Please stand by @BC as I search the knowledge base. I normally take about 20 seconds or so. :wave:
----------------------
To query multiple vectors in LlamaIndex, you can use the
VectorStoreQuery method. This method allows you to define a query string and generate an embedding for it. You can then use this embedding to query your vector store.
Here is an example of how you can do this:
# Define query and generate embedding for it
query_str = "What's new with TimescaleDB functions?"
embed_model = OpenAIEmbedding()
query_embedding = embed_model.get_query_embedding(query_str)
# Query the vector database
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=5
)You can also add time filters to your query. For example, you can filter your results within a provided start date and end date:
# Time filter variables for query
start_dt = datetime(
2023, 8, 1, 22, 10, 35
) # Start date = 1 August 2023, 22:10:35
end_dt = datetime(
2023, 8, 30, 22, 10, 35
) # End date = 30 August 2023, 22:10:35
# Query the vector database with time filters
query_result = ts_vector_store.query(
vector_store_query, start_date=start_dt, end_date=end_dt
)For more advanced retrieval techniques, you can rewrite your queries into multiple queries and execute all these queries against a retriever. This is a key step in ensemble retrieval and fusion, leading to higher-quality retrieved results.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai Whats an approach if im querying multiple vectors in a single question. So if im asking about one vector about windows and one vector about ubuntu both os's two different kernels and if my question asks between those differences without stating it in the pipeline of data
Let me check the knowledge base for you. One moment! :books:
----------------------
To query multiple vectors in a single question, especially when you're comparing or contrasting different entities (like Windows and Ubuntu in your case), you can use the
SubQuestionQueryEngine in LlamaIndex. This engine can execute any number of sub-queries against any subset of query engine tools before synthesizing the final answer. This makes it especially well-suited for compare/contrast queries across documents as well as queries pertaining to a specific document.
Here is an example of how you can do this:
# Define your query engines for each vector
vector_index_windows = VectorStoreIndex(nodes_windows)
vector_query_engine_windows = vector_index_windows.as_query_engine(similarity_top_k=2)
vector_index_ubuntu = VectorStoreIndex(nodes_ubuntu)
vector_query_engine_ubuntu = vector_index_ubuntu.as_query_engine(similarity_top_k=2)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=vector_query_engine_windows,
metadata=ToolMetadata(
name="windows",
description=("Provides information about Windows OS"),
),
),
QueryEngineTool(
query_engine=vector_query_engine_ubuntu,
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu OS"),
),
),
]
# Define your SubQuestionQueryEngine
base_sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm,
use_async=True,
)
# Query the differences between Windows and Ubuntu
response = base_sub_query_engine.query(
"Tell me the differences between Windows and Ubuntu in terms of their kernels"
)
print(str(response))In this example, nodes_windows and nodes_ubuntu are the nodes of the vectors related to Windows and Ubuntu respectively. llm is the language model used for the query.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai can you show what this system would look like with metadata filter instead of two different indexes
Let me check the knowledge base for you. One moment! :books:
----------------------
Sure, you can use metadata filters to query different vectors in a single question. In this case, you would have a single index with metadata associated with each vector. You can then use the
MetadataFilters and
ExactMatchFilter classes to filter your queries based on the metadata.
Here is an example of how you can do this:
from llama_index.core.schema import TextNode
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
# Define your nodes with metadata
nodes = [
TextNode(
text="Information about Windows",
metadata={
"os": "windows",
},
),
TextNode(
text="Information about Ubuntu",
metadata={
"os": "ubuntu",
},
),
]
# Create your index
vector_store = YourVectorStore() # Replace with your vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)
# Define your metadata filters
filters_windows = MetadataFilters(
filters=[ExactMatchFilter(key="os", value="windows")]
)
filters_ubuntu = MetadataFilters(
filters=[ExactMatchFilter(key="os", value="ubuntu")]
)
# Query the differences between Windows and Ubuntu
retriever = index.as_retriever(filters=filters_windows)
response_windows = retriever.retrieve("What is the kernel of Windows?")
retriever = index.as_retriever(filters=filters_ubuntu)
response_ubuntu = retriever.retrieve("What is the kernel of Ubuntu?")
print("Windows kernel: ", response_windows)
print("Ubuntu kernel: ", response_ubuntu)In this example,
YourVectorStore() should be replaced with the vector store you are using. The
retrieve method is used to retrieve the information about the kernel of each operating system.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai for the metadata filters they were already defined with the key "filename" and is it possible to list the text for the node after it's embedded since the metadata name covers multiple documents in the store. so both sets of documents windows and ubuntu are in one store.
