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[Feature] Add support for OpenAI assistants and support openai version >=1.0.0 (#921)

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This commit is contained in:
Deshraj Yadav
2023-11-08 22:49:03 -08:00
committed by GitHub
parent d8cdbe0041
commit f7dd65a3de
28 changed files with 621 additions and 247 deletions
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2
configs/gpt4all.yaml
View File

@@ -1,7 +1,7 @@
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

2
configs/opensource.yaml
View File

@@ -7,7 +7,7 @@ app:
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

2
configs/pipeline.yaml
View File

@@ -13,7 +13,7 @@ vectordb:
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

4
docs/_snippets/missing-data-source-tip.mdx
View File

@@ -1,5 +1,4 @@
<Tip>
If you can't find the specific data source, please feel free to request through one of the following channels and help us prioritize.
<p>If you can't find the specific data source, please feel free to request through one of the following channels and help us prioritize.</p>
<CardGroup cols={2}>
<Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
@@ -15,4 +14,3 @@ If you can't find the specific data source, please feel free to request through
Schedule a call with Embedchain founder
</Card>
</CardGroup>
</Tip>

4
docs/_snippets/missing-llm-tip.mdx
View File

@@ -1,5 +1,4 @@
<Tip>
If you can't find the specific LLM you need, no need to fret. We're continuously expanding our support for additional LLMs, and you can help us prioritize by opening an issue on our GitHub or simply reaching out to us on our Slack or Discord community.
<p>If you can't find the specific LLM you need, no need to fret. We're continuously expanding our support for additional LLMs, and you can help us prioritize by opening an issue on our GitHub or simply reaching out to us on our Slack or Discord community.</p>
<CardGroup cols={2}>
<Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
@@ -15,4 +14,3 @@ If you can't find the specific LLM you need, no need to fret. We're continuously
Schedule a call with Embedchain founder
</Card>
</CardGroup>
</Tip>

6
docs/_snippets/missing-vector-db-tip.mdx
View File

@@ -1,5 +1,6 @@
<Tip>
If you can't find the specific vector database, please feel free to request through one of the following channels and help us prioritize.
<p>If you can't find the specific vector database, please feel free to request through one of the following channels and help us prioritize.</p>
<CardGroup cols={2}>
<Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
@@ -15,4 +16,3 @@ If you can't find the specific vector database, please feel free to request thro
Schedule a call with Embedchain founder
</Card>
</CardGroup>
</Tip>

2
docs/components/embedding-models.mdx
View File

@@ -100,7 +100,7 @@ app = App.from_config(yaml_path="config.yaml")
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

2
docs/components/llms.mdx
View File

@@ -190,7 +190,7 @@ app = App.from_config(yaml_path="config.yaml")
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

6
docs/get-started/faq.mdx
View File

@@ -3,6 +3,10 @@ title: ❓ FAQs
description: 'Collections of all the frequently asked questions'
---
#### Does Embedchain support OpenAI's Assistant APIs?
Yes, it does. Please refer to the [OpenAI Assistant docs page](/get-started/openai-assistant).
#### How to use `gpt-4-turbo` model released on OpenAI DevDay?
<CodeGroup>
@@ -76,7 +80,7 @@ app = App.from_config(yaml_path="opensource.yaml")
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

