463 lines
18 KiB
Python
463 lines
18 KiB
Python
import ast
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import json
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import logging
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import os
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import sqlite3
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import uuid
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from typing import Any, Dict, Optional
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import requests
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import yaml
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from embedchain.cache import (
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Config,
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ExactMatchEvaluation,
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SearchDistanceEvaluation,
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cache,
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gptcache_data_manager,
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gptcache_pre_function,
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)
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from embedchain.client import Client
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from embedchain.config import AppConfig, CacheConfig, ChunkerConfig
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from embedchain.constants import SQLITE_PATH
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from embedchain.embedchain import EmbedChain
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from embedchain.embedder.base import BaseEmbedder
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from embedchain.embedder.openai import OpenAIEmbedder
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from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
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from embedchain.helpers.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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from embedchain.llm.openai import OpenAILlm
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from embedchain.telemetry.posthog import AnonymousTelemetry
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from embedchain.utils.misc import validate_config
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from embedchain.vectordb.base import BaseVectorDB
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from embedchain.vectordb.chroma import ChromaDB
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# Set up the user directory if it doesn't exist already
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Client.setup_dir()
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@register_deserializable
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class App(EmbedChain):
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"""
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EmbedChain App lets you create a LLM powered app for your unstructured
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data by defining your chosen data source, embedding model,
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and vector database.
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"""
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def __init__(
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self,
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id: str = None,
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name: str = None,
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config: AppConfig = None,
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db: BaseVectorDB = None,
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embedding_model: BaseEmbedder = None,
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llm: BaseLlm = None,
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config_data: dict = None,
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log_level=logging.WARN,
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auto_deploy: bool = False,
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chunker: ChunkerConfig = None,
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cache_config: CacheConfig = None,
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):
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"""
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Initialize a new `App` instance.
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:param config: Configuration for the pipeline, defaults to None
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:type config: AppConfig, optional
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:param db: The database to use for storing and retrieving embeddings, defaults to None
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:type db: BaseVectorDB, optional
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:param embedding_model: The embedding model used to calculate embeddings, defaults to None
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:type embedding_model: BaseEmbedder, optional
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:param llm: The LLM model used to calculate embeddings, defaults to None
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:type llm: BaseLlm, optional
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:param config_data: Config dictionary, defaults to None
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:type config_data: dict, optional
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:param log_level: Log level to use, defaults to logging.WARN
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:type log_level: int, optional
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:param auto_deploy: Whether to deploy the pipeline automatically, defaults to False
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:type auto_deploy: bool, optional
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:raises Exception: If an error occurs while creating the pipeline
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"""
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if id and config_data:
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raise Exception("Cannot provide both id and config. Please provide only one of them.")
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if id and name:
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raise Exception("Cannot provide both id and name. Please provide only one of them.")
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if name and config:
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raise Exception("Cannot provide both name and config. Please provide only one of them.")
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logging.basicConfig(level=log_level, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
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self.logger = logging.getLogger(__name__)
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self.auto_deploy = auto_deploy
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# Store the dict config as an attribute to be able to send it
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self.config_data = config_data if (config_data and validate_config(config_data)) else None
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self.client = None
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# pipeline_id from the backend
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self.id = None
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self.chunker = None
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if chunker:
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self.chunker = ChunkerConfig(**chunker)
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self.cache_config = cache_config
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self.config = config or AppConfig()
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self.name = self.config.name
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self.config.id = self.local_id = str(uuid.uuid4()) if self.config.id is None else self.config.id
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if id is not None:
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# Init client first since user is trying to fetch the pipeline
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# details from the platform
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self._init_client()
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pipeline_details = self._get_pipeline(id)
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self.config.id = self.local_id = pipeline_details["metadata"]["local_id"]
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self.id = id
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if name is not None:
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self.name = name
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self.embedding_model = embedding_model or OpenAIEmbedder()
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self.db = db or ChromaDB()
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self.llm = llm or OpenAILlm()
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self._init_db()
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# If cache_config is provided, initializing the cache ...
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if self.cache_config is not None:
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self._init_cache()
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# Send anonymous telemetry
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self._telemetry_props = {"class": self.__class__.__name__}
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self.telemetry = AnonymousTelemetry(enabled=self.config.collect_metrics)
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# Establish a connection to the SQLite database
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self.connection = sqlite3.connect(SQLITE_PATH, check_same_thread=False)
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self.cursor = self.connection.cursor()
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# Create the 'data_sources' table if it doesn't exist
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self.cursor.execute(
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"""
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CREATE TABLE IF NOT EXISTS data_sources (
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pipeline_id TEXT,
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hash TEXT,
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type TEXT,
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value TEXT,
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metadata TEXT,
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is_uploaded INTEGER DEFAULT 0,
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PRIMARY KEY (pipeline_id, hash)
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)
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"""
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)
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self.connection.commit()
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# Send anonymous telemetry
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self.telemetry.capture(event_name="init", properties=self._telemetry_props)
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self.user_asks = []
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if self.auto_deploy:
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self.deploy()
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def _init_db(self):
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"""
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Initialize the database.
