feat: add validation to llm env variables (#558)

… needed

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## Description
<!-- Provide a clear description of the changes in this PR -->

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin


<!-- This is an auto-generated comment: release notes by coderabbit.ai
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## Summary by CodeRabbit

- **New Features**
- Implemented enhanced configuration validation for environment-based
settings. Now, if any configuration parameter is provided via the
environment, all required settings must be present. This improvement
helps catch misconfigurations early, reducing potential errors and
ensuring a smoother, more reliable user experience. These proactive
measures significantly enhance overall system stability and performance.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: Boris <boris@topoteretes.com>

cognee/infrastructure/llm/config.py

@@ -1,6 +1,8 @@
 from typing import Optional
 from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
+from pydantic import model_validator, Field
+import os
 
 
 class LLMConfig(BaseSettings):
@@ -16,6 +18,60 @@ class LLMConfig(BaseSettings):
 
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
+    @model_validator(mode="after")
+    def ensure_env_vars_for_ollama(self) -> "LLMConfig":
+        """
+        Only if llm_provider is 'ollama':
+          - If any of (LLM_MODEL, LLM_ENDPOINT, LLM_API_KEY) is set, all must be set.
+          - If any of (EMBEDDING_PROVIDER, EMBEDDING_MODEL, EMBEDDING_DIMENSIONS,
+            HUGGINGFACE_TOKENIZER) is set, all must be set.
+        Otherwise, skip these checks.
+        """
+
+        if self.llm_provider != "ollama":
+            # Skip checks unless provider is "ollama"
+            return self
+
+        def is_env_set(var_name: str) -> bool:
+            """Return True if environment variable is present and non-empty."""
+            val = os.environ.get(var_name)
+            return val is not None and val.strip() != ""
+
+        #
+        # 1. Check LLM environment variables
+        #
+        llm_env_vars = {
+            "LLM_MODEL": is_env_set("LLM_MODEL"),
+            "LLM_ENDPOINT": is_env_set("LLM_ENDPOINT"),
+            "LLM_API_KEY": is_env_set("LLM_API_KEY"),
+        }
+        if any(llm_env_vars.values()) and not all(llm_env_vars.values()):
+            missing_llm = [key for key, is_set in llm_env_vars.items() if not is_set]
+            raise ValueError(
+                "You have set some but not all of the required environment variables "
+                f"for LLM usage (LLM_MODEL, LLM_ENDPOINT, LLM_API_KEY). Missing: {missing_llm}"
+            )
+
+        #
+        # 2. Check embedding environment variables
+        #
+        embedding_env_vars = {
+            "EMBEDDING_PROVIDER": is_env_set("EMBEDDING_PROVIDER"),
+            "EMBEDDING_MODEL": is_env_set("EMBEDDING_MODEL"),
+            "EMBEDDING_DIMENSIONS": is_env_set("EMBEDDING_DIMENSIONS"),
+            "HUGGINGFACE_TOKENIZER": is_env_set("HUGGINGFACE_TOKENIZER"),
+        }
+        if any(embedding_env_vars.values()) and not all(embedding_env_vars.values()):
+            missing_embed = [key for key, is_set in embedding_env_vars.items() if not is_set]
+            raise ValueError(
+                "You have set some but not all of the required environment variables "
+                "for embeddings (EMBEDDING_PROVIDER, EMBEDDING_MODEL, "
+                "EMBEDDING_DIMENSIONS, HUGGINGFACE_TOKENIZER). Missing: "
+                f"{missing_embed}"
+            )
+
+        return self
+
     def to_dict(self) -> dict:
         return {
             "provider": self.llm_provider,