Support setting seed for reproducible LLM response generation

Anthropic models do not support seed. But offline, gemini and openai
models do. Use these to debug and test Khoj via KHOJ_LLM_SEED env var

pyproject.toml

@@ -87,7 +87,7 @@ dependencies = [
     "django_apscheduler == 0.6.2",
     "anthropic == 0.26.1",
     "docx2txt == 0.8",
-    "google-generativeai == 0.7.2"
+    "google-generativeai == 0.8.3"
 ]
 dynamic = ["version"]
 

src/khoj/processor/conversation/google/utils.py

@@ -1,4 +1,5 @@
 import logging
+import os
 import random
 from threading import Thread
 

src/khoj/processor/conversation/offline/chat_model.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import os
 from datetime import datetime, timedelta
 from threading import Thread
 from typing import Any, Iterator, List, Optional, Union
@@ -265,8 +266,14 @@ def send_message_to_model_offline(
     assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
     offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size)
     messages_dict = [{"role": message.role, "content": message.content} for message in messages]
+    seed = int(os.getenv("KHOJ_LLM_SEED")) if os.getenv("KHOJ_LLM_SEED") else None
     response = offline_chat_model.create_chat_completion(
-        messages_dict, stop=stop, stream=streaming, temperature=temperature, response_format={"type": response_type}
+        messages_dict,
+        stop=stop,
+        stream=streaming,
+        temperature=temperature,
+        response_format={"type": response_type},
+        seed=seed,
     )
 
     if streaming:

src/khoj/processor/conversation/openai/utils.py

@@ -1,4 +1,5 @@
 import logging
+import os
 from threading import Thread
 from typing import Dict
 
@@ -60,6 +61,9 @@ def completion_with_backoff(
         model_kwargs.pop("stop", None)
         model_kwargs.pop("response_format", None)
 
+    if os.getenv("KHOJ_LLM_SEED"):
+        model_kwargs["seed"] = int(os.getenv("KHOJ_LLM_SEED"))
+
     chat = client.chat.completions.create(
         stream=stream,
         messages=formatted_messages,  # type: ignore
@@ -157,6 +161,9 @@ def llm_thread(
             model_kwargs.pop("stop", None)
             model_kwargs.pop("response_format", None)
 
+        if os.getenv("KHOJ_LLM_SEED"):
+            model_kwargs["seed"] = int(os.getenv("KHOJ_LLM_SEED"))
+
         chat = client.chat.completions.create(
             stream=stream,
             messages=formatted_messages,