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README.md
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@ -1,8 +1,9 @@
## talk-codebase
[![Node.js Package](https://github.com/rsaryev/talk-codebase/actions/workflows/python-publish.yml/badge.svg)](https://github.com/rsaryev/talk-codebase/actions/workflows/python-publish.yml)
* Simple configuration in just a couple of clicks
* Talk-codebase is a tool that allows you to converse with your codebase using LLMs (Large Language Models) to answer your queries.
* It supports offline code processing using [GPT4All](https://github.com/nomic-ai/gpt4all) without sharing your code with third parties, or you can use OpenAI if privacy is not a concern for you.
* It supports offline code processing using LlamaCpp and [GPT4All](https://github.com/nomic-ai/gpt4all) without sharing your code with third parties, or you can use OpenAI if privacy is not a concern for you.
* Talk-codebase is still under development, but it is a tool that can help you to improve your code. It is only recommended for educational purposes and not for production use.
<p align="center">
@ -15,20 +16,22 @@ To install talk-codebase, you need to have:
* Python 3.9
* An OpenAI API [api-keys](https://platform.openai.com/account/api-keys)
* (Optional) [GPT4All](https://gpt4all.io) model
```bash
# Install talk-codebase
pip install talk-codebase
# Configure talk-codebase
talk-codebase configure
# If you want some files to be ignored, add them to .gitignore.
# Once `talk-codebase` is installed, you can use it to chat with your codebase in the current directory by running the following command:
talk-codebase chat .
```
## Reset configuration
```bash
# If you want to reset the configuration, you can run the following command:
talk-codebase configure
```
## Advanced configuration
You can also edit the configuration manually by editing the `~/.config.yaml` file.
@ -48,9 +51,12 @@ k: 4
max_tokens: 1048
# Configuration for the LLM model
model_name: gpt-3.5-turbo
model_path: models/ggml-gpt4all-j-v1.3-groovy.bin
openai_model_name: gpt-3.5-turbo
# Type of model to use. You can choose between `openai` and `local`.
model_type: openai
local_model_name: orca-mini-7b.ggmlv3.q4_0.bin
# Path to local model. If you want to use a local model, you need to specify the path to it.
model_path: 'absolute path to local model'
```
## Supports the following extensions:

496
poetry.lock generated
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@ -122,57 +122,32 @@ files = [
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frozenlist = ">=1.1.0"
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name = "anyio"
version = "3.7.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.7"
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idna = ">=2.8"
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doc = ["Sphinx (>=6.1.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme", "sphinxcontrib-jquery"]
test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (<0.22)"]
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tests = ["cleanlab (>=2.0.0,<2.1.0)", "datasets (>1.17.0,!=2.3.2)", "evaluate", "factory-boy (>=3.2.1,<3.3.0)", "faiss-cpu", "flair (>=0.12.2)", "flyingsquid", "huggingface-hub (>=0.5.0,<0.13)", "openai", "pgmpy", "plotly (>=4.1.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-mock", "rich (==13.0.1)", "seqeval", "setfit", "snorkel (>=0.9.7)", "spacy (==3.5.0)", "span-marker", "transformers[torch] (>=4.19.0)"]
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[[package]]
name = "async-timeout"
@ -421,20 +396,6 @@ files = [
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]
[[package]]
name = "commonmark"
version = "0.9.1"
description = "Python parser for the CommonMark Markdown spec"
optional = false
python-versions = "*"
files = [
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[package.extras]
test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
[[package]]
name = "cryptography"
version = "41.0.1"
@ -478,13 +439,13 @@ test-randomorder = ["pytest-randomly"]
[[package]]
name = "dataclasses-json"
version = "0.5.8"
version = "0.5.9"
description = "Easily serialize dataclasses to and from JSON"
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python-versions = ">=3.6"
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]
[package.dependencies]
@ -493,7 +454,7 @@ marshmallow-enum = ">=1.5.1,<2.0.0"
typing-inspect = ">=0.4.0"
[package.extras]
dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=7.2.0)", "simplejson", "types-dataclasses"]
dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=7.2.0)", "setuptools", "simplejson", "twine", "types-dataclasses", "wheel"]
[[package]]
name = "deprecated"
@ -512,6 +473,17 @@ wrapt = ">=1.10,<2"
[package.extras]
dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
[[package]]
name = "diskcache"
version = "5.6.1"
description = "Disk Cache -- Disk and file backed persistent cache."
