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docs: update docstrings (#565)

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This commit is contained in:
cachho
2023-09-07 02:04:44 +02:00
committed by GitHub
parent 4754372fcd
commit 1ac8aef4de
25 changed files with 736 additions and 298 deletions
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25
embedchain/apps/App.py
View File

@@ -12,12 +12,13 @@ from embedchain.vectordb.chroma_db import ChromaDB
@register_deserializable
class App(EmbedChain):
"""
The EmbedChain app.
Has two functions: add and query.
The EmbedChain app in it's simplest and most straightforward form.
An opinionated choice of LLM, vector database and embedding model.
adds(data_type, url): adds the data from the given URL to the vector db.
Methods:
add(source, data_type): adds the data from the given URL to the vector db.
query(query): finds answer to the given query using vector database and LLM.
dry_run(query): test your prompt without consuming tokens.
chat(query): finds answer to the given query using vector database and LLM, with conversation history.
"""
def __init__(
@@ -28,8 +29,20 @@ class App(EmbedChain):
system_prompt: Optional[str] = None,
):
"""
:param config: AppConfig instance to load as configuration. Optional.
:param system_prompt: System prompt string. Optional.
Initialize a new `CustomApp` instance. You only have a few choices to make.
:param config: Config for the app instance.
This is the most basic configuration, that does not fall into the LLM, database or embedder category,
defaults to None
:type config: AppConfig, optional
:param llm_config: Allows you to configure the LLM, e.g. how many documents to return,
example: `from embedchain.config import LlmConfig`, defaults to None
:type llm_config: BaseLlmConfig, optional
:param chromadb_config: Allows you to configure the vector database,
example: `from embedchain.config import ChromaDbConfig`, defaults to None
:type chromadb_config: Optional[ChromaDbConfig], optional
:param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
:type system_prompt: Optional[str], optional
"""
if config is None:
config = AppConfig()

32
embedchain/apps/CustomApp.py
View File

@@ -11,26 +11,42 @@ from embedchain.vectordb.base_vector_db import BaseVectorDB
@register_deserializable
class CustomApp(EmbedChain):
"""
The custom EmbedChain app.
Has two functions: add and query.
Embedchain's custom app allows for most flexibility.
adds(data_type, url): adds the data from the given URL to the vector db.
You can craft your own mix of various LLMs, vector databases and embedding model/functions.
Methods:
add(source, data_type): adds the data from the given URL to the vector db.
query(query): finds answer to the given query using vector database and LLM.
dry_run(query): test your prompt without consuming tokens.
chat(query): finds answer to the given query using vector database and LLM, with conversation history.
"""
def __init__(
self,
config: CustomAppConfig = None,
config: Optional[CustomAppConfig] = None,
llm: BaseLlm = None,
db: BaseVectorDB = None,
embedder: BaseEmbedder = None,
system_prompt: Optional[str] = None,
):
"""
:param config: Optional. `CustomAppConfig` instance to load as configuration.
:raises ValueError: Config must be provided for custom app
:param system_prompt: Optional. System prompt string.
Initialize a new `CustomApp` instance. You have to choose a LLM, database and embedder.
:param config: Config for the app instance. This is the most basic configuration,
that does not fall into the LLM, database or embedder category, defaults to None
:type config: Optional[CustomAppConfig], optional
:param llm: LLM Class instance. example: `from embedchain.llm.openai_llm import OpenAiLlm`, defaults to None
:type llm: BaseLlm
:param db: The database to use for storing and retrieving embeddings,
example: `from embedchain.vectordb.chroma_db import ChromaDb`, defaults to None
:type db: BaseVectorDB
:param embedder: The embedder (embedding model and function) use to calculate embeddings.
example: `from embedchain.embedder.gpt4all_embedder import GPT4AllEmbedder`, defaults to None
:type embedder: BaseEmbedder
:param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
:type system_prompt: Optional[str], optional
:raises ValueError: LLM, database or embedder has not been defined.
:raises TypeError: LLM, database or embedder is not a valid class instance.
"""
# Config is not required, it has a default
if config is None:

5
embedchain/apps/Llama2App.py
View File

@@ -12,10 +12,11 @@ from embedchain.vectordb.chroma_db import ChromaDB
class Llama2App(CustomApp):
"""
The EmbedChain Llama2App class.
Has two functions: add and query.
adds(data_type, url): adds the data from the given URL to the vector db.
Methods:
add(source, data_type): adds the data from the given URL to the vector db.
query(query): finds answer to the given query using vector database and LLM.
chat(query): finds answer to the given query using vector database and LLM, with conversation history.
"""
def __init__(self, config: CustomAppConfig = None, system_prompt: Optional[str] = None):

