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Update version to v0.0.77 (#851)

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This commit is contained in:
Deshraj Yadav
2023-10-26 09:51:51 -07:00
committed by GitHub
parent 0f8a2e624a
commit ab9598d00a
3 changed files with 24 additions and 33 deletions
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8
embedchain/llm/gpt4all.py
View File

@@ -1,7 +1,7 @@
from typing import Iterable, Optional, Union
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from embedchain.config import BaseLlmConfig
from embedchain.helper.json_serializable import register_deserializable
@@ -48,11 +48,7 @@ class GPT4ALLLlm(BaseLlm):
if config.top_p:
kwargs["top_p"] = config.top_p
callbacks = None
if config.stream:
callbacks = [StreamingStdOutCallbackHandler()]
else:
callbacks =[StdOutCallbackHandler()]
callbacks = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
response = self.instance.generate(prompts=messages, callbacks=callbacks, **kwargs)
answer = ""

47
embedchain/pipeline.py
View File

@@ -17,6 +17,7 @@ from embedchain.embedder.openai import OpenAIEmbedder
from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
from embedchain.helper.json_serializable import register_deserializable
from embedchain.llm.base import BaseLlm
from embedchain.llm.openai import OpenAILlm
from embedchain.vectordb.base import BaseVectorDB
from embedchain.vectordb.chroma import ChromaDB
@@ -77,11 +78,12 @@ class Pipeline(EmbedChain):
self.config = config or PipelineConfig()
self.name = self.config.name
self.local_id = self.config.id or str(uuid.uuid4())
self.config.id = self.local_id = str(uuid.uuid4()) if self.config.id is None else self.config.id
self.embedding_model = embedding_model or OpenAIEmbedder()
self.db = db or ChromaDB()
self.llm = llm or None
self.llm = llm or OpenAILlm()
self._init_db()
# setup user id and directory
@@ -128,7 +130,7 @@ class Pipeline(EmbedChain):
self.client = Client()
else:
api_key = input(
"Enter Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/ \n"
"🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/ \n" # noqa: E501
)
self.client = Client(api_key=api_key)
@@ -150,7 +152,7 @@ class Pipeline(EmbedChain):
headers={"Authorization": f"Token {self.client.api_key}"},
)
if r.status_code not in [200, 201]:
raise Exception(f"Error occurred while creating pipeline. Response from API: {r.text}")
raise Exception(f"❌ Error occurred while creating pipeline. API response: {r.text}")
print(
f"🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n" # noqa: E501
@@ -207,8 +209,7 @@ class Pipeline(EmbedChain):
printed_value = metadata.get("file_path") if metadata.get("file_path") else data_value
print(f"✅ Data of type: {data_type}, value: {printed_value} added successfully.")
except Exception as e:
self.logger.error(f"Error occurred during data upload: {str(e)}")
print(f"❌ Error occurred during data upload for type {data_type}!")
print(f"❌ Error occurred during data upload for type {data_type}!. Error: {str(e)}")
def _send_api_request(self, endpoint, payload):
url = f"{self.client.host}{endpoint}"
@@ -237,39 +238,33 @@ class Pipeline(EmbedChain):
self._upload_data_to_pipeline(data_type, data_value, metadata)
self._mark_data_as_uploaded(data_hash)
return True
except Exception as e:
self.logger.error(f"Error occurred during data upload: {str(e)}")
except Exception:
print(f"❌ Error occurred during data upload for hash {data_hash}!")
return False
def _mark_data_as_uploaded(self, data_hash):
self.cursor.execute(
"UPDATE data_sources SET is_uploaded = 1 WHERE hash = ? AND pipeline_id = ? AND is_uploaded = 0",
"UPDATE data_sources SET is_uploaded = 1 WHERE hash = ? AND pipeline_id = ?",
(data_hash, self.local_id),
)
self.connection.commit()
def deploy(self):
try:
if self.client is None:
self._init_client()
if self.client is None:
self._init_client()
pipeline_data = self._create_pipeline()
self.id = pipeline_data["id"]
pipeline_data = self._create_pipeline()
self.id = pipeline_data["id"]
results = self.cursor.execute(
"SELECT * FROM data_sources WHERE pipeline_id = ? AND is_uploaded = 0", (self.local_id,)
).fetchall()
results = self.cursor.execute(
"SELECT * FROM data_sources WHERE pipeline_id = ? AND is_uploaded = 0", (self.local_id,)
).fetchall()
if len(results) > 0:
print("🛠️ Adding data to your pipeline...")
for result in results:
data_hash, data_type, data_value = result[0], result[2], result[3]
self._process_and_upload_data(data_hash, data_type, data_value)
except Exception as e:
self.logger.exception(f"Error occurred during deployment: {str(e)}")
raise HTTPException(status_code=500, detail="Error occurred during deployment.")
if len(results) > 0:
print("🛠️ Adding data to your pipeline...")
for result in results:
data_hash, data_type, data_value = result[1], result[2], result[3]
self._process_and_upload_data(data_hash, data_type, data_value)
@classmethod
def from_config(cls, yaml_path: str, auto_deploy: bool = False):

2
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "embedchain"
version = "0.0.76"
version = "0.0.77"
description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
authors = ["Taranjeet Singh, Deshraj Yadav"]
license = "Apache License"