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#1128 | Remove deprecated type hints from typing module (#1131)

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This commit is contained in:
Sandra Serrano
2024-01-09 18:35:24 +01:00
committed by GitHub
parent c9df7a2020
commit 0de9491c61
41 changed files with 272 additions and 267 deletions
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6
embedchain/app.py
View File

@@ -4,7 +4,7 @@ import logging
import os
import sqlite3
import uuid
from typing import Any, Dict, Optional
from typing import Any, Optional
import requests
import yaml
@@ -364,7 +364,7 @@ class App(EmbedChain):
def from_config(
cls,
config_path: Optional[str] = None,
config: Optional[Dict[str, Any]] = None,
config: Optional[dict[str, Any]] = None,
auto_deploy: bool = False,
yaml_path: Optional[str] = None,
):
@@ -374,7 +374,7 @@ class App(EmbedChain):
:param config_path: Path to the YAML or JSON configuration file.
:type config_path: Optional[str]
:param config: A dictionary containing the configuration.
:type config: Optional[Dict[str, Any]]
:type config: Optional[dict[str, Any]]
:param auto_deploy: Whether to deploy the pipeline automatically, defaults to False
:type auto_deploy: bool, optional
:param yaml_path: (Deprecated) Path to the YAML configuration file. Use config_path instead.

6
embedchain/bots/poe.py
View File

@@ -1,7 +1,7 @@
import argparse
import logging
import os
from typing import List, Optional
from typing import Optional
from embedchain.helpers.json_serializable import register_deserializable
@@ -53,7 +53,7 @@ class PoeBot(BaseBot, PoeBot):
answer = self.handle_message(last_message, history)
yield self.text_event(answer)
def handle_message(self, message, history: Optional[List[str]] = None):
def handle_message(self, message, history: Optional[list[str]] = None):
if message.startswith("/add "):
response = self.add_data(message)
else:
@@ -70,7 +70,7 @@ class PoeBot(BaseBot, PoeBot):
# response = "Some error occurred while adding data."
# return response
def ask_bot(self, message, history: List[str]):
def ask_bot(self, message, history: list[str]):
try:
self.app.llm.set_history(history=history)
response = self.query(message)

4
embedchain/cache.py
View File

@@ -1,6 +1,6 @@
import logging
import os # noqa: F401
from typing import Any, Dict
from typing import Any
from gptcache import cache # noqa: F401
from gptcache.adapter.adapter import adapt # noqa: F401
@@ -15,7 +15,7 @@ from gptcache.similarity_evaluation.exact_match import \
ExactMatchEvaluation # noqa: F401
def gptcache_pre_function(data: Dict[str, Any], **params: Dict[str, Any]):
def gptcache_pre_function(data: dict[str, Any], **params: dict[str, Any]):
return data["input_query"]

3
embedchain/config/add_config.py
View File

@@ -1,7 +1,8 @@
import builtins
import logging
from collections.abc import Callable
from importlib import import_module
from typing import Callable, Optional
from typing import Optional
from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import register_deserializable

6
embedchain/config/base_config.py
View File

@@ -1,4 +1,4 @@
from typing import Any, Dict
from typing import Any
from embedchain.helpers.json_serializable import JSONSerializable
@@ -12,10 +12,10 @@ class BaseConfig(JSONSerializable):
"""Initializes a configuration class for a class."""
pass
def as_dict(self) -> Dict[str, Any]:
def as_dict(self) -> dict[str, Any]:
"""Return config object as a dict
:return: config object as dict
:rtype: Dict[str, Any]
:rtype: dict[str, Any]
"""
return vars(self)

8
embedchain/config/cache_config.py
View File

@@ -1,4 +1,4 @@
from typing import Any, Dict, Optional
from typing import Any, Optional
from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -30,7 +30,7 @@ class CacheSimilarityEvalConfig(BaseConfig):
self.positive = positive
@staticmethod
def from_config(config: Optional[Dict[str, Any]]):
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheSimilarityEvalConfig()
else:
@@ -65,7 +65,7 @@ class CacheInitConfig(BaseConfig):
self.auto_flush = auto_flush
@staticmethod
def from_config(config: Optional[Dict[str, Any]]):
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheInitConfig()
else:
@@ -86,7 +86,7 @@ class CacheConfig(BaseConfig):
self.init_config = init_config
@staticmethod
def from_config(config: Optional[Dict[str, Any]]):
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheConfig()
else:

12
embedchain/config/llm/base.py
View File

@@ -1,7 +1,7 @@
import logging
import re
from string import Template
from typing import Any, Dict, List, Optional
from typing import Any, Optional
from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -68,12 +68,12 @@ class BaseLlmConfig(BaseConfig):
stream: bool = False,
deployment_name: Optional[str] = None,
system_prompt: Optional[str] = None,
where: Dict[str, Any] = None,
where: dict[str, Any] = None,
query_type: Optional[str] = None,
callbacks: Optional[List] = None,
callbacks: Optional[list] = None,
api_key: Optional[str] = None,
endpoint: Optional[str] = None,
model_kwargs: Optional[Dict[str, Any]] = None,
model_kwargs: Optional[dict[str, Any]] = None,
):
"""
Initializes a configuration class instance for the LLM.
@@ -106,7 +106,7 @@ class BaseLlmConfig(BaseConfig):
:param system_prompt: System prompt string, defaults to None
:type system_prompt: Optional[str], optional
:param where: A dictionary of key-value pairs to filter the database results., defaults to None
:type where: Dict[str, Any], optional
:type where: dict[str, Any], optional
:param api_key: The api key of the custom endpoint, defaults to None
:type api_key: Optional[str], optional
:param endpoint: The api url of the custom endpoint, defaults to None
@@ -114,7 +114,7 @@ class BaseLlmConfig(BaseConfig):
:param model_kwargs: A dictionary of key-value pairs to pass to the model, defaults to None
:type model_kwargs: Optional[Dict[str, Any]], optional
:param callbacks: Langchain callback functions to use, defaults to None
:type callbacks: Optional[List], optional
:type callbacks: Optional[list], optional
:param query_type: The type of query to use, defaults to None
:type query_type: Optional[str], optional
:raises ValueError: If the template is not valid as template should

