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Resolve conflicts (#208)

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This commit is contained in:
Deshraj Yadav
2023-07-10 21:50:05 -07:00
committed by GitHub
parent 6936d6983d
commit 9ca836520f
32 changed files with 396 additions and 207 deletions
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24
Makefile Normal file
View File

@@ -0,0 +1,24 @@
# Variables
PYTHON := python3
PIP := $(PYTHON) -m pip
PROJECT_NAME := embedchain
# Targets
.PHONY: install format lint clean test
install:
$(PIP) install --upgrade pip
$(PIP) install .[dev]
format:
$(PYTHON) -m black .
$(PYTHON) -m isort .
lint:
$(PYTHON) -m ruff .
clean:
rm -rf dist build *.egg-info
test:
$(PYTHON) -m pytest

22
README.md
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@@ -44,6 +44,7 @@ embedchain is a framework to easily create LLM powered bots over any dataset. If
- [Reset](#reset)
- [Count](#count)
- [How does it work?](#how-does-it-work)
- [Contribution Guidelines](#contribution-guidelines)
- [Tech Stack](#tech-stack)
- [Team](#team)
- [Author](#author)
@@ -551,6 +552,27 @@ embedchain is a framework which takes care of all these nuances and provides a s
In the first release, we are making it easier for anyone to get a chatbot over any dataset up and running in less than a minute. All you need to do is create an app instance, add the data sets using `.add` function and then use `.query` function to get the relevant answer.
# Contribution Guidelines
Thank you for your interest in contributing to the EmbedChain project! We welcome your ideas and contributions to help improve the project. Please follow the instructions below to get started:
1. **Fork the repository**: Click on the "Fork" button at the top right corner of this repository page. This will create a copy of the repository in your own GitHub account.
2. **Install the required dependencies**: Ensure that you have the necessary dependencies installed in your Python environment. You can do this by running the following command:
```bash
make install
```
3. **Make changes in the code**: Create a new branch in your forked repository and make your desired changes in the codebase.
4. **Format code**: Before creating a pull request, it's important to ensure that your code follows our formatting guidelines. Run the following commands to format the code:
```bash
make lint format
```
5. **Create a pull request**: When you are ready to contribute your changes, submit a pull request to the EmbedChain repository. Provide a clear and descriptive title for your pull request, along with a detailed description of the changes you have made.
# Tech Stack
embedchain is built on the following stack:

2
embedchain/__init__.py
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@@ -1 +1 @@
from .embedchain import App, OpenSourceApp, PersonApp, PersonOpenSourceApp
from .embedchain import App, OpenSourceApp, PersonApp, PersonOpenSourceApp

10
embedchain/chunkers/base_chunker.py
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@@ -3,15 +3,17 @@ import hashlib
class BaseChunker:
def __init__(self, text_splitter):
''' Initialize the chunker. '''
"""Initialize the chunker."""
self.text_splitter = text_splitter
def create_chunks(self, loader, src):
"""
Loads data and chunks it.
:param loader: The loader which's `load_data` method is used to create the raw data.
:param src: The data to be handled by the loader. Can be a URL for remote sources or local content for local loaders.
:param loader: The loader which's `load_data` method is used to create
the raw data.
:param src: The data to be handled by the loader. Can be a URL for
remote sources or local content for local loaders.
"""
documents = []
ids = []
@@ -27,7 +29,7 @@ class BaseChunker:
for chunk in chunks:
chunk_id = hashlib.sha256((chunk + url).encode()).hexdigest()
if (idMap.get(chunk_id) is None):
if idMap.get(chunk_id) is None:
idMap[chunk_id] = True
ids.append(chunk_id)
documents.append(chunk)

8
embedchain/chunkers/docx_file.py
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@@ -1,10 +1,9 @@
from typing import Optional
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
TEXT_SPLITTER_CHUNK_PARAMS = {
"chunk_size": 1000,
@@ -14,7 +13,8 @@ TEXT_SPLITTER_CHUNK_PARAMS = {
class DocxFileChunker(BaseChunker):
''' Chunker for .docx file. '''
"""Chunker for .docx file."""
def __init__(self, config: Optional[ChunkerConfig] = None):
if config is None:
config = TEXT_SPLITTER_CHUNK_PARAMS

7
embedchain/chunkers/pdf_file.py
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@@ -1,9 +1,9 @@
from typing import Optional
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
TEXT_SPLITTER_CHUNK_PARAMS = {
"chunk_size": 1000,
@@ -13,7 +13,8 @@ TEXT_SPLITTER_CHUNK_PARAMS = {
class PdfFileChunker(BaseChunker):
''' Chunker for PDF file. '''
"""Chunker for PDF file."""
def __init__(self, config: Optional[ChunkerConfig] = None):
if config is None:
config = TEXT_SPLITTER_CHUNK_PARAMS

