response from a specific method or function? Or are you encountering an issue with a response in your code? The more details you provide, the better I can assist you.llama_index.file. However, if you're referring to the llama_index package itself, you can install it using pip:pip install llama-index
llamafile module within the llama_index package, you don't need to install it separately. Once you've installed llama_index, you can import and use the llamafile module like this:from llama_index.llms.llamafile import Llamafile
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-34-c71342e7c987> in <cell line: 1>()
----> 1 from llama_index.file.sdl.base import SDLReader
2 from llama_index.tools.ondemand_loader_tool import OnDemandLoaderTool
3
4 documentation_tool = OnDemandLoaderTool.from_defaults(
5 SDLReader(),
ModuleNotFoundError: No module named 'llama_index.file'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------ModuleNotFoundError: No module named 'llama_index.file' indicates that Python cannot find a module named llama_index.file. This could be due to several reasons:llama_index.file module does not exist. Based on the provided knowledge sources, there is no mention of a llama_index.file module. It's possible that you may have made a typo or misunderstood the module's name.llama_index package is not installed. You can install it using pip:pip install llama-index
llama_index package is installed, but the Python interpreter you're using doesn't have access to it. This could happen if you're using a virtual environment or if you have multiple Python installations on your system.llama_index package, make sure you're using the correct module path. For example, to import the SDLReader class, you would do:from llama_index.sdl.base import SDLReader
llama_index.sdl.base with the correct module path if it's different. If you're unsure about the module path, you can check the LlamaIndex documentation () or the source code of the llama_index package.from llama_index.file.sdl.base import SDLReader
llama_index package. If you encounter a ModuleNotFoundError with this import statement, I recommend checking the LlamaIndex documentation () or the source code of the llama_index package to find the correct module path.from llama_index.file.sdl.base import SDLReader
from llama_index.tools.ondemand_loader_tool import OnDemandLoaderTool
documentation_tool = OnDemandLoaderTool.from_defaults(
SDLReader(),
name="graphql_writer",
description="""
The GraphQL schema file is located at './data/shopify_graphql.txt', this is always the file argument.
A tool for processing the Shopify GraphQL spec, and writing queries from the documentation.
You should pass a query_str to this tool in the form of a request to write a GraphQL query.
Examples:
file: './data/shopify_graphql.txt', query_str='Write a graphql query to find unshipped orders'
file: './data/shopify_graphql.txt', query_str='Write a graphql query to retrieve the stores products'
file: './data/shopify_graphql.txt', query_str='What fields can you retrieve from the orders object'
""",
)
print(
documentation_tool(
"./data/shopify_graphql.txt",
query_str="Write a graphql query to retrieve the first 3 products from a store",
)
)
print(
documentation_tool(
"./data/shopify_graphql.txt",
query_str="what fields can you retrieve from the products object",
)
)ModuleNotFoundError Traceback (most recent call last)
<ipython-input-51-2337a079e4f5> in <cell line: 1>()
----> 1 from llama_index.file.sdl.base import SDLReader
2 from llama_index.tools.ondemand_loader_tool import OnDemandLoaderTool
3
4 documentation_tool = OnDemandLoaderTool.from_defaults(
5 SDLReader(),
ModuleNotFoundError: No module named 'llama_index.file'OnDemandLoaderTool in LlamaIndex is a utility tool that allows for "on-demand" data querying from any data source. It takes in a BaseReader data loader, and when called, it will load data, index data, and query the data.OnDemandLoaderTool with the WikipediaReader as the data loader:from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
from llama_index.readers.wikipedia import WikipediaReader
reader = WikipediaReader()
tool = OnDemandLoaderTool.from_defaults(
reader,
name="Wikipedia Tool",
description="A tool for loading and querying articles from Wikipedia",
)WikipediaReader is used to load data from Wikipedia. The OnDemandLoaderTool is then used to index this data and provide a query interface.WikipediaReader() with an instance of your desired reader. For example, if you have a FileReader class for reading data from a file, you could do:reader = FileReader()
tool = OnDemandLoaderTool.from_defaults(
reader,
name="File Tool",
description="A tool for loading and querying data from a file",
)FileReader class is just an example and may not exist in the llama_index package. You would need to replace it with a valid reader class that suits your needs.FileReader class available in the LlamaIndex package. The OnDemandLoaderTool requires a reader that inherits from the BaseReader class and implements the load_data method.from llama_index.core.readers.base import BaseReader