12
embedchain/config/vectordb/elasticsearch.py
View File

@@ -1,5 +1,5 @@
import os
from typing import Dict, List, Optional, Union
from typing import Optional, Union
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -11,9 +11,9 @@ class ElasticsearchDBConfig(BaseVectorDbConfig):
self,
collection_name: Optional[str] = None,
dir: Optional[str] = None,
es_url: Union[str, List[str]] = None,
es_url: Union[str, list[str]] = None,
cloud_id: Optional[str] = None,
**ES_EXTRA_PARAMS: Dict[str, any],
**ES_EXTRA_PARAMS: dict[str, any],
):
"""
Initializes a configuration class instance for an Elasticsearch client.
@@ -23,13 +23,13 @@ class ElasticsearchDBConfig(BaseVectorDbConfig):
:param dir: Path to the database directory, where the database is stored, defaults to None
:type dir: Optional[str], optional
:param es_url: elasticsearch url or list of nodes url to be used for connection, defaults to None
:type es_url: Union[str, List[str]], optional
:type es_url: Union[str, list[str]], optional
:param ES_EXTRA_PARAMS: extra params dict that can be passed to elasticsearch.
:type ES_EXTRA_PARAMS: Dict[str, Any], optional
:type ES_EXTRA_PARAMS: dict[str, Any], optional
"""
if es_url and cloud_id:
raise ValueError("Only one of `es_url` and `cloud_id` can be set.")
# self, es_url: Union[str, List[str]] = None, **ES_EXTRA_PARAMS: Dict[str, any]):
# self, es_url: Union[str, list[str]] = None, **ES_EXTRA_PARAMS: dict[str, any]):
self.ES_URL = es_url or os.environ.get("ELASTICSEARCH_URL")
self.CLOUD_ID = cloud_id or os.environ.get("ELASTICSEARCH_CLOUD_ID")
if not self.ES_URL and not self.CLOUD_ID:

8
embedchain/config/vectordb/opensearch.py
View File

@@ -1,4 +1,4 @@
from typing import Dict, Optional, Tuple
from typing import Optional
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -9,11 +9,11 @@ class OpenSearchDBConfig(BaseVectorDbConfig):
def __init__(
self,
opensearch_url: str,
http_auth: Tuple[str, str],
http_auth: tuple[str, str],
vector_dimension: int = 1536,
collection_name: Optional[str] = None,
dir: Optional[str] = None,
**extra_params: Dict[str, any],
**extra_params: dict[str, any],
):
"""
Initializes a configuration class instance for an OpenSearch client.
@@ -23,7 +23,7 @@ class OpenSearchDBConfig(BaseVectorDbConfig):
:param opensearch_url: URL of the OpenSearch domain
:type opensearch_url: str, Eg, "http://localhost:9200"
:param http_auth: Tuple of username and password
:type http_auth: Tuple[str, str], Eg, ("username", "password")
:type http_auth: tuple[str, str], Eg, ("username", "password")
:param vector_dimension: Dimension of the vector, defaults to 1536 (openai embedding model)
:type vector_dimension: int, optional
:param dir: Path to the database directory, where the database is stored, defaults to None

4
embedchain/config/vectordb/pinecone.py
View File

@@ -1,4 +1,4 @@
from typing import Dict, Optional
from typing import Optional
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -12,7 +12,7 @@ class PineconeDBConfig(BaseVectorDbConfig):
dir: Optional[str] = None,
vector_dimension: int = 1536,
metric: Optional[str] = "cosine",
**extra_params: Dict[str, any],
**extra_params: dict[str, any],
):
self.metric = metric
self.vector_dimension = vector_dimension

12
embedchain/config/vectordb/qdrant.py
View File

@@ -1,4 +1,4 @@
from typing import Dict, Optional
from typing import Optional
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -15,10 +15,10 @@ class QdrantDBConfig(BaseVectorDbConfig):
self,
collection_name: Optional[str] = None,
dir: Optional[str] = None,
hnsw_config: Optional[Dict[str, any]] = None,
quantization_config: Optional[Dict[str, any]] = None,
hnsw_config: Optional[dict[str, any]] = None,
quantization_config: Optional[dict[str, any]] = None,
on_disk: Optional[bool] = None,
**extra_params: Dict[str, any],
**extra_params: dict[str, any],
):
"""
Initializes a configuration class instance for a qdrant client.
@@ -28,9 +28,9 @@ class QdrantDBConfig(BaseVectorDbConfig):
:param dir: Path to the database directory, where the database is stored, defaults to None
:type dir: Optional[str], optional
:param hnsw_config: Params for HNSW index
:type hnsw_config: Optional[Dict[str, any]], defaults to None
:type hnsw_config: Optional[dict[str, any]], defaults to None
:param quantization_config: Params for quantization, if None - quantization will be disabled
:type quantization_config: Optional[Dict[str, any]], defaults to None
:type quantization_config: Optional[dict[str, any]], defaults to None
:param on_disk: If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.