7
embedchain/chunkers/qna_pair.py
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@@ -1,9 +1,9 @@
from typing import Optional
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
TEXT_SPLITTER_CHUNK_PARAMS = {
"chunk_size": 300,
@@ -13,7 +13,8 @@ TEXT_SPLITTER_CHUNK_PARAMS = {
class QnaPairChunker(BaseChunker):
''' Chunker for QnA pair. '''
"""Chunker for QnA pair."""
def __init__(self, config: Optional[ChunkerConfig] = None):
if config is None:
config = TEXT_SPLITTER_CHUNK_PARAMS

7
embedchain/chunkers/text.py
View File

@@ -1,9 +1,9 @@
from typing import Optional
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
TEXT_SPLITTER_CHUNK_PARAMS = {
"chunk_size": 300,
@@ -13,7 +13,8 @@ TEXT_SPLITTER_CHUNK_PARAMS = {
class TextChunker(BaseChunker):
''' Chunker for text. '''
"""Chunker for text."""
def __init__(self, config: Optional[ChunkerConfig] = None):
if config is None:
config = TEXT_SPLITTER_CHUNK_PARAMS

7
embedchain/chunkers/web_page.py
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@@ -1,9 +1,9 @@
from typing import Optional
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
TEXT_SPLITTER_CHUNK_PARAMS = {
"chunk_size": 500,
@@ -13,7 +13,8 @@ TEXT_SPLITTER_CHUNK_PARAMS = {
class WebPageChunker(BaseChunker):
''' Chunker for web page. '''
"""Chunker for web page."""
def __init__(self, config: Optional[ChunkerConfig] = None):
if config is None:
config = TEXT_SPLITTER_CHUNK_PARAMS

7
embedchain/chunkers/youtube_video.py
View File

@@ -1,9 +1,9 @@
from typing import Optional
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from embedchain.chunkers.base_chunker import BaseChunker
from embedchain.config.AddConfig import ChunkerConfig
TEXT_SPLITTER_CHUNK_PARAMS = {
"chunk_size": 2000,
@@ -13,7 +13,8 @@ TEXT_SPLITTER_CHUNK_PARAMS = {
class YoutubeVideoChunker(BaseChunker):
''' Chunker for Youtube video. '''
"""Chunker for Youtube video."""
def __init__(self, config: Optional[ChunkerConfig] = None):
if config is None:
config = TEXT_SPLITTER_CHUNK_PARAMS

24
embedchain/config/AddConfig.py
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@@ -1,4 +1,5 @@
from typing import Callable, Optional
from embedchain.config.BaseConfig import BaseConfig
@@ -6,27 +7,36 @@ class ChunkerConfig(BaseConfig):
"""
Config for the chunker used in `add` method
"""
def __init__(self,
chunk_size: Optional[int] = 4000,
chunk_overlap: Optional[int] = 200,
length_function: Optional[Callable[[str], int]] = len):
def __init__(
self,
chunk_size: Optional[int] = 4000,
chunk_overlap: Optional[int] = 200,
length_function: Optional[Callable[[str], int]] = len,
):
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
self.length_function = length_function
class LoaderConfig(BaseConfig):
"""
Config for the chunker used in `add` method
"""
def __init__(self):
pass
class AddConfig(BaseConfig):
"""
Config for the `add` method.
"""
def __init__(self,
chunker: Optional[ChunkerConfig] = None,
loader: Optional[LoaderConfig] = None):
def __init__(
self,
chunker: Optional[ChunkerConfig] = None,
loader: Optional[LoaderConfig] = None,
):
self.loader = loader
self.chunker = chunker

1
embedchain/config/BaseConfig.py
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@@ -2,6 +2,7 @@ class BaseConfig:
"""
Base config.
"""
def __init__(self):
pass

28
embedchain/config/ChatConfig.py
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@@ -1,8 +1,10 @@
from embedchain.config.QueryConfig import QueryConfig
from string import Template
from embedchain.config.QueryConfig import QueryConfig
DEFAULT_PROMPT = """
You are a chatbot having a conversation with a human. You are given chat history and context.
You are a chatbot having a conversation with a human. You are given chat
history and context.
You need to answer the query considering context, chat history and your knowledge base. If you don't know the answer or the answer is neither contained in the context nor in history, then simply say "I don't know".
$context
@@ -12,35 +14,41 @@ DEFAULT_PROMPT = """
Query: $query
Helpful Answer:
"""
""" # noqa:E501
DEFAULT_PROMPT_TEMPLATE = Template(DEFAULT_PROMPT)
class ChatConfig(QueryConfig):
"""
Config for the `chat` method, inherits from `QueryConfig`.
"""
def __init__(self, template: Template = None, stream: bool = False):
"""
Initializes the ChatConfig instance.
:param template: Optional. The `Template` instance to use as a template for prompt.
:param stream: Optional. Control if response is streamed back to the user
:raises ValueError: If the template is not valid as template should contain $context and $query and $history
:param template: Optional. The `Template` instance to use as a
template for prompt.
:param stream: Optional. Control if response is streamed back to the
user
:raises ValueError: If the template is not valid as template should
contain $context and $query and $history
"""
if template is None:
template = DEFAULT_PROMPT_TEMPLATE
# History is set as 0 to ensure that there is always a history, that way, there don't have to be two templates.
# Having two templates would make it complicated because the history is not user controlled.
# History is set as 0 to ensure that there is always a history, that
# way, there don't have to be two templates.
# Having two templates would make it complicated because the history
# is not user controlled.
super().__init__(template, history=[0], stream=stream)
def set_history(self, history):
"""
Chat history is not user provided and not set at initialization time
:param history: (string) history to set
"""
self.history = history
return