49
embedchain/apps/OpenSourceApp.py
View File

@@ -15,43 +15,64 @@ gpt4all_model = None
@register_deserializable
class OpenSourceApp(EmbedChain):
"""
The OpenSource app.
Same as App, but uses an open source embedding model and LLM.
The embedchain Open Source App.
Comes preconfigured with the best open source LLM, embedding model, database.
Has two function: add and query.
adds(data_type, url): adds the data from the given URL to the vector db.
Methods:
add(source, data_type): adds the data from the given URL to the vector db.
query(query): finds answer to the given query using vector database and LLM.
chat(query): finds answer to the given query using vector database and LLM, with conversation history.
"""
def __init__(
self,
config: OpenSourceAppConfig = None,
llm_config: BaseLlmConfig = None,
chromadb_config: Optional[ChromaDbConfig] = None,
system_prompt: Optional[str] = None,
):
"""
:param config: OpenSourceAppConfig instance to load as configuration. Optional.
`ef` defaults to open source.
:param system_prompt: System prompt string. Optional.
Initialize a new `CustomApp` instance.
Since it's opinionated you don't have to choose a LLM, database and embedder.
However, you can configure those.
:param config: Config for the app instance. This is the most basic configuration,
that does not fall into the LLM, database or embedder category, defaults to None
:type config: OpenSourceAppConfig, optional
:param llm_config: Allows you to configure the LLM, e.g. how many documents to return.
example: `from embedchain.config import LlmConfig`, defaults to None
:type llm_config: BaseLlmConfig, optional
:param chromadb_config: Allows you to configure the open source database,
example: `from embedchain.config import ChromaDbConfig`, defaults to None
:type chromadb_config: Optional[ChromaDbConfig], optional
:param system_prompt: System prompt that will be provided to the LLM as such.
Please don't use for the time being, as it's not supported., defaults to None
:type system_prompt: Optional[str], optional
:raises TypeError: `OpenSourceAppConfig` or `LlmConfig` invalid.
"""
logging.info("Loading open source embedding model. This may take some time...") # noqa:E501
if not config:
config = OpenSourceAppConfig()
if not isinstance(config, OpenSourceAppConfig):
raise ValueError(
raise TypeError(
"OpenSourceApp needs a OpenSourceAppConfig passed to it. "
"You can import it with `from embedchain.config import OpenSourceAppConfig`"
)
if not config.model:
raise ValueError("OpenSourceApp needs a model to be instantiated. Maybe you passed the wrong config type?")
if not llm_config:
llm_config = BaseLlmConfig(model="orca-mini-3b.ggmlv3.q4_0.bin")
elif not isinstance(llm_config, BaseLlmConfig):
raise TypeError(
"The LlmConfig passed to OpenSourceApp is invalid. "
"You can import it with `from embedchain.config import LlmConfig`"
)
elif not llm_config.model:
llm_config.model = "orca-mini-3b.ggmlv3.q4_0.bin"
logging.info("Successfully loaded open source embedding model.")
llm = GPT4ALLLlm(config=BaseLlmConfig(model="orca-mini-3b.ggmlv3.q4_0.bin"))
llm = GPT4ALLLlm(config=llm_config)
embedder = GPT4AllEmbedder(config=BaseEmbedderConfig(model="all-MiniLM-L6-v2"))
logging.error("Successfully loaded open source embedding model.")
database = ChromaDB(config=chromadb_config)
super().__init__(config, llm=llm, db=database, embedder=embedder, system_prompt=system_prompt)

18
embedchain/apps/PersonApp.py
View File

@@ -19,7 +19,14 @@ class EmbedChainPersonApp:
:param config: BaseAppConfig instance to load as configuration.
"""
def __init__(self, person, config: BaseAppConfig = None):
def __init__(self, person: str, config: BaseAppConfig = None):
"""Initialize a new person app
:param person: Name of the person that's imitated.
:type person: str
:param config: Configuration class instance, defaults to None
:type config: BaseAppConfig, optional
"""
self.person = person
self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style." # noqa:E501
super().__init__(config)
@@ -30,9 +37,12 @@ class EmbedChainPersonApp:
if yes it adds the person prompt to it and return the updated config
else it creates a config object with the default prompt added to the person prompt
:param default_prompt: it is the default prompt for query or chat methods
:param config: Optional. The `ChatConfig` instance to use as
configuration options.
:param default_prompt: it is the default prompt for query or chat methods
:type default_prompt: str
:param config: _description_, defaults to None
:type config: BaseLlmConfig, optional
:return: The `ChatConfig` instance to use as configuration options.
:rtype: _type_
"""
template = Template(self.person_prompt + " " + default_prompt)