4
embedchain/config/vectordb/weaviate.py
View File

@@ -1,4 +1,4 @@
from typing import Dict, Optional
from typing import Optional
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -10,7 +10,7 @@ class WeaviateDBConfig(BaseVectorDbConfig):
self,
collection_name: Optional[str] = None,
dir: Optional[str] = None,
**extra_params: Dict[str, any],
**extra_params: dict[str, any],
):
self.extra_params = extra_params
super().__init__(collection_name=collection_name, dir=dir)

50
embedchain/embedchain.py
View File

@@ -2,7 +2,7 @@ import hashlib
import json
import logging
import sqlite3
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Optional, Union
from dotenv import load_dotenv
from langchain.docstore.document import Document
@@ -136,12 +136,12 @@ class EmbedChain(JSONSerializable):
self,
source: Any,
data_type: Optional[DataType] = None,
metadata: Optional[Dict[str, Any]] = None,
metadata: Optional[dict[str, Any]] = None,
config: Optional[AddConfig] = None,
dry_run=False,
loader: Optional[BaseLoader] = None,
chunker: Optional[BaseChunker] = None,
**kwargs: Optional[Dict[str, Any]],
**kwargs: Optional[dict[str, Any]],
):
"""
Adds the data from the given URL to the vector db.
@@ -154,7 +154,7 @@ class EmbedChain(JSONSerializable):
defaults to None
:type data_type: Optional[DataType], optional
:param metadata: Metadata associated with the data source., defaults to None
:type metadata: Optional[Dict[str, Any]], optional
:type metadata: Optional[dict[str, Any]], optional
:param config: The `AddConfig` instance to use as configuration options., defaults to None
:type config: Optional[AddConfig], optional
:raises ValueError: Invalid data type
@@ -243,9 +243,9 @@ class EmbedChain(JSONSerializable):
self,
source: Any,
data_type: Optional[DataType] = None,
metadata: Optional[Dict[str, Any]] = None,
metadata: Optional[dict[str, Any]] = None,
config: Optional[AddConfig] = None,
**kwargs: Optional[Dict[str, Any]],
**kwargs: Optional[dict[str, Any]],
):
"""
Adds the data from the given URL to the vector db.
@@ -261,7 +261,7 @@ class EmbedChain(JSONSerializable):
defaults to None
:type data_type: Optional[DataType], optional
:param metadata: Metadata associated with the data source., defaults to None
:type metadata: Optional[Dict[str, Any]], optional
:type metadata: Optional[dict[str, Any]], optional
:param config: The `AddConfig` instance to use as configuration options., defaults to None
:type config: Optional[AddConfig], optional
:raises ValueError: Invalid data type
@@ -342,11 +342,11 @@ class EmbedChain(JSONSerializable):
loader: BaseLoader,
chunker: BaseChunker,
src: Any,
metadata: Optional[Dict[str, Any]] = None,
metadata: Optional[dict[str, Any]] = None,
source_hash: Optional[str] = None,
add_config: Optional[AddConfig] = None,
dry_run=False,
**kwargs: Optional[Dict[str, Any]],
**kwargs: Optional[dict[str, Any]],
):
"""
Loads the data from the given URL, chunks it, and adds it to database.
@@ -359,7 +359,7 @@ class EmbedChain(JSONSerializable):
:param source_hash: Hexadecimal hash of the source.
:param dry_run: Optional. A dry run returns chunks and doesn't update DB.
:type dry_run: bool, defaults to False
:return: (List) documents (embedded text), (List) metadata, (list) ids, (int) number of chunks
:return: (list) documents (embedded text), (list) metadata, (list) ids, (int) number of chunks
"""
existing_doc_id = self._get_existing_doc_id(chunker=chunker, src=src)
app_id = self.config.id if self.config is not None else None
@@ -464,8 +464,8 @@ class EmbedChain(JSONSerializable):
config: Optional[BaseLlmConfig] = None,
where=None,
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, str, str]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, str, str]], list[str]]:
"""
Queries the vector database based on the given input query.
Gets relevant doc based on the query
@@ -479,7 +479,7 @@ class EmbedChain(JSONSerializable):
:param citations: A boolean to indicate if db should fetch citation source
:type citations: bool
:return: List of contents of the document that matched your query
:rtype: List[str]
:rtype: list[str]
"""
query_config = config or self.llm.config
if where is not None:
@@ -507,10 +507,10 @@ class EmbedChain(JSONSerializable):
input_query: str,
config: BaseLlmConfig = None,
dry_run=False,
where: Optional[Dict] = None,
where: Optional[dict] = None,
citations: bool = False,
**kwargs: Dict[str, Any],
) -> Union[Tuple[str, List[Tuple[str, Dict]]], str]:
**kwargs: dict[str, Any],
) -> Union[tuple[str, list[tuple[str, dict]]], str]:
"""
Queries the vector database based on the given input query.
Gets relevant doc based on the query and then passes it to an
@@ -525,13 +525,13 @@ class EmbedChain(JSONSerializable):
the LLM. The purpose is to test the prompt, not the response., defaults to False
:type dry_run: bool, optional
:param where: A dictionary of key-value pairs to filter the database results., defaults to None
:type where: Optional[Dict[str, str]], optional
:type where: Optional[dict[str, str]], optional
:param kwargs: To read more params for the query function. Ex. we use citations boolean
param to return context along with the answer
:type kwargs: Dict[str, Any]
:type kwargs: dict[str, Any]
:return: The answer to the query, with citations if the citation flag is True
or the dry run result
:rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
:rtype: str, if citations is False, otherwise tuple[str, list[tuple[str,str,str]]]
"""
contexts = self._retrieve_from_database(
input_query=input_query, config=config, where=where, citations=citations, **kwargs
@@ -572,10 +572,10 @@ class EmbedChain(JSONSerializable):
config: Optional[BaseLlmConfig] = None,
dry_run=False,
session_id: str = "default",
where: Optional[Dict[str, str]] = None,
where: Optional[dict[str, str]] = None,
citations: bool = False,
**kwargs: Dict[str, Any],
) -> Union[Tuple[str, List[Tuple[str, Dict]]], str]:
**kwargs: dict[str, Any],
) -> Union[tuple[str, list[tuple[str, dict]]], str]:
"""
Queries the vector database on the given input query.
Gets relevant doc based on the query and then passes it to an
@@ -594,13 +594,13 @@ class EmbedChain(JSONSerializable):
:param session_id: The session id to use for chat history, defaults to 'default'.
:type session_id: Optional[str], optional
:param where: A dictionary of key-value pairs to filter the database results., defaults to None
:type where: Optional[Dict[str, str]], optional
:type where: Optional[dict[str, str]], optional
:param kwargs: To read more params for the query function. Ex. we use citations boolean
param to return context along with the answer
:type kwargs: Dict[str, Any]
:type kwargs: dict[str, Any]
:return: The answer to the query, with citations if the citation flag is True
or the dry run result
:rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
:rtype: str, if citations is False, otherwise tuple[str, list[tuple[str,str,str]]]
"""
contexts = self._retrieve_from_database(
input_query=input_query, config=config, where=where, citations=citations, **kwargs