17
embedchain/config/InitConfig.py
View File

@@ -1,8 +1,9 @@
import os
import logging
import os
from embedchain.config.BaseConfig import BaseConfig
class InitConfig(BaseConfig):
"""
Config to initialize an embedchain `App` instance.
@@ -10,7 +11,8 @@ class InitConfig(BaseConfig):
def __init__(self, log_level=None, ef=None, db=None):
"""
:param log_level: Optional. (String) Debug level ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
:param log_level: Optional. (String) Debug level
['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'].
:param ef: Optional. Embedding function to use.
:param db: Optional. (Vector) database to use for embeddings.
"""
@@ -19,16 +21,18 @@ class InitConfig(BaseConfig):
# Embedding Function
if ef is None:
from chromadb.utils import embedding_functions
self.ef = embedding_functions.OpenAIEmbeddingFunction(
api_key=os.getenv("OPENAI_API_KEY"),
organization_id=os.getenv("OPENAI_ORGANIZATION"),
model_name="text-embedding-ada-002"
model_name="text-embedding-ada-002",
)
else:
self.ef = ef
if db is None:
from embedchain.vectordb.chroma_db import ChromaDB
self.db = ChromaDB(ef=self.ef)
else:
self.db = db
@@ -44,9 +48,10 @@ class InitConfig(BaseConfig):
if debug_level is not None:
level = getattr(logging, debug_level.upper(), None)
if not isinstance(level, int):
raise ValueError(f'Invalid log level: {debug_level}')
raise ValueError(f"Invalid log level: {debug_level}")
logging.basicConfig(format="%(asctime)s [%(name)s] [%(levelname)s] %(message)s",
level=level)
logging.basicConfig(
format="%(asctime)s [%(name)s] [%(levelname)s] %(message)s", level=level
)
self.logger = logging.getLogger(__name__)
return

36
embedchain/config/QueryConfig.py
View File

@@ -1,6 +1,7 @@
from embedchain.config.BaseConfig import BaseConfig
from string import Template
import re
from string import Template
from embedchain.config.BaseConfig import BaseConfig
DEFAULT_PROMPT = """
Use the following pieces of context to answer the query at the end.
@@ -11,7 +12,7 @@ DEFAULT_PROMPT = """
Query: $query
Helpful Answer:
"""
""" # noqa:E501
DEFAULT_PROMPT_WITH_HISTORY = """
Use the following pieces of context to answer the query at the end.
@@ -25,7 +26,7 @@ DEFAULT_PROMPT_WITH_HISTORY = """
Query: $query
Helpful Answer:
"""
""" # noqa:E501
DEFAULT_PROMPT_TEMPLATE = Template(DEFAULT_PROMPT)
DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE = Template(DEFAULT_PROMPT_WITH_HISTORY)
@@ -38,14 +39,17 @@ class QueryConfig(BaseConfig):
"""
Config for the `query` method.
"""
def __init__(self, template: Template = None, history = None, stream: bool = False):
def __init__(self, template: Template = None, history=None, stream: bool = False):
"""
Initializes the QueryConfig instance.
:param template: Optional. The `Template` instance to use as a template for prompt.
:param template: Optional. The `Template` instance to use as a
template for prompt.
:param history: Optional. A list of strings to consider as history.
:param stream: Optional. Control if response is streamed back to the user
:raises ValueError: If the template is not valid as template should contain $context and $query (and optionally $history).
:param stream: Optional. Control if response is streamed back to user
:raises ValueError: If the template is not valid as template should
contain $context and $query (and optionally $history).
"""
if not history:
self.history = None
@@ -67,12 +71,13 @@ class QueryConfig(BaseConfig):
if self.history is None:
raise ValueError("`template` should have `query` and `context` keys")
else:
raise ValueError("`template` should have `query`, `context` and `history` keys")
raise ValueError(
"`template` should have `query`, `context` and `history` keys"
)
if not isinstance(stream, bool):
raise ValueError("`stream` should be bool")
self.stream = stream
def validate_template(self, template: Template):
"""
@@ -82,9 +87,12 @@ class QueryConfig(BaseConfig):
:return: Boolean, valid (true) or invalid (false)
"""
if self.history is None:
return (re.search(query_re, template.template) \
and re.search(context_re, template.template))
return re.search(query_re, template.template) and re.search(
context_re, template.template
)
else:
return (re.search(query_re, template.template) \
return (
re.search(query_re, template.template)
and re.search(context_re, template.template)
and re.search(history_re, template.template))
and re.search(history_re, template.template)
)