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, it is possible to list the text for the node after it's embedded. You can do this by iterating over the results returned by the retriever. Each result contains a
node attribute which has the
text and
metadata of the original node.
Here is an example of how you can do this:
# Query the differences between Windows and Ubuntu
retriever = index.as_retriever(filters=filters_windows)
response_windows = retriever.retrieve("What is the kernel of Windows?")
for r in response_windows:
print("\n", r.node.text)
print("Metadata:", r.node.metadata)
retriever = index.as_retriever(filters=filters_ubuntu)
response_ubuntu = retriever.retrieve("What is the kernel of Ubuntu?")
for r in response_ubuntu:
print("\n", r.node.text)
print("Metadata:", r.node.metadata)In this example,
r.node.text is the text of the node and
r.node.metadata is the metadata of the node.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai going back to the previous answer can you tell me how this would work if i only gave a single query and wanted the query split into 2 still keeping the idea but requiring sub queries combined to make a single answer at the end?
Let me check the knowledge base for you. One moment! :books:
----------------------
To split a single query into multiple sub-queries and then combine the results into a single answer, you can use the
SubQuestionQueryEngine in LlamaIndex. This engine breaks down a complex query into many sub-questions and executes them. After executing all sub-questions, all responses are gathered and sent to a response synthesizer to produce the final response.
Here is an example of how you can do this:
from llama_index.core import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
# Define your query engines for each vector
vector_index = VectorStoreIndex(nodes) # nodes is your data
vector_query_engine = vector_index.as_query_engine(similarity_top_k=2)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name="os",
description=("Provides information about OS"),
),
),
]
# Define your SubQuestionQueryEngine
base_sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm, # llm is your language model
use_async=True,
)
# Query the differences between Windows and Ubuntu
response = base_sub_query_engine.query(
"Tell me the differences between Windows and Ubuntu in terms of their kernels"
)
print(str(response))In this example,
nodes is your data and
llm is the language model used for the query. The
SubQuestionQueryEngine will automatically generate sub-questions, execute them, and synthesize the results into a single response.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai what would this system look like with sub question. And each sub question is associated with a metadata filter from the same index
Let me check the knowledge base for you. One moment! :books:
----------------------
To use the SubQuestionQueryEngine with metadata filters from the same index, you would first need to create QueryEngineTool instances for each metadata filter. Each QueryEngineTool would use the same vector index but with a different metadata filter.
Here is an example of how you can do this:
```python
from llama_index.core import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
Define your nodes with metadata
nodes = [
TextNode(
text="Information about Windows",
metadata={{
"os": "windows",
}},
),
TextNode(
text="Information about Ubuntu",
metadata={{
"os": "ubuntu",
}},
),
]
Create your index
vector_store = YourVectorStore() # Replace with your vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)
Define your metadata filters
filters_windows = MetadataFilters(
filters=[ExactMatchFilter(key="os", value="windows")]
)
filters_ubuntu = MetadataFilters(
filters=[ExactMatchFilter(key="os", value="ubuntu")]
)
Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_query_engine(filters=filters_windows),
metadata=ToolMetadata(
name="windows",
description=("Provides information about Windows OS"),
),
),
QueryEngineTool(
query_engine=index.as_query_engine(filters=filters_ubuntu),
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu OS"),
),
),
]Define your SubQuestionQueryEngine
base_sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm, # llm is your language model
use_async=True,
)
Query the differences between Windows and Ubuntu
response = base_sub_query_engine.query(
"Tell me the differences between Windows and Ubuntu in terms of their kernels"
)
print(str(response))
``
In this example, YourVectorStore() should be replaced with the vector store you are using. The SubQuestionQueryEngine` will automatically generate sub-questions, execute them, and synthesize the results into a single response.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------@kapa.ai the as_query_engine is facing baseindex() class and I want it to face vectorstoreindex() like presented here which it isn't doing:
index = VectorStoreIndex.from_vector_store(vector_store)
filters_windows = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_query_engine(filters=filters_windows),
metadata=ToolMetadata(
name="vyos",
description=(f"Provides information about vyos and it's contents"),
),
),
Let me check the knowledge base for you. One moment! :books:
----------------------
The
as_query_engine() method is indeed a method of the
VectorStoreIndex class. If you're facing issues with it, it might be due to the way you're initializing your
VectorStoreIndex.
