From a86978fb154decc3bdc280cddc1292101f782af3 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Mon, 25 Mar 2024 22:11:25 +0100 Subject: [PATCH] Added docs --- cognee/config.py | 4 + cognee/infrastructure/InfrastructureConfig.py | 7 + cognee/infrastructure/llm/get_llm_client.py | 21 +- cognee/infrastructure/llm/ollama/adapter.py | 108 ++ poetry.lock | 1008 ++++++++++++----- pyproject.toml | 7 +- 6 files changed, 858 insertions(+), 297 deletions(-) create mode 100644 cognee/infrastructure/llm/ollama/adapter.py diff --git a/cognee/config.py b/cognee/config.py index 22b80a07c..e5e92dc83 100644 --- a/cognee/config.py +++ b/cognee/config.py @@ -43,6 +43,10 @@ class Config: graph_filename = os.getenv("GRAPH_NAME", "cognee_graph.pkl") # Model parameters + llm_provider: str = "ollama" + ollama_endpoint: str = "http://localhost:11434/v1" + ollama_key: Optional[str] = os.getenv("OLLAMA_API_KEY") + ollama_model: str = "gpt-3.5-turbo" model: str = "gpt-4-0125-preview" # model: str = "gpt-3.5-turbo" model_endpoint: str = "openai" diff --git a/cognee/infrastructure/InfrastructureConfig.py b/cognee/infrastructure/InfrastructureConfig.py index b8a67c8aa..c57ba5106 100644 --- a/cognee/infrastructure/InfrastructureConfig.py +++ b/cognee/infrastructure/InfrastructureConfig.py @@ -1,6 +1,8 @@ from cognee.config import Config from .databases.relational import SqliteEngine, DatabaseEngine from .databases.vector import WeaviateAdapter, VectorDBInterface +from .llm.llm_interface import LLMInterface +from .llm.openai.adapter import OpenAIAdapter config = Config() config.load() @@ -8,11 +10,15 @@ config.load() class InfrastructureConfig(): database_engine: DatabaseEngine = None vector_engine: VectorDBInterface = None + llm_engine: LLMInterface = None def get_config(self) -> dict: if self.database_engine is None: self.database_engine = SqliteEngine(config.db_path, config.db_name) + if self.llm_engine is None: + self.llm_engine = OpenAIAdapter(config.openai_key, config.model) + if self.vector_engine is None: self.vector_engine = WeaviateAdapter( config.weaviate_url, @@ -28,5 +34,6 @@ class InfrastructureConfig(): def set_config(self, new_config: dict): self.database_engine = new_config["database_engine"] self.vector_engine = new_config["vector_engine"] + self.llm_engine = new_config["llm_engine"] infrastructure_config = InfrastructureConfig() diff --git a/cognee/infrastructure/llm/get_llm_client.py b/cognee/infrastructure/llm/get_llm_client.py index c9c9fc993..d6ac1cb08 100644 --- a/cognee/infrastructure/llm/get_llm_client.py +++ b/cognee/infrastructure/llm/get_llm_client.py @@ -1,10 +1,27 @@ """Get the LLM client.""" +from enum import Enum from cognee.config import Config from .openai.adapter import OpenAIAdapter +from .ollama.adapter import OllamaAPIAdapter + +# Define an Enum for LLM Providers +class LLMProvider(Enum): + OPENAI = "openai" + OLLAMA = "ollama" config = Config() config.load() def get_llm_client(): - """Get the LLM client.""" - return OpenAIAdapter(config.openai_key, config.model) \ No newline at end of file + """Get the LLM client based on the configuration using Enums.""" + provider = LLMProvider(config.llm_provider) + + if provider == LLMProvider.OPENAI: + return OpenAIAdapter(config.openai_key, config.model) + elif provider == LLMProvider.OLLAMA: + return OllamaAPIAdapter(config.ollama_endpoint, config.ollama_key, config.ollama_model) + else: + raise ValueError(f"Unsupported LLM provider: {provider}") + +# Usage example +llm_client = get_llm_client() diff --git a/cognee/infrastructure/llm/ollama/adapter.py b/cognee/infrastructure/llm/ollama/adapter.py new file mode 100644 index 000000000..a6069e5f6 --- /dev/null +++ b/cognee/infrastructure/llm/ollama/adapter.py @@ -0,0 +1,108 @@ +import asyncio + +import aiohttp +from typing import List, Type +from pydantic import BaseModel +import instructor +from tenacity import retry, stop_after_attempt +from openai import AsyncOpenAI + +import