4
embedchain/config/__init__.py
View File

@@ -1,5 +1,5 @@
from .BaseConfig import BaseConfig
from .AddConfig import AddConfig
from .BaseConfig import BaseConfig
from .ChatConfig import ChatConfig
from .InitConfig import InitConfig
from .QueryConfig import QueryConfig
from .QueryConfig import QueryConfig

2
embedchain/data_formatter/__init__.py
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@@ -1 +1 @@
from .data_formatter import DataFormatter
from .data_formatter import DataFormatter

47
embedchain/data_formatter/data_formatter.py
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@@ -1,24 +1,25 @@
from embedchain.config import AddConfig
from embedchain.loaders.youtube_video import YoutubeVideoLoader
from embedchain.loaders.pdf_file import PdfFileLoader
from embedchain.loaders.web_page import WebPageLoader
from embedchain.loaders.local_qna_pair import LocalQnaPairLoader
from embedchain.loaders.local_text import LocalTextLoader
from embedchain.loaders.docx_file import DocxFileLoader
from embedchain.chunkers.youtube_video import YoutubeVideoChunker
from embedchain.chunkers.docx_file import DocxFileChunker
from embedchain.chunkers.pdf_file import PdfFileChunker
from embedchain.chunkers.web_page import WebPageChunker
from embedchain.chunkers.qna_pair import QnaPairChunker
from embedchain.chunkers.text import TextChunker
from embedchain.chunkers.docx_file import DocxFileChunker
from embedchain.chunkers.web_page import WebPageChunker
from embedchain.chunkers.youtube_video import YoutubeVideoChunker
from embedchain.config import AddConfig
from embedchain.loaders.docx_file import DocxFileLoader
from embedchain.loaders.local_qna_pair import LocalQnaPairLoader
from embedchain.loaders.local_text import LocalTextLoader
from embedchain.loaders.pdf_file import PdfFileLoader
from embedchain.loaders.web_page import WebPageLoader
from embedchain.loaders.youtube_video import YoutubeVideoLoader
class DataFormatter:
"""
DataFormatter is an internal utility class which abstracts the mapping for
loaders and chunkers to the data_type entered by the user in their
.add or .add_local method call
.add or .add_local method call
"""
def __init__(self, data_type: str, config: AddConfig):
self.loader = self._get_loader(data_type, config.loader)
self.chunker = self._get_chunker(data_type, config.chunker)
@@ -32,12 +33,12 @@ class DataFormatter:
:raises ValueError: If an unsupported data type is provided.
"""
loaders = {
'youtube_video': YoutubeVideoLoader(),
'pdf_file': PdfFileLoader(),
'web_page': WebPageLoader(),
'qna_pair': LocalQnaPairLoader(),
'text': LocalTextLoader(),
'docx': DocxFileLoader(),
"youtube_video": YoutubeVideoLoader(),
"pdf_file": PdfFileLoader(),
"web_page": WebPageLoader(),
"qna_pair": LocalQnaPairLoader(),
"text": LocalTextLoader(),
"docx": DocxFileLoader(),
}
if data_type in loaders:
return loaders[data_type]
@@ -53,12 +54,12 @@ class DataFormatter:
:raises ValueError: If an unsupported data type is provided.
"""
chunkers = {
'youtube_video': YoutubeVideoChunker(config),
'pdf_file': PdfFileChunker(config),
'web_page': WebPageChunker(config),
'qna_pair': QnaPairChunker(config),
'text': TextChunker(config),
'docx': DocxFileChunker(config),
"youtube_video": YoutubeVideoChunker(config),
"pdf_file": PdfFileChunker(config),
"web_page": WebPageChunker(config),
"qna_pair": QnaPairChunker(config),
"text": TextChunker(config),
"docx": DocxFileChunker(config),
}
if data_type in chunkers:
return chunkers[data_type]