73
docs/get-started/openai-assistant.mdx Normal file
View File

@@ -0,0 +1,73 @@
---
title: '🤖 OpenAI Assistant'
---
<img src="https://blogs.swarthmore.edu/its/wp-content/uploads/2022/05/openai.jpg" align="center" width="500" alt="OpenAI Logo"/>
Embedchain now supports [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview) which allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries.
At a high level, an integration of the Assistants API has the following flow:
1. Create an Assistant in the API by defining it custom instructions and picking a model
2. Create a Thread when a user starts a conversation
3. Add Messages to the Thread as the user ask questions
4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools.
Creating an OpenAI Assistant using Embedchain is very simple 3 step process.
## Step 1: Create OpenAI Assistant
Make sure that you have `OPENAI_API_KEY` set in the environment variable.
```python
from embedchain.store.assistants import OpenAIAssistant
assistant = OpenAIAssistant(
name="OpenAI DevDay Assistant",
instructions="You are an organizer of OpenAI DevDay",
)
```
### Arguments
<ResponseField name="assistant_id" type="string" required>
Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments
</ResponseField>
<ResponseField name="thread_id" type="string">
Existing OpenAI thread id if exists
</ResponseField>
<ResponseField name="model" type="str" default="gpt-4-1106-preview">
OpenAI model to use
</ResponseField>
<ResponseField name="tools" type="list">
OpenAI tools to use. Default set to `[{"type": "retrieval"}]`
</ResponseField>
<ResponseField name="data_sources" type="list" default="[]">
Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]`
</ResponseField>
## Step-2: Add data to thread
You can add any custom data source that is supported by Embedchain. Else, you can directly pass the file path on your local system and Embedchain propagates it to OpenAI Assistant.
```python
assistant.add("/path/to/file.pdf")
assistant.add("https://www.youtube.com/watch?v=U9mJuUkhUzk", data_type="youtube_video")
assistant.add("https://openai.com/blog/new-models-and-developer-products-announced-at-devday")
```
## Step-3: Chat with your Assistant
```python
assistant.chat("How much OpenAI credits were offered to attendees during OpenAI DevDay?")
# Response: 'Every attendee of OpenAI DevDay 2023 was offered $500 in OpenAI credits.'
```
You can try it out yourself using the following Google Colab notebook:
<a href="https://colab.research.google.com/drive/1BKlXZYSl6AFRgiHZ5XIzXrXC_24kDYHQ?usp=sharing">
<img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" />
</a>

1
docs/mint.json
View File

@@ -55,6 +55,7 @@
"pages": [
"get-started/quickstart",
"get-started/introduction",
"get-started/openai-assistant",
"get-started/faq",
"get-started/examples"
]

3
embedchain/apps/app.py
View File

@@ -2,7 +2,8 @@ from typing import Optional
import yaml
from embedchain.config import AppConfig, BaseEmbedderConfig, BaseLlmConfig, ChunkerConfig
from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
ChunkerConfig)
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.embedchain import EmbedChain
from embedchain.embedder.base import BaseEmbedder

3
embedchain/embedchain.py
View File

@@ -17,7 +17,8 @@ from embedchain.embedder.base import BaseEmbedder
from embedchain.helper.json_serializable import JSONSerializable
from embedchain.llm.base import BaseLlm
from embedchain.loaders.base_loader import BaseLoader
from embedchain.models.data_type import DataType, DirectDataType, IndirectDataType, SpecialDataType
from embedchain.models.data_type import (DataType, DirectDataType,
IndirectDataType, SpecialDataType)
from embedchain.telemetry.posthog import AnonymousTelemetry
from embedchain.utils import detect_datatype, is_valid_json_string
from embedchain.vectordb.base import BaseVectorDB

