Llama_index model as a tool for lang chain
Labels: name: auto:bug

### System Info

I got error when try to load custom LLM for Llama-Index

```
# setup prompts - specific to StableLM
from llama_index.prompts.prompts import SimpleInputPrompt

system_prompt = """
# StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""

# This will wrap the default prompts that are internal to llama-index
query_wrapper_prompt = SimpleInputPrompt("
{query_str}
")
import torch

llm = HuggingFaceLLM(
    context_window=4096,
    max_new_tokens=256,    
    generate_kwargs={"temperature": 0.7, "do_sample": False, "return_dict_in_generate":True},
    system_prompt=system_prompt,
    query_wrapper_prompt=query_wrapper_prompt,
    tokenizer_name="StabilityAI/stablelm-tuned-alpha-3b",
    model_name="StabilityAI/stablelm-tuned-alpha-3b",
    device_map="auto",
    stopping_ids=[50278, 50279, 50277, 1, 0],
    tokenizer_kwargs={"max_length": 4096},    
)
# load in HF embedding model from langchain
embed_model = LangchainEmbedding(HuggingFaceEmbeddings())
service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm,embed_model=embed_model)
documents = SimpleDirectoryReader('data\\abnamro').load_data()
index = VectorStoreIndex.from_documents(documents, service_context=service_context,show_progress=True)
from langchain.agents import Tool
tools = [
    Tool(
        name="LlamaIndex",
        func=lambda q: str(index.as_query_engine(
            retriever_mode="embedding", 
            verbose=True, 
            service_context=service_context
        ).query(q)),
        description="useful for when you want to answer questions about finance. The input to this tool should be a complete english sentence.",
        return_direct=True,
    ),
]
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.agents import initialize_agent
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent_executor = initialize_agent(
    tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,memory=memory
)
agent_executor.run(input="What is inflation in the Czech Republic?")
```

got

```
ValidationError                           Traceback (most recent call last)
Cell In[13], line 1
----> 1 agent_executor = initialize_agent(
      2     tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,memory=memory
      3 )
      4 agent_executor.run(input="What is inflation in the Czech Republic?")

File F:\anaconda\lib\site-packages\langchain\agents\initialize.py:57, in initialize_agent(tools, llm, agent, callback_manager, agent_path, agent_kwargs, tags, **kwargs)
     55     agent_cls = AGENT_TO_CLASS[agent]
     56     agent_kwargs = agent_kwargs or {}
---> 57     agent_obj = agent_cls.from_llm_and_tools(
     58         llm, tools, callback_manager=callback_manager, **agent_kwargs
     59     )
     60 elif agent_path is not None:
     61     agent_obj = load_agent(
     62         agent_path, llm=llm, tools=tools, callback_manager=callback_manager
     63     )

File F:\anaconda\lib\site-packages\langchain\agents\conversational\base.py:115, in ConversationalAgent.from_llm_and_tools(cls, llm, tools, callback_manager, output_parser, prefix, suffix, format_instructions, ai_prefix, human_prefix, input_variables, **kwargs)
    105 cls._validate_tools(tools)
    106 prompt = cls.create_prompt(
    107     tools,
    108     ai_prefix=ai_prefix,
   (...)
    113     input_variables=input_variables,
    114 )
--> 115 llm_chain = LLMChain(
    116     llm=llm,
    117     prompt=prompt,
    118     callback_manager=callback_manager,
    119 )
    120 tool_names = [tool.name for tool in tools]
    121 _output_parser = output_parser or cls._get_default_output_parser(
    122     ai_prefix=ai_prefix
    123 )

File F:\anaconda\lib\site-packages\langchain\load\serializable.py:74, in Serializable.__init__(self, **kwargs)
     73 def __init__(self, **kwargs: Any) -> None:
---> 74     super().__init__(**kwargs)
     75     self._lc_kwargs = kwargs

File F:\anaconda\lib\site-packages\pydantic\main.py:341, in pydantic.main.BaseModel.__init__()

ValidationError: 1 validation error for LLMChain
llm
  value is not a valid dict (type=type_error.dict)
```

### Who can help?

@hwchase17 @agola11 

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

Connect LlamaIndex with Langchain

### Expected behavior

Load custom LLM