128
embedchain/embedchain.py
View File

@@ -1,17 +1,16 @@
import openai
import os
import logging
import os
from string import Template
import openai
from chromadb.utils import embedding_functions
from dotenv import load_dotenv
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.memory import ConversationBufferMemory
from embedchain.config import InitConfig, AddConfig, QueryConfig, ChatConfig
from embedchain.config import AddConfig, ChatConfig, InitConfig, QueryConfig
from embedchain.config.QueryConfig import DEFAULT_PROMPT
from embedchain.data_formatter import DataFormatter
from string import Template
gpt4all_model = None
@@ -45,7 +44,8 @@ class EmbedChain:
:param data_type: The type of the data to add.
:param url: The URL where the data is located.
:param config: Optional. The `AddConfig` instance to use as configuration options.
:param config: Optional. The `AddConfig` instance to use as configuration
options.
"""
if config is None:
config = AddConfig()
@@ -62,22 +62,28 @@ class EmbedChain:
:param data_type: The type of the data to add.
:param content: The local data. Refer to the `README` for formatting.
:param config: Optional. The `AddConfig` instance to use as configuration options.
:param config: Optional. The `AddConfig` instance to use as
configuration options.
"""
if config is None:
config = AddConfig()
data_formatter = DataFormatter(data_type, config)
self.user_asks.append([data_type, content])
self.load_and_embed(data_formatter.loader, data_formatter.chunker, content)
self.load_and_embed(
data_formatter.loader,
data_formatter.chunker,
content,
)
def load_and_embed(self, loader, chunker, src):
"""
Loads the data from the given URL, chunks it, and adds it to the database.
Loads the data from the given URL, chunks it, and adds it to database.
:param loader: The loader to use to load the data.
:param chunker: The chunker to use to chunk the data.
:param src: The data to be handled by the loader. Can be a URL for remote sources or local content for local loaders.
:param src: The data to be handled by the loader. Can be a URL for
remote sources or local content for local loaders.
"""
embeddings_data = chunker.create_chunks(loader, src)
documents = embeddings_data["documents"]
@@ -91,8 +97,12 @@ class EmbedChain:
existing_ids = set(existing_docs["ids"])
if len(existing_ids):
data_dict = {id: (doc, meta) for id, doc, meta in zip(ids, documents, metadatas)}
data_dict = {id: value for id, value in data_dict.items() if id not in existing_ids}
data_dict = {
id: (doc, meta) for id, doc, meta in zip(ids, documents, metadatas)
}
data_dict = {
id: value for id, value in data_dict.items() if id not in existing_ids
}
if not data_dict:
print(f"All data from {src} already exists in the database.")
@@ -103,12 +113,10 @@ class EmbedChain:
chunks_before_addition = self.count()
self.collection.add(
documents=documents,
metadatas=list(metadatas),
ids=ids
self.collection.add(documents=documents, metadatas=list(metadatas), ids=ids)
print(
f"Successfully saved {src}. New chunks count: {self.count() - chunks_before_addition}" # noqa:E501
)
print(f"Successfully saved {src}. New chunks count: {self.count() - chunks_before_addition}")
def _format_result(self, results):
return [
@@ -132,7 +140,9 @@ class EmbedChain:
:return: The content of the document that matched your query.
"""
result = self.collection.query(
query_texts=[input_query,],
query_texts=[
input_query,
],
n_results=1,
)
result_formatted = self._format_result(result)
@@ -144,17 +154,21 @@ class EmbedChain:
def generate_prompt(self, input_query, context, config: QueryConfig):
"""
Generates a prompt based on the given query and context, ready to be passed to an LLM
Generates a prompt based on the given query and context, ready to be
passed to an LLM
:param input_query: The query to use.
:param context: Similar documents to the query used as context.
:param config: Optional. The `QueryConfig` instance to use as configuration options.
:param config: Optional. The `QueryConfig` instance to use as
configuration options.
:return: The prompt
"""
if not config.history:
prompt = config.template.substitute(context = context, query = input_query)
prompt = config.template.substitute(context=context, query=input_query)
else:
prompt = config.template.substitute(context = context, query = input_query, history = config.history)
prompt = config.template.substitute(
context=context, query=input_query, history=config.history
)
return prompt
def get_answer_from_llm(self, prompt, config: ChatConfig):
@@ -166,7 +180,7 @@ class EmbedChain:
:param context: Similar documents to the query used as context.
:return: The answer.
"""
return self.get_llm_model_answer(prompt, config)
def query(self, input_query, config: QueryConfig = None):
@@ -176,7 +190,8 @@ class EmbedChain:
LLM as context to get the answer.
:param input_query: The query to use.
:param config: Optional. The `QueryConfig` instance to use as configuration options.
:param config: Optional. The `QueryConfig` instance to use as
configuration options.
:return: The answer to the query.
"""
if config is None:
@@ -188,7 +203,6 @@ class EmbedChain:
logging.info(f"Answer: {answer}")
return answer
def chat(self, input_query, config: ChatConfig = None):
"""
Queries the vector database on the given input query.
@@ -197,30 +211,31 @@ class EmbedChain:
Maintains last 5 conversations in memory.
:param input_query: The query to use.
:param config: Optional. The `ChatConfig` instance to use as configuration options.