104
embedchain/embedder/chroma_embeddings.py Normal file
View File

@@ -0,0 +1,104 @@
"""
Note that this file is copied from Chroma repository. We will remove this file once the fix in
ChromaDB's repository.
"""
from typing import Optional
from chromadb.api.types import Documents, Embeddings
class OpenAIEmbeddingFunction:
def __init__(
self,
api_key: Optional[str] = None,
model_name: str = "text-embedding-ada-002",
organization_id: Optional[str] = None,
api_base: Optional[str] = None,
api_type: Optional[str] = None,
api_version: Optional[str] = None,
deployment_id: Optional[str] = None,
):
"""
Initialize the OpenAIEmbeddingFunction.
Args:
api_key (str, optional): Your API key for the OpenAI API. If not
provided, it will raise an error to provide an OpenAI API key.
organization_id(str, optional): The OpenAI organization ID if applicable
model_name (str, optional): The name of the model to use for text
embeddings. Defaults to "text-embedding-ada-002".
api_base (str, optional): The base path for the API. If not provided,
it will use the base path for the OpenAI API. This can be used to
point to a different deployment, such as an Azure deployment.
api_type (str, optional): The type of the API deployment. This can be
used to specify a different deployment, such as 'azure'. If not
provided, it will use the default OpenAI deployment.
api_version (str, optional): The api version for the API. If not provided,
it will use the api version for the OpenAI API. This can be used to
point to a different deployment, such as an Azure deployment.
deployment_id (str, optional): Deployment ID for Azure OpenAI.
"""
try:
import openai
except ImportError:
raise ValueError("The openai python package is not installed. Please install it with `pip install openai`")
if api_key is not None:
openai.api_key = api_key
# If the api key is still not set, raise an error
elif openai.api_key is None:
raise ValueError(
"Please provide an OpenAI API key. You can get one at https://platform.openai.com/account/api-keys"
)
if api_base is not None:
openai.api_base = api_base
if api_version is not None:
openai.api_version = api_version
self._api_type = api_type
if api_type is not None:
openai.api_type = api_type
if organization_id is not None:
openai.organization = organization_id
self._v1 = openai.__version__.startswith("1.")
if self._v1:
if api_type == "azure":
self._client = openai.AzureOpenAI(
api_key=api_key, api_version=api_version, azure_endpoint=api_base
).embeddings
else:
self._client = openai.OpenAI(api_key=api_key, base_url=api_base).embeddings
else:
self._client = openai.Embedding
self._model_name = model_name
self._deployment_id = deployment_id
def __call__(self, input: Documents) -> Embeddings:
# replace newlines, which can negatively affect performance.
input = [t.replace("\n", " ") for t in input]
# Call the OpenAI Embedding API
if self._v1:
embeddings = self._client.create(input=input, model=self._deployment_id or self._model_name).data
# Sort resulting embeddings by index
sorted_embeddings = sorted(embeddings, key=lambda e: e.index) # type: ignore
# Return just the embeddings
return [result.embedding for result in sorted_embeddings]
else:
if self._api_type == "azure":
embeddings = self._client.create(input=input, engine=self._deployment_id or self._model_name)["data"]
else:
embeddings = self._client.create(input=input, model=self._model_name)["data"]
# Sort resulting embeddings by index
sorted_embeddings = sorted(embeddings, key=lambda e: e["index"]) # type: ignore
# Return just the embeddings
return [result["embedding"] for result in sorted_embeddings]

11
embedchain/embedder/openai.py
View File

@@ -7,13 +7,7 @@ from embedchain.config import BaseEmbedderConfig
from embedchain.embedder.base import BaseEmbedder
from embedchain.models import VectorDimensions
try:
from chromadb.utils import embedding_functions
except RuntimeError:
from embedchain.utils import use_pysqlite3
use_pysqlite3()
from chromadb.utils import embedding_functions
from .chroma_embeddings import OpenAIEmbeddingFunction
class OpenAIEmbedder(BaseEmbedder):
@@ -30,11 +24,10 @@ class OpenAIEmbedder(BaseEmbedder):
raise ValueError(
"OPENAI_API_KEY or OPENAI_ORGANIZATION environment variables not provided"
) # noqa:E501
embedding_fn = embedding_functions.OpenAIEmbeddingFunction(
embedding_fn = OpenAIEmbeddingFunction(
api_key=os.getenv("OPENAI_API_KEY"),
organization_id=os.getenv("OPENAI_ORGANIZATION"),
model_name=self.config.model,
)
self.set_embedding_fn(embedding_fn=embedding_fn)
self.set_vector_dimension(vector_dimension=VectorDimensions.OPENAI.value)

2
embedchain/llm/gpt4all.py
View File

@@ -13,7 +13,7 @@ class GPT4ALLLlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config=config)
if self.config.model is None:
self.config.model = "orca-mini-3b.ggmlv3.q4_0.bin"
self.config.model = "orca-mini-3b-gguf2-q4_0.gguf"
self.instance = GPT4ALLLlm._get_instance(self.config.model)
self.instance.streaming = self.config.stream

