Add yaml config validation (#890)

embedchain/apps/app.py

@@ -11,6 +11,7 @@ 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.utils import validate_yaml_config
 from embedchain.vectordb.base import BaseVectorDB
 from embedchain.vectordb.chroma import ChromaDB
 
@@ -127,6 +128,11 @@ class App(EmbedChain):
         with open(yaml_path, "r") as file:
             config_data = yaml.safe_load(file)
 
+        try:
+            validate_yaml_config(config_data)
+        except Exception as e:
+            raise Exception(f"❌ Error occurred while validating the YAML config. Error: {str(e)}")
+
         app_config_data = config_data.get("app", {})
         llm_config_data = config_data.get("llm", {})
         db_config_data = config_data.get("vectordb", {})

embedchain/pipeline.py

@@ -19,6 +19,7 @@ from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 from embedchain.llm.openai import OpenAILlm
 from embedchain.telemetry.posthog import AnonymousTelemetry
+from embedchain.utils import validate_yaml_config
 from embedchain.vectordb.base import BaseVectorDB
 from embedchain.vectordb.chroma import ChromaDB
 
@@ -357,6 +358,11 @@ class Pipeline(EmbedChain):
         with open(yaml_path, "r") as file:
             config_data = yaml.safe_load(file)
 
+        try:
+            validate_yaml_config(config_data)
+        except Exception as e:
+            raise Exception(f"❌ Error occurred while validating the YAML config. Error: {str(e)}")
+
         pipeline_config_data = config_data.get("app", {}).get("config", {})
         db_config_data = config_data.get("vectordb", {})
         embedding_model_config_data = config_data.get("embedding_model", config_data.get("embedder", {}))

embedchain/utils.py

@@ -5,6 +5,8 @@ import re
 import string
 from typing import Any
 
+from schema import Optional, Or, Schema
+
 from embedchain.models.data_type import DataType
 
 
@@ -136,8 +138,7 @@ 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`.")
@@ -283,3 +284,73 @@ def is_valid_json_string(source: str):
             Check the docs to see the supported formats - `https://docs.embedchain.ai/data-sources/json`"
         )
         return False
+
+
+def validate_yaml_config(config_data):
+    schema = Schema(
+        {
+            Optional("app"): {
+                Optional("config"): {
+                    Optional("id"): str,
+                    Optional("name"): str,
+                    Optional("log_level"): Or("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"),
+                    Optional("collect_metrics"): bool,
+                    Optional("collection_name"): str,
+                }
+            },
+            Optional("llm"): {
+                Optional("provider"): Or(
+                    "openai",
+                    "azure_openai",
+                    "anthropic",
+                    "huggingface",
+                    "cohere",
+                    "gpt4all",
+                    "jina",
+                    "llama2",
+                    "vertex_ai",
+                ),
+                Optional("config"): {
+                    Optional("model"): str,
+                    Optional("number_documents"): int,
+                    Optional("temperature"): float,
+                    Optional("max_tokens"): int,
+                    Optional("top_p"): Or(float, int),
+                    Optional("stream"): bool,
+                    Optional("template"): str,
+                    Optional("system_prompt"): str,
+                    Optional("deployment_name"): str,
+                    Optional("where"): dict,
+                    Optional("query_type"): str,
+                },
+            },
+            Optional("vectordb"): {
+                Optional("provider"): Or(
+                    "chroma", "elasticsearch", "opensearch", "pinecone", "qdrant", "weaviate", "zilliz"
+                ),
+                Optional("config"): {
+                    Optional("collection_name"): str,
+                    Optional("dir"): str,
+                    Optional("allow_reset"): bool,
+                    Optional("host"): str,
+                    Optional("port"): str,
+                },
+            },
+            Optional("embedder"): {
+                Optional("provider"): Or("openai", "gpt4all", "huggingface", "vertexai"),
+                Optional("config"): {
+                    Optional("model"): Optional(str),
+                    Optional("deployment_name"): Optional(str),
+                },
+            },
+            Optional("embedding_model"): {
+                Optional("provider"): Or("openai", "gpt4all", "huggingface", "vertexai"),
+                Optional("config"): {
+                    Optional("model"): str,
+                    Optional("deployment_name"): str,
+                },
+            },
+        }
+    )
+
+    return schema.validate(config_data)