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214 | class LLMChat:
def __init__(self):
self.chat_model = None
self.retriever = None
self.handler_name = "PyMuPDFParser"
@property
def handler(self) -> Handler:
return FileProcessor.get_handler(self.handler_name)
def initialize_client(
self,
provider: str,
model: str,
credentials: str | None = None,
base_url: str | None = None,
temperature: float | None = 0.0,
) -> str:
"""Initialize the LangChain chat model based on selected provider."""
gr.Info("Initializaing LLM")
base_url = base_url if base_url else None
self.provider = provider
self.model = model
if TYPE_CHECKING:
assert isinstance(credentials, SecretStr)
try:
match (provider):
case LLMProvider.ollama:
from langchain_ollama.llms import OllamaLLM # noqa: I900
self.chat_model = OllamaLLM(model=model, temperature=temperature, base_url=base_url)
case LLMProvider.anthropic:
from langchain_anthropic import ChatAnthropic # noqa: I900
self.chat_model = ChatAnthropic(
model_name=model,
temperature=temperature,
timeout=None,
api_key=credentials,
stop=None,
base_url=base_url,
)
case LLMProvider.openai:
from langchain_openai import ChatOpenAI # noqa: I900
temperature = 0.7 if temperature is None else temperature
self.chat_model = ChatOpenAI(
model=model, temperature=temperature, api_key=credentials, base_url=base_url
)
case LLMProvider.azure_openai:
from langchain_openai import AzureChatOpenAI
if not base_url:
raise gr.Error("Azure OpenAI requires API Enpoint")
api_version = None
api_version_match = re.search(r"[?&]api-version=([^&]+)", base_url)
if api_version_match:
api_version = api_version_match.group(1)
else:
raise gr.Error("Pass in `api_version` as query parameter in Azure API Endpoint")
self.chat_model = AzureChatOpenAI(
api_key=credentials,
azure_endpoint=base_url,
azure_deployment=model,
api_version=api_version,
temperature=0,
)
case _:
raise gr.Error(f"Invalid LLM Provider {provider}")
gr.Info(f"{self.chat_model.get_name()} initialized successfully!")
return f"{self.chat_model.get_name()} initialized successfully!"
except Exception as e:
raise gr.Error(f"Error initializing client: {str(e)}")
def update_handler(self, handler_name: str):
self.handler_name = handler_name
def parse_files(self, collection_name: str, pdf_files: Sequence[Path | str]) -> str:
gr.Info(f"Parsing {len(pdf_files)} files.")
pdf_files = list(map(Path, pdf_files))
if TYPE_CHECKING:
pdf_files = cast(list[Path], pdf_files)
if not all(f.exists() for f in pdf_files):
raise gr.Error("Few files do not exist.")
non_pdf_files = [f.name for f in pdf_files if f.suffix.lower() != ".pdf"]
if non_pdf_files:
raise gr.Error(f"Following files are not pdf: \n{'\n'.join(non_pdf_files)}")
parsed_files = Parallel(n_jobs=-1, backend="threading")(
delayed(self.handler.parse_file)(path) for path in pdf_files
)
if TYPE_CHECKING:
parsed_files = cast(list[ParsedDocumentType], parsed_files)
documents = list(chain.from_iterable(parsed_docs for parsed_docs in parsed_files))
if TYPE_CHECKING:
documents = cast(ParsedDocumentType, documents)
retriever = SupermatRetriever(
parsed_docs=documents,
vector_store=Chroma(
embedding_function=HuggingFaceEmbeddings(
model_name="thenlper/gte-base",
),
persist_directory="./chromadb",
collection_name=collection_name,
),
)
self.retriever = retriever
gr.Info("Files parsed successfully.")
return "Files parsed successfully."
def convert_history_to_messages(self, history: list[dict]) -> list[HumanMessage | AIMessage]:
"""Convert Gradio chat history to LangChain message format."""
return [
HumanMessage(content=msg["content"]) if msg["role"] == "user" else AIMessage(content=msg["content"])
for msg in history
]
@property
def chain(self) -> RunnableSerializable:
assert self.chat_model and self.retriever
chain = get_default_chain(self.retriever, self.chat_model, substitute_references=False, return_context=False)
return chain
def chat(self, message: str, _history):
"""Process chat message using LangChain chat model."""
if not self.chat_model:
raise gr.Error("Please initialize an LLM provider first!")
if not self.retriever:
raise gr.Error("Please parse relevant pdf documents!")
try:
# history_langchain_format = self.convert_history_to_messages(history)
# history_langchain_format.append(HumanMessage(content=message))
gpt_response = self.chain.invoke(message)
gpt_response = gpt_response if isinstance(gpt_response, str) else gpt_response.content
gpt_response = re.sub(r"<cite\b[^>]*?/>", r"``\g<0>``", gpt_response)
return gpt_response
except Exception as e:
raise gr.Error(f"Error: {str(e)}\n{traceback.format_exc()}")
def refresh(self) -> list[str]:
if not self.retriever:
raise gr.Error("Parse pdf documents first.")
return list(self.retriever._document_index_map.keys())
def get_document(self, document: str) -> list[dict]:
if not self.retriever:
raise gr.Error("Parse pdf documents first.")
if document == "All":
return ParsedDocument.dump_python(self.retriever.parsed_docs)
elif document == "None":
return []
else:
filtered_docs = [parsed_doc for parsed_doc in self.retriever.parsed_docs if parsed_doc.document == document]
return ParsedDocument.dump_python(filtered_docs)
|