from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
class FileReader(BaseReader):
def load_data(self, file_path):
with open(file_path, 'r') as file:
data = file.read().splitlines()
return data
reader = FileReader()
tool = OnDemandLoaderTool.from_defaults(
reader,
name="File Tool",
description="A tool for loading and querying data from a file",
)FileReader is a custom reader class that reads data from a text file. The load_data method takes a file path as input, reads the file, and returns a list of lines from the file.FileReader class to process the file data appropriately.'your_file_path.txt' with the actual path to your text file.from llama_index.core.readers.base import BaseReader
class FileReader(BaseReader):
def load_data(self, file_path):
with open(file_path, 'r') as file:
data = file.read().splitlines()
return data
reader = FileReader('bitquery-graphql.txt')load_data method in your FileReader class is expecting a file_path argument, but you're not providing it when you call reader = FileReader('bitquery-graphql.txt').from llama_index.core.readers.base import BaseReader
class FileReader(BaseReader):
def __init__(self, file_path):
self.file_path = file_path
def load_data(self):
with open(self.file_path, 'r') as file:
data = file.read().splitlines()
return data
reader = FileReader('bitquery-graphql.txt')
data = reader.load_data()FileReader class takes a file_path argument in its constructor and stores it as an instance variable. The load_data method then uses this instance variable to read the file. When you create the FileReader instance, you provide the file path as an argument.ModuleNotFoundError Traceback (most recent call last)
<ipython-input-57-5ee68c2593a2> in <cell line: 1>()
----> 1 from llama_index.tools.ondemand_loader_tool import OnDemandLoaderTool
2
3 documentation_tool = OnDemandLoaderTool.from_defaults(
4 data,
5 name="graphql_writer",
ModuleNotFoundError: No module named 'llama_index.tools.ondemand_loader_tool'ModuleNotFoundError: No module named 'llama_index.tools.ondemand_loader_tool' indicates that Python cannot find a module named llama_index.tools.ondemand_loader_tool. This could be due to several reasons:llama_index.tools.ondemand_loader_tool module does not exist. Based on the provided knowledge sources, the correct import statement should be:from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
llama_index package is not installed. You can install it using pip:pip install llama-index
llama_index package is installed, but the Python interpreter you're using doesn't have access to it. This could happen if you're using a virtual environment or if you have multiple Python installations on your system.OnDemandLoaderTool class from the llama_index package, make sure you're using the correct module path. If you encounter a ModuleNotFoundError with this import statement, I recommend checking the LlamaIndex documentation () or the source code of the llama_index package to find the correct module path.AttributeError Traceback (most recent call last)
<ipython-input-58-df865f5489e0> in <cell line: 3>()
1 from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
2
----> 3 documentation_tool = OnDemandLoaderTool.from_defaults(
4 data,
5 name="graphql_writer",
/usr/local/lib/python3.10/dist-packages/llama_index/core/tools/ondemand_loader_tool.py in from_defaults(cls, reader, index_cls, index_kwargs, use_query_str_in_loader, query_str_kwargs_key, name, description, fn_schema)
70 fn_schema = create_schema_from_function(
71 name or "LoadData",
---> 72 reader.load_data,
73 [(query_str_kwargs_key, str, None)],
74 )
AttributeError: 'list' object has no attribute 'load_data'AttributeError: 'list' object has no attribute 'load_data' indicates that you're trying to access a load_data attribute on a list object, which doesn't exist. OnDemandLoaderTool.from_defaults method, the first argument should be an instance of a reader class that inherits from BaseReader and implements the load_data method. data) as the first argument to OnDemandLoaderTool.from_defaults. OnDemandLoaderTool.from_defaults with a custom FileReader:from llama_index.core.readers.base import BaseReader
from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
class FileReader(BaseReader):
def __init__(self, file_path):
self.file_path = file_path
def load_data(self):
with open(self.file_path, 'r') as file:
data = file.read().splitlines()
return data
reader = FileReader('bitquery-graphql.txt')
tool = OnDemandLoaderTool.from_defaults(
reader,
name="File Tool",
description="A tool for loading and querying data from a file",
