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ValueError: wrapper has not been initialized

ValueError: wrapper has not been initialized
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Please stand by @tomi as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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The error message "ValueError: wrapper has not been initialized" typically means that an object or a variable that is expected to be initialized (or set to a certain value) has not been. In the context of LlamaIndex, this could be related to the initialization of a vector store wrapper or a document store.

For instance, in the case of initializing a vector store wrapper, you need to provide certain parameters. Here are examples for initializing Neo4j and Neptune Analytics vector stores:

For Neo4j:
Plain Text
from llama_index.vector_stores.neo4jvector import Neo4jVectorStore

username = "neo4j"
password = "pleaseletmein"
url = "bolt://localhost:7687"
embed_dim = 1536

neo4j_vector = Neo4jVectorStore(username, password, url, embed_dim)

Source ()

For Neptune Analytics:
Plain Text
from llama_index.vector_stores.neptune import NeptuneAnalyticsVectorStore

graph_identifier = ""
embed_dim = 1536

neptune_vector_store = NeptuneAnalyticsVectorStore(
    graph_identifier=graph_identifier, embedding_dimension=1536
)

Source ()

Ensure that you have correctly initialized the wrapper with the necessary parameters. If you're still encountering issues, it would be helpful to check the specific documentation for the wrapper you're trying to use.
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@kapa.ai all i did was agent = OpenAIAgent.from_tools()
Let me check the knowledge base for you. One moment! :books:
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The OpenAI.from_tools() function is not directly mentioned in the provided knowledge sources. However, it seems like you're trying to initialize an OpenAI agent. In the context of LlamaIndex, the OpenAI API key is often set up as follows:

Plain Text
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]

Source ()

This sets the OpenAI API key as an environment variable and then assigns it to openai.api_key. If you're trying to use OpenAI tools with LlamaIndex, make sure you've set up the OpenAI API key correctly.

If you're still encountering issues, it would be helpful to refer to the specific documentation for the OpenAI tools you're trying to use.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai all i did was agent = OpenAIAgent.from_tools()
Let me check the knowledge base for you. One moment! :books:
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The OpenAIAgent.from_tools() function is used to create an instance of the OpenAIAgent class in LlamaIndex. This function requires certain parameters to be passed in order to initialize the agent correctly.

Here is an example of how to use it:

Plain Text
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI

# Define your tools here
tools = [tool1, tool2, ...]  # replace with your actual tools

# Initialize the language model
llm = OpenAI(model="gpt-3.5-turbo-0613")

# Initialize the OpenAI agent
agent = OpenAIAgent.from_tools(tools, llm=llm, verbose=True)

Source ()

In this example, tools is a list of tools that the agent will use. The llm parameter is an instance of the OpenAI class, which represents the language model that the agent will use. The verbose parameter is a boolean that controls whether the agent will print detailed logs.

If you call OpenAIAgent.from_tools() without passing any parameters, it will likely result in an error because the function needs these parameters to initialize the agent. Make sure to pass the required parameters when calling this function.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai all i did was agent = OpenAIAgent.from_tools() and now i get:
ValueError: wrapper has not been initialized
Let me check the knowledge base for you. One moment! :books:
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The error message "ValueError: wrapper has not been initialized" typically indicates that an object or a variable that is expected to be initialized (or set to a certain value) has not been. In this case, it seems like you're trying to initialize an OpenAIAgent without providing the necessary parameters.

The OpenAIAgent.from_tools() function is used to create an instance of the OpenAIAgent class in LlamaIndex. This function requires certain parameters to be passed in order to initialize the agent correctly.

Here is an example of how to use it:

Plain Text
from llama_index.agent.openai import OpenAIAgent

# Define your tools here
tools = [tool1, tool2, ...]  # replace with your actual tools

# Initialize the OpenAI agent
agent = OpenAIAgent.from_tools(tools, verbose=True)

Source ()

In this example, tools is a list of tools that the agent will use. The verbose parameter is a boolean that controls whether the agent will print detailed logs.

If you call OpenAIAgent.from_tools() without passing any parameters, it will likely result in an error because the function needs these parameters to initialize the agent. Make sure to pass the required parameters when calling this function.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai why does Settings.llm have to be set?
Let me check the knowledge base for you. One moment! :books:
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The Settings.llm attribute in LlamaIndex is used to set the language learning model (LLM) that will be used to respond to prompts and queries, and is responsible for writing natural language responses. This attribute is part of the Settings object, which is a bundle of commonly used resources used during the indexing and querying stage in a LlamaIndex pipeline or application.

Setting Settings.llm is important because it determines the model that will be used for generating responses. If it's not set, the system won't know which model to use and may not function correctly.

Here is an example of how to set Settings.llm:

Plain Text
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)

Source ()

In this example, Settings.llm is set to an instance of the OpenAI class with the model "gpt-3.5-turbo" and a temperature of 0.1. This means that the OpenAI model "gpt-3.5-turbo" will be used as the LLM for generating responses.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai what's an alternative to this? it looks like i won't be able to switch models whenever i want
Let me check the knowledge base for you. One moment! :books:
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In LlamaIndex, you can switch models by reassigning the Settings.llm attribute to a different model. However, it's important to note that if you change your embedding model, you must re-index your data. Furthermore, you should ensure the same embedding model is used for both indexing and querying.

