468 lines
19 KiB
Python
468 lines
19 KiB
Python
import hashlib
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import importlib.metadata
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import json
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import logging
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import os
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import threading
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import uuid
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import requests
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from dotenv import load_dotenv
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from tenacity import retry, stop_after_attempt, wait_fixed
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from embedchain.chunkers.base_chunker import BaseChunker
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from embedchain.config import AddConfig, BaseLlmConfig
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from embedchain.config.apps.BaseAppConfig import BaseAppConfig
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from embedchain.data_formatter import DataFormatter
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from embedchain.embedder.base_embedder import BaseEmbedder
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from embedchain.helper_classes.json_serializable import JSONSerializable
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from embedchain.llm.base_llm import BaseLlm
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from embedchain.loaders.base_loader import BaseLoader
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from embedchain.models.data_type import DataType
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from embedchain.utils import detect_datatype
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from embedchain.vectordb.base_vector_db import BaseVectorDB
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load_dotenv()
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ABS_PATH = os.getcwd()
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HOME_DIR = str(Path.home())
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CONFIG_DIR = os.path.join(HOME_DIR, ".embedchain")
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CONFIG_FILE = os.path.join(CONFIG_DIR, "config.json")
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class EmbedChain(JSONSerializable):
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def __init__(
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self,
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config: BaseAppConfig,
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llm: BaseLlm,
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db: BaseVectorDB = None,
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embedder: BaseEmbedder = None,
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system_prompt: Optional[str] = None,
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):
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"""
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Initializes the EmbedChain instance, sets up a vector DB client and
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creates a collection.
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:param config: Configuration just for the app, not the db or llm or embedder.
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:type config: BaseAppConfig
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:param llm: Instance of the LLM you want to use.
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:type llm: BaseLlm
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:param db: Instance of the Database to use, defaults to None
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:type db: BaseVectorDB, optional
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:param embedder: instance of the embedder to use, defaults to None
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:type embedder: BaseEmbedder, optional
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:param system_prompt: System prompt to use in the llm query, defaults to None
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:type system_prompt: Optional[str], optional
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:raises ValueError: No database or embedder provided.
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"""
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self.config = config
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# Add subclasses
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## Llm
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self.llm = llm
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## Database
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# Database has support for config assignment for backwards compatibility
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if db is None and (not hasattr(self.config, "db") or self.config.db is None):
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raise ValueError("App requires Database.")
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self.db = db or self.config.db
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## Embedder
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if embedder is None:
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raise ValueError("App requires Embedder.")
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self.embedder = embedder
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# Initialize database
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self.db._set_embedder(self.embedder)
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self.db._initialize()
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# Set collection name from app config for backwards compatibility.
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if config.collection_name:
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self.db.set_collection_name(config.collection_name)
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# Add variables that are "shortcuts"
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if system_prompt:
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self.llm.config.system_prompt = system_prompt
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# Attributes that aren't subclass related.
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self.user_asks = []
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# Send anonymous telemetry
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self.s_id = self.config.id if self.config.id else str(uuid.uuid4())
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self.u_id = self._load_or_generate_user_id()
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# NOTE: Uncomment the next two lines when running tests to see if any test fires a telemetry event.
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# if (self.config.collect_metrics):
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# raise ConnectionRefusedError("Collection of metrics should not be allowed.")
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thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("init",))
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thread_telemetry.start()
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def _load_or_generate_user_id(self) -> str:
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"""
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Loads the user id from the config file if it exists, otherwise generates a new
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one and saves it to the config file.
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:return: user id
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:rtype: str
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"""
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if not os.path.exists(CONFIG_DIR):
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os.makedirs(CONFIG_DIR)
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if os.path.exists(CONFIG_FILE):
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with open(CONFIG_FILE, "r") as f:
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data = json.load(f)
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if "user_id" in data:
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return data["user_id"]
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u_id = str(uuid.uuid4())
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with open(CONFIG_FILE, "w") as f:
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json.dump({"user_id": u_id}, f)
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return u_id
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def add(
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self,
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source: Any,
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data_type: Optional[DataType] = None,
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metadata: Optional[Dict[str, Any]] = None,
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config: Optional[AddConfig] = None,
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):
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"""
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Adds the data from the given URL to the vector db.
