235 lines
8.4 KiB
Python
235 lines
8.4 KiB
Python
import os
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from typing import Dict
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from llama_index.core.constants import DEFAULT_TEMPERATURE
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from llama_index.core.settings import Settings
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from llama_index.llms.xinference import Xinference
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from llama_index.llms.xinference.base import DEFAULT_XINFERENCE_TEMP
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from app.xinference.base import XinferenceEmbedding, XinferenceRerank
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def get_node_postprocessors():
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rerank_enabled = os.getenv("RERANK_ENABLED")
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if rerank_enabled is None or rerank_enabled is False:
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return []
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rerank_model = os.getenv("RERANK_MODEL")
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rerank_url = os.getenv("RERANK_BASE_URL")
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rerank_top_n = os.getenv("RERANK_TOP_N")
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rerank_threshold = os.getenv("RERANK_THRESHOLD")
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postprocess = None
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if rerank_model is not None:
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postprocess = [XinferenceRerank(rerank_model, rerank_url, top_n=rerank_top_n, threshold=rerank_threshold)]
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return postprocess
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def init_settings():
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model_provider = os.getenv("MODEL_PROVIDER")
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match model_provider:
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case "openai":
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init_openai()
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case "dashscope":
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init_dashscope()
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case "groq":
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init_groq()
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case "ollama":
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init_ollama()
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case "anthropic":
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init_anthropic()
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case "gemini":
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init_gemini()
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case "mistral":
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init_mistral()
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case "azure-openai":
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init_azure_openai()
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case "t-systems":
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from .llmhub import init_llmhub
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init_llmhub()
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case "xinference":
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init_xinference()
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case _:
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raise ValueError(f"Invalid model provider: {model_provider}")
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Settings.chunk_size = int(os.getenv("CHUNK_SIZE", "1024"))
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Settings.chunk_overlap = int(os.getenv("CHUNK_OVERLAP", "20"))
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def init_ollama():
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# from llama_index.embeddings.ollama import OllamaEmbedding
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# from llama_index.llms.ollama.base import DEFAULT_REQUEST_TIMEOUT, Ollama
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#
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# base_url = os.getenv("OLLAMA_BASE_URL") or "http://127.0.0.1:11434"
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# request_timeout = float(
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# os.getenv("OLLAMA_REQUEST_TIMEOUT", DEFAULT_REQUEST_TIMEOUT)
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# )
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# Settings.embed_model = OllamaEmbedding(
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# base_url=base_url,
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# model_name=os.getenv("EMBEDDING_MODEL"),
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# )
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# Settings.llm = Ollama(
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# base_url=base_url, model=os.getenv("MODEL"), request_timeout=request_timeout
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# )
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pass
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def init_xinference():
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base_url = os.getenv("BASE_URL")
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model = os.getenv("MODEL")
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max_tokens = int(os.getenv("LLM_MAX_TOKENS")) if os.getenv("LLM_MAX_TOKENS") is not None else None
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temperature = float(os.getenv("LLM_TEMPERATURE", DEFAULT_XINFERENCE_TEMP))
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Settings.llm = Xinference(model, base_url, temperature, max_tokens)
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embedding_base_url = os.getenv("EMBEDDING_BASE_URL")
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embedding_base_url = embedding_base_url if embedding_base_url != None and embedding_base_url != "" else base_url
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embed_model_name = os.getenv("EMBEDDING_MODEL")
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dimensions = os.getenv("EMBEDDING_DIM")
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dimensions = int(dimensions) if dimensions is not None else None
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Settings.embed_model = XinferenceEmbedding(embed_model_name, embedding_base_url, dimensions=dimensions)
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def init_openai():
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from llama_index.core.constants import DEFAULT_TEMPERATURE
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from llama_index.embeddings.openai import OpenAIEmbedding
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from llama_index.llms.openai import OpenAI
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max_tokens = os.getenv("LLM_MAX_TOKENS")
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config = {
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"model": os.getenv("MODEL"),
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"temperature": float(os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)),
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"max_tokens": int(max_tokens) if max_tokens is not None else None,
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}
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Settings.llm = OpenAI(**config)
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dimensions = os.getenv("EMBEDDING_DIM")
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config = {
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"model": os.getenv("EMBEDDING_MODEL"),
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"dimensions": int(dimensions) if dimensions is not None else None,
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}
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Settings.embed_model = OpenAIEmbedding(**config)
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def init_dashscope():
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from llama_index.llms.dashscope import DashScope,DashScopeGenerationModels
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from llama_index.embeddings.dashscope import DashScopeEmbedding,DashScopeBatchTextEmbeddingModels,DashScopeTextEmbeddingType,DashScopeTextEmbeddingModels
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max_tokens = os.getenv("LLM_MAX_TOKENS")
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config = {
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"model": os.getenv("MODEL"),
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"temperature": float(os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)),
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"max_tokens": int(max_tokens) if max_tokens is not None else None,