Here's an example of how you might switch models:

Plain Text
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

# Set the LLM to one model
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)

# Later in your code, you can switch to a different model
Settings.llm = OpenAI(model="text-davinci-002", temperature=0.1)

Source ()

In this example, Settings.llm is initially set to the "gpt-3.5-turbo" model, and later it's switched to the "text-davinci-002" model.

Remember, if you're using embeddings, you'll need to re-index your data after switching models. You can check the current best overall embeddings models on the MTEB Leaderboard ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai what if i have multiple agents running simulatenously?
Let me check the knowledge base for you. One moment! :books:
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If you have multiple agents running simultaneously, you can use the ParallelAgentRunner class in LlamaIndex. This class allows you to execute steps in a queue in parallel, which can improve performance when running multiple agents concurrently.

Here's an example of how you might use it:

Plain Text
responses = await asyncio.gather(
    *[agent.arun_task(task_id) for task_id in next_tasks]
)
for task_id in next_tasks:
    agent.mark_task_complete(plan_id, task_id)

Source ()

In this example, agent.arun_task(task_id) is called for each task in next_tasks, and the results are gathered using asyncio.gather(). This allows all the tasks to be run concurrently. After all the tasks have been run, agent.mark_task_complete(plan_id, task_id) is called for each task to mark it as complete.

Remember, the ParallelAgentRunner class requires async support. If you're not familiar with async programming in Python, you might want to read up on it before using this class.

Also, if you're using the OpenAIAgent class, you can create multiple instances of it, each with its own set of tools and settings. This allows you to have multiple agents running simultaneously, each doing different things. However, keep in mind that each instance of OpenAIAgent will consume resources, so you'll need to manage your resources carefully if you're running a lot of agents at the same time.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai i'm asking about settings.llm... if i have many agents running with different models, won't Settings.llm be an issue?
Let me check the knowledge base for you. One moment! :books:
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Yes, you're correct. If you're running multiple agents simultaneously and each agent needs to use a different language learning model (LLM), setting a global Settings.llm could indeed be an issue.

In such a scenario, you can use local settings instead of global settings. This means you would specify the LLM for each agent individually when you create the agent, rather than setting a global LLM that all agents use.

Here's an example of how you might do this:

Plain Text
from llama_index.llms.ollama import Ollama
from llama_index.core import Index

# Local settings
index.as_query_engine(llm=Ollama(model="mistral", request_timeout=60.0))

Source ()

In this example, the as_query_engine() method is called on an Index object, and the llm parameter is set to an instance of the Ollama class with the model "mistral". This means that this particular index will use the "mistral" model as its LLM, regardless of what Settings.llm is set to.

By using local settings, you can have each agent use a different LLM, allowing you to run multiple agents simultaneously with different models.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai how do i give the ReActAgentWorker a system prompt
Let me check the knowledge base for you. One moment! :books:
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To give the ReActAgentWorker a system prompt, you can use the update_prompts method. This method allows you to update the system prompt that the agent uses.

Here's an example of how you might do this:

Plain Text
from llama_index.core import PromptTemplate

# Define your system prompt
react_system_prompt = PromptTemplate(react_system_header_str)

# Update the system prompt
agent.update_prompts({"agent_worker:system_prompt": react_system_prompt})

Source ()

In this example, a PromptTemplate object is created with react_system_header_str as the template string. This object is then passed to the update_prompts method of the ReActAgentWorker object as a dictionary, with the key being "agent_worker:system_prompt".

Remember, the react_system_header_str should be a string that defines the system prompt. This string can include placeholders for variables that will be replaced with actual values when the prompt is used.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai assume that when my agent starts, there are files in a data/ folder. the agent will immediately index these files. now when i add new files, how do i get the agent to update the index accordingly?
Let me check the knowledge base for you. One moment! :books:
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To update the index when new files are added, you can use the refresh() function provided by LlamaIndex. This function allows you to add new documents to the index without having to rebuild the entire index from scratch.

Here's an example of how you might use it:

Plain Text
# Load new data
new_docs = SimpleDirectoryReader(
    input_files=["./data/new_file.pdf"]
).load_data()

# Refresh the index with new data
index.refresh(new_docs)

Source ()

In this example, SimpleDirectoryReader is used to load the new data from new_file.pdf, and then index.refresh(new_docs) is called to add the new documents to the index.

Remember, the refresh() function only adds new documents to the index. If a document with the same doc_id already exists in the index, it won't be added again. If you want to update an existing document in the index, you can use the update() function:

Plain Text
# Update a document in the index
index.update_ref_doc(doc, update_kwargs={"delete_kwargs": {"delete_from_docstore": True}})

Source ()

In this example, index.update_ref_doc(doc) is called to update the document doc in the index. The update_kwargs parameter is used to specify additional options for the update operation. In this case, {"delete_kwargs": {"delete_from_docstore": True}} is passed to ensure the document is deleted from the docstore before it's updated.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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