3
embedchain/embedder/base.py
View File

@@ -1,4 +1,5 @@
from typing import Any, Callable, Optional
from collections.abc import Callable
from typing import Any, Optional
from embedchain.config.embedder.base import BaseEmbedderConfig

4
embedchain/helpers/callbacks.py
View File

@@ -1,5 +1,5 @@
import queue
from typing import Any, Dict, List, Union
from typing import Any, Union
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import LLMResult
@@ -29,7 +29,7 @@ class StreamingStdOutCallbackHandlerYield(StreamingStdOutCallbackHandler):
super().__init__()
self.q = q
def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:
def on_llm_start(self, serialized: dict[str, Any], prompts: list[str], **kwargs: Any) -> None:
"""Run when LLM starts running."""
with self.q.mutex:
self.q.queue.clear()

6
embedchain/helpers/json_serializable.py
View File

@@ -1,7 +1,7 @@
import json
import logging
from string import Template
from typing import Any, Dict, Type, TypeVar, Union
from typing import Any, Type, TypeVar, Union
T = TypeVar("T", bound="JSONSerializable")
@@ -84,7 +84,7 @@ class JSONSerializable:
return cls()
@staticmethod
def _auto_encoder(obj: Any) -> Union[Dict[str, Any], None]:
def _auto_encoder(obj: Any) -> Union[dict[str, Any], None]:
"""
Automatically encode an object for JSON serialization.
@@ -126,7 +126,7 @@ class JSONSerializable:
raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
@classmethod
def _auto_decoder(cls, dct: Dict[str, Any]) -> Any:
def _auto_decoder(cls, dct: dict[str, Any]) -> Any:
"""
Automatically decode a dictionary to an object during JSON deserialization.

21
embedchain/llm/base.py
View File

@@ -1,5 +1,6 @@
import logging
from typing import Any, Dict, Generator, List, Optional
from collections.abc import Generator
from typing import Any, Optional
from langchain.schema import BaseMessage as LCBaseMessage
@@ -55,7 +56,7 @@ class BaseLlm(JSONSerializable):
app_id: str,
question: str,
answer: str,
metadata: Optional[Dict[str, Any]] = None,
metadata: Optional[dict[str, Any]] = None,
session_id: str = "default",
):
chat_message = ChatMessage()
@@ -64,7 +65,7 @@ class BaseLlm(JSONSerializable):
self.memory.add(app_id=app_id, chat_message=chat_message, session_id=session_id)
self.update_history(app_id=app_id, session_id=session_id)
def generate_prompt(self, input_query: str, contexts: List[str], **kwargs: Dict[str, Any]) -> str:
def generate_prompt(self, input_query: str, contexts: list[str], **kwargs: dict[str, Any]) -> str:
"""
Generates a prompt based on the given query and context, ready to be
passed to an LLM
@@ -72,7 +73,7 @@ class BaseLlm(JSONSerializable):
:param input_query: The query to use.
:type input_query: str
:param contexts: List of similar documents to the query used as context.
:type contexts: List[str]
:type contexts: list[str]
:return: The prompt
:rtype: str
"""
@@ -170,7 +171,7 @@ class BaseLlm(JSONSerializable):
yield chunk
logging.info(f"Answer: {streamed_answer}")
def query(self, input_query: str, contexts: List[str], config: BaseLlmConfig = None, dry_run=False):
def query(self, input_query: str, contexts: list[str], config: BaseLlmConfig = None, dry_run=False):
"""
Queries the vector database based on the given input query.
Gets relevant doc based on the query and then passes it to an
@@ -179,7 +180,7 @@ class BaseLlm(JSONSerializable):
:param input_query: The query to use.
:type input_query: str
:param contexts: Embeddings retrieved from the database to be used as context.
:type contexts: List[str]
:type contexts: list[str]
:param config: The `BaseLlmConfig` instance to use as configuration options. This is used for one method call.
To persistently use a config, declare it during app init., defaults to None
:type config: Optional[BaseLlmConfig], optional
@@ -223,7 +224,7 @@ class BaseLlm(JSONSerializable):
self.config: BaseLlmConfig = BaseLlmConfig.deserialize(prev_config)
def chat(
self, input_query: str, contexts: List[str], config: BaseLlmConfig = None, dry_run=False, session_id: str = None
self, input_query: str, contexts: list[str], config: BaseLlmConfig = None, dry_run=False, session_id: str = None
):
"""
Queries the vector database on the given input query.
@@ -235,7 +236,7 @@ class BaseLlm(JSONSerializable):
:param input_query: The query to use.
:type input_query: str
:param contexts: Embeddings retrieved from the database to be used as context.
:type contexts: List[str]
:type contexts: list[str]
:param config: The `BaseLlmConfig` instance to use as configuration options. This is used for one method call.
To persistently use a config, declare it during app init., defaults to None
:type config: Optional[BaseLlmConfig], optional
@@ -281,7 +282,7 @@ class BaseLlm(JSONSerializable):
self.config: BaseLlmConfig = BaseLlmConfig.deserialize(prev_config)
@staticmethod
def _get_messages(prompt: str, system_prompt: Optional[str] = None) -> List[LCBaseMessage]:
def _get_messages(prompt: str, system_prompt: Optional[str] = None) -> list[LCBaseMessage]:
"""
Construct a list of langchain messages
@@ -290,7 +291,7 @@ class BaseLlm(JSONSerializable):
:param system_prompt: System prompt, defaults to None
:type system_prompt: Optional[str], optional
:return: List of messages
:rtype: List[BaseMessage]
:rtype: list[BaseMessage]
"""
from langchain.schema import HumanMessage, SystemMessage

