changed to oss120b, updated readme tags

README.md

@@ -7,4 +7,9 @@ sdk: gradio
 sdk_version: "5.34.2"
 app_file: app.py
 pinned: false
+tags:
+    - 3D
+    - 3D Modeling
+    - CAD
+    - FreeCAD
 ---

generated/result_script.py

@@ -1,89 +1,23 @@
 import FreeCAD as App
 import FreeCADGui as Gui
-from FreeCAD import Vector
-import math
-
-
-def createFlangeAssembly():
-    doc = App.newDocument("Flange")
-
-    # === Parameters ===
-    FLANGE_OUTER_DIAMETER = 100.0
-    FLANGE_THICKNESS = 7.5
-    BORE_INNER_DIAMETER = 50.0
-    NECK_HEIGHT = 15.0
-    NECK_OUTER_DIAMETER = 60.0
-    NUM_BOLT_HOLES = 6
-    BOLT_HOLE_DIAMETER = 12.0
-    PCD = 75.0
-
-    total_height = FLANGE_THICKNESS + NECK_HEIGHT
-
-    # === 1. Create flange base ===
-    flange = doc.addObject("Part::Cylinder", "Flange")
-    flange.Radius = FLANGE_OUTER_DIAMETER / 2
-    flange.Height = FLANGE_THICKNESS
-
-    # === 2. Cut central bore from flange ===
-    bore = doc.addObject("Part::Cylinder", "CentralBore")
-    bore.Radius = BORE_INNER_DIAMETER / 2
-    bore.Height = FLANGE_THICKNESS
-    bore_cut = doc.addObject("Part::Cut", "FlangeWithBore")
-    bore_cut.Base = flange
-    bore_cut.Tool = bore
-
-    # === 3. Create neck ===
-    neck_outer = doc.addObject("Part::Cylinder", "NeckOuter")
-    neck_outer.Radius = NECK_OUTER_DIAMETER / 2
-    neck_outer.Height = NECK_HEIGHT
-    neck_outer.Placement.Base = Vector(0, 0, FLANGE_THICKNESS)
-
-    neck_inner = doc.addObject("Part::Cylinder", "NeckInner")
-    neck_inner.Radius = BORE_INNER_DIAMETER / 2
-    neck_inner.Height = NECK_HEIGHT
-    neck_inner.Placement.Base = Vector(0, 0, FLANGE_THICKNESS)
-
-    neck_hollow = doc.addObject("Part::Cut", "HollowNeck")
-    neck_hollow.Base = neck_outer
-    neck_hollow.Tool = neck_inner
-
-    # === 4. Fuse flange (with central hole) and neck ===
-    fused = doc.addObject("Part::Fuse", "FlangeAndNeck")
-    fused.Base = bore_cut
-    fused.Tool = neck_hollow
-
-    # === 5. Cut bolt holes sequentially ===
-    current_shape = fused
-    bolt_radius = BOLT_HOLE_DIAMETER / 2
-    bolt_circle_radius = PCD / 2
-
-    for i in range(NUM_BOLT_HOLES):
-        angle_deg = 360 * i / NUM_BOLT_HOLES
-        angle_rad = math.radians(angle_deg)
-        x = bolt_circle_radius * math.cos(angle_rad)
-        y = bolt_circle_radius * math.sin(angle_rad)
-
-        hole = doc.addObject("Part::Cylinder", f"BoltHole_{i+1:02d}")
-        hole.Radius = bolt_radius
-        hole.Height = total_height
-        hole.Placement.Base = Vector(x, y, 0)
-
-        cut = doc.addObject("Part::Cut", f"Cut_Bolt_{i+1:02d}")
-        cut.Base = current_shape
-        cut.Tool = hole
-        current_shape = cut
-
-    # === 6. Final result ===
-
-
+from FreeCAD import Vector, Placement, Rotation
+import Part
+
+def createBox(length=10.0, width=20.0, height=30.0):
+    doc = App.newDocument("Box")
+    box = doc.addObject("Part::Box", "Box")
+    box.Length = length
+    box.Width = width
+    box.Height = height
+    box.Placement = Placement(Vector(0, 0, 0), Rotation())
+    box.ViewObject.ShapeColor = (0.8, 0.8, 0.8)
     doc.recompute()
-    Gui.activeDocument().activeView().viewAxometric()
+    Gui.ActiveDocument.ActiveView.viewAxometric()
     Gui.SendMsgToActiveView("ViewFit")
-
     return doc
 