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"""
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self.db._set_embedder(self.embedding_model)
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self.db._initialize()
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self.db.set_collection_name(self.db.config.collection_name)
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def _init_cache(self):
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if self.cache_config.similarity_eval_config.strategy == "exact":
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similarity_eval_func = ExactMatchEvaluation()
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else:
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similarity_eval_func = SearchDistanceEvaluation(
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max_distance=self.cache_config.similarity_eval_config.max_distance,
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positive=self.cache_config.similarity_eval_config.positive,
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)
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cache.init(
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pre_embedding_func=gptcache_pre_function,
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embedding_func=self.embedding_model.to_embeddings,
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data_manager=gptcache_data_manager(vector_dimension=self.embedding_model.vector_dimension),
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similarity_evaluation=similarity_eval_func,
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config=Config(**self.cache_config.init_config.as_dict()),
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)
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def _init_client(self):
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"""
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Initialize the client.
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"""
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config = Client.load_config()
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if config.get("api_key"):
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self.client = Client()
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else:
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api_key = input(
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"🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/ \n" # noqa: E501
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)
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self.client = Client(api_key=api_key)
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def _get_pipeline(self, id):
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"""
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Get existing pipeline
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"""
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print("🛠️ Fetching pipeline details from the platform...")
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url = f"{self.client.host}/api/v1/pipelines/{id}/cli/"
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r = requests.get(
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url,
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headers={"Authorization": f"Token {self.client.api_key}"},
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)
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if r.status_code == 404:
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raise Exception(f"❌ Pipeline with id {id} not found!")
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print(
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f"🎉 Pipeline loaded successfully! Pipeline url: https://app.embedchain.ai/pipelines/{r.json()['id']}\n" # noqa: E501
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)
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return r.json()
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def _create_pipeline(self):
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"""
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Create a pipeline on the platform.
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"""
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print("🛠️ Creating pipeline on the platform...")
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# self.config_data is a dict. Pass it inside the key 'yaml_config' to the backend
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payload = {
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"yaml_config": json.dumps(self.config_data),
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"name": self.name,
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"local_id": self.local_id,
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}
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url = f"{self.client.host}/api/v1/pipelines/cli/create/"
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r = requests.post(
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url,
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json=payload,
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headers={"Authorization": f"Token {self.client.api_key}"},
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)
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if r.status_code not in [200, 201]:
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raise Exception(f"❌ Error occurred while creating pipeline. API response: {r.text}")
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if r.status_code == 200:
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print(
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f"🎉🎉🎉 Existing pipeline found! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n" # noqa: E501
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) # noqa: E501
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elif r.status_code == 201:
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print(
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f"🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n" # noqa: E501
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)
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return r.json()
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def _get_presigned_url(self, data_type, data_value):
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payload = {"data_type": data_type, "data_value": data_value}
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r = requests.post(
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f"{self.client.host}/api/v1/pipelines/{self.id}/cli/presigned_url/",
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json=payload,
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headers={"Authorization": f"Token {self.client.api_key}"},
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)
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r.raise_for_status()
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return r.json()
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def search(self, query, num_documents=3):
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"""
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Search for similar documents related to the query in the vector database.
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"""
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# Send anonymous telemetry
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self.telemetry.capture(event_name="search", properties=self._telemetry_props)
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# TODO: Search will call the endpoint rather than fetching the data from the db itself when deploy=True.
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if self.id is None:
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where = {"app_id": self.local_id}
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context = self.db.query(
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query,
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n_results=num_documents,
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where=where,
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citations=True,
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)
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result = []
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for c in context:
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result.append({"context": c[0], "metadata": c[1]})
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return result
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else:
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# Make API call to the backend to get the results
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NotImplementedError("Search is not implemented yet for the prod mode.")
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def _upload_file_to_presigned_url(self, presigned_url, file_path):
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try:
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with open(file_path, "rb") as file:
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response = requests.put(presigned_url, data=file)
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response.raise_for_status()
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return response.status_code == 200
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except Exception as e:
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self.logger.exception(f"Error occurred during file upload: {str(e)}")
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print("❌ Error occurred during file upload!")
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return False
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def _upload_data_to_pipeline(self, data_type, data_value, metadata=None):
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payload = {
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"data_type": data_type,
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"data_value": data_value,
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"metadata": metadata,
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}
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try:
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self._send_api_request(f"/api/v1/pipelines/{self.id}/cli/add/", payload)
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# print the local file path if user tries to upload a local file
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printed_value = metadata.get("file_path") if metadata.get("file_path") else data_value
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print(f"✅ Data of type: {data_type}, value: {printed_value} added successfully.")