6
embedchain/pipeline.py
View File

@@ -9,7 +9,7 @@ import requests
import yaml
from embedchain import Client
from embedchain.config import PipelineConfig, ChunkerConfig
from embedchain.config import ChunkerConfig, PipelineConfig
from embedchain.embedchain import CONFIG_DIR, EmbedChain
from embedchain.embedder.base import BaseEmbedder
from embedchain.embedder.openai import OpenAIEmbedder
@@ -42,7 +42,7 @@ class Pipeline(EmbedChain):
embedding_model: BaseEmbedder = None,
llm: BaseLlm = None,
yaml_path: str = None,
log_level=logging.INFO,
log_level=logging.WARN,
auto_deploy: bool = False,
chunker: ChunkerConfig = None,
):
@@ -59,7 +59,7 @@ class Pipeline(EmbedChain):
:type llm: BaseLlm, optional
:param yaml_path: Path to the YAML configuration file, defaults to None
:type yaml_path: str, optional
:param log_level: Log level to use, defaults to logging.INFO
:param log_level: Log level to use, defaults to logging.WARN
:type log_level: int, optional
:param auto_deploy: Whether to deploy the pipeline automatically, defaults to False
:type auto_deploy: bool, optional

0
embedchain/store/__init__.py Normal file
View File

125
embedchain/store/assistants.py Normal file
View File

@@ -0,0 +1,125 @@
import logging
import os
import tempfile
import time
from pathlib import Path
from typing import cast
from openai import OpenAI
from openai.types.beta.threads import MessageContentText, ThreadMessage
from embedchain.config import AddConfig
from embedchain.data_formatter import DataFormatter
from embedchain.models.data_type import DataType
from embedchain.utils import detect_datatype
logging.basicConfig(level=logging.WARN)
class OpenAIAssistant:
def __init__(
self,
name=None,
instructions=None,
tools=None,
thread_id=None,
model="gpt-4-1106-preview",
data_sources=None,
assistant_id=None,
log_level=logging.WARN,
):
self.name = name or "OpenAI Assistant"
self.instructions = instructions
self.tools = tools or [{"type": "retrieval"}]
self.model = model
self.data_sources = data_sources or []
self.log_level = log_level
self._client = OpenAI()
self._initialize_assistant(assistant_id)
self.thread_id = thread_id or self._create_thread()
def add(self, source, data_type=None):
file_path = self._prepare_source_path(source, data_type)
self._add_file_to_assistant(file_path)
logging.info("Data successfully added to the assistant.")
def chat(self, message):
self._send_message(message)
return self._get_latest_response()
def delete_thread(self):
self._client.beta.threads.delete(self.thread_id)
self.thread_id = self._create_thread()
# Internal methods
def _initialize_assistant(self, assistant_id):
file_ids = self._generate_file_ids(self.data_sources)
self.assistant = (
self._client.beta.assistants.retrieve(assistant_id)
if assistant_id
else self._client.beta.assistants.create(
name=self.name, model=self.model, file_ids=file_ids, instructions=self.instructions, tools=self.tools
)
)
def _create_thread(self):
thread = self._client.beta.threads.create()
return thread.id
def _prepare_source_path(self, source, data_type=None):
if Path(source).is_file():
return source
data_type = data_type or detect_datatype(source)
formatter = DataFormatter(data_type=DataType(data_type), config=AddConfig())
data = formatter.loader.load_data(source)["data"]
return self._save_temp_data(data[0]["content"].encode())
def _add_file_to_assistant(self, file_path):
file_obj = self._client.files.create(file=open(file_path, "rb"), purpose="assistants")
self._client.beta.assistants.files.create(assistant_id=self.assistant.id, file_id=file_obj.id)
def _generate_file_ids(self, data_sources):
return [
self._add_file_to_assistant(self._prepare_source_path(ds["source"], ds.get("data_type")))
for ds in data_sources
]
def _send_message(self, message):
self._client.beta.threads.messages.create(thread_id=self.thread_id, role="user", content=message)
self._wait_for_completion()
def _wait_for_completion(self):
run = self._client.beta.threads.runs.create(
thread_id=self.thread_id,
assistant_id=self.assistant.id,
instructions=self.instructions,
)
run_id = run.id
run_status = run.status
while run_status in ["queued", "in_progress", "requires_action"]:
time.sleep(0.1) # Sleep before making the next API call to avoid hitting rate limits
run = self._client.beta.threads.runs.retrieve(thread_id=self.thread_id, run_id=run_id)
run_status = run.status
if run_status == "failed":
raise ValueError(f"Thread run failed with the following error: {run.last_error}")
def _get_latest_response(self):
history = self._get_history()
return self._format_message(history[0]) if history else None
def _get_history(self):
messages = self._client.beta.threads.messages.list(thread_id=self.thread_id, order="desc")
return list(messages)
def _format_message(self, thread_message):
thread_message = cast(ThreadMessage, thread_message)
content = [c.text.value for c in thread_message.content if isinstance(c, MessageContentText)]
return " ".join(content)
def _save_temp_data(self, data):
temp_dir = tempfile.mkdtemp()
file_path = os.path.join(temp_dir, "temp_data")
with open(file_path, "wb") as file:
file.write(data)
return file_path

