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[Improvement] Set a default app id if not provided in the app configuration (#1300)

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This commit is contained in:
Deshraj Yadav
2024-03-02 15:10:34 -08:00
committed by GitHub
parent 8d7e8b6fb9
commit faacfeb891
4 changed files with 42 additions and 41 deletions
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40
docs/get-started/quickstart.mdx
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@@ -31,41 +31,47 @@ This section gives a quickstart example of using Mistral as the Open source LLM
We are using Mistral hosted at Hugging Face, so will you need a Hugging Face token to run this example. Its *free* and you can create one [here](https://huggingface.co/docs/hub/security-tokens).
<CodeGroup>
```python quickstart.py
```python huggingface_demo.py
import os
# replace this with your HF key
# Replace this with your HF token
os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_xxxx"
from embedchain import App
app = App.from_config("mistral.yaml")
config = {
'llm': {
'provider': 'huggingface',
'config': {
'model': 'mistralai/Mistral-7B-Instruct-v0.2',
'top_p': 0.5
}
},
'embedder': {
'provider': 'huggingface',
'config': {
'model': 'sentence-transformers/all-mpnet-base-v2'
}
}
}
app = App.from_config(config=config)
app.add("https://www.forbes.com/profile/elon-musk")
app.add("https://en.wikipedia.org/wiki/Elon_Musk")
app.query("What is the net worth of Elon Musk today?")
# Answer: The net worth of Elon Musk today is $258.7 billion.
```
```yaml mistral.yaml
llm:
provider: huggingface
config:
model: 'mistralai/Mistral-7B-Instruct-v0.2'
top_p: 0.5
embedder:
provider: huggingface
config:
model: 'sentence-transformers/all-mpnet-base-v2'
```
</CodeGroup>
## Paid Models
In this section, we will use both LLM and embedding model from OpenAI.
```python quickstart.py
```python openai_demo.py
import os
# replace this with your OpenAI key
from embedchain import App
# Replace this with your OpenAI key
os.environ["OPENAI_API_KEY"] = "sk-xxxx"
from embedchain import App
app = App()
app.add("https://www.forbes.com/profile/elon-musk")
app.add("https://en.wikipedia.org/wiki/Elon_Musk")

17
embedchain/app.py
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@@ -3,21 +3,15 @@ import concurrent.futures
import json
import logging
import os
import uuid
from typing import Any, Optional, Union
import requests
import yaml
from tqdm import tqdm
from embedchain.cache import (
Config,
ExactMatchEvaluation,
SearchDistanceEvaluation,
cache,
gptcache_data_manager,
gptcache_pre_function,
)
from embedchain.cache import (Config, ExactMatchEvaluation,
SearchDistanceEvaluation, cache,
gptcache_data_manager, gptcache_pre_function)
from embedchain.client import Client
from embedchain.config import AppConfig, CacheConfig, ChunkerConfig
from embedchain.core.db.database import get_session, init_db, setup_engine
@@ -26,7 +20,8 @@ from embedchain.embedchain import EmbedChain
from embedchain.embedder.base import BaseEmbedder
from embedchain.embedder.openai import OpenAIEmbedder
from embedchain.evaluation.base import BaseMetric
from embedchain.evaluation.metrics import AnswerRelevance, ContextRelevance, Groundedness
from embedchain.evaluation.metrics import (AnswerRelevance, ContextRelevance,
Groundedness)
from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
from embedchain.helpers.json_serializable import register_deserializable
from embedchain.llm.base import BaseLlm
@@ -106,7 +101,7 @@ class App(EmbedChain):
self.config = config or AppConfig()
self.name = self.config.name
self.config.id = self.local_id = str(uuid.uuid4()) if self.config.id is None else self.config.id
self.config.id = self.local_id = "default-app-id" if self.config.id is None else self.config.id
if id is not None:
# Init client first since user is trying to fetch the pipeline

2
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "embedchain"
version = "0.1.91"
version = "0.1.92"
description = "Simplest open source retrieval(RAG) framework"
authors = [
"Taranjeet Singh <taranjeet@embedchain.ai>",

24
tests/vectordb/test_qdrant.py
View File

@@ -29,7 +29,7 @@ class TestQdrantDB(unittest.TestCase):
def test_initialize(self, qdrant_client_mock):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_vector_dimension(1536)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Qdrant instance
@@ -37,7 +37,7 @@ class TestQdrantDB(unittest.TestCase):
app_config = AppConfig(collect_metrics=False)
App(config=app_config, db=db, embedding_model=embedder)
self.assertEqual(db.collection_name, "embedchain-store-1526")
self.assertEqual(db.collection_name, "embedchain-store-1536")
self.assertEqual(db.client, qdrant_client_mock.return_value)
qdrant_client_mock.return_value.get_collections.assert_called_once()
@@ -47,7 +47,7 @@ class TestQdrantDB(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_vector_dimension(1536)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Qdrant instance
@@ -67,7 +67,7 @@ class TestQdrantDB(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_vector_dimension(1536)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Qdrant instance
@@ -80,9 +80,9 @@ class TestQdrantDB(unittest.TestCase):
ids = ["123", "456"]
db.add(documents, metadatas, ids)
qdrant_client_mock.return_value.upsert.assert_called_once_with(
collection_name="embedchain-store-1526",
collection_name="embedchain-store-1536",
points=Batch(
ids=["def", "ghi"],
ids=["abc", "def"],
payloads=[
{
"identifier": "123",
@@ -103,7 +103,7 @@ class TestQdrantDB(unittest.TestCase):
def test_query(self, qdrant_client_mock):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_vector_dimension(1536)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Qdrant instance
@@ -115,7 +115,7 @@ class TestQdrantDB(unittest.TestCase):
db.query(input_query=["This is a test document."], n_results=1, where={"doc_id": "123"})
qdrant_client_mock.return_value.search.assert_called_once_with(
collection_name="embedchain-store-1526",
collection_name="embedchain-store-1536",
query_filter=models.Filter(
must=[
models.FieldCondition(
@@ -134,7 +134,7 @@ class TestQdrantDB(unittest.TestCase):
def test_count(self, qdrant_client_mock):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_vector_dimension(1536)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Qdrant instance
@@ -143,13 +143,13 @@ class TestQdrantDB(unittest.TestCase):
App(config=app_config, db=db, embedding_model=embedder)
db.count()
qdrant_client_mock.return_value.get_collection.assert_called_once_with(collection_name="embedchain-store-1526")
qdrant_client_mock.return_value.get_collection.assert_called_once_with(collection_name="embedchain-store-1536")
@patch("embedchain.vectordb.qdrant.QdrantClient")
def test_reset(self, qdrant_client_mock):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_vector_dimension(1536)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Qdrant instance
@@ -159,7 +159,7 @@ class TestQdrantDB(unittest.TestCase):
db.reset()
qdrant_client_mock.return_value.delete_collection.assert_called_once_with(
collection_name="embedchain-store-1526"
collection_name="embedchain-store-1536"
)