mirror of
https://github.com/rsaryev/talk-codebase
synced 2024-11-10 07:10:31 +00:00
126 lines
5.0 KiB
Python
126 lines
5.0 KiB
Python
import os
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import time
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from typing import Optional
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import gpt4all
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import questionary
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from halo import Halo
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from langchain.vectorstores import FAISS
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from langchain.callbacks.manager import CallbackManager
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from langchain.chains import RetrievalQA
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
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from langchain.llms import LlamaCpp
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from talk_codebase.consts import MODEL_TYPES
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from talk_codebase.utils import load_files, get_local_vector_store, calculate_cost, StreamStdOut
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class BaseLLM:
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def __init__(self, root_dir, config):
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self.config = config
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self.llm = self._create_model()
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self.root_dir = root_dir
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self.vector_store = self._create_store(root_dir)
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def _create_store(self, root_dir):
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raise NotImplementedError("Subclasses must implement this method.")
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def _create_model(self):
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raise NotImplementedError("Subclasses must implement this method.")
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def embedding_search(self, query, k):
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return self.vector_store.search(query, k=k, search_type="similarity")
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def _create_vector_store(self, embeddings, index, root_dir):
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k = int(self.config.get("k"))
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index_path = os.path.join(root_dir, f"vector_store/{index}")
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new_db = get_local_vector_store(embeddings, index_path)
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if new_db is not None:
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return new_db.as_retriever(search_kwargs={"k": k})
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docs = load_files()
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if len(docs) == 0:
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print("✘ No documents found")
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exit(0)
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=int(self.config.get("chunk_size")),
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chunk_overlap=int(self.config.get("chunk_overlap")))
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texts = text_splitter.split_documents(docs)
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if index == MODEL_TYPES["OPENAI"]:
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cost = calculate_cost(docs, self.config.get("openai_model_name"))
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approve = questionary.select(
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f"Creating a vector store will cost ~${cost:.5f}. Do you want to continue?",
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choices=[
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{"name": "Yes", "value": True},
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{"name": "No", "value": False},
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]
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).ask()
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if not approve:
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exit(0)
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spinners = Halo(text=f"Creating vector store", spinner='dots').start()
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db = FAISS.from_documents([texts[0]], embeddings)
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for i, text in enumerate(texts[1:]):
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spinners.text = f"Creating vector store ({i + 1}/{len(texts)})"
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db.add_documents([text])
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db.save_local(index_path)
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time.sleep(1.5)
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spinners.succeed(f"Created vector store")
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return db.as_retriever(search_kwargs={"k": k})
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def send_query(self, query):
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retriever = self._create_store(self.root_dir)
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qa = RetrievalQA.from_chain_type(
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llm=self.llm,
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chain_type="stuff",
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retriever=retriever,
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return_source_documents=True
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)
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docs = qa(query)
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file_paths = [os.path.abspath(s.metadata["source"]) for s in docs['source_documents']]
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print('\n'.join([f'📄 {file_path}:' for file_path in file_paths]))
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class LocalLLM(BaseLLM):
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def _create_store(self, root_dir: str) -> Optional[FAISS]:
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embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
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return self._create_vector_store(embeddings, MODEL_TYPES["LOCAL"], root_dir)
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def _create_model(self):
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os.makedirs(self.config.get("model_path"), exist_ok=True)
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gpt4all.GPT4All.retrieve_model(model_name=self.config.get("local_model_name"),
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model_path=self.config.get("model_path"))
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model_path = os.path.join(self.config.get("model_path"), self.config.get("local_model_name"))
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model_n_ctx = int(self.config.get("max_tokens"))
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model_n_batch = int(self.config.get("n_batch"))
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callbacks = CallbackManager([StreamStdOut()])
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llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks,
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verbose=False)
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llm.client.verbose = False
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return llm
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class OpenAILLM(BaseLLM):
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def _create_store(self, root_dir: str) -> Optional[FAISS]:
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embeddings = OpenAIEmbeddings(openai_api_key=self.config.get("api_key"))
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return self._create_vector_store(embeddings, MODEL_TYPES["OPENAI"], root_dir)
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def _create_model(self):
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return ChatOpenAI(model_name=self.config.get("openai_model_name"),
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openai_api_key=self.config.get("api_key"),
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streaming=True,
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max_tokens=int(self.config.get("max_tokens")),
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callback_manager=CallbackManager([StreamStdOut()]),
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temperature=float(self.config.get("temperature")))
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def factory_llm(root_dir, config):
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if config.get("model_type") == "openai":
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return OpenAILLM(root_dir, config)
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else:
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return LocalLLM(root_dir, config)
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