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File size: 2,984 Bytes
2fe4e9d e34be6c 2fe4e9d 532a759 1543ec3 532a759 2fe4e9d e34be6c 2fe4e9d 532a759 2fe4e9d 1543ec3 532a759 2fe4e9d 532a759 dfeefd3 1543ec3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import os
from functools import lru_cache
from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
from langchain.chat_models.base import BaseChatModel
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import Document
from langchain.vectorstores import Qdrant, VectorStore
from edu_assistant.utils.qdrant_utils import load_qdrant_client
@lru_cache(maxsize=1)
def load_llm() -> BaseChatModel:
if os.environ.get("AZURE_OPENAI"):
llm = AzureChatOpenAI(
openai_api_type="azure",
openai_api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
openai_api_base=os.environ.get("AZURE_OPENAI_API_BASE"),
openai_api_version="2023-05-15",
deployment_name=os.environ.get("AZURE_OPENAI_DEPLOYMENT_ID", "gpt-35-turbo"),
model="gpt-3.5-turbo",
temperature=0,
)
else:
llm = ChatOpenAI(
openai_api_key=os.environ.get("OPENAI_API_KEY"),
openai_proxy=os.environ.get("OPENAI_PROXY", ""),
model="gpt-3.5-turbo",
)
return llm
@lru_cache(maxsize=1)
def load_gpt4_llm() -> BaseChatModel:
llm = ChatOpenAI(
openai_api_key=os.environ.get("OPENAI_API_KEY"),
openai_proxy=os.environ.get("OPENAI_PROXY", ""),
model="gpt-4",
)
return llm
@lru_cache(maxsize=1)
def load_gpt4_flag() -> bool:
return os.environ.get("CODEDOG_ENABLE_GPT4") is not None
@lru_cache(maxsize=1)
def load_embeddings():
if os.environ.get("AZURE_OPENAI"):
embeddings = OpenAIEmbeddings(
openai_api_type="azure",
openai_api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
openai_api_base=os.environ.get("AZURE_OPENAI_API_BASE"),
openai_api_version="2023-05-15",
deployment=os.environ.get("AZURE_OPENAI_EMBEDDING_DEP_ID", ""),
)
else:
embeddings = OpenAIEmbeddings(
openai_api_key=os.environ.get("OPENAI_API_KEY"),
openai_proxy=os.environ.get("OPENAI_PROXY", ""),
)
return embeddings
@lru_cache(maxsize=10)
def load_vectorstore(collection_name: str = "default") -> VectorStore:
if os.environ.get("QDRANT_API"):
client = load_qdrant_client()
embeddings = load_embeddings()
doc_store = Qdrant(client=client, collection_name=collection_name, embeddings=embeddings)
return doc_store
return None
@lru_cache(maxsize=20)
def escape_for_prompt(text: str) -> str:
"""escape cruly brackets in text for generate prompt.
Args:
text (str): input string.
Returns:
str: escaped string.
"""
return text.replace("{", "{{").replace("}", "}}")
def shrink_docs(docs: list[Document], max_size=50):
"""shrink source docs content size for display.
Args:
docs (dict): Retrieval Chain returned docs.
"""
for doc in docs:
doc.page_content = doc.page_content[:max_size] + ".."
return docs
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