Here's how you can initialize your
VectorStoreIndex from a vector store and then use it to create a query engine with metadata filters:
from llama_index.core import VectorStoreIndex, QueryEngineTool, ToolMetadata
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Initialize your VectorStoreIndex from a vector store
index = VectorStoreIndex.from_vector_store(vector_store)
# Define your metadata filters
filters_vyos = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_query_engine(filters=filters_vyos),
metadata=ToolMetadata(
name="vyos",
description=("Provides information about vyos and its contents"),
),
),
QueryEngineTool(
query_engine=index.as_query_engine(filters=filters_ubuntu),
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu and its contents"),
),
),
]In this example,
vector_store is your vector store. The
as_query_engine() method is used to create a query engine with the specified metadata filters.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
as_query_engine doesn't have filters
@kapa.ai as_query_engine doesn't have filters
Let me check the knowledge base for you. One moment! :books:
----------------------
I apologize for the confusion. You're correct. The
as_query_engine() method in LlamaIndex does not directly accept filters. Instead, you can apply filters when you're using the query engine to retrieve data.
Here's how you can do it:
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
# Define your metadata filters
filters_vyos = MetadataFilters(
filters=[ExactMatchFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[ExactMatchFilter(key="file_name", value="ubuntu")]
)
# Create your query engine
query_engine = index.as_query_engine()
# Use the query engine with filters to retrieve data
response_vyos = query_engine.retrieve("Your query here", filters=filters_vyos)
response_ubuntu = query_engine.retrieve("Your query here", filters=filters_ubuntu)In this example,
index is your
VectorStoreIndex, and
retrieve is a method you would replace with the appropriate method for retrieving data from your query engine.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai can you show me what this would look like with the subqueries
Let me check the knowledge base for you. One moment! :books:
----------------------
Sure, to use sub-queries with metadata filters, you would need to create a
SubQuestionQueryEngine and define sub-questions for each metadata filter. Here's an example of how you can do this:
from llama_index.core import QueryEngineTool, ToolMetadata, SubQuestionQueryEngine
from llama_index.core.query_engine import SubQuestion
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Initialize your VectorStoreIndex from a vector store
index = VectorStoreIndex.from_vector_store(vector_store)
# Define your metadata filters
filters_vyos = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_query_engine(),
metadata=ToolMetadata(
name="vyos",
description=("Provides information about vyos and its contents"),
),
),
QueryEngineTool(
query_engine=index.as_query_engine(),
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu and its contents"),
),
),
]
# Define your SubQuestionQueryEngine
sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm, # llm is your language model
use_async=True,
)
# Define your sub-questions
sub_questions = [
SubQuestion(sub_question="Your query here", tool_name="vyos", filters=filters_vyos),
SubQuestion(sub_question="Your query here", tool_name="ubuntu", filters=filters_ubuntu),
]
# Query using the sub-questions
response = sub_query_engine.query(sub_questions)
print(str(response))In this example, vector_store is your vector store, llm is your language model, and "Your query here" should be replaced with your actual query. The SubQuestionQueryEngine will execute each sub-question with the specified metadata filter and then synthesize the results into a single response.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai I want just one question that is split into two sub questions leveraging those filters and I want the queryengine tools to have access specifically to the files associated with the metadata to help answer the subquestions with the filters
Let me check the knowledge base for you. One moment! :books:
----------------------
To split a single question into two sub-questions and use metadata filters to guide the retrieval of information from specific files, you can use the
SubQuestionQueryEngine in LlamaIndex. Here's an example of how you can do this:
from llama_index.core import QueryEngineTool, ToolMetadata, SubQuestionQueryEngine
from llama_index.core.query_engine import SubQuestion
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Initialize your VectorStoreIndex from a vector store
index = VectorStoreIndex.from_vector_store(vector_store)
# Define your metadata filters
filters_vyos = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_query_engine(),
metadata=ToolMetadata(
name="vyos",
description=("Provides information about vyos and its contents"),
),
),
QueryEngineTool(
query_engine=index.as_query_engine(),
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu and its contents"),
),
),
]
# Define your SubQuestionQueryEngine
sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm, # llm is your language model
use_async=True,
)
# Define your sub-questions
sub_questions = [
SubQuestion(sub_question="Your query here", tool_name="vyos", filters=filters_vyos),
SubQuestion(sub_question="Your query here", tool_name="ubuntu", filters=filters_ubuntu),
]
# Query using the sub-questions
response = sub_query_engine.query(sub_questions)
print(str(response))In this example, vector_store is your vector store, llm is your language model, and "Your query here" should be replaced with your actual query. The SubQuestionQueryEngine will execute each sub-question with the specified metadata filter and then synthesize the results into a single response.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai will this system keep the context of the original question because what I want is the system to understand the difference between the topics when the documents are pulled and I want just one question asked and for it to remember what the purpose of the one question was
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, the
SubQuestionQueryEngine in LlamaIndex is designed to handle complex queries by breaking them down into sub-questions, each associated with a specific context or topic. The context of the original question is preserved in the sense that each sub-question is a part of the original question and is designed to retrieve information relevant to that part.
When the responses to the sub-questions are synthesized into a final response, the synthesis process takes into account the context of the original question. This is because the synthesis process is guided by the responses to the sub-questions, which are themselves derived from the original question.
Here's a simplified example of how this might work:
- Original question: "What are the differences between Ubuntu and VyOS in terms of their kernel architecture?"
- This question might be broken down into two sub-questions:
- Sub-question 1 (associated with Ubuntu): "What is the kernel architecture of Ubuntu?"
- Sub-question 2 (associated with VyOS): "What is the kernel architecture of VyOS?"
- Each sub-question is processed using the appropriate metadata filter to retrieve information from the relevant documents.
- The responses to the sub-questions are then synthesized into a final response that answers the original question.
Here's how you can implement this in code:
# Define your sub-questions
sub_questions = [
SubQuestion(sub_question="What is the kernel architecture of Ubuntu?", tool_name="ubuntu", filters=filters_ubuntu),
SubQuestion(sub_question="What is the kernel architecture of VyOS?", tool_name="vyos", filters=filters_vyos),
]
# Query using the sub-questions
response = sub_query_engine.query(sub_questions)
print(str(response))In this example, filters_ubuntu and filters_vyos are your metadata filters for Ubuntu and VyOS respectively, and sub_query_engine is your SubQuestionQueryEngine.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
I want my one question broken down into separate questions with the filters @kapa.ai
Let me check the knowledge base for you. One moment! :books:
----------------------
To break down a single question into separate sub-questions using metadata filters, you would need to use a question generator that can generate sub-questions based on the context of the original question. In LlamaIndex, this can be done using the
OpenAIQuestionGenerator or
LLMQuestionGenerator.
However, the current version of LlamaIndex does not support directly applying metadata filters when generating sub-questions. The filters are typically applied when retrieving data from the query engine, not when generating the sub-questions.