3
embedchain/llm/google.py
View File

@@ -1,7 +1,8 @@
import importlib
import logging
import os
from typing import Any, Generator, Optional, Union
from collections.abc import Generator
from typing import Any, Optional, Union
import google.generativeai as genai

3
embedchain/llm/gpt4all.py
View File

@@ -1,6 +1,7 @@
import os
from collections.abc import Iterable
from pathlib import Path
from typing import Iterable, Optional, Union
from typing import Optional, Union
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

3
embedchain/llm/ollama.py
View File

@@ -1,4 +1,5 @@
from typing import Iterable, Optional, Union
from collections.abc import Iterable
from typing import Optional, Union
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.stdout import StdOutCallbackHandler

4
embedchain/llm/openai.py
View File

@@ -1,6 +1,6 @@
import json
import os
from typing import Any, Dict, Optional
from typing import Any, Optional
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage
@@ -12,7 +12,7 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
class OpenAILlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None, functions: Optional[Dict[str, Any]] = None):
def __init__(self, config: Optional[BaseLlmConfig] = None, functions: Optional[dict[str, Any]] = None):
self.functions = functions
super().__init__(config=config)

4
embedchain/loaders/directory_loader.py
View File

@@ -1,7 +1,7 @@
import hashlib
import logging
from pathlib import Path
from typing import Any, Dict, Optional
from typing import Any, Optional
from embedchain.config import AddConfig
from embedchain.data_formatter.data_formatter import DataFormatter
@@ -15,7 +15,7 @@ from embedchain.utils.misc import detect_datatype
class DirectoryLoader(BaseLoader):
"""Load data from a directory."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
def __init__(self, config: Optional[dict[str, Any]] = None):
super().__init__()
config = config or {}
self.recursive = config.get("recursive", True)

4
embedchain/loaders/discourse.py
View File

@@ -1,7 +1,7 @@
import hashlib
import logging
import time
from typing import Any, Dict, Optional
from typing import Any, Optional
import requests
@@ -10,7 +10,7 @@ from embedchain.utils.misc import clean_string
class DiscourseLoader(BaseLoader):
def __init__(self, config: Optional[Dict[str, Any]] = None):
def __init__(self, config: Optional[dict[str, Any]] = None):
super().__init__()
if not config:
raise ValueError(

3
embedchain/loaders/dropbox.py
View File

@@ -1,6 +1,5 @@
import hashlib
import os
from typing import List
from dropbox.files import FileMetadata
@@ -29,7 +28,7 @@ class DropboxLoader(BaseLoader):
except exceptions.AuthError as ex:
raise ValueError("Invalid Dropbox access token. Please verify your token and try again.") from ex
def _download_folder(self, path: str, local_root: str) -> List[FileMetadata]:
def _download_folder(self, path: str, local_root: str) -> list[FileMetadata]:
"""Download a folder from Dropbox and save it preserving the directory structure."""
entries = self.dbx.files_list_folder(path).entries
for entry in entries:

4
embedchain/loaders/github.py
View File

@@ -4,7 +4,7 @@ import logging
import os
import re
import shlex
from typing import Any, Dict, Optional
from typing import Any, Optional
from tqdm import tqdm
@@ -20,7 +20,7 @@ VALID_SEARCH_TYPES = set(["code", "repo", "pr", "issue", "discussion"])
class GithubLoader(BaseLoader):
"""Load data from GitHub search query."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
def __init__(self, config: Optional[dict[str, Any]] = None):
super().__init__()
if not config:
raise ValueError(

10
embedchain/loaders/gmail.py
View File

@@ -5,7 +5,7 @@ import os
from email import message_from_bytes
from email.utils import parsedate_to_datetime
from textwrap import dedent
from typing import Dict, List, Optional
from typing import Optional
from bs4 import BeautifulSoup
@@ -57,7 +57,7 @@ class GmailReader:
token.write(creds.to_json())
return creds
def load_emails(self) -> List[Dict]:
def load_emails(self) -> list[dict]:
response = self.service.users().messages().list(userId="me", q=self.query).execute()
messages = response.get("messages", [])
@@ -67,7 +67,7 @@ class GmailReader:
raw_message = self.service.users().messages().get(userId="me", id=message_id, format="raw").execute()
return base64.urlsafe_b64decode(raw_message["raw"])
def _parse_email(self, raw_email) -> Dict:
def _parse_email(self, raw_email) -> dict:
mime_msg = message_from_bytes(raw_email)
return {
"subject": self._get_header(mime_msg, "Subject"),
@@ -124,7 +124,7 @@ class GmailLoader(BaseLoader):
return {"doc_id": self._generate_doc_id(query, data), "data": data}
@staticmethod
def _process_email(email: Dict) -> str:
def _process_email(email: dict) -> str:
content = BeautifulSoup(email["body"], "html.parser").get_text()
content = clean_string(content)
return dedent(
@@ -137,6 +137,6 @@ class GmailLoader(BaseLoader):
)
@staticmethod
def _generate_doc_id(query: str, data: List[Dict]) -> str:
def _generate_doc_id(query: str, data: list[dict]) -> str:
content_strings = [email["content"] for email in data]
return hashlib.sha256((query + ", ".join(content_strings)).encode()).hexdigest()

8
embedchain/loaders/json.py
View File

@@ -2,7 +2,7 @@ import hashlib
import json
import os
import re
from typing import Dict, List, Union
from typing import Union
import requests
@@ -16,14 +16,14 @@ class JSONReader:
pass
@staticmethod
def load_data(json_data: Union[Dict, str]) -> List[str]:
def load_data(json_data: Union[dict, str]) -> list[str]:
"""Load data from a JSON structure.
Args:
json_data (Union[Dict, str]): The JSON data to load.
json_data (Union[dict, str]): The JSON data to load.
Returns:
List[str]: A list of strings representing the leaf nodes of the JSON.
list[str]: A list of strings representing the leaf nodes of the JSON.
"""
if isinstance(json_data, str):
json_data = json.loads(json_data)

