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import os
import json
from langchain.schema import SystemMessage, HumanMessage
from langchain.prompts.chat import (
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
ChatPromptTemplate
)
from langchain.prompts.prompt import PromptTemplate
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_aws import BedrockEmbeddings
from langchain_aws.chat_models.bedrock_converse import ChatBedrockConverse
from langchain_cohere import ChatCohere
from langchain_fireworks.chat_models import ChatFireworks
from langchain_fireworks.embeddings import FireworksEmbeddings
from langchain_groq.chat_models import ChatGroq
from langchain_openai import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_ollama.chat_models import ChatOllama
from langchain_ollama.embeddings import OllamaEmbeddings
from langchain_cohere.embeddings import CohereEmbeddings
from langchain_cohere.chat_models import ChatCohere
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings
from langchain_community.chat_models import ChatPerplexity
from langchain_together import ChatTogether
from langchain_together.embeddings import TogetherEmbeddings
def split_provider_model(provider_model):
parts = provider_model.split(':', 1)
provider = parts[0]
model = parts[1] if len(parts) > 1 else None
return provider, model
def get_model(provider_model, temperature=0.0):
provider, model = split_provider_model(provider_model)
match provider:
case 'bedrock':
if model is None:
model = "anthropic.claude-3-sonnet-20240229-v1:0"
chat_llm = ChatBedrockConverse(model=model, temperature=temperature)
case 'cohere':
if model is None:
model = 'command-r-plus'
chat_llm = ChatCohere(model=model, temperature=temperature)
case 'fireworks':
if model is None:
model = 'accounts/fireworks/models/llama-v3p1-8b-instruct'
chat_llm = ChatFireworks(model_name=model, temperature=temperature, max_tokens=120000)
case 'googlegenerativeai':
if model is None:
model = "gemini-1.5-flash"
chat_llm = ChatGoogleGenerativeAI(model=model, temperature=temperature,
max_tokens=None, timeout=None, max_retries=2,)
case 'groq':
if model is None:
model = 'llama-3.1-8b-instant'
chat_llm = ChatGroq(model_name=model, temperature=temperature)
case 'ollama':
if model is None:
model = 'llama3.1'
chat_llm = ChatOllama(model=model, temperature=temperature)
case 'openai':
if model is None:
model = "gpt-4o-mini"
chat_llm = ChatOpenAI(model_name=model, temperature=temperature)
case 'perplexity':
if model is None:
model = 'llama-3.1-sonar-small-128k-online'
chat_llm = ChatPerplexity(model=model, temperature=temperature)
case 'together':
if model is None:
model = 'meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo'
chat_llm = ChatTogether(model=model, temperature=temperature)
case _:
raise ValueError(f"Unknown LLM provider {provider}")
return chat_llm
def get_embedding_model(provider_model):
provider, model = split_provider_model(provider_model)
match provider:
case 'bedrock':
if model is None:
model = "amazon.titan-embed-text-v2:0"
embedding_model = BedrockEmbeddings(model_id=model)
case 'cohere':
if model is None:
model = "embed-english-light-v3.0"
embedding_model = CohereEmbeddings(model=model)
case 'fireworks':
if model is None:
model = 'nomic-ai/nomic-embed-text-v1.5'
embedding_model = FireworksEmbeddings(model=model)
case 'ollama':
if model is None:
model = 'nomic-embed-text:latest'
embedding_model = OllamaEmbeddings(model=model)
case 'openai':
if model is None:
model = "text-embedding-3-small"
embedding_model = OpenAIEmbeddings(model=model)
case 'googlegenerativeai':
if model is None:
model = "models/embedding-001"
embedding_model = GoogleGenerativeAIEmbeddings(model=model)
case 'groq':
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
case 'perplexity':
raise ValueError(f"Cannot use Perplexity for embedding model")
case 'together':
if model is None:
model = 'togethercomputer/m2-bert-80M-2k-retrieval'
embedding_model = TogetherEmbeddings(model=model)
case _:
raise ValueError(f"Unknown LLM provider {provider}")
return embedding_model
import unittest
from unittest.mock import patch
from models import get_embedding_model # Make sure this import is correct
class TestGetEmbeddingModel(unittest.TestCase):
@patch('models.BedrockEmbeddings')
def test_bedrock_embedding(self, mock_bedrock):
result = get_embedding_model('bedrock')
mock_bedrock.assert_called_once_with(model_id='cohere.embed-multilingual-v3')
self.assertEqual(result, mock_bedrock.return_value)
@patch('models.CohereEmbeddings')
def test_cohere_embedding(self, mock_cohere):
result = get_embedding_model('cohere')
mock_cohere.assert_called_once_with(model='embed-english-light-v3.0')
self.assertEqual(result, mock_cohere.return_value)
