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)
from llama_index.vector_stores.neptune import NeptuneAnalyticsVectorStore
graph_identifier = ""
embed_dim = 1536
neptune_vector_store = NeptuneAnalyticsVectorStore(
graph_identifier=graph_identifier, embedding_dimension=1536
)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:import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]openai.api_key. If you're trying to use OpenAI tools with LlamaIndex, make sure you've set up the OpenAI API key correctly.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. 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)
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.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.agent = OpenAIAgent.from_tools() and now i get: ValueError: wrapper has not been initializedOpenAIAgent without providing the necessary parameters.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. 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)
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.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.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.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.Settings.llm:from llama_index.llms.openai import OpenAI from llama_index.core import Settings Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)
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.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.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)
Settings.llm is initially set to the "gpt-3.5-turbo" model, and later it's switched to the "text-davinci-002" model.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.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)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.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.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.Settings.llm be an issue?Settings.llm could indeed be an issue. 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))
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.ReActAgentWorker a system prompt, you can use the update_prompts method. This method allows you to update the system prompt that the agent uses. 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})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".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.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?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. # Load new data
new_docs = SimpleDirectoryReader(
input_files=["./data/new_file.pdf"]
).load_data()
# Refresh the index with new data
index.refresh(new_docs)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.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:# Update a document in the index
index.update_ref_doc(doc, update_kwargs={"delete_kwargs": {"delete_from_docstore": True}})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.