Llama_index model as a tool for lang chain

[ghost]

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 = """<|SYSTEM|># 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("<|USER|>{query_str}<|ASSISTANT|>")
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

[dosubot[bot]]

Answer generated by a 🤖

Answer

From your description, it appears that you're encountering a ValidationError when trying to initialize an agent in LangChain using a custom language model. This error is likely due to the initialize_agent function expecting an instance of a class that inherits from BaseLanguageModel, but instead receiving an instance of HuggingFaceLLM.

In your code, you've initialized llm as an instance of HuggingFaceLLM and then passed it to initialize_agent. However, HuggingFaceLLM does not inherit from BaseLanguageModel, which is likely causing the ValidationError.

To address this issue, you should initialize llm as an instance of a class that does inherit from BaseLanguageModel. For instance, you could use the OpenAI class from LangChain:

from langchain.llms import OpenAI

llm = OpenAI()

Then, you can pass this llm instance to initialize_agent:

agent_executor = initialize_agent(
    tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, memory=memory
)

This should hopefully resolve the ValidationError you're encountering. However, please note that this is just one potential solution based on the information provided. If you're still experiencing issues after trying this, it might be worth checking if you're using the latest version of LangChain, as updates often include bug fixes and improvements.

If you believe this is a bug that could impact other users, you're encouraged to make a pull request to help improve LangChain. Your contributions would be greatly appreciated by the community.

Sources

• docs/extras/modules/model_io/models/llms/how_to/human_input_llm.ipynb
• docs/extras/use_cases/agents/wikibase_agent.ipynb
• docs/extras/modules/model_io/models/llms/integrations/openlm.ipynb
• langchain/chains/llm.py
• docs/snippets/get_started/quickstart/chains_chat_models.mdx
• docs/snippets/modules/chains/foundational/llm_chain.mdx
• docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx
• docs/extras/modules/model_io/models/llms/integrations/openai.ipynb

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[devstein]

@vladimircape The bot's answer is correct about the error message. You cannot pass the LlamaIndex model to initialize_agent

agent_executor = initialize_agent(
    # llm must be a LangChain LLM
    tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,memory=memory
)

You have to use a LangChain native llm for the Agent then the LlamaIndex model will be used when the agent uses the tool

[ghost]

@vladimircape The bot's answer is correct. You cannot pass the LlamaIndex model to initialize_agent

agent_executor = initialize_agent(
    # llm must be a LangChain LLM
    tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,memory=memory
)

You have to use a LangChain native llm for the Agent then the LlamaIndex model will be used when the agent uses the tool

When i used not a custom LLM in LlamaIndex, it was working

[devstein]

@vladimircape Can you share code?

[ghost]

@vladimircape Can you share code? Sorry, in previous comment, i wrote incorrect, here is example what i mean

from llama_index import ListIndex, SimpleDirectoryReader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, ServiceContext, VectorStoreIndex

documents = SimpleDirectoryReader('data').load_data() index = VectorStoreIndex.from_documents(documents,show_progress=True)

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, ), ]

set Logging to DEBUG for more detailed outputs

memory = ConversationBufferMemory(memory_key="chat_history") llm = ChatOpenAI(temperature=0) agent_executor = initialize_agent( tools, llm, agent="conversational-react-description", memory=memory )

yes, you are right, you can close issue