)FileReader is a custom reader class that reads data from a text file. The load_data method takes a file path as input, reads the file, and returns a list of lines from the file. SDLReader() class from the LlamaIndex package is deprecated. llama_index package. If a class or method is deprecated, it's usually mentioned in the documentation or in the source code as a comment.from llama_index.tools.tool_spec import ToolMetadata, ToolSpec
ToolMetadata and ToolSpec from the LlamaIndex package should be:from llama_index.core.tools import ToolMetadata from llama_index.core.tools.types import BaseTool as ToolSpec
ToolSpec is not directly available in LlamaIndex, but there is a BaseTool class which might be what you're referring to. If you're looking for a different class, please check the LlamaIndex documentation () or the source code of the llama_index package.from llama_index.tools.function_tool import FunctionTool
FunctionTool from the LlamaIndex package should be:from llama_index.core.tools.function_tool import FunctionTool
AttributeError Traceback (most recent call last)
<ipython-input-111-319ffd34e8cd> in <cell line: 114>()
112 )
113
--> 114 print(agent.chat('I need to analyze the most recent Ethereum activity. Please generate a GraphQL query to fetch the details of the last 10 transactions, including sender, receiver, and amount. from the graphql server'))
11 frames
/usr/local/lib/python3.10/dist-packages/llama_index/agent/openai/step.py in <listcomp>(.0)
548 # TODO: see if we want to do step-based inputs
549 tools = self.get_tools(task.input)
--> 550 openai_tools = [tool.metadata.to_openai_tool() for tool in tools]
551
552 llm_chat_kwargs = self._get_llm_chat_kwargs(task, openai_tools, tool_choice)
AttributeError: 'list' object has no attribute 'metadata'agent = OpenAIAgent.from_tools(
[tool_spec.to_tool_list(),documentation_tool],
system_prompt=f"""
You are a specialized Agent with access to the Shopify Admin GraphQL API for this Users online store.
Your job is to chat with store owners and help them run GraphQL queries, interpreting the results for the user
For you conveinence, the QueryRoot objects are listed here.
{balance_update_fields}
QueryRoots are the schema's entry-point for queries. This acts as the public, top-level API from which all queries must start.
You can use graphql_writer to query the schema and assist in writing queries.
If the GraphQL you execute returns an error, either directly fix the query, or directly ask the graphql_writer questions about the schema instead of writing graphql queries.
Then use that information to write the correct graphql query
""",
verbose=True
)AttributeError: 'list' object has no attribute 'metadata' indicates that you're trying to access a metadata attribute on a list object, which doesn't exist. OpenAIAgent.from_tools method, the argument should be a list of tool instances, not a list of lists of tools.from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
from llama_index.core.tools.graphql import GraphQLToolSpec
from llama_index.agent.openai import OpenAIAgent
# Assuming tool_spec and documentation_tool are correctly defined
tools = tool_spec.to_tool_list() + [documentation_tool]
agent = OpenAIAgent.from_tools(
tools,
system_prompt=f"""
You are a specialized Agent with access to the Shopify Admin GraphQL API for this Users online store.
Your job is to chat with store owners and help them run GraphQL queries, interpreting the results for the user
For your convenience, the QueryRoot objects are listed here.
{balance_update_fields}
QueryRoots are the schema's entry-point for queries. This acts as the public, top-level API from which all queries must start.
You can use graphql_writer to query the schema and assist in writing queries.
If the GraphQL you execute returns an error, either directly fix the query, or directly ask the graphql_writer questions about the schema instead of writing graphql queries.
Then use that information to write the correct graphql query
""",
verbose=True
)tools is a list of tool instances. The to_tool_list method of tool_spec returns a list of tools, and documentation_tool is a single tool, so they are combined into a single list using the + operator.from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool
from llama_index.core.tools.graphql import GraphQLToolSpec
from llama_index.agent.openai import OpenAIAgent
# Assuming tool_spec and documentation_tool are correctly defined
tools = tool_spec.to_tool_list() + [documentation_tool]
agent = OpenAIAgent.from_tools(
tools,
system_prompt=f"""
You are a specialized Agent with access to the Shopify Admin GraphQL API for this Users online store.
Your job is to chat with store owners and help them run GraphQL queries, interpreting the results for the user
For your convenience, the QueryRoot objects are listed here.
{balance_update_fields}
QueryRoots are the schema's entry-point for queries. This acts as the public, top-level API from which all queries must start.
You can use graphql_writer to query the schema and assist in writing queries.