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Loads the data, chunks it, create embedding for each chunk
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and then stores the embedding to vector database.
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:param source: The data to embed, can be a URL, local file or raw content, depending on the data type.
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:type source: Any
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:param data_type: Automatically detected, but can be forced with this argument. The type of the data to add,
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defaults to None
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:type data_type: Optional[DataType], optional
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:param metadata: Metadata associated with the data source., defaults to None
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:type metadata: Optional[Dict[str, Any]], optional
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:param config: The `AddConfig` instance to use as configuration options., defaults to None
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:type config: Optional[AddConfig], optional
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:raises ValueError: Invalid data type
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:return: source_id, a md5-hash of the source, in hexadecimal representation.
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:rtype: str
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"""
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if config is None:
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config = AddConfig()
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try:
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DataType(source)
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logging.warning(
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f"""Starting from version v0.0.40, Embedchain can automatically detect the data type. So, in the `add` method, the argument order has changed. You no longer need to specify '{source}' for the `source` argument. So the code snippet will be `.add("{data_type}", "{source}")`""" # noqa #E501
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)
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logging.warning(
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"Embedchain is swapping the arguments for you. This functionality might be deprecated in the future, so please adjust your code." # noqa #E501
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)
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source, data_type = data_type, source
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except ValueError:
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pass
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if data_type:
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try:
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data_type = DataType(data_type)
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except ValueError:
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raise ValueError(
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f"Invalid data_type: '{data_type}'.",
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f"Please use one of the following: {[data_type.value for data_type in DataType]}",
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) from None
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if not data_type:
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data_type = detect_datatype(source)
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# `source_id` is the hash of the source argument
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hash_object = hashlib.md5(str(source).encode("utf-8"))
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source_id = hash_object.hexdigest()
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data_formatter = DataFormatter(data_type, config)
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self.user_asks.append([source, data_type.value, metadata])
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documents, _metadatas, _ids, new_chunks = self.load_and_embed(
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data_formatter.loader, data_formatter.chunker, source, metadata, source_id
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)
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if data_type in {DataType.DOCS_SITE}:
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self.is_docs_site_instance = True
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# Send anonymous telemetry
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if self.config.collect_metrics:
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# it's quicker to check the variable twice than to count words when they won't be submitted.
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word_count = sum([len(document.split(" ")) for document in documents])
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extra_metadata = {"data_type": data_type.value, "word_count": word_count, "chunks_count": new_chunks}
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thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("add", extra_metadata))
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thread_telemetry.start()
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return source_id
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def add_local(
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self,
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source: Any,
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data_type: Optional[DataType] = None,
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metadata: Optional[Dict[str, Any]] = None,
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config: Optional[AddConfig] = None,
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):
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"""
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Adds the data from the given URL to the vector db.
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Loads the data, chunks it, create embedding for each chunk
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and then stores the embedding to vector database.
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Warning:
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This method is deprecated and will be removed in future versions. Use `add` instead.
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:param source: The data to embed, can be a URL, local file or raw content, depending on the data type.
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:type source: Any
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:param data_type: Automatically detected, but can be forced with this argument. The type of the data to add,
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defaults to None
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:type data_type: Optional[DataType], optional
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:param metadata: Metadata associated with the data source., defaults to None
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:type metadata: Optional[Dict[str, Any]], optional
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:param config: The `AddConfig` instance to use as configuration options., defaults to None
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:type config: Optional[AddConfig], optional
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:raises ValueError: Invalid data type
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:return: source_id, a md5-hash of the source, in hexadecimal representation.
