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b347548
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1 Parent(s): 6409d51

docs: added engineering curricula

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Files changed (7) hide show
  1. data/.gitkeep +0 -0
  2. data/engineerings/.gitkeep +0 -0
  3. pyproject.toml +5 -0
  4. src/ingest.py +0 -17
  5. src/loader/ingest.py +90 -0
  6. src/rag/rag_pipeline.py +57 -0
  7. uv.lock +190 -0
data/.gitkeep ADDED
File without changes
data/engineerings/.gitkeep ADDED
File without changes
pyproject.toml CHANGED
@@ -10,4 +10,9 @@ dependencies = [
10
  "langchain==1.0.2",
11
  "langchain-classic==1.0.0",
12
  "langchain-community==0.4.0", # explicitly pinned to prebuilt wheel
 
 
 
 
 
13
  ]
 
10
  "langchain==1.0.2",
11
  "langchain-classic==1.0.0",
12
  "langchain-community==0.4.0", # explicitly pinned to prebuilt wheel
13
+ "langchain-openai>=0.3.14",
14
+ "langchain-text-splitters>=0.3.4",
15
+ "playwright>=1.55.0",
16
+ "python-dotenv>=1.0.0",
17
+ "faiss-cpu>=1.9.0",
18
  ]
src/ingest.py DELETED
@@ -1,17 +0,0 @@
1
- import bs4
2
- from langchain_community.document_loaders import WebBaseLoader
3
- import os
4
- from dotenv import load_dotenv
5
-
6
- load_dotenv()
7
- headers = {"User-Agent": os.getenv("USER_AGENT")}
8
-
9
- bs4_strainer = bs4.SoupStrainer(class_=("post-title", "post-header", "post-content"))
10
- loader = WebBaseLoader(
11
- web_paths=("https://www.upb.edu.co/es/pregrados/ingenieria-ciencia-datos-medellin",),
12
- bs_kwargs={"parse_only": bs4_strainer},
13
- )
14
- docs = loader.load()
15
-
16
- assert len(docs) == 1
17
- print(f"Total characters: {len(docs[0].page_content)}")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/loader/ingest.py ADDED
@@ -0,0 +1,90 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ UPB Career Data Ingestion Pipeline
3
+ Scrapes UPB engineering program pages and saves documents for RAG
4
+ """
5
+
6
+ from pathlib import Path
7
+ import json
8
+ from data_loader import load_upb_careers
9
+ from config import UPB_ENGINEERING_URLS, TEST_URLS
10
+
11
+ # Paths
12
+ CURRENT_DIR = Path(__file__).resolve().parent
13
+ DATA_DIR = CURRENT_DIR.parent / "data"
14
+ RAW_HTML_DIR = DATA_DIR / "raw_html"
15
+ PROCESSED_DIR = DATA_DIR / "processed"
16
+
17
+ # Create directories
18
+ RAW_HTML_DIR.mkdir(parents=True, exist_ok=True)
19
+ PROCESSED_DIR.mkdir(parents=True, exist_ok=True)
20
+
21
+
22
+ def save_documents_json(documents, output_file: Path):
23
+ """Save documents to JSON file"""
24
+ doc_dicts = [
25
+ {
26
+ "page_content": doc.page_content,
27
+ "metadata": doc.metadata
28
+ }
29
+ for doc in documents
30
+ ]
31
+
32
+ with open(output_file, "w", encoding="utf-8") as f:
33
+ json.dump(doc_dicts, f, ensure_ascii=False, indent=2)
34
+
35
+ print(f"💾 Saved {len(documents)} documents to {output_file}")
36
+
37
+
38
+ def ingest_upb_data(test_mode: bool = False):
39
+ """
40
+ Main ingestion pipeline
41
+
42
+ Args:
43
+ test_mode: If True, only scrape TEST_URLS. Otherwise scrape all programs.
44
+ """
45
+ urls = TEST_URLS if test_mode else UPB_ENGINEERING_URLS
46
+
47
+ print("=" * 70)
48
+ print("UPB CAREER DATA INGESTION")
49
+ print("=" * 70)
50
+ print(f"Mode: {'TEST' if test_mode else 'FULL'}")
51
+ print(f"URLs to scrape: {len(urls)}\n")
52
+
53
+ # Load documents
54
+ print("🚀 Starting data collection...\n")
55
+ documents = load_upb_careers(urls, save_html=True)
56
+
57
+ # Save processed documents
58
+ output_file = PROCESSED_DIR / ("upb_careers_test.json" if test_mode else "upb_careers_all.json")
59
+ save_documents_json(documents, output_file)
60
+
61
+ # Print summary
62
+ print("\n" + "=" * 70)
63
+ print("INGESTION SUMMARY")
64
+ print("=" * 70)
65
+ print(f"✅ Documents loaded: {len(documents)}")
66
+ print(f"📊 Total characters: {sum(doc.metadata['char_count'] for doc in documents):,}")
67
+ print(f"📁 Raw HTML saved to: {RAW_HTML_DIR}")
68
+ print(f"📁 Processed data saved to: {output_file}")
69
+
70
+ # Show document titles
71
+ print("\n📚 Loaded programs:")
72
+ for i, doc in enumerate(documents, 1):
73
+ print(f" {i}. {doc.metadata['title']} ({doc.metadata['char_count']:,} chars)")
