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Use either embedder or embedding_model as YAML key (#905)

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Sidharth Mohanty
2023-11-04 02:10:16 +05:30
committed by GitHub
parent 53037b5ed8
commit 9c4b023297
4 changed files with 7 additions and 7 deletions
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6
embedchain/apps/app.py
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@@ -130,7 +130,7 @@ class App(EmbedChain):
app_config_data = config_data.get("app", {}) app_config_data = config_data.get("app", {})
llm_config_data = config_data.get("llm", {}) llm_config_data = config_data.get("llm", {})
db_config_data = config_data.get("vectordb", {}) db_config_data = config_data.get("vectordb", {})
embedder_config_data = config_data.get("embedder", {}) embedding_model_config_data = config_data.get("embedding_model", config_data.get("embedder", {}))
app_config = AppConfig(**app_config_data.get("config", {})) app_config = AppConfig(**app_config_data.get("config", {}))
@@ -140,6 +140,6 @@ class App(EmbedChain):
db_provider = db_config_data.get("provider", "chroma") db_provider = db_config_data.get("provider", "chroma")
db = VectorDBFactory.create(db_provider, db_config_data.get("config", {})) db = VectorDBFactory.create(db_provider, db_config_data.get("config", {}))
embedder_provider = embedder_config_data.get("provider", "openai") embedder_provider = embedding_model_config_data.get("provider", "openai")
embedder = EmbedderFactory.create(embedder_provider, embedder_config_data.get("config", {})) embedder = EmbedderFactory.create(embedder_provider, embedding_model_config_data.get("config", {}))
return cls(config=app_config, llm=llm, db=db, embedder=embedder) return cls(config=app_config, llm=llm, db=db, embedder=embedder)

2
embedchain/pipeline.py
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@@ -359,7 +359,7 @@ class Pipeline(EmbedChain):
pipeline_config_data = config_data.get("app", {}).get("config", {}) pipeline_config_data = config_data.get("app", {}).get("config", {})
db_config_data = config_data.get("vectordb", {}) db_config_data = config_data.get("vectordb", {})
embedding_model_config_data = config_data.get("embedding_model", {}) embedding_model_config_data = config_data.get("embedding_model", config_data.get("embedder", {}))
llm_config_data = config_data.get("llm", {}) llm_config_data = config_data.get("llm", {})
pipeline_config = PipelineConfig(**pipeline_config_data) pipeline_config = PipelineConfig(**pipeline_config_data)

4
examples/rest-api/requirements.txt
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@@ -1,6 +1,6 @@
fastapi==0.104.0 fastapi==0.104.0
uvicorn==0.23.2 uvicorn==0.23.2
embedchain==0.0.90 embedchain==0.0.91
embedchain[streamlit, community, opensource, elasticsearch, opensearch, poe, discord, slack, whatsapp, weaviate, pinecone, qdrant, images, huggingface_hub, cohere, milvus, dataloaders, vertexai, llama2, gmail, json]==0.0.90 embedchain[streamlit, community, opensource, elasticsearch, opensearch, poe, discord, slack, whatsapp, weaviate, pinecone, qdrant, images, huggingface_hub, cohere, milvus, dataloaders, vertexai, llama2, gmail, json]==0.0.91
sqlalchemy==2.0.22 sqlalchemy==2.0.22
python-multipart==0.0.6 python-multipart==0.0.6

2
pyproject.toml
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@@ -1,6 +1,6 @@
[tool.poetry] [tool.poetry]
name = "embedchain" name = "embedchain"
version = "0.0.90" version = "0.0.91"
description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data" description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
authors = [ authors = [
"Taranjeet Singh <taranjeet@embedchain.ai>", "Taranjeet Singh <taranjeet@embedchain.ai>",