If the GraphQL you execute returns an error, either directly fix the query, or directly ask the graphql_writer questions about the schema instead of writing graphql queries.
Then use that information to write the correct graphql query
""",
verbose=True
)max_tokens parameter when you instantiate the OpenAI LLM (Language Learning Model). Here's an example:from llama_index.core import Settings from llama_index.llms.openai import OpenAI # define global LLM with max_tokens set to a lower value Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo", max_tokens=512)
max_tokens is set to 512, which means the LLM will generate a maximum of 512 tokens for each output. You can adjust this number according to your needs.from llama_index.tools.graphql import GraphQLToolSpec
from llama_index.agent.openai import OpenAIAgent
tool_spec = GraphQLToolSpec(
url="https://streaming.bitquery.io",
headers={
'accept-language': 'en-US,en;q=0.9',
"content-type": "application/json",
'Authorization': 'Bearer '
}
)
tools = tool_spec.to_tool_list() + [documentation_tool]
agent = OpenAIAgent.from_tools(
tools,
system_prompt=f"""
You are a specialized Agent with access to the Shopify Admin GraphQL API for this Users online store.
Your job is to chat with store owners and help them run GraphQL queries, interpreting the results for the user
For you conveinence, the QueryRoot objects are listed here.
{balance_update_fields}
QueryRoots are the schema's entry-point for queries. This acts as the public, top-level API from which all queries must start.
You can use graphql_writer to query the schema and assist in writing queries.
If the GraphQL you execute returns an error, either directly fix the query, or directly ask the graphql_writer questions about the schema instead of writing graphql queries.
Then use that information to write the correct graphql query
""",
verbose=True,
max_function_calls=20,
)
print(agent.chat('Write a GraphQL query to get the last 10 Ethereum transactions'))max_tokens parameter when you instantiate the OpenAI LLM (Language Learning Model). However, in your provided code, the LLM is not explicitly defined. from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
from llama_index.tools.graphql import GraphQLToolSpec
from llama_index.agent.openai import OpenAIAgent
# define global LLM with max_tokens set to a lower value
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo", max_tokens=512)
tool_spec = GraphQLToolSpec(
url="<https://streaming.bitquery.io",>
headers={
'accept-language': 'en-US,en;q=0.9',
"content-type": "application/json",
'Authorization': 'Bearer '
}
)
tools = tool_spec.to_tool_list() + [documentation_tool]
agent = OpenAIAgent.from_tools(
tools,
system_prompt=f"""
You are a specialized Agent with access to the Shopify Admin GraphQL API for this Users online store.
Your job is to chat with store owners and help them run GraphQL queries, interpreting the results for the user
For your convenience, the QueryRoot objects are listed here.
{balance_update_fields}
QueryRoots are the schema's entry-point for queries. This acts as the public, top-level API from which all queries must start.
You can use graphql_writer to query the schema and assist in writing queries.
If the GraphQL you execute returns an error, either directly fix the query, or directly ask the graphql_writer questions about the schema instead of writing graphql queries.
Then use that information to write the correct graphql query
""",
verbose=True,
max_function_calls=20,
)
print(agent.chat('Write a GraphQL query to get the last 10 Ethereum transactions'))max_tokens is set to 512, which means the LLM will generate a maximum of 512 tokens for each output. You can adjust this number according to your needs.RateLimitError: Error code: 429 - {'error': {'message': 'Request too large for gpt-3.5-turbo in organization org-kC3LMTPajxXK2HwO961U4Yz1 on tokens per min (TPM): Limit 60000, Requested 180186. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}RateLimitError: Error code: 429 indicates that you've exceeded the rate limit for your OpenAI API key. The rate limit for the gpt-3.5-turbo model is 60,000 tokens per minute, but your request used 180,186 tokens, which is well above the limit.max_tokens parameter. However, based on the error message, it seems like your request is significantly over the limit, so this might not be enough to solve the problem.time.sleep() call between requests.max_tokens parameter:from llama_index.core import Settings from llama_index.llms.openai import OpenAI # define global LLM with max_tokens set to a lower value Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo", max_tokens=100) # reduce max_tokens
import time
for i in range(100):
# make API request
print(agent.chat('Write a GraphQL query to get the last 10 Ethereum transactions'))
# wait for 1 second between requests
time.sleep(1)from llama_index.tools.graphql import GraphQLToolSpec
from llama_index.agent.openai import OpenAIAgent
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
# define global LLM with max_tokens set to a lower value
Settings.llm = OpenAI(temperature=0, model="gpt-4-turbo-preview", max_tokens=100)
tool_spec = GraphQLToolSpec(
url="https://streaming.bitquery.io",
headers={
'accept-language': 'en-US,en;q=0.9',
"content-type": "application/json",
'Authorization': 'Bearer '
}
)
tools = tool_spec.to_tool_list() + [documentation_tool]
agent = OpenAIAgent.from_tools(
tools,
system_prompt=f"""
You are a specialized Agent with access to the Shopify Admin GraphQL API for this Users online store.