6
embedchain/loaders/mysql.py
View File

@@ -1,13 +1,13 @@
import hashlib
import logging
from typing import Any, Dict, Optional
from typing import Any, Optional
from embedchain.loaders.base_loader import BaseLoader
from embedchain.utils.misc import clean_string
class MySQLLoader(BaseLoader):
def __init__(self, config: Optional[Dict[str, Any]]):
def __init__(self, config: Optional[dict[str, Any]]):
super().__init__()
if not config:
raise ValueError(
@@ -20,7 +20,7 @@ class MySQLLoader(BaseLoader):
self.cursor = None
self._setup_loader(config=config)
def _setup_loader(self, config: Dict[str, Any]):
def _setup_loader(self, config: dict[str, Any]):
try:
import mysql.connector as sqlconnector
except ImportError as e:

6
embedchain/loaders/notion.py
View File

@@ -1,7 +1,7 @@
import hashlib
import logging
import os
from typing import Any, Dict, List, Optional
from typing import Any, Optional
import requests
@@ -15,7 +15,7 @@ class NotionDocument:
A simple Document class to hold the text and additional information of a page.
"""
def __init__(self, text: str, extra_info: Dict[str, Any]):
def __init__(self, text: str, extra_info: dict[str, Any]):
self.text = text
self.extra_info = extra_info
@@ -82,7 +82,7 @@ class NotionPageLoader:
result_lines = "\n".join(result_lines_arr)
return result_lines
def load_data(self, page_ids: List[str]) -> List[NotionDocument]:
def load_data(self, page_ids: list[str]) -> list[NotionDocument]:
"""Load data from the given list of page IDs."""
docs = []
for page_id in page_ids:

6
embedchain/loaders/postgres.py
View File

@@ -1,12 +1,12 @@
import hashlib
import logging
from typing import Any, Dict, Optional
from typing import Any, Optional
from embedchain.loaders.base_loader import BaseLoader
class PostgresLoader(BaseLoader):
def __init__(self, config: Optional[Dict[str, Any]] = None):
def __init__(self, config: Optional[dict[str, Any]] = None):
super().__init__()
if not config:
raise ValueError(f"Must provide the valid config. Received: {config}")
@@ -15,7 +15,7 @@ class PostgresLoader(BaseLoader):
self.cursor = None
self._setup_loader(config=config)
def _setup_loader(self, config: Dict[str, Any]):
def _setup_loader(self, config: dict[str, Any]):
try:
import psycopg
except ImportError as e:

6
embedchain/loaders/slack.py
View File

@@ -2,7 +2,7 @@ import hashlib
import logging
import os
import ssl
from typing import Any, Dict, Optional
from typing import Any, Optional
import certifi
@@ -13,7 +13,7 @@ SLACK_API_BASE_URL = "https://www.slack.com/api/"
class SlackLoader(BaseLoader):
def __init__(self, config: Optional[Dict[str, Any]] = None):
def __init__(self, config: Optional[dict[str, Any]] = None):
super().__init__()
self.config = config if config else {}
@@ -24,7 +24,7 @@ class SlackLoader(BaseLoader):
self.client = None
self._setup_loader(self.config)
def _setup_loader(self, config: Dict[str, Any]):
def _setup_loader(self, config: dict[str, Any]):
try:
from slack_sdk import WebClient
except ImportError as e:

6
embedchain/memory/base.py
View File

@@ -2,7 +2,7 @@ import json
import logging
import sqlite3
import uuid
from typing import Any, Dict, List, Optional
from typing import Any, Optional
from embedchain.constants import SQLITE_PATH
from embedchain.memory.message import ChatMessage
@@ -67,7 +67,7 @@ class ChatHistory:
self.cursor.execute(DELETE_CHAT_HISTORY_QUERY, (app_id, session_id))
self.connection.commit()
def get(self, app_id, session_id, num_rounds=10, display_format=False) -> List[ChatMessage]:
def get(self, app_id, session_id, num_rounds=10, display_format=False) -> list[ChatMessage]:
"""
Get the most recent num_rounds rounds of conversations
between human and AI, for a given app_id.
@@ -114,7 +114,7 @@ class ChatHistory:
return count
@staticmethod
def _serialize_json(metadata: Dict[str, Any]):
def _serialize_json(metadata: dict[str, Any]):
return json.dumps(metadata)
@staticmethod

6
embedchain/memory/message.py
View File

@@ -1,5 +1,5 @@
import logging
from typing import Any, Dict, Optional
from typing import Any, Optional
from embedchain.helpers.json_serializable import JSONSerializable
@@ -18,9 +18,9 @@ class BaseMessage(JSONSerializable):
created_by: str
# Any additional info.
metadata: Dict[str, Any]
metadata: dict[str, Any]
def __init__(self, content: str, created_by: str, metadata: Optional[Dict[str, Any]] = None) -> None:
def __init__(self, content: str, created_by: str, metadata: Optional[dict[str, Any]] = None) -> None:
super().__init__()
self.content = content
self.created_by = created_by

10
embedchain/memory/utils.py
View File

@@ -1,16 +1,16 @@
from typing import Any, Dict, Optional
from typing import Any, Optional
def merge_metadata_dict(left: Optional[Dict[str, Any]], right: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
def merge_metadata_dict(left: Optional[dict[str, Any]], right: Optional[dict[str, Any]]) -> Optional[dict[str, Any]]:
"""
Merge the metadatas of two BaseMessage types.
Args:
left (Dict[str, Any]): metadata of human message
right (Dict[str, Any]): metadata of AI message
left (dict[str, Any]): metadata of human message
right (dict[str, Any]): metadata of AI message
Returns:
Dict[str, Any]: combined metadata dict with dedup
dict[str, Any]: combined metadata dict with dedup
to be saved in db.
"""
if not left and not right:

44
embedchain/vectordb/chroma.py
View File

@@ -1,5 +1,5 @@
import logging
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Optional, Union
from chromadb import Collection, QueryResult
from langchain.docstore.document import Document
@@ -76,7 +76,7 @@ class ChromaDB(BaseVectorDB):
return self.client
@staticmethod
def _generate_where_clause(where: Dict[str, any]) -> Dict[str, any]:
def _generate_where_clause(where: dict[str, any]) -> dict[str, any]:
# If only one filter is supplied, return it as is
# (no need to wrap in $and based on chroma docs)
if len(where.keys()) <= 1:
@@ -105,18 +105,18 @@ class ChromaDB(BaseVectorDB):
)
return self.collection
def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
def get(self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None):
"""
Get existing doc ids present in vector database
:param ids: list of doc ids to check for existence
:type ids: List[str]
:type ids: list[str]
:param where: Optional. to filter data
:type where: Dict[str, Any]
:type where: dict[str, Any]
:param limit: Optional. maximum number of documents
:type limit: Optional[int]
:return: Existing documents.
:rtype: List[str]
:rtype: list[str]
"""
args = {}
if ids:
@@ -129,23 +129,23 @@ class ChromaDB(BaseVectorDB):
def add(
self,
embeddings: List[List[float]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, Any]],
embeddings: list[list[float]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, Any]],
) -> Any:
"""
Add vectors to chroma database
:param embeddings: list of embeddings to add
:type embeddings: List[List[str]]
:type embeddings: list[list[str]]
:param documents: Documents
:type documents: List[str]
:type documents: list[str]
:param metadatas: Metadatas
:type metadatas: List[object]
:type metadatas: list[object]
:param ids: ids
:type ids: List[str]
:type ids: list[str]
"""
size = len(documents)
if len(documents) != size or len(metadatas) != size or len(ids) != size:
@@ -182,27 +182,27 @@ class ChromaDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
Query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: to filter data
:type where: Dict[str, Any]
:type where: dict[str, Any]
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
:raises InvalidDimensionException: Dimensions do not match.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
try:
result = self.collection.query(

40
embedchain/vectordb/elasticsearch.py
View File

@@ -1,5 +1,5 @@
import logging
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Optional, Union
try:
from elasticsearch import Elasticsearch
@@ -84,14 +84,14 @@ class ElasticsearchDB(BaseVectorDB):
def _get_or_create_collection(self, name):
"""Note: nothing to return here. Discuss later"""
def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
def get(self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None):
"""
Get existing doc ids present in vector database
:param ids: _list of doc ids to check for existence
:type ids: List[str]
:type ids: list[str]
:param where: to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:return: ids
:rtype: Set[str]
"""
@@ -110,22 +110,22 @@ class ElasticsearchDB(BaseVectorDB):
def add(
self,
embeddings: List[List[float]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, any]],
embeddings: list[list[float]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, any]],
) -> Any:
"""
add data in vector database
:param embeddings: list of embeddings to add
:type embeddings: List[List[str]]
:type embeddings: list[list[str]]
:param documents: list of texts to add
:type documents: List[str]
:type documents: list[str]
:param metadatas: list of metadata associated with docs
:type metadatas: List[object]
:type metadatas: list[object]
:param ids: ids of docs
:type ids: List[str]
:type ids: list[str]
"""
embeddings = self.embedder.embedding_fn(documents)
@@ -154,27 +154,27 @@ class ElasticsearchDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:return: The context of the document that matched your query, url of the source, doc_id
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
input_query_vector = self.embedder.embedding_fn(input_query)
query_vector = input_query_vector[0]

44
embedchain/vectordb/opensearch.py
View File

@@ -1,6 +1,6 @@
import logging
import time
from typing import Any, Dict, List, Optional, Set, Tuple, Union
from typing import Any, Optional, Union
from tqdm import tqdm
@@ -78,17 +78,17 @@ class OpenSearchDB(BaseVectorDB):
"""Note: nothing to return here. Discuss later"""
def get(
self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None
) -> Set[str]:
self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None
) -> set[str]:
"""
Get existing doc ids present in vector database
:param ids: _list of doc ids to check for existence
:type ids: List[str]
:type ids: list[str]
:param where: to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:return: ids
:type: Set[str]
:type: set[str]
"""
query = {}
if ids:
@@ -116,19 +116,19 @@ class OpenSearchDB(BaseVectorDB):
def add(
self,
embeddings: List[List[str]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, any]],
embeddings: list[list[str]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, any]],
):
"""Add data in vector database.
Args:
embeddings (List[List[str]]): List of embeddings to add.
documents (List[str]): List of texts to add.
metadatas (List[object]): List of metadata associated with docs.
ids (List[str]): IDs of docs.
embeddings (list[list[str]]): list of embeddings to add.
documents (list[str]): list of texts to add.
metadatas (list[object]): list of metadata associated with docs.
ids (list[str]): IDs of docs.
"""
for batch_start in tqdm(range(0, len(documents), self.BATCH_SIZE), desc="Inserting batches in opensearch"):
batch_end = batch_start + self.BATCH_SIZE
@@ -156,26 +156,26 @@ class OpenSearchDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
embeddings = OpenAIEmbeddings()
docsearch = OpenSearchVectorSearch(

38
embedchain/vectordb/pinecone.py
View File

@@ -1,5 +1,5 @@
import os
from typing import Dict, List, Optional, Tuple, Union
from typing import Optional, Union
try:
import pinecone
@@ -67,14 +67,14 @@ class PineconeDB(BaseVectorDB):
)
return pinecone.Index(self.index_name)
def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
def get(self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None):
"""
Get existing doc ids present in vector database
:param ids: _list of doc ids to check for existence
:type ids: List[str]
:type ids: list[str]
:param where: to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:return: ids
:rtype: Set[str]
"""
@@ -88,20 +88,20 @@ class PineconeDB(BaseVectorDB):
def add(
self,
embeddings: List[List[float]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, any]],
embeddings: list[list[float]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, any]],
):
"""add data in vector database
:param documents: list of texts to add
:type documents: List[str]
:type documents: list[str]
:param metadatas: list of metadata associated with docs
:type metadatas: List[object]
:type metadatas: list[object]
:param ids: ids of docs
:type ids: List[str]
:type ids: list[str]
"""
docs = []
print("Adding documents to Pinecone...")
@@ -120,25 +120,25 @@ class PineconeDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
query_vector = self.embedder.embedding_fn([input_query])[0]
data = self.client.query(vector=query_vector, filter=where, top_k=n_results, include_metadata=True, **kwargs)