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:rtype: str
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"""
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logging.warning(
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"The `add_local` method is deprecated and will be removed in future versions. Please use the `add` method for both local and remote files." # noqa: E501
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)
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return self.add(source=source, data_type=data_type, metadata=metadata, config=config)
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def load_and_embed(
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self,
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loader: BaseLoader,
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chunker: BaseChunker,
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src: Any,
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metadata: Optional[Dict[str, Any]] = None,
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source_id: Optional[str] = None,
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) -> Tuple[List[str], Dict[str, Any], List[str], int]:
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"""The loader to use to load the data.
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:param loader: The loader to use to load the data.
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:type loader: BaseLoader
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:param chunker: The chunker to use to chunk the data.
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:type chunker: BaseChunker
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:param src: The data to be handled by the loader.
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Can be a URL for remote sources or local content for local loaders.
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:type src: Any
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:param metadata: Metadata associated with the data source., defaults to None
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:type metadata: Dict[str, Any], optional
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:param source_id: Hexadecimal hash of the source., defaults to None
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:type source_id: str, optional
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:return: (List) documents (embedded text), (List) metadata, (list) ids, (int) number of chunks
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:rtype: Tuple[List[str], Dict[str, Any], List[str], int]
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"""
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embeddings_data = chunker.create_chunks(loader, src)
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# spread chunking results
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documents = embeddings_data["documents"]
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metadatas = embeddings_data["metadatas"]
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ids = embeddings_data["ids"]
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# get existing ids, and discard doc if any common id exist.
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where = {"app_id": self.config.id} if self.config.id is not None else {}
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# where={"url": src}
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existing_ids = self.db.get(
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ids=ids,
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where=where, # optional filter
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)
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if len(existing_ids):
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data_dict = {id: (doc, meta) for id, doc, meta in zip(ids, documents, metadatas)}
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data_dict = {id: value for id, value in data_dict.items() if id not in existing_ids}
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if not data_dict:
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print(f"All data from {src} already exists in the database.")
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# Make sure to return a matching return type
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return [], [], [], 0
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ids = list(data_dict.keys())
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documents, metadatas = zip(*data_dict.values())
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# Loop though all metadatas and add extras.
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new_metadatas = []
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for m in metadatas:
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# Add app id in metadatas so that they can be queried on later
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if self.config.id:
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m["app_id"] = self.config.id
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# Add hashed source
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m["hash"] = source_id
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# Note: Metadata is the function argument
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if metadata:
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# Spread whatever is in metadata into the new object.
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m.update(metadata)
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new_metadatas.append(m)
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metadatas = new_metadatas
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# Count before, to calculate a delta in the end.
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chunks_before_addition = self.db.count()
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self.db.add(documents=documents, metadatas=metadatas, ids=ids)
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count_new_chunks = self.db.count() - chunks_before_addition
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print((f"Successfully saved {src} ({chunker.data_type}). New chunks count: {count_new_chunks}"))
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return list(documents), metadatas, ids, count_new_chunks
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def retrieve_from_database(self, input_query: str, config: Optional[BaseLlmConfig] = None, where=None) -> List[str]:
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"""
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Queries the vector database based on the given input query.
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Gets relevant doc based on the query
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:param input_query: The query to use.
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:type input_query: str
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:param config: The query configuration, defaults to None
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:type config: Optional[BaseLlmConfig], optional
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:param where: A dictionary of key-value pairs to filter the database results, defaults to None
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:type where: _type_, optional
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:return: List of contents of the document that matched your query
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:rtype: List[str]
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"""
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query_config = config or self.llm.config
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if where is not None:
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where = where
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elif query_config is not None and query_config.where is not None:
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where = query_config.where
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else:
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where = {}
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if self.config.id is not None:
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where.update({"app_id": self.config.id})
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contents = self.db.query(
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input_query=input_query,
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n_results=query_config.number_documents,
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where=where,
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)
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return contents
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def query(self, input_query: str, config: BaseLlmConfig = None, dry_run=False, where: Optional[Dict] = None) -> str:
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"""
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Queries the vector database based on the given input query.
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Gets relevant doc based on the query and then passes it to an
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LLM as context to get the answer.
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:param input_query: The query to use.