Your job is to chat with store owners and help them run GraphQL queries, interpreting the results for the user
For you conveinence, the QueryRoot objects are listed here.
{balance_update_fields}
QueryRoots are the schema's entry-point for queries. This acts as the public, top-level API from which all queries must start.
You can use graphql_writer to query the schema and assist in writing queries.
If the GraphQL you execute returns an error, either directly fix the query, or directly ask the graphql_writer questions about the schema instead of writing graphql queries.
Then use that information to write the correct graphql query
""",
verbose=True,
max_function_calls=20,
)
print(agent.chat('Write a GraphQL query to get the last 10 Ethereum transactions'))gpt-4-turbo-preview model is 60,000 tokens per minute, but your request used 180,186 tokens, which is well above the limit.max_tokens parameter you set for the OpenAI instance in Settings.llm only limits the number of output tokens, not the total number of tokens used in the request. If your input text is very long, it could still result in a request that exceeds the rate limit.max_tokens parameter: You've already set max_tokens to 100, which is quite low. Reducing it further might not have a significant impact on the total number of tokens, and it could result in less informative responses from the model.from typing import Optional, Union import openai import logging from llama_index.agent import OpenAIAgent import requests from llama_index.bridge.langchain import FunctionMessage from llama_hub.tools.graphql.base import GraphQLToolSpec import json from typing import Sequence from llama_index.llms import OpenAI as OpenAI_LLM # from langchain.chat_models import ChatOpenAI # from langchain.memory import ChatMessageHistory from llama_index.tools import BaseTool, FunctionTool # from langchain.tools import BaseTool, StructuredTool import asyncio import os
from typing import Optional, Union import openai import logging from llama_index.agent import OpenAIAgent import requests from llama_index.bridge.langchain import FunctionMessage from llama_hub.tools.graphql.base import GraphQLToolSpec import json from typing import Sequence from llama_index.llms import OpenAI as OpenAI_LLM # from langchain.chat_models import ChatOpenAI # from langchain.memory import ChatMessageHistory from llama_index.tools import BaseTool, FunctionTool # from langchain.tools import BaseTool, StructuredTool import asyncio import os
pip install --upgrade openai
pip install --upgrade requests
llama_index, might be part of a larger project and might need to be updated in a different way. Also, updating a library might require changes to the code if the library's API has changed.from llama_index.bridge.langchain import FunctionMessage
from llama_index.bridge.langchain import FunctionMessage. llama_index.bridge.langchain module in the context of importing Tool and StructuredTool classes in the LlamaIndex API reference () and LlamaIndex API reference (). FunctionMessage is a class or function within the llama_index.bridge.langchain module, then the import statement could be correct. However, without a direct reference to FunctionMessage in the provided knowledge sources, I cannot confirm its correctness. llama_index.bridge.langchain module for more information.from llama_index.llms import OpenAI as OpenAI_LLM is correct and it is used in multiple instances in the provided knowledge sources. OpenAI class from the llama_index.llms module and renaming it as OpenAI_LLM. This can be useful to avoid naming conflicts if there's another OpenAI class or function in your code.from llama_index.tools import BaseTool, FunctionTool is correct and it is used in multiple instances in the provided knowledge sources. BaseTool and FunctionTool classes from the llama_index.tools module. These classes are used to define and work with tools in the LlamaIndex framework.LLMPredictor class in LlamaIndex has been deprecated and is no longer intended to be used by users. Instead, you can set up an LLM (Language Learning Model) directly and pass it into the Settings or the interface using the LLM. The LLM class itself has similar attributes and methods as the LLMPredictor.from llama_index.core import Settings from llama_index.llms.openai import OpenAI # define global LLM Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
OpenAI class (which is a type of LLM) is created and set as the global LLM in the Settings.