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except Exception as e:
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print(f"❌ Error occurred during data upload for type {data_type}!. Error: {str(e)}")
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def _send_api_request(self, endpoint, payload):
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url = f"{self.client.host}{endpoint}"
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headers = {"Authorization": f"Token {self.client.api_key}"}
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response = requests.post(url, json=payload, headers=headers)
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response.raise_for_status()
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return response
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def _process_and_upload_data(self, data_hash, data_type, data_value):
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if os.path.isabs(data_value):
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presigned_url_data = self._get_presigned_url(data_type, data_value)
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presigned_url = presigned_url_data["presigned_url"]
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s3_key = presigned_url_data["s3_key"]
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if self._upload_file_to_presigned_url(presigned_url, file_path=data_value):
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metadata = {"file_path": data_value, "s3_key": s3_key}
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data_value = presigned_url
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else:
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self.logger.error(f"File upload failed for hash: {data_hash}")
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return False
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else:
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if data_type == "qna_pair":
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data_value = list(ast.literal_eval(data_value))
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metadata = {}
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try:
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self._upload_data_to_pipeline(data_type, data_value, metadata)
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self._mark_data_as_uploaded(data_hash)
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return True
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except Exception:
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print(f"❌ Error occurred during data upload for hash {data_hash}!")
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return False
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def _mark_data_as_uploaded(self, data_hash):
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self.cursor.execute(
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"UPDATE data_sources SET is_uploaded = 1 WHERE hash = ? AND pipeline_id = ?",
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(data_hash, self.local_id),
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)
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self.connection.commit()
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def get_data_sources(self):
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db_data = self.cursor.execute("SELECT * FROM data_sources WHERE pipeline_id = ?", (self.local_id,)).fetchall()
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data_sources = []
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for data in db_data:
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data_sources.append({"data_type": data[2], "data_value": data[3], "metadata": data[4]})
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return data_sources
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def deploy(self):
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if self.client is None:
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self._init_client()
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pipeline_data = self._create_pipeline()
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self.id = pipeline_data["id"]
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results = self.cursor.execute(
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"SELECT * FROM data_sources WHERE pipeline_id = ? AND is_uploaded = 0", (self.local_id,) # noqa:E501
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).fetchall()
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if len(results) > 0:
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print("🛠️ Adding data to your pipeline...")
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for result in results:
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data_hash, data_type, data_value = result[1], result[2], result[3]
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self._process_and_upload_data(data_hash, data_type, data_value)
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# Send anonymous telemetry
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self.telemetry.capture(event_name="deploy", properties=self._telemetry_props)
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@classmethod
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def from_config(
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cls,
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config_path: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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auto_deploy: bool = False,
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yaml_path: Optional[str] = None,
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):
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"""
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Instantiate a Pipeline object from a configuration.
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:param config_path: Path to the YAML or JSON configuration file.
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:type config_path: Optional[str]
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:param config: A dictionary containing the configuration.
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:type config: Optional[Dict[str, Any]]
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:param auto_deploy: Whether to deploy the pipeline automatically, defaults to False
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:type auto_deploy: bool, optional
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:param yaml_path: (Deprecated) Path to the YAML configuration file. Use config_path instead.
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:type yaml_path: Optional[str]
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:return: An instance of the Pipeline class.
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:rtype: Pipeline
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"""
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# Backward compatibility for yaml_path
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if yaml_path and not config_path:
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config_path = yaml_path
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if config_path and config:
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raise ValueError("Please provide only one of config_path or config.")
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config_data = None
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if config_path:
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file_extension = os.path.splitext(config_path)[1]
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with open(config_path, "r") as file:
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if file_extension in [".yaml", ".yml"]:
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config_data = yaml.safe_load(file)
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elif file_extension == ".json":
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config_data = json.load(file)
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else:
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raise ValueError("config_path must be a path to a YAML or JSON file.")
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elif config and isinstance(config, dict):
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config_data = config
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else:
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logging.error(
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"Please provide either a config file path (YAML or JSON) or a config dictionary. Falling back to defaults because no config is provided.", # noqa: E501
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)
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config_data = {}
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try:
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validate_config(config_data)
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except Exception as e:
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raise Exception(f"Error occurred while validating the config. Error: {str(e)}")
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app_config_data = config_data.get("app", {}).get("config", {})
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db_config_data = config_data.get("vectordb", {})
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embedding_model_config_data = config_data.get("embedding_model", config_data.get("embedder", {}))
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llm_config_data = config_data.get("llm", {})
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chunker_config_data = config_data.get("chunker", {})
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cache_config_data = config_data.get("cache", None)
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app_config = AppConfig(**app_config_data)
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db_provider = db_config_data.get("provider", "chroma")
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db = VectorDBFactory.create(db_provider, db_config_data.get("config", {}))
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if llm_config_data:
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llm_provider = llm_config_data.get("provider", "openai")
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llm = LlmFactory.create(llm_provider, llm_config_data.get("config", {}))
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else:
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llm = None
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embedding_model_provider = embedding_model_config_data.get("provider", "openai")
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embedding_model = EmbedderFactory.create(
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embedding_model_provider, embedding_model_config_data.get("config", {})
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)
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if cache_config_data is not None:
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cache_config = CacheConfig.from_config(cache_config_data)
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else:
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cache_config = None
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# Send anonymous telemetry
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event_properties = {"init_type": "config_data"}
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AnonymousTelemetry().capture(event_name="init", properties=event_properties)
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return cls(
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config=app_config,
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llm=llm,
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db=db,
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embedding_model=embedding_model,
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config_data=config_data,
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auto_deploy=auto_deploy,
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chunker=chunker_config_data,
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cache_config=cache_config,
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)
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