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:type input_query: str
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:param config: The `LlmConfig` instance to use as configuration options. This is used for one method call.
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To persistently use a config, declare it during app init., defaults to None
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:type config: Optional[BaseLlmConfig], optional
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:param dry_run: A dry run does everything except send the resulting prompt to
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the LLM. The purpose is to test the prompt, not the response., defaults to False
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:type dry_run: bool, optional
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:param where: A dictionary of key-value pairs to filter the database results., defaults to None
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:type where: Optional[Dict[str, str]], optional
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:return: The answer to the query or the dry run result
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:rtype: str
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"""
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contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where)
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answer = self.llm.query(input_query=input_query, contexts=contexts, config=config, dry_run=dry_run)
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# Send anonymous telemetry
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thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("query",))
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thread_telemetry.start()
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return answer
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def chat(
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self,
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input_query: str,
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config: Optional[BaseLlmConfig] = None,
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dry_run=False,
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where: Optional[Dict[str, str]] = None,
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) -> str:
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"""
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Queries the vector database on the given input query.
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Gets relevant doc based on the query and then passes it to an
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LLM as context to get the answer.
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Maintains the whole conversation in memory.
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:param input_query: The query to use.
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:type input_query: str
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:param config: The `LlmConfig` instance to use as configuration options. This is used for one method call.
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To persistently use a config, declare it during app init., defaults to None
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:type config: Optional[BaseLlmConfig], optional
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:param dry_run: A dry run does everything except send the resulting prompt to
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the LLM. The purpose is to test the prompt, not the response., defaults to False
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:type dry_run: bool, optional
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:param where: A dictionary of key-value pairs to filter the database results., defaults to None
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:type where: Optional[Dict[str, str]], optional
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:return: The answer to the query or the dry run result
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:rtype: str
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"""
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contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where)
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answer = self.llm.chat(input_query=input_query, contexts=contexts, config=config, dry_run=dry_run)
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# Send anonymous telemetry
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thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("chat",))
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thread_telemetry.start()
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return answer
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def set_collection_name(self, name: str):
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"""
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Set the name of the collection. A collection is an isolated space for vectors.
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Using `app.db.set_collection_name` method is preferred to this.
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:param name: Name of the collection.
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:type name: str
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"""
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self.db.set_collection_name(name)
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# Create the collection if it does not exist
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self.db._get_or_create_collection(name)
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# TODO: Check whether it is necessary to assign to the `self.collection` attribute,
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# since the main purpose is the creation.
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def count(self) -> int:
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"""
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Count the number of embeddings.
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DEPRECATED IN FAVOR OF `db.count()`
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:return: The number of embeddings.
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:rtype: int
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"""
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logging.warning("DEPRECATION WARNING: Please use `app.db.count()` instead of `app.count()`.")
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return self.db.count()
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def reset(self):
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"""
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Resets the database. Deletes all embeddings irreversibly.
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`App` does not have to be reinitialized after using this method.
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DEPRECATED IN FAVOR OF `db.reset()`
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"""
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# Send anonymous telemetry
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thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("reset",))
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thread_telemetry.start()
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logging.warning("DEPRECATION WARNING: Please use `app.db.reset()` instead of `App.reset()`.")
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self.db.reset()
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@retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
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def _send_telemetry_event(self, method: str, extra_metadata: Optional[dict] = None):
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"""
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Send telemetry event to the embedchain server. This is anonymous. It can be toggled off in `AppConfig`.
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"""
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if not self.config.collect_metrics:
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return
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with threading.Lock():
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url = "https://api.embedchain.ai/api/v1/telemetry/"
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metadata = {
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"s_id": self.s_id,
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"version": importlib.metadata.version(__package__ or __name__),
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"method": method,
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"language": "py",
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"u_id": self.u_id,
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}
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if extra_metadata:
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metadata.update(extra_metadata)
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response = requests.post(url, json={"metadata": metadata})
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if response.status_code != 200:
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logging.warning(f"Telemetry event failed with status code {response.status_code}")
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