Here's an example of how you can generate sub-questions without filters:
from llama_index.core import QueryBundle
from llama_index.question_gen.openai import OpenAIQuestionGenerator
from llama_index.llms.openai import OpenAI
# Initialize your language model and question generator
llm = OpenAI()
question_gen = OpenAIQuestionGenerator.from_defaults(llm=llm)
# Define your tools
tools = [
ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu and its contents"),
),
ToolMetadata(
name="vyos",
description=("Provides information about VyOS and its contents"),
),
]
# Generate sub-questions
sub_questions = question_gen.generate(
tools=tools,
query=QueryBundle("Your original question here"),
)In this example,
"Your original question here" should be replaced with your actual question. The
generate method will return a list of
SubQuestion objects, each containing a sub-question and the name of the tool that should be used to answer it.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai for this system can you show me for the query_engine parameter a way to include a retriever filter instead of the as_query_engine so as_retriever(filters= filter_ubuntu)
Let me check the knowledge base for you. One moment! :books:
----------------------
Sure, you can use the
as_retriever() method with metadata filters to create a retriever for each sub-question. Here's an example of how you can do this:
from llama_index.core import QueryEngineTool, ToolMetadata, SubQuestionQueryEngine
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Initialize your VectorStoreIndex from a vector store
index = VectorStoreIndex.from_vector_store(vector_store)
# Define your metadata filters
filters_vyos = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_retriever(filters=filters_vyos),
metadata=ToolMetadata(
name="vyos",
description=("Provides information about vyos and its contents"),
),
),
QueryEngineTool(
query_engine=index.as_retriever(filters=filters_ubuntu),
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu and its contents"),
),
),
]
# Define your SubQuestionQueryEngine
sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm, # llm is your language model
use_async=True,
)
# Define your sub-questions
sub_questions = [
SubQuestion(sub_question="Your query here", tool_name="vyos"),
SubQuestion(sub_question="Your query here", tool_name="ubuntu"),
]
# Query using the sub-questions
response = sub_query_engine.query(sub_questions)
print(str(response))In this example, vector_store is your vector store, llm is your language model, and "Your query here" should be replaced with your actual query. The SubQuestionQueryEngine will execute each sub-question with the specified retriever and then synthesize the results into a single response.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai for this system can you show me for the query_engine parameter a way to include a retriever filter instead of the as_query_engine so as_retriever(filters= filter_ubuntu):
filters_windows = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_query_engine(),
metadata=ToolMetadata(
name="vyos",
description=(f"Provides information about vyos and it's contents"),
),
),
QueryEngineTool(
query_engine=index.as_query_engine(),
metadata=ToolMetadata(
name="ubuntu",
description=(f"Provides information about ubuntu and its commands"),
),
),
]
base_sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=Settings.llm, # llm is your language model
use_async=True,
)
response = base_sub_query_engine.query(question)
print(str(response))
Let me check the knowledge base for you. One moment! :books:
----------------------
Sure, you can use the
as_retriever() method with metadata filters to create a retriever for each sub-question. Here's an example of how you can do this:
from llama_index.core import QueryEngineTool, ToolMetadata, SubQuestionQueryEngine
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Initialize your VectorStoreIndex from a vector store
index = VectorStoreIndex.from_vector_store(vector_store)
# Define your metadata filters
filters_vyos = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="vyos")]
)
filters_ubuntu = MetadataFilters(
filters=[MetadataFilter(key="file_name", value="ubuntu")]
)
# Setup base query engine as tool
query_engine_tools = [
QueryEngineTool(
query_engine=index.as_retriever(filters=filters_vyos),
metadata=ToolMetadata(
name="vyos",
description=("Provides information about vyos and its contents"),
),
),
QueryEngineTool(
query_engine=index.as_retriever(filters=filters_ubuntu),
metadata=ToolMetadata(
name="ubuntu",
description=("Provides information about Ubuntu and its contents"),
),
),
]
# Define your SubQuestionQueryEngine
base_sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=Settings.llm, # llm is your language model
use_async=True,
)
# Query using the sub-questions
response = base_sub_query_engine.query(question)
print(str(response))In this example,
vector_store is your vector store,