:param config: Optional. The `ChatConfig` instance to use as
configuration options.
:return: The answer to the query.
"""
context = self.retrieve_from_database(input_query)
global memory
chat_history = memory.load_memory_variables({})["history"]
if config is None:
config = ChatConfig()
if chat_history:
config.set_history(chat_history)
prompt = self.generate_prompt(input_query, context, config)
logging.info(f"Prompt: {prompt}")
answer = self.get_answer_from_llm(prompt, config)
memory.chat_memory.add_user_message(input_query)
if isinstance(answer, str):
memory.chat_memory.add_ai_message(answer)
logging.info(f"Answer: {answer}")
return answer
else:
#this is a streamed response and needs to be handled differently.
# this is a streamed response and needs to be handled differently.
return self._stream_chat_response(answer)
def _stream_chat_response(self, answer):
@@ -230,7 +245,6 @@ class EmbedChain:
yield chunk
memory.chat_memory.add_ai_message(streamed_answer)
logging.info(f"Answer: {streamed_answer}")
def dry_run(self, input_query, config: QueryConfig = None):
"""
@@ -242,7 +256,8 @@ class EmbedChain:
the `max_tokens` parameter.
:param input_query: The query to use.
:param config: Optional. The `QueryConfig` instance to use as configuration options.
:param config: Optional. The `QueryConfig` instance to use as
configuration options.
:return: The prompt that would be sent to the LLM
"""
if config is None:
@@ -260,7 +275,6 @@ class EmbedChain:
"""
return self.collection.count()
def reset(self):
"""
Resets the database. Deletes all embeddings irreversibly.
@@ -288,35 +302,31 @@ class App(EmbedChain):
super().__init__(config)
def get_llm_model_answer(self, prompt, config: ChatConfig):
messages = []
messages.append({
"role": "user", "content": prompt
})
messages.append({"role": "user", "content": prompt})
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-0613",
messages=messages,
temperature=0,
max_tokens=1000,
top_p=1,
stream=config.stream
stream=config.stream,
)
if config.stream:
return self._stream_llm_model_response(response)
else:
return response["choices"][0]["message"]["content"]
def _stream_llm_model_response(self, response):
"""
This is a generator for streaming response from the OpenAI completions API
"""
for line in response:
chunk = line['choices'][0].get('delta', {}).get('content', '')
chunk = line["choices"][0].get("delta", {}).get("content", "")
yield chunk
class OpenSourceApp(EmbedChain):
"""
The OpenSource app.
@@ -330,20 +340,24 @@ class OpenSourceApp(EmbedChain):
def __init__(self, config: InitConfig = None):
"""
:param config: InitConfig instance to load as configuration. Optional. `ef` defaults to open source.
:param config: InitConfig instance to load as configuration. Optional.
`ef` defaults to open source.
"""
print("Loading open source embedding model. This may take some time...")
print(
"Loading open source embedding model. This may take some time..."
) # noqa:E501
if not config:
config = InitConfig(
ef = embedding_functions.SentenceTransformerEmbeddingFunction(
ef=embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="all-MiniLM-L6-v2"
)
)
elif not config.ef:
config._set_embedding_function(
embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="all-MiniLM-L6-v2"
))
embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="all-MiniLM-L6-v2"
)
)
print("Successfully loaded open source embedding model.")
super().__init__(config)
@@ -353,10 +367,7 @@ class OpenSourceApp(EmbedChain):
global gpt4all_model
if gpt4all_model is None:
gpt4all_model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
response = gpt4all_model.generate(
prompt=prompt,
streaming=config.stream
)
response = gpt4all_model.generate(prompt=prompt, streaming=config.stream)
return response
@@ -368,12 +379,11 @@ class EmbedChainPersonApp:
:param person: name of the person, better if its a well known person.
:param config: InitConfig instance to load as configuration.
"""
def __init__(self, person, config: InitConfig = None):
self.person = person
self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style."
self.template = Template(
self.person_prompt + " " + DEFAULT_PROMPT
)
self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style." # noqa:E501
self.template = Template(self.person_prompt + " " + DEFAULT_PROMPT)
if config is None:
config = InitConfig()
super().__init__(config)
@@ -384,6 +394,7 @@ class PersonApp(EmbedChainPersonApp, App):
The Person app.
Extends functionality from EmbedChainPersonApp and App
"""
def query(self, input_query, config: QueryConfig = None):
query_config = QueryConfig(
template=self.template,
@@ -392,7 +403,7 @@ class PersonApp(EmbedChainPersonApp, App):
def chat(self, input_query, config: ChatConfig = None):
chat_config = ChatConfig(
template = self.template,
template=self.template,
)
return super().chat(input_query, chat_config)
@@ -402,6 +413,7 @@ class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
The Person app.
Extends functionality from EmbedChainPersonApp and OpenSourceApp
"""
def query(self, input_query, config: QueryConfig = None):
query_config = QueryConfig(
template=self.template,
@@ -410,6 +422,6 @@ class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
def chat(self, input_query, config: ChatConfig = None):
chat_config = ChatConfig(
template = self.template,
template=self.template,
)
return super().chat(input_query, chat_config)
return super().chat(input_query, chat_config)