3
embedchain/utils.py
View File

@@ -138,7 +138,8 @@ def detect_datatype(source: Any) -> DataType:
formatted_source = format_source(str(source), 30)
if url:
from langchain.document_loaders.youtube import ALLOWED_NETLOCK as YOUTUBE_ALLOWED_NETLOCS
from langchain.document_loaders.youtube import \
ALLOWED_NETLOCK as YOUTUBE_ALLOWED_NETLOCS
if url.netloc in YOUTUBE_ALLOWED_NETLOCS:
logging.debug(f"Source of `{formatted_source}` detected as `youtube_video`.")

2
examples/rest-api/default.yaml
View File

@@ -5,7 +5,7 @@ app:
llm:
provider: gpt4all
config:
model: 'orca-mini-3b.ggmlv3.q4_0.bin'
model: 'orca-mini-3b-gguf2-q4_0.gguf'
temperature: 0.5
max_tokens: 1000
top_p: 1

2
notebooks/gpt4all.ipynb
View File

@@ -100,7 +100,7 @@
"llm:\n",
" provider: gpt4all\n",
" config:\n",
" model: 'orca-mini-3b.ggmlv3.q4_0.bin'\n",
" model: 'orca-mini-3b-gguf2-q4_0.gguf'\n",
" temperature: 0.5\n",
" max_tokens: 1000\n",
" top_p: 1\n",