74
+
75
+ return documents
76
+
77
+
78
+ if __name__ == "__main__":
79
+ import sys
80
+
81
+ # Check if user wants full ingestion
82
+ test_mode = True
83
+ if len(sys.argv) > 1 and sys.argv[1] == "--full":
84
+ test_mode = False
85
+ print("⚠️ Running FULL ingestion (all engineering programs)")
86
+ print("This will take several minutes...\n")
87
+
88
+ documents = ingest_upb_data(test_mode=test_mode)
89
+
90
+ print("\n✨ Ingestion complete! Documents are ready for RAG processing.")
src/rag/rag_pipeline.py ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from document_utils import load_test_documents
2
+ from langchain_text_splitters import RecursiveCharacterTextSplitter
3
+ from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI
4
+ from langchain_community.vectorstores import FAISS
5
+ from dotenv import load_dotenv
6
+ import os
7
+
8
+ # Load environment
9
+ load_dotenv()
10
+ os.environ["AZURE_OPENAI_API_KEY"] = os.getenv("AZURE_OPENAI_API_KEY")
11
+ os.environ["AZURE_OPENAI_ENDPOINT"] = os.getenv("AZURE_OPENAI_ENDPOINT")
12
+
13
+ # 1. Load documents
14
+ documents = load_test_documents()
15
+
16
+ # 2. Split into chunks
17
+ text_splitter = RecursiveCharacterTextSplitter(
18
+ chunk_size=500,
19
+ chunk_overlap=50
20
+ )
21
+ chunks = text_splitter.split_documents(documents)
22
+
23
+ # 3. Create embeddings
24
+ embeddings = AzureOpenAIEmbeddings(
25
+ azure_deployment=os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT"),
26
+ api_version="2024-10-21"
27
+ )
28
+
29
+ # 4. Create vector store
30
+ vectorstore = FAISS.from_documents(chunks, embeddings)
31
+
32
+ # 5. Create retriever
33
+ retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
34
+
35
+ # 6. Create LLM
36
+ llm = AzureChatOpenAI(
37
+ azure_deployment=os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT"),
38
+ api_version="2024-12-01-preview",
39
+ temperature=0
40
+ )
41
+
42
+ # 7. Create QA chain (using LangChain classic)
43
+ from langchain_classic.chains import RetrievalQA
44
+
45
+ qa_chain = RetrievalQA.from_chain_type(
46
+ llm=llm,
47
+ chain_type="stuff",
48
+ retriever=retriever,
49
+ return_source_documents=True
50
+ )
51
+
52
+ # 8. Query!
53
+ result = qa_chain.invoke({
54
+ "query": "¿Qué carrera debo estudiar si me gusta la inteligencia artificial?"
55
+ })
56
+
57
+ print(result["result"])
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742
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743
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744
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@@ -779,17 +959,27 @@ version = "0.1.0"
779
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780
  dependencies = [
781
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782
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783
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784
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785
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786
 
787
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788
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789
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888
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891
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921
+
922
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924
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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972
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973
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974
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975
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976
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977
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978
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979
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980
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981
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982
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983
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985
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