3
embedchain/loaders/docx_file.py
View File

@@ -1,8 +1,9 @@
from langchain.document_loaders import Docx2txtLoader
class DocxFileLoader:
def load_data(self, url):
''' Load data from a .docx file. '''
"""Load data from a .docx file."""
loader = Docx2txtLoader(url)
output = []
data = loader.load()

13
embedchain/loaders/local_qna_pair.py
View File

@@ -1,13 +1,14 @@
class LocalQnaPairLoader:
def load_data(self, content):
''' Load data from a local QnA pair. '''
"""Load data from a local QnA pair."""
question, answer = content
content = f"Q: {question}\nA: {answer}"
meta_data = {
"url": "local",
}
return [{
"content": content,
"meta_data": meta_data,
}]
return [
{
"content": content,
"meta_data": meta_data,
}
]

13
embedchain/loaders/local_text.py
View File

@@ -1,11 +1,12 @@
class LocalTextLoader:
def load_data(self, content):
''' Load data from a local text file. '''
"""Load data from a local text file."""
meta_data = {
"url": "local",
}
return [{
"content": content,
"meta_data": meta_data,
}]
return [
{
"content": content,
"meta_data": meta_data,
}
]

13
embedchain/loaders/pdf_file.py
View File

@@ -4,9 +4,8 @@ from embedchain.utils import clean_string
class PdfFileLoader:
def load_data(self, url):
''' Load data from a PDF file. '''
"""Load data from a PDF file."""
loader = PyPDFLoader(url)
output = []
pages = loader.load_and_split()
@@ -17,8 +16,10 @@ class PdfFileLoader:
content = clean_string(content)
meta_data = page.metadata
meta_data["url"] = url
output.append({
"content": content,
"meta_data": meta_data,
})
output.append(
{
"content": content,
"meta_data": meta_data,
}
)
return output

37
embedchain/loaders/web_page.py
View File

@@ -1,22 +1,29 @@
import requests
from bs4 import BeautifulSoup
from embedchain.utils import clean_string
class WebPageLoader:
def load_data(self, url):
''' Load data from a web page. '''
"""Load data from a web page."""
response = requests.get(url)
data = response.content
soup = BeautifulSoup(data, 'html.parser')
for tag in soup([
"nav", "aside", "form", "header",
"noscript", "svg", "canvas",
"footer", "script", "style"
]):
soup = BeautifulSoup(data, "html.parser")
for tag in soup(
[
"nav",
"aside",
"form",
"header",
"noscript",
"svg",
"canvas",
"footer",
"script",
"style",
]
):
tag.string = " "
output = []
content = soup.get_text()
@@ -24,8 +31,10 @@ class WebPageLoader:
meta_data = {
"url": url,
}
output.append({
"content": content,
"meta_data": meta_data,
})
return output
output.append(
{
"content": content,
"meta_data": meta_data,
}
)
return output

13
embedchain/loaders/youtube_video.py
View File

@@ -4,9 +4,8 @@ from embedchain.utils import clean_string
class YoutubeVideoLoader:
def load_data(self, url):
''' Load data from a Youtube video. '''
"""Load data from a Youtube video."""
loader = YoutubeLoader.from_youtube_url(url, add_video_info=True)
doc = loader.load()
output = []
@@ -16,8 +15,10 @@ class YoutubeVideoLoader:
content = clean_string(content)
meta_data = doc[0].metadata
meta_data["url"] = url
output.append({
"content": content,
"meta_data": meta_data,
})
output.append(
{
"content": content,
"meta_data": meta_data,
}
)
return output

21
embedchain/utils.py
View File

@@ -3,30 +3,33 @@ import re
def clean_string(text):
"""
This function takes in a string and performs a series of text cleaning operations.
This function takes in a string and performs a series of text cleaning operations.
Args:
text (str): The text to be cleaned. This is expected to be a string.
Returns:
cleaned_text (str): The cleaned text after all the cleaning operations have been performed.
cleaned_text (str): The cleaned text after all the cleaning operations
have been performed.
"""
# Replacement of newline characters:
text = text.replace('\n', ' ')
text = text.replace("\n", " ")
# Stripping and reducing multiple spaces to single:
cleaned_text = re.sub(r'\s+', ' ', text.strip())
cleaned_text = re.sub(r"\s+", " ", text.strip())
# Removing backslashes:
cleaned_text = cleaned_text.replace('\\', '')
cleaned_text = cleaned_text.replace("\\", "")
# Replacing hash characters:
cleaned_text = cleaned_text.replace('#', ' ')
cleaned_text = cleaned_text.replace("#", " ")
# Eliminating consecutive non-alphanumeric characters:
# This regex identifies consecutive non-alphanumeric characters (i.e., not a word character [a-zA-Z0-9_] and not a whitespace) in the string
# and replaces each group of such characters with a single occurrence of that character.
# This regex identifies consecutive non-alphanumeric characters (i.e., not
# a word character [a-zA-Z0-9_] and not a whitespace) in the string
# and replaces each group of such characters with a single occurrence of
# that character.
# For example, "!!! hello !!!" would become "! hello !".
cleaned_text = re.sub(r'([^\w\s])\1*', r'\1', cleaned_text)
cleaned_text = re.sub(r"([^\w\s])\1*", r"\1", cleaned_text)
return cleaned_text