openai +from cognee.infrastructure.llm.llm_interface import LLMInterface +from cognee.infrastructure.llm.prompts import read_query_prompt + + +class OllamaAPIAdapter(LLMInterface): + """Adapter for Ollama's API""" + + def __init__(self, ollama_endpoint, api_key: str, model: str): + + self.aclient = instructor.patch( + AsyncOpenAI( + base_url=ollama_endpoint, + api_key=api_key, # required, but unused + ), + mode=instructor.Mode.JSON, + ) + self.model = model + + @retry(stop=stop_after_attempt(5)) + def completions_with_backoff(self, **kwargs): + """Wrapper around ChatCompletion.create w/ backoff""" + # Local model + return openai.chat.completions.create(**kwargs) + + @retry(stop=stop_after_attempt(5)) + async def acompletions_with_backoff(self, **kwargs): + """Wrapper around ChatCompletion.acreate w/ backoff""" + return await openai.chat.completions.acreate(**kwargs) + + @retry(stop=stop_after_attempt(5)) + async def acreate_embedding_with_backoff(self, input: List[str], model: str = "text-embedding-3-large"): + """Wrapper around Embedding.acreate w/ backoff""" + + return await self.aclient.embeddings.create(input=input, model=model) + + async def async_get_embedding_with_backoff(self, text, model="text-embedding-3-large"): + """To get text embeddings, import/call this function + It specifies defaults + handles rate-limiting + is async""" + text = text.replace("\n", " ") + response = await self.aclient.embeddings.create(input=text, model=model) + embedding = response.data[0].embedding + return embedding + + @retry(stop=stop_after_attempt(5)) + def create_embedding_with_backoff(self, **kwargs): + """Wrapper around Embedding.create w/ backoff""" + return openai.embeddings.create(**kwargs) + + def get_embedding_with_backoff(self, text: str, model: str = "text-embedding-3-large"): + """To get text embeddings, import/call this function + It specifies defaults + handles rate-limiting + :param text: str + :param model: str + """ + text = text.replace("\n", " ") + response = self.create_embedding_with_backoff(input=[text], model=model) + embedding = response.data[0].embedding + return embedding + + async def async_get_batch_embeddings_with_backoff(self, texts: List[str], models: List[str]): + """To get multiple text embeddings in parallel, import/call this function + It specifies defaults + handles rate-limiting + is async""" + # Collect all coroutines + coroutines = (self.async_get_embedding_with_backoff(text, model) + for text, model in zip(texts, models)) + + # Run the coroutines in parallel and gather the results + embeddings = await asyncio.gather(*coroutines) + + return embeddings + + @retry(stop=stop_after_attempt(5)) + async def acreate_structured_output(self, text_input: str, system_prompt: str, + response_model: Type[BaseModel]) -> BaseModel: + """Generate a response from a user query.""" + return await self.aclient.chat.completions.create( + model=self.model, + messages=[ + { + "role": "user", + "content": f"""Use the given format to + extract information from the following input: {text_input}. """, + }, + {"role": "system", "content": system_prompt}, + ], + response_model=response_model, + ) + + def show_prompt(self, text_input: str, system_prompt: str) -> str: + """Format and display the prompt for a user query.""" + if not text_input: + text_input = "No user input provided." + if not system_prompt: + raise ValueError("No system prompt path provided.") + system_prompt = read_query_prompt(system_prompt) + + formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" if system_prompt else None + return formatted_prompt diff --git a/poetry.lock b/poetry.lock index 03a5f0875..17e1e8ade 100644 --- a/poetry.lock +++ b/poetry.lock @@ -11,6 +11,116 @@ files = [ {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"}, ] +[[package]] +name = "aiohttp" +version = "3.9.3" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = 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