 if __name__ == "__main__":
-    createFlangeAssembly()
+    createBox()
 
 
 import FreeCADGui

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "cadomatic"
-version = "0.1.0"
+version = "0.2.0"
 description = "This is a code to create editable CAD files from natural language prompts"
 readme = "README.md"
 requires-python = ">=3.13"
@@ -12,5 +12,7 @@ dependencies = [
     "langchain-community>=0.3.27",
     "faiss-cpu>=1.11.0",
     "huggingface-hub>=0.34.3",
-    "gradio>=5.39.0"
+    "gradio>=5.39.0",
+    "langchain-huggingface>=0.3.1",
+    "sentence-transformers>=5.1.0",
 ]

src/llm_client.py

@@ -1,34 +1,41 @@
 import os
 from pathlib import Path
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, InferenceClient
 from langchain_community.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 from dotenv import load_dotenv
 
-# Load API key
+# Load HF token from .env
 load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+if not HF_TOKEN:
+    raise ValueError("HF_TOKEN missing in .env")
 
-# Set up Hugging Face repo and files
+# FAISS vectorstore setup
 REPO_ID = "Yas1n/CADomatic_vectorstore"
-FILENAME_FAISS = "index.faiss"
-FILENAME_PKL = "index.pkl"
+FILENAME_FAISS = "index_oss120b.faiss"
+FILENAME_PKL = "index_oss120b.pkl"
 
-# Download once (uses HF cache internally)
 faiss_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME_FAISS)
 pkl_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME_PKL)
-
-# Use same folder for both files
 download_dir = Path(faiss_path).parent
 
-# Load vectorstore
-embedding = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-vectorstore = FAISS.load_local(str(download_dir), embeddings=embedding, allow_dangerous_deserialization=True)
+embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectorstore = FAISS.load_local(
+    str(download_dir),
+    embeddings=embedding,
+    allow_dangerous_deserialization=True,
+    index_name="index_oss120b"
+)
 retriever = vectorstore.as_retriever(search_kwargs={"k": 40})
 
-# Gemini 2.5 Flash
-llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=1.2)
+# Initialize HF InferenceClient
+client = InferenceClient(provider="fireworks-ai", api_key=HF_TOKEN)
+MODEL_NAME = "openai/gpt-oss-120b"
 
+# Prompt function
 def prompt_llm(user_prompt: str) -> str:
+    # Retrieve context from FAISS
     docs = retriever.invoke(user_prompt)
     context = "\n\n".join(doc.page_content for doc in docs)
 
@@ -41,12 +48,28 @@ Use the following FreeCAD wiki documentation as context:
 Instruction:
 {user_prompt}
 
-Respond with valid FreeCAD Python code only, no extra commentary.
+Respond with valid FreeCAD 1.0.1 Python code only, no extra comments.
 """
 
     try:
-        response = llm.invoke(final_prompt)
-        return response.content
+        completion = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[{"role": "user", "content": final_prompt}],
+        )
+        return completion.choices[0].message.content
     except Exception as e:
-        print("❌ Error generating FreeCAD code:", e)
+        print("❌ Error in HF response:", e)
         return ""
+
+# Main CLI
+if __name__ == "__main__":
+    prompt = input("Describe your FreeCAD part: ")
+    code = prompt_llm(prompt)
+
+    # Save generated code
+    output_path = Path("generated/result_script.py")
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write(code)
+
+    print(f"✅ Code generated and written to {output_path}")

src/rag_builder.py

@@ -4,16 +4,11 @@ from bs4 import BeautifulSoup
 from urllib.parse import urljoin
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-
-# --- Step 1: ENV setup ---
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from dotenv import load_dotenv
-import google.generativeai as genai
 
 load_dotenv()
-genai.configure(api_key=os.environ.get("GEMINI_API_KEY"))
 
