Creating An Index With An Openai-like Llm

from llama_index.embeddings.openai import OpenAIEmbedding

Settings.embed_model = OpenAIEmbedding(model_name="your model", api_key="fake", api_base="http://localhost:8000/v1")

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
import os
from dotenv import load_dotenv
from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model

load_dotenv()

Settings.embed_model = OpenAIEmbedding(
    api_base=os.getenv("TENTRIS_BASE_URL_EMBEDDINGS"),
    api_key=os.getenv("TENTRIS_API_KEY"),
    model_name=os.getenv("tentris"),
)

# Create index
index = VectorStoreIndex.from_documents(
    documents, 
    show_progress=True)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[2], line 10
      6 from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model
      8 load_dotenv()
---> 10 Settings.embed_model = OpenAIEmbedding(
     11     api_base=os.getenv("TENTRIS_BASE_URL_EMBEDDINGS"),
     12     api_key=os.getenv("TENTRIS_API_KEY"),
     13     model_name=os.getenv("tentris"),
     14 )
     16 # Create index
     17 index = VectorStoreIndex.from_documents(
     18     documents, 
     19     show_progress=True)

File c:\Users\KUNJAN SHAH\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_index\embeddings\openai\base.py:310, in OpenAIEmbedding.__init__(self, mode, model, embed_batch_size, dimensions, additional_kwargs, api_key, api_base, api_version, max_retries, timeout, reuse_client, callback_manager, default_headers, http_client, async_http_client, num_workers, **kwargs)
    307 else:
    308     model_name = model
--> 310 super().__init__(
    311     embed_batch_size=embed_batch_size,
    312     dimensions=dimensions,
    313     callback_manager=callback_manager,
    314     model_name=model_name,
    315     additional_kwargs=additional_kwargs,
    316     api_key=api_key,
...

ValidationError: 1 validation error for OpenAIEmbedding
model_name
  Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]
    For further information visit https://errors.pydantic.dev/2.10/v/string_type

# Set up the OpenAI LLM
llm = OpenAILike(
  model='tentris',
  api_key=os.getenv("TENTRIS_API_KEY"),
  api_base=os.getenv("TENTRIS_BASE_URL_CHAT"),
)
chat_engine = index.as_chat_engine()
str(chat_engine.chat("Where is dice group located?"))

load_dotenv()

# Set up the OpenAI LLM
Settings.llm = OpenAILike(
  model='tentris',
  api_key=os.getenv("TENTRIS_API_KEY"),
  api_base=os.getenv("TENTRIS_BASE_URL_CHAT"),

)

Settings.embed_model = OpenAIEmbedding(
    api_base=os.getenv("TENTRIS_BASE_URL_EMBEDDINGS"),
    api_key=os.getenv("TENTRIS_API_KEY"),
    model_name='tentris',
)

# Create index
index = VectorStoreIndex.from_documents(
    documents, 
    show_progress=True)

chat_engine = index.as_chat_engine()

str(chat_engine.chat("Where is dice group located?"))

ValueError                                Traceback (most recent call last)
Cell In[14], line 1
----> 1 str(chat_engine.chat("Where is dice group located?"))

File c:\Users\KUNJAN SHAH\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_index\core\instrumentation\dispatcher.py:321, in Dispatcher.span.<locals>.wrapper(func, instance, args, kwargs)
    318             _logger.debug(f"Failed to reset active_span_id: {e}")
    320 try:
--> 321     result = func(*args, **kwargs)
    322     if isinstance(result, asyncio.Future):
    323         # If the result is a Future, wrap it
    324         new_future = asyncio.ensure_future(result)

File c:\Users\KUNJAN SHAH\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_index\core\callbacks\utils.py:41, in trace_method.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     39 callback_manager = cast(CallbackManager, callback_manager)
     40 with callback_manager.as_trace(trace_id):
---> 41     return func(self, *args, **kwargs)

File c:\Users\KUNJAN SHAH\AppData\Local\Programs\Python\Python311\Lib\site-packages\llama_index\core\agent\runner\base.py:692, in AgentRunner.chat(self, message, chat_history, tool_choice)
    687     tool_choice = self.default_tool_choice
>     688 with self.callback_manager.event(
    689     CBEventType.AGENT_STEP,
    690     payload={EventPayload.MESSAGES: [message]},
    691 ) as e:
--> 692     chat_response = self._chat(
...
--> 437     raise ValueError("Reached max iterations.")
    439 if isinstance(current_reasoning[-1], ResponseReasoningStep):
    440     response_step = cast(ResponseReasoningStep, current_reasoning[-1])

ValueError: Reached max iterations.