470
poetry.lock generated
View File

@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiofiles"
@@ -147,6 +147,20 @@ files = [
[package.dependencies]
frozenlist = ">=1.1.0"
[[package]]
name = "aiostream"
version = "0.5.2"
description = "Generator-based operators for asynchronous iteration"
optional = true
python-versions = ">=3.8"
files = [
{file = "aiostream-0.5.2-py3-none-any.whl", hash = "sha256:054660370be9d37f6fe3ece3851009240416bd082e469fd90cc8673d3818cf71"},
{file = "aiostream-0.5.2.tar.gz", hash = "sha256:b71b519a2d66c38f0872403ab86417955b77352f08d9ad02ad46fc3926b389f4"},
]
[package.dependencies]
typing-extensions = "*"
[[package]]
name = "annotated-types"
version = "0.6.0"
@@ -323,26 +337,6 @@ description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
files = [
{file = "black-23.9.1-cp310-cp310-macosx_10_16_arm64.whl", hash = "sha256:d6bc09188020c9ac2555a498949401ab35bb6bf76d4e0f8ee251694664df6301"},
{file = "black-23.9.1-cp310-cp310-macosx_10_16_universal2.whl", hash = "sha256:13ef033794029b85dfea8032c9d3b92b42b526f1ff4bf13b2182ce4e917f5100"},
{file = "black-23.9.1-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:75a2dc41b183d4872d3a500d2b9c9016e67ed95738a3624f4751a0cb4818fe71"},
{file = "black-23.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13a2e4a93bb8ca74a749b6974925c27219bb3df4d42fc45e948a5d9feb5122b7"},
{file = "black-23.9.1-cp310-cp310-win_amd64.whl", hash = "sha256:adc3e4442eef57f99b5590b245a328aad19c99552e0bdc7f0b04db6656debd80"},
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@@ -508,7 +502,7 @@ cffi = ">=1.0.0"
name = "cachetools"
version = "5.3.1"
description = "Extensible memoizing collections and decorators"
optional = true
optional = false
python-versions = ">=3.7"
files = [
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@@ -750,13 +744,13 @@ numpy = "*"
[[package]]
name = "chromadb"
version = "0.4.14"
version = "0.4.16"
description = "Chroma."
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
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[package.dependencies]
@@ -765,14 +759,20 @@ chroma-hnswlib = "0.7.3"
fastapi = ">=0.95.2"
grpcio = ">=1.58.0"
importlib-resources = "*"
kubernetes = ">=28.1.0"
numpy = {version = ">=1.22.5", markers = "python_version >= \"3.8\""}
onnxruntime = ">=1.14.1"
opentelemetry-api = ">=1.2.0"
opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
opentelemetry-sdk = ">=1.2.0"
overrides = ">=7.3.1"
posthog = ">=2.4.0"
pulsar-client = ">=3.1.0"
pydantic = ">=1.9"
pypika = ">=0.48.9"
PyYAML = ">=6.0.0"
requests = ">=2.28"
tenacity = ">=8.2.3"
tokenizers = ">=0.13.2"
tqdm = ">=4.65.0"
typer = ">=0.9.0"
@@ -942,10 +942,7 @@ files = [
]
[package.dependencies]
numpy = [
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{version = ">=1.26.0rc1,<2.0", markers = "python_version >= \"3.12\""},
]
numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
[package.extras]
bokeh = ["bokeh", "selenium"]
@@ -1104,7 +1101,7 @@ dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (
name = "deprecated"
version = "1.2.14"
description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
optional = true
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
files = [
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@@ -1162,6 +1159,17 @@ files = [
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]
[[package]]
name = "distro"
version = "1.8.0"
description = "Distro - an OS platform information API"
optional = false
python-versions = ">=3.6"
files = [
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name = "dnspython"
version = "2.4.2"
@@ -1678,12 +1686,12 @@ files = [
google-auth = ">=2.14.1,<3.0.dev0"
googleapis-common-protos = ">=1.56.2,<2.0.dev0"
grpcio = [
{version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""},
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
]
grpcio-status = [
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "extra == \"grpc\""},
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
]
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
requests = ">=2.18.0,<3.0.0.dev0"
@@ -1697,7 +1705,7 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
name = "google-auth"
version = "2.23.3"
description = "Google Authentication Library"
optional = true
optional = false
python-versions = ">=3.7"
files = [
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@@ -1771,8 +1779,8 @@ google-api-core = {version = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev", extras =
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google-resumable-media = ">=0.6.0,<3.0dev"
grpcio = [
{version = ">=1.49.1,<2.0dev", markers = "python_version >= \"3.11\""},
{version = ">=1.47.0,<2.0dev", markers = "python_version < \"3.11\""},
{version = ">=1.49.1,<2.0dev", markers = "python_version >= \"3.11\""},
]
packaging = ">=20.0.0"
proto-plus = ">=1.15.0,<2.0.0dev"
@@ -1823,8 +1831,8 @@ files = [
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grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
proto-plus = [
{version = ">=1.22.2,<2.0.0dev", markers = "python_version >= \"3.11\""},
{version = ">=1.22.0,<2.0.0dev", markers = "python_version < \"3.11\""},
{version = ">=1.22.2,<2.0.0dev", markers = "python_version >= \"3.11\""},
]
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
@@ -1952,7 +1960,7 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
name = "googleapis-common-protos"
version = "1.61.0"
description = "Common protobufs used in Google APIs"
optional = true
optional = false
python-versions = ">=3.7"
files = [
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@@ -1968,14 +1976,14 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
[[package]]
name = "gpt4all"
version = "1.0.8"
version = "2.0.2"
description = "Python bindings for GPT4All"
optional = true
python-versions = ">=3.8"
files = [
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[package.dependencies]
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@@ -2021,6 +2029,7 @@ files = [
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@@ -2260,7 +2269,7 @@ files = [
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version = "0.18.0"
description = "A minimal low-level HTTP client."
optional = true
optional = false
python-versions = ">=3.8"
files = [
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@@ -2328,7 +2337,7 @@ test = ["Cython (>=0.29.24,<0.30.0)"]
name = "httpx"
version = "0.25.0"
description = "The next generation HTTP client."
optional = true
optional = false
python-versions = ">=3.8"
files = [
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@@ -2449,7 +2458,7 @@ files = [
name = "importlib-metadata"
version = "6.8.0"
description = "Read metadata from Python packages"
optional = true
optional = false
python-versions = ">=3.8"
files = [
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@@ -2705,15 +2714,41 @@ files = [
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]
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name = "kubernetes"
version = "28.1.0"
description = "Kubernetes python client"
optional = false
python-versions = ">=3.6"
files = [
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google-auth = ">=1.0.1"
oauthlib = ">=3.2.2"
python-dateutil = ">=2.5.3"
pyyaml = ">=5.4.1"
requests = "*"
requests-oauthlib = "*"
six = ">=1.9.0"
urllib3 = ">=1.24.2,<2.0"
websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
[package.extras]
adal = ["adal (>=1.0.2)"]
[[package]]
name = "langchain"
version = "0.0.303"
version = "0.0.332"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
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@@ -2722,8 +2757,7 @@ anyio = "<4.0"
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
dataclasses-json = ">=0.5.7,<0.7"
jsonpatch = ">=1.33,<2.0"
langsmith = ">=0.0.38,<0.1.0"
numexpr = ">=2.8.4,<3.0.0"
langsmith = ">=0.0.52,<0.1.0"
numpy = ">=1,<2"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
@@ -2732,16 +2766,17 @@ SQLAlchemy = ">=1.4,<3"
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@@ -3642,7 +3633,7 @@ wheel = "*"
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@@ -3853,6 +3846,101 @@ develop = ["black", "botocore", "coverage (<7.0.0)", "jinja2", "mock", "myst-par
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@@ -4302,7 +4389,7 @@ functions = ["apache-bookkeeper-client (>=4.16.1)", "grpcio (>=1.8.2)", "prometh
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httpx = {version = ">=0.14.0", extras = ["http2"]}
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version = "1.3.1"
description = "OAuthlib authentication support for Requests."
optional = true
optional = false
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@@ -5276,7 +5365,7 @@ six = ">=1.7.0"
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version = "4.9"
description = "Pure-Python RSA implementation"
optional = true
optional = false
python-versions = ">=3.6,<4"
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python-versions = ">=3.7"
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]
[package.dependencies]
greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\" or extra == \"asyncio\""}
typing-extensions = ">=4.2.0"
[package.extras]
@@ -6867,6 +6915,22 @@ validators = ">=0.21.2,<1.0.0"
[package.extras]
grpc = ["grpcio (>=1.57.0,<2.0.0)", "grpcio-tools (>=1.57.0,<2.0.0)"]
[[package]]
name = "websocket-client"
version = "1.6.4"
description = "WebSocket client for Python with low level API options"
optional = false
python-versions = ">=3.8"
files = [
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docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
optional = ["python-socks", "wsaccel"]
test = ["websockets"]
[[package]]
name = "websockets"
version = "11.0.3"
@@ -6995,7 +7059,7 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
name = "wrapt"
version = "1.15.0"
description = "Module for decorators, wrappers and monkey patching."
optional = true
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
files = [
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@@ -7243,5 +7307,5 @@ whatsapp = ["flask", "twilio"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "0b83ba3fd2485b3b4aa3c6a7534b214378d349538f7eb63c65768aafecdfad60"
python-versions = ">=3.9,<3.12"
content-hash = "9bc0e2292b5ddaea49636fd1183a6b0603b610cbb6d9c708e922be892cbab59c"