-# --- Step 2: Crawler ---
 BASE_URL_WIKI = "https://wiki.freecad.org/Power_users_hub"
 BASE_URL_GITHUB = "https://github.com/shaise/FreeCAD_FastenersWB"
 
@@ -22,21 +17,22 @@ DOMAIN_WHITELIST = [
     "https://github.com/shaise"
 ]
 
-# List of language identifiers to exclude (only for wiki)
 LANG_IDENTIFIERS = [
     "/id", "/de", "/tr", "/es", "/fr", "/hr", "/it", "/pl",
     "/pt", "/pt-br", "/ro", "/fi", "/sv", "/cs", "/ru", "/zh-cn",
     "/zh-tw", "/ja", "/ko"
 ]
 
+CHECKPOINT_INTERVAL = 500  # save every 500 pages
+
+VECTORSTORE_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../vectorstore")
+os.makedirs(VECTORSTORE_PATH, exist_ok=True)
+
 def is_excluded_url(url):
     url_lower = url.lower()
-
-    # Apply language filters only to FreeCAD wiki URLs
     if "wiki.freecad.org" in url_lower:
         if any(lang in url_lower for lang in LANG_IDENTIFIERS):
             return True
-
     return (
         ".jpg" in url_lower or
         ".png" in url_lower or
@@ -71,40 +67,42 @@ def crawl_wiki(start_url, max_pages):
                 if any(full.startswith(domain) for domain in DOMAIN_WHITELIST):
                     if full not in visited and not is_excluded_url(full):
                         to_visit.append(full)
+
+            # --- Checkpoint: save every N pages ---
+            if len(pages) % CHECKPOINT_INTERVAL == 0:
+                save_vectorstore_checkpoint(pages, checkpoint_suffix=f"checkpoint_{len(pages)}")
+                print(f"Checkpoint saved after {len(pages)} pages")
         except Exception as e:
             print(f"Error fetching {url}: {e}")
 
-    print(f"Crawled {len(pages)} pages from {start_url}")
     return pages
 
-# --- Step 3: RAG Build ---
-def build_vectorstore():
-    wiki_pages = crawl_wiki(BASE_URL_WIKI, max_pages=2000)  # Uncomment if you want both
-    github_pages = crawl_wiki(BASE_URL_GITHUB, max_pages=450)
-    pages = wiki_pages + github_pages
-
-    if not pages:
-        print("No pages crawled. Exiting.")
-        return
-
+def save_vectorstore_checkpoint(pages, checkpoint_suffix="latest"):
     texts = [p["text"] for p in pages]
     metadatas = [{"source": p["url"]} for p in pages]
 
     splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
     docs = splitter.create_documents(texts, metadatas=metadatas)
 
-    print(f"Split into {len(docs)} chunks")
-
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
     vectorstore = FAISS.from_documents(docs, embeddings)
 
-    src_path = os.path.dirname(os.path.abspath(__file__))
-    root_dir_path = os.path.dirname(src_path)
-    vectorstore_path = os.path.join(root_dir_path, "vectorstore")
+    checkpoint_path = os.path.join(VECTORSTORE_PATH, checkpoint_suffix)
+    os.makedirs(checkpoint_path, exist_ok=True)
+    vectorstore.save_local(checkpoint_path)
+
+def build_vectorstore():
+    wiki_pages = crawl_wiki(BASE_URL_WIKI, max_pages=2000) #2000
+    github_pages = crawl_wiki(BASE_URL_GITHUB, max_pages=450) #450
+    all_pages = wiki_pages + github_pages
+
+    if not all_pages:
+        print("No pages crawled. Exiting.")
+        return
 