@patch('models.FireworksEmbeddings')
def test_fireworks_embedding(self, mock_fireworks):
result = get_embedding_model('fireworks')
mock_fireworks.assert_called_once_with(model='nomic-ai/nomic-embed-text-v1.5')
self.assertEqual(result, mock_fireworks.return_value)
@patch('models.OllamaEmbeddings')
def test_ollama_embedding(self, mock_ollama):
result = get_embedding_model('ollama')
mock_ollama.assert_called_once_with(model='nomic-embed-text:latest')
self.assertEqual(result, mock_ollama.return_value)
@patch('models.OpenAIEmbeddings')
def test_openai_embedding(self, mock_openai):
result = get_embedding_model('openai')
mock_openai.assert_called_once_with(model='text-embedding-3-small')
self.assertEqual(result, mock_openai.return_value)
@patch('models.GoogleGenerativeAIEmbeddings')
def test_google_embedding(self, mock_google):
result = get_embedding_model('googlegenerativeai')
mock_google.assert_called_once_with(model='models/embedding-001')
self.assertEqual(result, mock_google.return_value)
@patch('models.TogetherEmbeddings')
def test_together_embedding(self, mock_together):
result = get_embedding_model('together')
mock_together.assert_called_once_with(model='BAAI/bge-base-en-v1.5')
self.assertEqual(result, mock_together.return_value)
def test_invalid_provider(self):
with self.assertRaises(ValueError):
get_embedding_model('invalid_provider')
def test_groq_provider(self):
with self.assertRaises(ValueError):
get_embedding_model('groq')
def test_perplexity_provider(self):
with self.assertRaises(ValueError):
get_embedding_model('perplexity')
import unittest
from unittest.mock import patch
from models import get_model # Make sure this import is correct
class TestGetModel(unittest.TestCase):
@patch('models.ChatBedrockConverse')
def test_bedrock_model(self, mock_bedrock):
result = get_model('bedrock')
mock_bedrock.assert_called_once_with(
model="anthropic.claude-3-sonnet-20240229-v1:0",
temperature=0.0
)
self.assertEqual(result, mock_bedrock.return_value)
@patch('models.ChatCohere')
def test_cohere_model(self, mock_cohere):
result = get_model('cohere')
mock_cohere.assert_called_once_with(model='command-r-plus', temperature=0.0)
self.assertEqual(result, mock_cohere.return_value)
@patch('models.ChatFireworks')
def test_fireworks_model(self, mock_fireworks):
result = get_model('fireworks')
mock_fireworks.assert_called_once_with(
model_name='accounts/fireworks/models/llama-v3p1-8b-instruct',
temperature=0.0,
max_tokens=120000
)
self.assertEqual(result, mock_fireworks.return_value)
@patch('models.ChatGoogleGenerativeAI')
def test_google_model(self, mock_google):
result = get_model('googlegenerativeai')
mock_google.assert_called_once_with(
model="gemini-1.5-pro",
temperature=0.0,
max_tokens=None,
timeout=None,
max_retries=2
)
self.assertEqual(result, mock_google.return_value)
@patch('models.ChatGroq')
def test_groq_model(self, mock_groq):
result = get_model('groq')
mock_groq.assert_called_once_with(model_name='llama-3.1-8b-instant', temperature=0.0)
self.assertEqual(result, mock_groq.return_value)
@patch('models.ChatOllama')
def test_ollama_model(self, mock_ollama):
result = get_model('ollama')
mock_ollama.assert_called_once_with(model='llama3.1', temperature=0.0)
self.assertEqual(result, mock_ollama.return_value)
@patch('models.ChatOpenAI')
def test_openai_model(self, mock_openai):
result = get_model('openai')
mock_openai.assert_called_once_with(model_name='gpt-4o-mini', temperature=0.0)
self.assertEqual(result, mock_openai.return_value)
@patch('models.ChatPerplexity')
def test_perplexity_model(self, mock_perplexity):
result = get_model('perplexity')
mock_perplexity.assert_called_once_with(model='llama-3.1-sonar-small-128k-online', temperature=0.0)
self.assertEqual(result, mock_perplexity.return_value)
@patch('models.ChatTogether')
def test_together_model(self, mock_together):
result = get_model('together')
mock_together.assert_called_once_with(model='meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo', temperature=0.0)
self.assertEqual(result, mock_together.return_value)
def test_invalid_provider(self):
with self.assertRaises(ValueError):
get_model('invalid_provider')
def test_custom_temperature(self):
with patch('models.ChatOpenAI') as mock_openai:
result = get_model('openai', temperature=0.5)
mock_openai.assert_called_once_with(model_name='gpt-4o-mini', temperature=0.5)
self.assertEqual(result, mock_openai.return_value)
def test_custom_model(self):
with patch('models.ChatOpenAI') as mock_openai:
result = get_model('openai/gpt-4')
mock_openai.assert_called_once_with(model_name='gpt-4', temperature=0.0)
self.assertEqual(result, mock_openai.return_value)
if __name__ == '__main__':
unittest.main()
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