_text_splitters import RecursiveCharacterTextSplitter #type: ignore +from langchain.embeddings.base import Embeddings #type: ignore +from langchain_community.vectorstores import Chroma #type: ignore + +from app.log.logger import logger +from app.libs.extract_pdf.extract_new.extract_supplier import extract_supplier, SupplierInfo + + +# Caminhos base (ajusta o PDF aqui quando quiser testar outro) +BASE_DIR = Path(__file__).resolve().parent +PDF_PATH = BASE_DIR / "exemplo_pdf_entrada.pdf" +VECTORSTORE_PATH = BASE_DIR / "vectorstore_supplier" + +# Modelo de embedding do Ollama (apenas para debug com Chroma, se quiser) +EMBEDDING_MODEL = "qwen3-embedding:0.6b" +OLLAMA_URL = "http://localhost:11434" +TIMEOUT = 120 + + +class CustomOllamaEmbeddingFunction(Embeddings): + def __init__(self, model_name: str, url: str = OLLAMA_URL, timeout: int = TIMEOUT): + self.model_name = model_name + self.url = url + self.timeout = timeout + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + import requests + + vectors: List[List[float]] = [] + for text in texts: + if not text or not text.strip(): + vectors.append([0.0] * 768) + continue + + try: + resp = requests.post( + f"{self.url}/api/embeddings", + json={"model": self.model_name, "prompt": text}, + timeout=self.timeout, + ) + resp.raise_for_status() + emb = resp.json().get("embedding", []) + if not emb: + emb = [0.0] * 768 + vectors.append(emb) + except Exception as e: + logger.error(f"[EMBEDDING] Error: {e}") + vectors.append([0.0] * 768) + + return vectors + + def embed_query(self, text: str) -> List[float]: + import requests + + try: + resp = requests.post( + f"{self.url}/api/embeddings", + json={"model": self.model_name, "prompt": text}, + timeout=self.timeout, + ) + resp.raise_for_status() + emb = resp.json().get("embedding", []) + if not emb: + emb = [0.0] * 768 + return emb + except Exception as e: + logger.error(f"[EMBEDDING] Error: {e}") + return [0.0] * 768 + + +class SupplierChromaManager: + def __init__(self): + logger.info("[CHROMADB-SUPPLIER] Initializing") + self.embedding_function = CustomOllamaEmbeddingFunction(EMBEDDING_MODEL) + self.vectorstore = self._load_or_create_vectorstore() + self.retriever = self.vectorstore.as_retriever( + search_type="similarity", + search_kwargs={"k": 10}, + ) + + # texto completo do PDF (para o extractor de supplier) + pages = PyPDFLoader(str(PDF_PATH)).load() + self.full_text = "\n\n".join(doc.page_content for doc in pages) + + logger.info("[CHROMADB-SUPPLIER] Ready") + + def _load_or_create_vectorstore(self): + try: + store = Chroma( + persist_directory=str(VECTORSTORE_PATH), + embedding_function=self.embedding_function, + ) + if store._collection.count() > 0: + logger.info(f"[CHROMADB-SUPPLIER] Loaded with {store._collection.count()} documents") + return store + except Exception as e: + logger.warning(f"[CHROMADB-SUPPLIER] Failed to load: {e}") + + return self._create_vectorstore_from_pdf(PDF_PATH) + + def _create_vectorstore_from_pdf(self, path: Path): + logger.info(f"[CHROMADB-SUPPLIER] Creating new store from: {path}") + + pages = PyPDFLoader(str(path)).load() + logger.info(f"[CHROMADB-SUPPLIER] Loaded {len(pages)} pages") + + splitter = RecursiveCharacterTextSplitter( + chunk_size=500, + chunk_overlap=80, + length_function=len, + separators=["\n\n", "\n", "\t"], + ) + + chunks = splitter.split_documents(pages) + logger.info(f"[CHROMADB-SUPPLIER] Produced {len(chunks)} chunks") + + unique = {} + for doc in chunks: + uid = str(uuid.uuid5(uuid.NAMESPACE_DNS, doc.page_content)) + unique[uid] = doc.page_content + + ids = list(unique.keys()) + texts = list(unique.values()) + + logger.info(f"[CHROMADB-SUPPLIER] Deduplicated to {len(texts)} chunks") + + store = Chroma( + persist_directory=str(VECTORSTORE_PATH), + embedding_function=self.embedding_function, + ) + store.add_texts(texts=texts, ids=ids) + + logger.info("[CHROMADB-SUPPLIER] Store created and populated") + return store + + def search_chunks(self, query: str) -> List[str]: + try: + logger.info(f"[CHROMADB-SUPPLIER] Searching: {query}") + docs = self.retriever.invoke(query) + return [doc.page_content for doc in docs] + except Exception as e: + logger.error(f"[CHROMADB-SUPPLIER] Search error: {e}") + return [] + + +if __name__ == "__main__": + manager = SupplierChromaManager() + + print("=== CHUNKS RELEVANTES (debug) ===") + for q in ["Mouser", "Avnet", "XWORK", "TECSYS", "Electronics"]: + parts = manager.search_chunks(q) + if not parts: + continue + print(f"\n--- Query: {q} ---") + for i, part in enumerate(parts, 1): + print(f"\n[{i}] ----------------------------") + print(part) + + print("\n=== SUPPLIER INFO EXTRAÍDO DO PDF ===\n") + info: SupplierInfo = extract_supplier(manager.full_text) + print(f"supplier_name: {info.supplier_name}") + print(f"supplier_address: {info.supplier_address}") + print(f"supplier_email: {info.supplier_email}") + print(f"supplier_phone: {info.supplier_phone}") diff --git a/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/data_level0.bin b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/data_level0.bin new file mode 100644 index 0000000..32550f3 Binary files /dev/null and b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/data_level0.bin differ diff --git a/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/header.bin b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/header.bin new file mode 100644 index 0000000..b4a33c1 Binary files /dev/null and b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/header.bin differ diff --git a/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/length.bin b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/length.bin new file mode 100644 index 0000000..e329a29 Binary files /dev/null and b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/length.bin differ diff --git a/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/link_lists.bin b/app/libs/extract_pdf/extract_new/vectorstore_supplier/3db63767-5153-4dd1-90ee-6f837ef71115/link_lists.bin new file mode 100644 index 0000000..e69de29 diff --git a/app/libs/extract_pdf/extract_new/vectorstore_supplier/chroma.sqlite3 b/app/libs/extract_pdf/extract_new/vectorstore_supplier/chroma.sqlite3 new file mode 100644 index 0000000..f686c74 Binary files /dev/null and b/app/libs/extract_pdf/extract_new/vectorstore_supplier/chroma.sqlite3 differ diff --git a/poetry.lock b/poetry.lock index 98e7212..bba4251 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -213,25 +213,29 @@ typing-extensions = ">=4.12" tz = ["tzdata"] [[package]] -name = "alembic" -version = "1.17.1" -description = "A database migration tool for SQLAlchemy." +name = "altair" +version = "5.5.0" +description = "Vega-Altair: A declarative statistical visualization library for Python." optional = false -python-versions = ">=3.10" +python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.11\"" files = [ - {file = "alembic-1.17.1-py3-none-any.whl", hash = "sha256:cbc2386e60f89608bb63f30d2d6cc66c7aaed1fe105bd862828600e5ad167023"}, - {file = "alembic-1.17.1.tar.gz", hash = "sha256:8a289f6778262df31571d29cca4c7fbacd2f0f582ea0816f4c399b6da7528486"}, + {file = "altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c"}, + {file = "altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d"}, ] [package.dependencies] -Mako = 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@@ -268,15 +272,15 @@ trio = ["trio (>=0.31.0)"] [[package]] name = "async-timeout" -version = "5.0.1" +version = "4.0.3" description = "Timeout context manager for asyncio programs" optional = false -python-versions = ">=3.8" +python-versions = ">=3.7" groups = ["main"] -markers = "python_version < \"3.11\"" +markers = "python_version == \"3.10\"" files = [ - {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"}, - {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"}, + {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"}, + {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"}, ] [[package]] @@ -358,19 +362,6 @@ files = [ {file = "attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11"}, ] -[[package]] -name = "backoff" -version = "1.11.1" -description = "Function decoration for backoff and retry" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" -groups = ["main"] -markers = "python_version >= \"3.13\"" -files = [ - {file = "backoff-1.11.1-py2.py3-none-any.whl", hash = "sha256:61928f8fa48d52e4faa81875eecf308eccfb1016b018bb6bd21e05b5d90a96c5"}, - {file = "backoff-1.11.1.tar.gz", hash = "sha256:ccb962a2378418c667b3c979b504fdeb7d9e0d29c0579e3b13b86467177728cb"}, -] - [[package]] name = "backoff" version = "2.2.1" @@ -378,7 +369,6 @@ description = "Function decoration for backoff and retry" optional = false python-versions = ">=3.7,<4.0" groups = ["main"] -markers = "python_version < \"3.13\"" files = [ {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"}, {file = "backoff-2.2.1.tar.gz", 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