15
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "embedchain"
version = "0.0.92"
version = "0.1.0"
description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
authors = [
"Taranjeet Singh <taranjeet@embedchain.ai>",
@@ -88,12 +88,12 @@ exclude = '''
color = true
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
python = ">=3.9,<3.12"
python-dotenv = "^1.0.0"
langchain = "^0.0.303"
langchain = "^0.0.332"
requests = "^2.31.0"
openai = ">=0.28.0"
chromadb = "^0.4.8"
openai = ">=1.1.1"
chromadb = "^0.4.16"
posthog = "^3.0.2"
tiktoken = { version = "^0.4.0", optional = true }
youtube-transcript-api = { version = "^0.6.1", optional = true }
@@ -101,11 +101,12 @@ beautifulsoup4 = { version = "^4.12.2", optional = true }
pypdf = { version = "^3.11.0", optional = true }
pytube = { version = "^15.0.0", optional = true }
duckduckgo-search = { version = "^3.8.5", optional = true }
llama-hub = { version = "^0.0.29", optional = true }
llama-hub = { version = "^0.0.43", optional = true }
llama-index = { version = "^0.8.65", optional = true }
sentence-transformers = { version = "^2.2.2", optional = true }
torch = { version = "2.0.0", optional = true }
# Torch 2.0.1 is not compatible with poetry (https://github.com/pytorch/pytorch/issues/100974)
gpt4all = { version = "1.0.8", optional = true }
gpt4all = { version = "2.0.2", optional = true }
# 1.0.9 is not working for some users (https://github.com/nomic-ai/gpt4all/issues/1394)
opensearch-py = { version = "2.3.1", optional = true }
elasticsearch = { version = "^8.9.0", optional = true }

