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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| from abc import ABC, abstractmethod | ||
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| import instructor | ||
| from dotenv import load_dotenv | ||
| from instructor.client import T | ||
| from litellm import completion | ||
| from llama_index.core.base.llms.types import ( | ||
| CompletionResponse, | ||
| CompletionResponseAsyncGen, | ||
| ) | ||
| from llama_index.llms.litellm import LiteLLM | ||
|
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| load_dotenv() | ||
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| class BaseLLM(ABC): | ||
| @abstractmethod | ||
| async def astream(self, prompt: str) -> CompletionResponseAsyncGen: | ||
| pass | ||
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| @abstractmethod | ||
| def complete(self, prompt: str) -> CompletionResponse: | ||
| pass | ||
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| @abstractmethod | ||
| def structured_complete(self, response_model: type[T], prompt: str) -> T: | ||
| pass | ||
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| class EveryLLM(BaseLLM): | ||
| def __init__( | ||
| self, | ||
| model: str, | ||
| ): | ||
| self.llm = LiteLLM(model=model) | ||
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| self.client = instructor.from_litellm(completion) | ||
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| async def astream(self, prompt: str) -> CompletionResponseAsyncGen: | ||
| return await self.llm.astream_complete(prompt) | ||
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| def complete(self, prompt: str) -> CompletionResponse: | ||
| return self.llm.complete(prompt) | ||
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| def structured_complete(self, response_model: type[T], prompt: str) -> T: | ||
| return self.client.chat.completions.create( | ||
| model=self.llm.model, | ||
| messages=[{"role": "user", "content": prompt}], | ||
| response_model=response_model, | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,85 +1,15 @@ | ||
| import os | ||
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| import groq | ||
| import instructor | ||
| import openai | ||
| from dotenv import load_dotenv | ||
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| from backend.constants import ChatModel, model_mappings | ||
| from backend.llm.base import BaseLLM | ||
| from backend.prompts import RELATED_QUESTION_PROMPT | ||
| from backend.schemas import RelatedQueries, SearchResult | ||
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| load_dotenv() | ||
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| OLLAMA_HOST = os.environ.get("OLLAMA_HOST", "http://localhost:11434") | ||
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| def get_openai_client() -> openai.AsyncOpenAI: | ||
| openai_mode = os.environ.get("OPENAI_MODE", "openai") | ||
| if openai_mode == "openai": | ||
| return openai.AsyncOpenAI() | ||
| elif openai_mode == "azure": | ||
| return openai.AsyncAzureOpenAI( | ||
| azure_deployment=os.environ.get("AZURE_DEPLOYMENT_NAME"), | ||
| azure_endpoint=os.environ["AZURE_CHAT_ENDPOINT"], | ||
| api_key=os.environ.get("AZURE_API_KEY"), | ||
| api_version="2024-04-01-preview", | ||
| ) | ||
| else: | ||
| raise ValueError(f"Unknown openai mode: {openai_mode}") | ||
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| def instructor_client(model: ChatModel) -> instructor.AsyncInstructor: | ||
| if model == ChatModel.GPT_3_5_TURBO: | ||
| return instructor.from_openai( | ||
| get_openai_client(), | ||
| ) | ||
| elif model in [ | ||
| ChatModel.GPT_3_5_TURBO, | ||
| ChatModel.GPT_4o, | ||
| ]: | ||
| return instructor.from_openai(openai.AsyncOpenAI()) | ||
| elif model in [ | ||
| ChatModel.LOCAL_GEMMA, | ||
| ChatModel.LOCAL_LLAMA_3, | ||
| ChatModel.LOCAL_MISTRAL, | ||
| ChatModel.LOCAL_PHI3_14B, | ||
| ]: | ||
| return instructor.from_openai( | ||
| openai.AsyncOpenAI( | ||
| base_url=f"{OLLAMA_HOST}/v1", | ||
| api_key="ollama", | ||
| ), | ||
| mode=instructor.Mode.JSON, | ||
| ) | ||
| elif model == ChatModel.LLAMA_3_70B: | ||
| return instructor.from_groq(groq.AsyncGroq(), mode=instructor.Mode.JSON) # type: ignore | ||
| else: | ||
| raise ValueError(f"Unknown model: {model}") | ||
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| async def generate_related_queries( | ||
| query: str, search_results: list[SearchResult], model: ChatModel | ||
| query: str, search_results: list[SearchResult], llm: BaseLLM | ||
| ) -> list[str]: | ||
| context = "\n\n".join([f"{str(result)}" for result in search_results]) | ||
| # Truncate the context to 4000 characters (mainly for smaller models) | ||
| context = context[:4000] | ||
|
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| client = instructor_client(model) | ||
| model_name = model_mappings[model] | ||
|
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| print(RELATED_QUESTION_PROMPT.format(query=query, context=context)) | ||
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| related = await client.chat.completions.create( | ||
| model=model_name, | ||
| response_model=RelatedQueries, | ||
| messages=[ | ||
| { | ||
| "role": "user", | ||
| "content": RELATED_QUESTION_PROMPT.format(query=query, context=context), | ||
| }, | ||
| ], | ||
| related = llm.structured_complete( | ||
| RelatedQueries, RELATED_QUESTION_PROMPT.format(query=query, context=context) | ||
| ) | ||
|
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| return [query.lower().replace("?", "") for query in related.related_questions] |