optional = false
python-versions = ">=3"
files = [
{file = "diskcache-5.6.1-py3-none-any.whl", hash = "sha256:558c6a2d5d7c721bb00e40711803d6804850c9f76c426ed81ecc627fe9d2ce2d"},
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]
[[package]]
name = "et-xmlfile"
version = "1.1.0"
@ -523,20 +495,6 @@ files = [
{file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"},
]
[[package]]
name = "exceptiongroup"
version = "1.1.1"
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
files = [
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]
[package.extras]
test = ["pytest (>=6)"]
[[package]]
name = "faiss-cpu"
version = "1.7.4"
@ -748,14 +706,14 @@ gitdb = ">=4.0.1,<5"
[[package]]
name = "gpt4all"
version = "0.2.3"
version = "1.0.1"
description = "Python bindings for GPT4All"
optional = false
python-versions = ">=3.8"
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@ -763,7 +721,7 @@ requests = "*"
tqdm = "*"
[package.extras]
dev = ["mkautodoc", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]", "pytest", "setuptools", "twine", "wheel"]
dev = ["black", "isort", "mkautodoc", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]", "pytest", "setuptools", "twine", "wheel"]
[[package]]
name = "greenlet"
@ -840,13 +798,13 @@ test = ["objgraph", "psutil"]
[[package]]
name = "h11"
version = "0.14.0"
version = "0.9.0"
description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
optional = false
python-versions = ">=3.7"
python-versions = "*"
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[[package]]
@ -872,47 +830,42 @@ ipython = ["IPython (==5.7.0)", "ipywidgets (==7.1.0)"]
[[package]]
name = "httpcore"
version = "0.16.3"
version = "0.11.1"
description = "A minimal low-level HTTP client."
optional = false
python-versions = ">=3.7"
python-versions = ">=3.6"
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certifi = "*"
h11 = ">=0.13,<0.15"
h11 = ">=0.8,<0.10"
sniffio = "==1.*"
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http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
http2 = ["h2 (==3.*)"]
[[package]]
name = "httpx"
version = "0.23.3"
version = "0.15.5"
description = "The next generation HTTP client."
optional = false
python-versions = ">=3.7"
python-versions = ">=3.6"
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certifi = "*"
httpcore = ">=0.15.0,<0.17.0"
httpcore = "==0.11.*"
rfc3986 = {version = ">=1.3,<2", extras = ["idna2008"]}
sniffio = "*"
[package.extras]
brotli = ["brotli", "brotlicffi"]
cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<13)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
brotli = ["brotlipy (==0.7.*)"]
http2 = ["h2 (==3.*)"]
[[package]]
name = "huggingface-hub"
@ -1005,20 +958,20 @@ files = [
[[package]]
name = "langchain"
version = "0.0.200"
version = "0.0.223"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
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]
[package.dependencies]
aiohttp = ">=3.8.3,<4.0.0"
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
dataclasses-json = ">=0.5.7,<0.6.0"
langchainplus-sdk = ">=0.0.9"
langchainplus-sdk = ">=0.0.20,<0.0.21"
numexpr = ">=2.8.4,<3.0.0"
numpy = ">=1,<2"
openapi-schema-pydantic = ">=1.2,<2.0"
@ -1029,26 +982,28 @@ SQLAlchemy = ">=1.4,<3"
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]
[[package]]
@ -3130,4 +3036,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "74f15ed94b5ed84e8f63071297b4897845473cc15df004bf40fd43622a7b03be"
content-hash = "746d76164cdfcacca19ad7d311ac16f6cf444918cb5cb79d2dcb4e3a8cf4ff63"

7
pyproject.toml
Unescape Escape View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "talk-codebase"
version = "0.1.37"
version = "0.1.41"
description = "talk-codebase is a powerful tool for querying and analyzing codebases."