4
embedchain/vectordb/base_vector_db.py
View File

@@ -1,12 +1,12 @@
class BaseVectorDB:
''' Base class for vector database. '''
"""Base class for vector database."""
def __init__(self):
self.client = self._get_or_create_db()
self.collection = self._get_or_create_collection()
def _get_or_create_db(self):
''' Get or create the database. '''
"""Get or create the database."""
raise NotImplementedError
def _get_or_create_collection(self):

17
embedchain/vectordb/chroma_db.py
View File

@@ -1,14 +1,14 @@
import chromadb
import os
import chromadb
from chromadb.utils import embedding_functions
from embedchain.vectordb.base_vector_db import BaseVectorDB
class ChromaDB(BaseVectorDB):
''' Vector database using ChromaDB. '''
"""Vector database using ChromaDB."""
def __init__(self, db_dir=None, ef=None):
if ef:
self.ef = ef
@@ -16,23 +16,24 @@ class ChromaDB(BaseVectorDB):
self.ef = embedding_functions.OpenAIEmbeddingFunction(
api_key=os.getenv("OPENAI_API_KEY"),
organization_id=os.getenv("OPENAI_ORGANIZATION"),
model_name="text-embedding-ada-002"
model_name="text-embedding-ada-002",
)
if db_dir is None:
db_dir = "db"
self.client_settings = chromadb.config.Settings(
chroma_db_impl="duckdb+parquet",
persist_directory=db_dir,
anonymized_telemetry=False
anonymized_telemetry=False,
)
super().__init__()
def _get_or_create_db(self):
''' Get or create the database. '''
"""Get or create the database."""
return chromadb.Client(self.client_settings)
def _get_or_create_collection(self):
''' Get or create the collection. '''
"""Get or create the collection."""
return self.client.get_or_create_collection(
'embedchain_store', embedding_function=self.ef,
"embedchain_store",
embedding_function=self.ef,
)

68
pyproject.toml Normal file
View File

@@ -0,0 +1,68 @@
[build-system]
requires = ["setuptools", "wheel"]
build-backend = "setuptools.build_meta"
[tool.ruff]
select = ["E", "F"]
ignore = []
fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
unfixable = []
exclude = [
".bzr",
".direnv",
".eggs",
".git",
".git-rewrite",
".hg",
".mypy_cache",
".nox",
".pants.d",
".pytype",
".ruff_cache",
".svn",
".tox",
".venv",
"__pypackages__",
"_build",
"buck-out",
"build",
"dist",
"node_modules",
"venv",
]
line-length = 88
dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
target-version = "py38"
[tool.ruff.mccabe]
max-complexity = 10
[tool.black]
line-length = 88
target-version = ["py38", "py39", "py310", "py311"]
include = '\.pyi?$'
exclude = '''
/(
\.eggs
| \.git
| \.hg
| \.mypy_cache
| \.nox
| \.pants.d
| \.pytype
| \.ruff_cache
| \.svn
| \.tox
| \.venv
| __pypackages__
| _build
| buck-out
| build
| dist
| node_modules
| venv
)/
'''
[tool.black.format]
color = true

5
requirements/dev.txt Normal file
View File

@@ -0,0 +1,5 @@
pip
black==23.3.0
isort==5.8.0
ruff==0.0.277
pytest==7.4.0

4
setup.py
View File

@@ -8,7 +8,7 @@ setuptools.setup(
version="0.0.18",
author="Taranjeet Singh",
author_email="reachtotj@gmail.com",
description="embedchain is a framework to easily create LLM powered bots over any dataset",
description="embedchain is a framework to easily create LLM powered bots over any dataset", # noqa:E501
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/embedchain/embedchain",
@@ -18,7 +18,7 @@ setuptools.setup(
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
],
python_requires='>=3.8',
python_requires=">=3.8",
py_modules=["embedchain"],
install_requires=[
"langchain>=0.0.205",

4
tests/test_embedchain.py
View File

@@ -1,7 +1,7 @@
import os
import unittest
from unittest.mock import patch, MagicMock
from unittest.mock import MagicMock, patch
from embedchain import App