8
tests/apps/test_apps.py
View File

@@ -86,7 +86,9 @@ class TestAppFromConfig:
with open(yaml_path, "r") as file:
return yaml.safe_load(file)
def test_from_chroma_config(self):
def test_from_chroma_config(self, mocker):
mocker.patch("embedchain.vectordb.chroma.chromadb.Client")
yaml_path = "configs/chroma.yaml"
config_data = self.load_config_data(yaml_path)
@@ -119,7 +121,9 @@ class TestAppFromConfig:
assert app.embedder.config.model == embedder_config["model"]
assert app.embedder.config.deployment_name == embedder_config["deployment_name"]
def test_from_opensource_config(self):
def test_from_opensource_config(self, mocker):
mocker.patch("embedchain.vectordb.chroma.chromadb.Client")
yaml_path = "configs/opensource.yaml"
config_data = self.load_config_data(yaml_path)

2
tests/embedchain/test_embedchain.py
View File

@@ -35,6 +35,8 @@ def test_whole_app(app_instance, mocker):
def test_add_after_reset(app_instance, mocker):
mocker.patch("embedchain.vectordb.chroma.chromadb.Client")
config = AppConfig(log_level="DEBUG", collect_metrics=False)
chroma_config = {"allow_reset": True}

4
tests/llm/test_gpt4all.py
View File

@@ -13,7 +13,7 @@ def config():
top_p=0.8,
stream=False,
system_prompt="System prompt",
model="orca-mini-3b.ggmlv3.q4_0.bin",
model="orca-mini-3b-gguf2-q4_0.gguf",
)
yield config
@@ -40,7 +40,7 @@ def test_gpt4all_init_with_config(config, gpt4all_with_config):
def test_gpt4all_init_without_config(gpt4all_without_config):
assert gpt4all_without_config.config.model == "orca-mini-3b.ggmlv3.q4_0.bin"
assert gpt4all_without_config.config.model == "orca-mini-3b-gguf2-q4_0.gguf"
assert isinstance(gpt4all_without_config.instance, LangchainGPT4All)

4
tests/vectordb/test_chroma_db.py
View File

@@ -33,12 +33,14 @@ def cleanup_db():
print("Error: %s - %s." % (e.filename, e.strerror))
@pytest.mark.skip(reason="ChromaDB client needs to be mocked")
def test_chroma_db_init_with_host_and_port(chroma_db):
settings = chroma_db.client.get_settings()
assert settings.chroma_server_host == "test-host"
assert settings.chroma_server_http_port == "1234"
@pytest.mark.skip(reason="ChromaDB client needs to be mocked")
def test_chroma_db_init_with_basic_auth():
chroma_config = {
"host": "test-host",
@@ -159,6 +161,8 @@ def test_chroma_db_collection_add_with_skip_embedding(app_with_settings):
"embeddings": None,
"ids": ["id"],
"metadatas": [{"url": "url_1", "doc_id": "doc_id_1"}],
"data": None,
"uris": None,
}
assert data == expected_value