authors = ["Saryev Rustam <rustam1997@gmail.com>"]
readme = "README.md"
@ -9,7 +9,6 @@ keywords = ["chatgpt", "openai", "cli"]
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = "^0.0.200"
fire = "^0.5.0"
openai = "^0.27.7"
tiktoken = "^0.4.0"
@ -18,9 +17,11 @@ halo = "^0.0.31"
urllib3 = "1.26.6"
gitpython = "^3.1.31"
questionary = "^1.10.0"
gpt4all = "^0.2.3"
sentence-transformers = "^2.2.2"
unstructured = "^0.6.10"
gpt4all = "^1.0.1"
langchain = "^0.0.223"
llama-cpp-python = "^0.1.67"
[tool.poetry.group.dev.dependencies]

85
talk_codebase/cli.py
Unescape Escape View File

@ -1,54 +1,28 @@
import os
import fire
import questionary
import yaml
from talk_codebase.llm import factory_llm
from talk_codebase.config import CONFIGURE_STEPS, save_config, get_config, config_path, remove_api_key, \
remove_model_type
from talk_codebase.consts import DEFAULT_CONFIG
config_path = os.path.join(os.path.expanduser("~"), ".talk_codebase_config.yaml")
def get_config():
print(f"🤖 Loading config from {config_path}:")
if os.path.exists(config_path):
with open(config_path, "r") as f:
config = yaml.safe_load(f)
else:
config = {}
return config
def save_config(config):
home_dir = os.path.expanduser("~")
with open(config_path, "w") as f:
yaml.dump(config, f)
from talk_codebase.llm import factory_llm
def configure():
def configure(reset=True):
if reset:
remove_api_key()
remove_model_type()
config = get_config()
model_type = questionary.select(
"🤖 Select model type:",
choices=[
{"name": "OpenAI", "value": "openai"},
{"name": "Local", "value": "local"},
]
).ask()
config["model_type"] = model_type
if model_type == "openai":
api_key = input("🤖 Enter your OpenAI API key: ")
model_name = input(f"🤖 Enter your model name (default: {DEFAULT_CONFIG['model_name']}): ")
config["model_name"] = model_name if model_name else DEFAULT_CONFIG["model_name"]
config["api_key"] = api_key
elif model_type == "local":
model_path = input(f"🤖 Enter your model path: (default: {DEFAULT_CONFIG['model_path']}) ")
config["model_path"] = model_path if model_path else DEFAULT_CONFIG["model_path"]
for key, value in DEFAULT_CONFIG.items():
if key not in config:
config[key] = value
for step in CONFIGURE_STEPS:
step(config)
save_config(config)
print("🤖 Configuration saved!")
def loop(llm):
def chat(root_dir=None):
configure(False)
config = get_config()
llm = factory_llm(root_dir, config)
while True:
query = input("👉 ").lower().strip()
if not query:
@ -59,35 +33,12 @@ def loop(llm):
llm.send_query(query)
def validate_config(config):
for key, value in DEFAULT_CONFIG.items():
if key not in config:
config[key] = value
if config.get("model_type") == "openai":
api_key = config.get("api_key")
if not api_key:
print("🤖 Please configure your API key. Use talk-codebase configure")
exit(0)
elif config.get("model_type") == "local":
model_path = config.get("model_path")
if not model_path:
print("🤖 Please configure your model path. Use talk-codebase configure")
exit(0)
save_config(config)
return config
def chat(root_dir):
config = validate_config(get_config())
llm = factory_llm(root_dir, config)
loop(llm)
def main():
print(f"🤖 Config path: {config_path}:")
try:
fire.Fire({
"chat": chat,
"configure": configure
"configure": lambda: configure(True)
})
except KeyboardInterrupt:
print("\n🤖 Bye!")

93
talk_codebase/config.py
Unescape Escape View File

@ -0,0 +1,93 @@
import os
import gpt4all
import questionary
import yaml
from talk_codebase.consts import MODEL_TYPES
config_path = os.path.join(os.path.expanduser("~"), ".talk_codebase_config.yaml")
def get_config():
if os.path.exists(config_path):
with open(config_path, "r") as f:
config = yaml.safe_load(f)
else:
config = {}
return config
def save_config(config):
with open(config_path, "w") as f:
yaml.dump(config, f)
def configure_model_name_local(config):
if config.get("model_type") != MODEL_TYPES["LOCAL"] or config.get("local_model_name"):
return
list_models = gpt4all.GPT4All.list_models()
def get_model_info(model):
return (
f"{model['name']} "
f"| {model['filename']} "
f"| {model['filesize']} "
f"| {model['parameters']} "
f"| {model['quant']} "
f"| {model['type']}"
)
choices = [
{"name": get_model_info(model), "value": model['filename']} for model in list_models
]
model_name = questionary.select("🤖 Select model name:", choices).ask()
config["local_model_name"] = model_name
save_config(config)
print("🤖 Model name saved!")
def configure_api_key(config):
if config.get("model_type") != MODEL_TYPES["OPENAI"] or config.get("api_key"):
return
api_key = input("🤖 Enter your OpenAI API key: ")
config["api_key"] = api_key
save_config(config)
print("🤖 API key saved!")
def remove_api_key():
config = get_config()
config["api_key"] = None
save_config(config)
def remove_model_type():
config = get_config()
config["model_type"] = None
save_config(config)
def configure_model_type(config):
if config.get("model_type"):
return
model_type = questionary.select(
"🤖 Select model type:",
choices=[
{"name": "Local", "value": MODEL_TYPES["LOCAL"]},
{"name": "OpenAI", "value": MODEL_TYPES["OPENAI"]},
]
).ask()
config["model_type"] = model_type
save_config(config)
print("🤖 Model type saved!")
