Refactor get_repo() and load_files() functions to use Repo() without root_dir.Refactored `load_files` added a delay when creating vector store.

.gitignore

@@ -3,4 +3,5 @@
 /.vscode/
 /.venv/
 /talk_codebase/__pycache__/
-.DS_Store
+.DS_Store
+/vector_store/

README.md

@@ -14,6 +14,7 @@ talk-codebase is still under development and is recommended for educational purp
 ## Installation
 
 Requirement Python 3.8.1 or higher
+Your project must be in a git repository
 
 ```bash
 pip install talk-codebase

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "talk-codebase"
-version = "0.1.46"
+version = "0.1.47"
 description = "talk-codebase is a powerful tool for querying and analyzing codebases."
 authors = ["Saryev Rustam <rustam1997@gmail.com>"]
 readme = "README.md"

talk_codebase/cli.py

@@ -6,6 +6,7 @@ from talk_codebase.config import CONFIGURE_STEPS, save_config, get_config, confi
     remove_model_type, remove_model_name_local
 from talk_codebase.consts import DEFAULT_CONFIG
 from talk_codebase.llm import factory_llm
+from talk_codebase.utils import get_repo
 
 
 def check_python_version():
@@ -44,10 +45,14 @@ def chat_loop(llm):
         llm.send_query(query)
 
 
-def chat(root_dir=None):
+def chat():
     configure(False)
     config = get_config()
-    llm = factory_llm(root_dir, config)
+    repo = get_repo()
+    if not repo:
+        print("🤖 Git repository not found")
+        sys.exit(1)
+    llm = factory_llm(repo.working_dir, config)
     chat_loop(llm)
 
 

talk_codebase/config.py

@@ -152,7 +152,6 @@ def configure_model_type(config):
     ).ask()
     config["model_type"] = model_type
     save_config(config)
-    print("🤖 Model type saved!")
 
 
 CONFIGURE_STEPS = [

talk_codebase/llm.py

@@ -1,4 +1,5 @@
 import os
+import time
 from typing import Optional
 
 import gpt4all
@@ -40,7 +41,7 @@ class BaseLLM:
         if new_db is not None:
             return new_db.as_retriever(search_kwargs={"k": k})
 
-        docs = load_files(root_dir)
+        docs = load_files()
         if len(docs) == 0:
             print("✘ No documents found")
             exit(0)
@@ -60,9 +61,13 @@ class BaseLLM:
                 exit(0)
 
         spinners = Halo(text=f"Creating vector store", spinner='dots').start()
-        db = FAISS.from_documents(texts, embeddings)
+        db = FAISS.from_documents([texts[0]], embeddings)
-        db.add_documents(texts)
+        for i, text in enumerate(texts[1:]):
-        db.save_local(index_path)
+            spinners.text = f"Creating vector store ({i + 1}/{len(texts)})"
+            db.add_documents([text])
+            db.save_local(index_path)
+            time.sleep(1.5)
+
         spinners.succeed(f"Created vector store")
         return db.as_retriever(search_kwargs={"k": k})
 
@@ -93,7 +98,8 @@ class LocalLLM(BaseLLM):
         model_n_ctx = int(self.config.get("max_tokens"))
         model_n_batch = int(self.config.get("n_batch"))
         callbacks = CallbackManager([StreamStdOut()])
-        llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=False)
+        llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks,
+                       verbose=False)
         llm.client.verbose = False
         return llm
 

talk_codebase/utils.py

@@ -1,6 +1,3 @@
-import glob
-import multiprocessing
-import os
 import sys
 
 import tiktoken
@@ -11,23 +8,13 @@ from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from talk_codebase.consts import LOADER_MAPPING, EXCLUDE_FILES
 
 
-def get_repo(root_dir):
+def get_repo():
     try:
-        return Repo(root_dir)
+        return Repo()
     except:
         return None
 
 
-def is_ignored(path, root_dir):
-    repo = get_repo(root_dir)
-    if repo is None:
-        return False
-    if not os.path.exists(path):
-        return False
-    ignored = repo.ignored(path)
-    return len(ignored) > 0
-
-
 class StreamStdOut(StreamingStdOutCallbackHandler):
     def on_llm_new_token(self, token: str, **kwargs) -> None:
         sys.stdout.write(token)
@@ -41,26 +28,24 @@ class StreamStdOut(StreamingStdOutCallbackHandler):
         sys.stdout.flush()
 
 
-def load_files(root_dir):
+def load_files():
-    num_cpus = multiprocessing.cpu_count()
+    repo = get_repo()
-    with multiprocessing.Pool(num_cpus) as pool:
+    if repo is None:
-        futures = []
+        return []
-        for file_path in glob.glob(os.path.join(root_dir, '**/*'), recursive=True):
+    files = []
-            if is_ignored(file_path, root_dir):
+    tree = repo.tree()
-                continue
+    for blob in tree.traverse():
-            if any(
+        path = blob.path
-                    file_path.endswith(exclude_file) for exclude_file in EXCLUDE_FILES):
+        if any(
-                continue
+                path.endswith(exclude_file) for exclude_file in EXCLUDE_FILES):
-            for ext in LOADER_MAPPING:
+            continue
-                if file_path.endswith(ext):
+        for ext in LOADER_MAPPING:
-                    print('\r' + f'📂 Loading files: {file_path}')
+            if path.endswith(ext):
-                    args = LOADER_MAPPING[ext]['args']
+                print('\r' + f'📂 Loading files: {path}')
-                    loader = LOADER_MAPPING[ext]['loader'](file_path, *args)
+                args = LOADER_MAPPING[ext]['args']
-                    futures.append(pool.apply_async(loader.load))
+                loader = LOADER_MAPPING[ext]['loader'](path, *args)
-        docs = []
+                files.extend(loader.load())
-        for future in futures:
+    return files
-            docs.extend(future.get())
-    return docs
 
 
 def calculate_cost(texts, model_name):