-    os.makedirs(vectorstore_path, exist_ok=True)
-    vectorstore.save_local(vectorstore_path)
-    print("Vectorstore saved to ./vectorstore")
+    # Final save
+    save_vectorstore_checkpoint(all_pages, checkpoint_suffix="final")
+    print(f"Vectorstore fully saved to {VECTORSTORE_PATH}/final")
 
 if __name__ == "__main__":
-    build_vectorstore()
+    build_vectorstore()

uv.lock

@@ -181,7 +181,7 @@ wheels = [
 
 [[package]]
 name = "cadomatic"
-version = "0.1.0"
+version = "0.2.0"
 source = { virtual = "." }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -191,7 +191,9 @@ dependencies = [
     { name = "langchain" },
     { name = "langchain-community" },
     { name = "langchain-google-genai" },
+    { name = "langchain-huggingface" },
     { name = "python-dotenv" },
+    { name = "sentence-transformers" },
 ]
 
 [package.metadata]
@@ -203,7 +205,9 @@ requires-dist = [
     { name = "langchain", specifier = ">=0.3.27" },
     { name = "langchain-community", specifier = ">=0.3.27" },
     { name = "langchain-google-genai", specifier = ">=2.1.9" },
+    { name = "langchain-huggingface", specifier = ">=0.3.1" },
     { name = "python-dotenv", specifier = ">=1.1.1" },
+    { name = "sentence-transformers", specifier = ">=5.1.0" },
 ]
 
 [[package]]
@@ -693,6 +697,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "joblib"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/fe/0f5a938c54105553436dbff7a61dc4fed4b1b2c98852f8833beaf4d5968f/joblib-1.5.1.tar.gz", hash = "sha256:f4f86e351f39fe3d0d32a9f2c3d8af1ee4cec285aafcb27003dda5205576b444", size = 330475, upload-time = "2025-05-23T12:04:37.097Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7d/4f/1195bbac8e0c2acc5f740661631d8d750dc38d4a32b23ee5df3cde6f4e0d/joblib-1.5.1-py3-none-any.whl", hash = "sha256:4719a31f054c7d766948dcd83e9613686b27114f190f717cec7eaa2084f8a74a", size = 307746, upload-time = "2025-05-23T12:04:35.124Z" },
+]
+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -788,6 +801,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/84/d8/e1162835d5d6eefaae341c2d1cf750ab53222a421252346905187e53b8a2/langchain_google_genai-2.1.9-py3-none-any.whl", hash = "sha256:8d3aab59706b8f8920a22bcfd63c5000ce430fe61db6ecdec262977d1a0be5b8", size = 49381, upload-time = "2025-08-04T18:51:50.51Z" },
 ]
 
+[[package]]
+name = "langchain-huggingface"
+version = "0.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "langchain-core" },
+    { name = "tokenizers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3f/15/f832ae485707bf52f9a8f055db389850de06c46bc6e3e4420a0ef105fbbf/langchain_huggingface-0.3.1.tar.gz", hash = "sha256:0a145534ce65b5a723c8562c456100a92513bbbf212e6d8c93fdbae174b41341", size = 25154, upload-time = "2025-07-22T17:22:26.77Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bf/26/7c5d4b4d3e1a7385863acc49fb6f96c55ccf941a750991d18e3f6a69a14a/langchain_huggingface-0.3.1-py3-none-any.whl", hash = "sha256:de10a692dc812885696fbaab607d28ac86b833b0f305bccd5d82d60336b07b7d", size = 27609, upload-time = "2025-07-22T17:22:25.282Z" },
+]
+
 [[package]]
 name = "langchain-text-splitters"
 version = "0.3.9"
@@ -879,6 +906,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" },
 ]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" },
+]
+
 [[package]]
 name = "multidict"
 version = "6.6.3"
@@ -933,6 +969,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "networkx"
+version = "3.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037", size = 2471065, upload-time = "2025-05-29T11:35:07.804Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406, upload-time = "2025-05-29T11:35:04.961Z" },
+]
+
 [[package]]
 name = "numpy"
 version = "2.3.2"
@@ -985,6 +1030,132 @@ wheels = [
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 ]
 
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.8.4.1"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921, upload-time = "2025-03-07T01:44:31.254Z" },
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.8.90"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182", size = 10248621, upload-time = "2025-03-07T01:40:21.213Z" },
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.8.93"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029, upload-time = "2025-03-07T01:42:13.562Z" },
+]
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+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.8.90"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90", size = 954765, upload-time = "2025-03-07T01:40:01.615Z" },
+]
+
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