CONFIGURE_STEPS = [
configure_model_type,
configure_model_name_local,
configure_api_key,
]

13
talk_codebase/consts.py
Unescape Escape View File

@ -1,3 +1,6 @@
import os
from pathlib import Path
from langchain.document_loaders import CSVLoader, UnstructuredWordDocumentLoader, UnstructuredEPubLoader, \
PDFMinerLoader, UnstructuredMarkdownLoader, TextLoader
@ -11,15 +14,19 @@ MODEL_TYPES = {
"OPENAI": "openai",
"LOCAL": "local",
}
DEFAULT_LOCAL_MODEL = "orca-mini-3b.ggmlv3.q4_0.bin"
DEFAULT_OPENAI_MODEL = "gpt-3.5-turbo"
DEFAULT_MODEL_DIRECTORY = os.path.join(str(Path.home()), ".cache", "gpt4all").replace("\\", "\\\\")
DEFAULT_CONFIG = {
"max_tokens": "2056",
"chunk_size": "2056",
"chunk_overlap": "256",
"k": "1",
"model_name": "gpt-3.5-turbo-0613",
"model_path": "models/ggml-gpt4all-j-v1.3-groovy.bin",
"model_type": MODEL_TYPES["OPENAI"],
"temperature": "0.7",
"openai_model_name": DEFAULT_OPENAI_MODEL,
"local_model_name": DEFAULT_LOCAL_MODEL,
"model_path": DEFAULT_MODEL_DIRECTORY,
}
LOADER_MAPPING = {

37
talk_codebase/llm.py
Unescape Escape View File

@ -1,6 +1,7 @@
import os
from typing import Optional
import gpt4all
import questionary
from halo import Halo
from langchain import FAISS
@ -8,7 +9,7 @@ from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
from langchain.llms import GPT4All
from langchain.llms import LlamaCpp
from langchain.schema import HumanMessage, SystemMessage
from langchain.text_splitter import RecursiveCharacterTextSplitter
@ -37,15 +38,7 @@ class BaseLLM:
index_path = os.path.join(root_dir, f"vector_store/{index}")
new_db = get_local_vector_store(embeddings, index_path)
if new_db is not None:
approve = questionary.select(
f"Found existing vector store. Do you want to use it?",
choices=[
{"name": "Yes", "value": True},
{"name": "No", "value": False},
]
).ask()
if approve:
return new_db
return new_db
docs = load_files(root_dir)
if len(docs) == 0:
@ -55,7 +48,7 @@ class BaseLLM:
chunk_overlap=int(self.config.get("chunk_overlap")))
texts = text_splitter.split_documents(docs)
if index == MODEL_TYPES["OPENAI"]:
cost = calculate_cost(docs, self.config.get("model_name"))
cost = calculate_cost(docs, self.config.get("openai_model_name"))
approve = questionary.select(
f"Creating a vector store will cost ~${cost:.5f}. Do you want to continue?",
choices=[
@ -81,7 +74,16 @@ class LocalLLM(BaseLLM):
return self._create_vector_store(embeddings, MODEL_TYPES["LOCAL"], root_dir)
def _create_model(self):
llm = GPT4All(model=self.config.get("model_path"), n_ctx=int(self.config.get("max_tokens")), streaming=True)
os.makedirs(self.config.get("model_path"), exist_ok=True)
gpt4all.GPT4All.retrieve_model(model_name=self.config.get("local_model_name"),
model_path=self.config.get("model_path"))
llm = LlamaCpp(
model_path=os.path.join(self.config.get("model_path"), self.config.get("local_model_name")),
n_ctx=int(self.config.get("max_tokens")),
callback_manager=CallbackManager([StreamStdOut()]),
temp=float(self.config.get("temperature")),
streaming=True,
allow_download=True)
return llm
def send_query(self, query):
@ -89,8 +91,15 @@ class LocalLLM(BaseLLM):
docs = self.embedding_search(query, k=int(k))
content = "\n".join([f"content: \n```{s.page_content}```" for s in docs])
template = "Given the following content, your task is to answer the question.\nQuestion: {question}\n{content}"
template = """
### System:
Given the following content, your task is to answer the question. {content}
### User:
{question}
### Response:
"""
prompt = PromptTemplate(template=template, input_variables=["content", "question"]).partial(content=content)
llm_chain = LLMChain(prompt=prompt, llm=self.llm)
@ -106,7 +115,7 @@ class OpenAILLM(BaseLLM):
return self._create_vector_store(embeddings, MODEL_TYPES["OPENAI"], root_dir)
def _create_model(self):
return ChatOpenAI(model_name=self.config.get("model_name"),
return ChatOpenAI(model_name=self.config.get("openai_model_name"),
openai_api_key=self.config.get("api_key"),
streaming=True,
max_tokens=int(self.config.get("max_tokens")),

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