40
embedchain/vectordb/qdrant.py
View File

@@ -1,7 +1,7 @@
import copy
import os
import uuid
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Optional, Union
try:
from qdrant_client import QdrantClient
@@ -69,14 +69,14 @@ class QdrantDB(BaseVectorDB):
def _get_or_create_collection(self):
return f"{self.config.collection_name}-{self.embedder.vector_dimension}".lower().replace("_", "-")
def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
def get(self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None):
"""
Get existing doc ids present in vector database
:param ids: _list of doc ids to check for existence
:type ids: List[str]
:type ids: list[str]
:param where: to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:param limit: The number of entries to be fetched
:type limit: Optional int, defaults to None
:return: All the existing IDs
@@ -122,21 +122,21 @@ class QdrantDB(BaseVectorDB):
def add(
self,
embeddings: List[List[float]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, any]],
embeddings: list[list[float]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, any]],
):
"""add data in vector database
:param embeddings: list of embeddings for the corresponding documents to be added
:type documents: List[List[float]]
:type documents: list[list[float]]
:param documents: list of texts to add
:type documents: List[str]
:type documents: list[str]
:param metadatas: list of metadata associated with docs
:type metadatas: List[object]
:type metadatas: list[object]
:param ids: ids of docs
:type ids: List[str]
:type ids: list[str]
"""
embeddings = self.embedder.embedding_fn(documents)
@@ -159,25 +159,25 @@ class QdrantDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
query_vector = self.embedder.embedding_fn([input_query])[0]
keys = set(where.keys() if where is not None else set())

40
embedchain/vectordb/weaviate.py
View File

@@ -1,6 +1,6 @@
import copy
import os
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Optional, Union
try:
import weaviate
@@ -117,13 +117,13 @@ class WeaviateDB(BaseVectorDB):
self.client.schema.create(class_obj)
def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
def get(self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None):
"""
Get existing doc ids present in vector database
:param ids: _list of doc ids to check for existance
:type ids: List[str]
:type ids: list[str]
:param where: to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:return: ids
:rtype: Set[str]
"""
@@ -153,21 +153,21 @@ class WeaviateDB(BaseVectorDB):
def add(
self,
embeddings: List[List[float]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, any]],
embeddings: list[list[float]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, any]],
):
"""add data in vector database
:param embeddings: list of embeddings for the corresponding documents to be added
:type documents: List[List[float]]
:type documents: list[list[float]]
:param documents: list of texts to add
:type documents: List[str]
:type documents: list[str]
:param metadatas: list of metadata associated with docs
:type metadatas: List[object]
:type metadatas: list[object]
:param ids: ids of docs
:type ids: List[str]
:type ids: list[str]
"""
embeddings = self.embedder.embedding_fn(documents)
self.client.batch.configure(batch_size=self.BATCH_SIZE, timeout_retries=3) # Configure batch
@@ -192,25 +192,25 @@ class WeaviateDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: Optional. to filter data
:type where: Dict[str, any]
:type where: dict[str, any]
:param citations: we use citations boolean param to return context along with the answer.
:type citations: bool, default is False.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
query_vector = self.embedder.embedding_fn([input_query])[0]
keys = set(where.keys() if where is not None else set())

32
embedchain/vectordb/zilliz.py
View File

@@ -1,5 +1,5 @@
import logging
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Optional, Union
from embedchain.config import ZillizDBConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -88,14 +88,14 @@ class ZillizVectorDB(BaseVectorDB):
self.collection.create_index("embeddings", index)
return self.collection
def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
def get(self, ids: Optional[list[str]] = None, where: Optional[dict[str, any]] = None, limit: Optional[int] = None):
"""
Get existing doc ids present in vector database
:param ids: list of doc ids to check for existence
:type ids: List[str]
:type ids: list[str]
:param where: Optional. to filter data
:type where: Dict[str, Any]
:type where: dict[str, Any]
:param limit: Optional. maximum number of documents
:type limit: Optional[int]
:return: Existing documents.
@@ -115,11 +115,11 @@ class ZillizVectorDB(BaseVectorDB):
def add(
self,
embeddings: List[List[float]],
documents: List[str],
metadatas: List[object],
ids: List[str],
**kwargs: Optional[Dict[str, any]],
embeddings: list[list[float]],
documents: list[str],
metadatas: list[object],
ids: list[str],
**kwargs: Optional[dict[str, any]],
):
"""Add to database"""
embeddings = self.embedder.embedding_fn(documents)
@@ -134,17 +134,17 @@ class ZillizVectorDB(BaseVectorDB):
def query(
self,
input_query: List[str],
input_query: list[str],
n_results: int,
where: Dict[str, any],
where: dict[str, any],
citations: bool = False,
**kwargs: Optional[Dict[str, Any]],
) -> Union[List[Tuple[str, Dict]], List[str]]:
**kwargs: Optional[dict[str, Any]],
) -> Union[list[tuple[str, dict]], list[str]]:
"""
Query contents from vector database based on vector similarity
:param input_query: list of query string
:type input_query: List[str]
:type input_query: list[str]
:param n_results: no of similar documents to fetch from database
:type n_results: int
:param where: to filter data
@@ -154,7 +154,7 @@ class ZillizVectorDB(BaseVectorDB):
:type citations: bool, default is False.
:return: The content of the document that matched your query,
along with url of the source and doc_id (if citations flag is true)
:rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
:rtype: list[str], if citations=False, otherwise list[tuple[str, str, str]]
"""
if self.collection.is_empty:
@@ -200,7 +200,7 @@ class ZillizVectorDB(BaseVectorDB):
"""
return self.collection.num_entities
def reset(self, collection_names: List[str] = None):
def reset(self, collection_names: list[str] = None):
"""
Resets the database. Deletes all embeddings irreversibly.
"""