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import os
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_ollama import ChatOllama, OllamaEmbeddings
from langchain_huggingface import HuggingFaceEndpoint, HuggingFaceEmbeddings
#from langchain_huggingface import HuggingFaceInference
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain_anthropic import ChatAnthropic
import streamlit as st
@st.cache_resource
def load_llm(provider: str = "ollama", model: str = "tinyllama"):
provider = provider.lower()
if provider == "ollama":
return ChatOllama(model=model)
elif provider == "openai":
return ChatOpenAI(
model=model,
temperature=0.2
)
elif provider == "huggingface":
return HuggingFaceEndpoint(
repo_id=model,
temperature=0.2
)
elif provider == "gemini":
return ChatGoogleGenerativeAI(
model=model,
temperature=0.2
)
elif provider == "anthropic":
"""anthropic-claude"""
return ChatAnthropic(
model=model,
temperature=0.2
)
else:
raise ValueError(f"Unknown provider: {provider}")
def load_embeddings(provider: str = "ollama", model: str = "tinyllama"):
provider = provider.lower()
if provider == "ollama":
return OllamaEmbeddings(model=model)
elif provider == "openai":
return OpenAIEmbeddings(model="text-embedding-3-small")
elif provider == "huggingface":
return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
elif provider == "gemini":
return GoogleGenerativeAIEmbeddings(model="models/embedding-001")
elif provider == "anthropic":
# Anthropic doesn't have Embeddings model, use HF's
return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
else:
raise ValueError(f"Unknown embedding provider: {provider}")
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