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}
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Settings.llm = llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX)
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dimensions = os.getenv("EMBEDDING_DIM")
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config = {
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"model": os.getenv("EMBEDDING_MODEL"),
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"dimensions": int(dimensions) if dimensions is not None else None,
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}
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Settings.embed_model = DashScopeEmbedding(model_name=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
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text_type=DashScopeTextEmbeddingType.TEXT_TYPE_QUERY)
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def init_azure_openai():
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# from llama_index.core.constants import DEFAULT_TEMPERATURE
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# from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
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# from llama_index.llms.azure_openai import AzureOpenAI
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#
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# llm_deployment = os.environ["AZURE_OPENAI_LLM_DEPLOYMENT"]
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# embedding_deployment = os.environ["AZURE_OPENAI_EMBEDDING_DEPLOYMENT"]
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# max_tokens = os.getenv("LLM_MAX_TOKENS")
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# temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
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# dimensions = os.getenv("EMBEDDING_DIM")
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#
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# azure_config = {
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# "api_key": os.environ["AZURE_OPENAI_KEY"],
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# "azure_endpoint": os.environ["AZURE_OPENAI_ENDPOINT"],
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# "api_version": os.getenv("AZURE_OPENAI_API_VERSION")
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# or os.getenv("OPENAI_API_VERSION"),
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# }
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#
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# Settings.llm = AzureOpenAI(
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# model=os.getenv("MODEL"),
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# max_tokens=int(max_tokens) if max_tokens is not None else None,
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# temperature=float(temperature),
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# deployment_name=llm_deployment,
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# **azure_config,
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# )
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#
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# Settings.embed_model = AzureOpenAIEmbedding(
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# model=os.getenv("EMBEDDING_MODEL"),
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# dimensions=int(dimensions) if dimensions is not None else None,
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# deployment_name=embedding_deployment,
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# **azure_config,
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# )
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pass
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def init_fastembed():
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"""
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Use Qdrant Fastembed as the local embedding provider.
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"""
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# from llama_index.embeddings.fastembed import FastEmbedEmbedding
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#
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# embed_model_map: Dict[str, str] = {
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# # Small and multilingual
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# "all-MiniLM-L6-v2": "sentence-transformers/all-MiniLM-L6-v2",
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# # Large and multilingual
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# "paraphrase-multilingual-mpnet-base-v2": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2", # noqa: E501
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# }
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#
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# # This will download the model automatically if it is not already downloaded
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# Settings.embed_model = FastEmbedEmbedding(
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# model_name=embed_model_map[os.getenv("EMBEDDING_MODEL")]
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# )
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pass
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def init_groq():
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# from llama_index.llms.groq import Groq
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#
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# model_map: Dict[str, str] = {
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# "llama3-8b": "llama3-8b-8192",
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# "llama3-70b": "llama3-70b-8192",
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# "mixtral-8x7b": "mixtral-8x7b-32768",
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# }
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#
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# Settings.llm = Groq(model=model_map[os.getenv("MODEL")])
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# # Groq does not provide embeddings, so we use FastEmbed instead
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# init_fastembed()
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pass
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def init_anthropic():
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# from llama_index.llms.anthropic import Anthropic
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#
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# model_map: Dict[str, str] = {
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# "claude-3-opus": "claude-3-opus-20240229",
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# "claude-3-sonnet": "claude-3-sonnet-20240229",
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# "claude-3-haiku": "claude-3-haiku-20240307",
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# "claude-2.1": "claude-2.1",
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# "claude-instant-1.2": "claude-instant-1.2",
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# }
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#
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# Settings.llm = Anthropic(model=model_map[os.getenv("MODEL")])
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# # Anthropic does not provide embeddings, so we use FastEmbed instead
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# init_fastembed()
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pass
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def init_gemini():
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# from llama_index.embeddings.gemini import GeminiEmbedding
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# from llama_index.llms.gemini import Gemini
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#
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# model_name = f"models/{os.getenv('MODEL')}"
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# embed_model_name = f"models/{os.getenv('EMBEDDING_MODEL')}"
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#
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# Settings.llm = Gemini(model=model_name)
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# Settings.embed_model = GeminiEmbedding(model_name=embed_model_name)
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pass
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def init_mistral():
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# from llama_index.embeddings.mistralai import MistralAIEmbedding
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# from llama_index.llms.mistralai import MistralAI
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#
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# Settings.llm = MistralAI(model=os.getenv("MODEL"))
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# Settings.embed_model = MistralAIEmbedding(model_name=os.getenv("EMBEDDING_MODEL"))
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pass |