Settings.llm is your language model, and
question should be replaced with your actual query. The
SubQuestionQueryEngine will execute each sub-question with the specified retriever and then synthesize the results into a single response.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai is there a way to grab all nodes associated with metadata
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can grab all nodes associated with metadata using the
VectorStoreIndex in LlamaIndex. Here's an example of how you can do this:
from llama_index.core import VectorStoreIndex, IndexNode
import copy
# Assume base_nodes is your list of base nodes
all_nodes = copy.deepcopy(base_nodes)
# Assume node_to_metadata is a dictionary mapping node IDs to metadata
for node_id, metadata in node_to_metadata.items():
for val in metadata.values():
all_nodes.append(IndexNode(text=val, index_id=node_id))
# Now all_nodes contains all nodes associated with metadataIn this example,
base_nodes is your list of base nodes and
node_to_metadata is a dictionary mapping node IDs to metadata. The
copy.deepcopy() function is used to create a copy of
base_nodes that you can modify without affecting the original list. The
IndexNode class is used to create new nodes from the metadata, which are then appended to
all_nodes.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai is there a way to use vectorindexretriever() with a filter to pull all nodes associated with a metadata name without needing a question:
def as_retriever(self,filtering_list: Any, kwargs: Any) -> BaseRetriever: # NOTE: lazy import from llama_index.core.indices.vector_store.retrievers import ( VectorIndexRetriever, ) return VectorIndexRetriever( self, filters=filtering_list, node_ids=list(self.index_struct.nodes_dict.values()), callback_manager=self._callback_manager, object_map=self._object_map, kwargs,
)
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can use the
VectorIndexRetriever with a filter to pull all nodes associated with a specific metadata name. Here's an example of how you can do this:
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Define your metadata filter
filters = MetadataFilters(
filters=[MetadataFilter(key="your_metadata_key", value="your_metadata_value")]
)
# Create your retriever
retriever = index.as_retriever(filters=filters)
# Retrieve all nodes associated with the specified metadata
nodes = retriever.retrieve_all()In this example,
index is your
VectorStoreIndex,
"your_metadata_key" should be replaced with the key of the metadata you're interested in, and
"your_metadata_value" should be replaced with the value of the metadata you're interested in. The
retrieve_all() method will return all nodes associated with the specified metadata.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai retrieve_all() is not a function associated with the retriever method is there another way to do it?
Let me check the knowledge base for you. One moment! :books:
----------------------
I apologize for the confusion. You're correct, the
retrieve_all() method does not exist in the LlamaIndex library.
To retrieve all nodes associated with a specific metadata, you can use the
retrieve() method with a very general query that matches a large number of nodes. However, please note that the number of nodes returned may be limited by the
similarity_top_k parameter.
Here's an example:
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Define your metadata filter
filters = MetadataFilters(
filters=[MetadataFilter(key="your_metadata_key", value="your_metadata_value")]
)
# Create your retriever with a large value for similarity_top_k
retriever = index.as_retriever(similarity_top_k=10000, filters=filters)
# Retrieve all nodes associated with the specified metadata
nodes = retriever.retrieve("general query that matches a large number of nodes")In this example,
index is your
VectorStoreIndex,
"your_metadata_key" should be replaced with the key of the metadata you're interested in, and
"your_metadata_value" should be replaced with the value of the metadata you're interested in. The
retrieve() method will return nodes that match the query and the specified metadata.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai is there a way without having a large top_k to call all nodes?
Let me check the knowledge base for you. One moment! :books:
----------------------
I apologize for the confusion earlier. Currently, in LlamaIndex, the
retrieve() method of the
VectorIndexRetriever class retrieves nodes based on the
similarity_top_k parameter, which limits the number of nodes returned. There isn't a built-in method to retrieve all nodes without specifying a large
similarity_top_k.
However, you can directly access the nodes in the
VectorStoreIndex without using a retriever. Here's an example:
# Get all nodes in the index
all_nodes = index.get_all_nodes()
# Filter nodes based on metadata
filtered_nodes = [node for node in all_nodes if node.metadata['your_metadata_key'] == 'your_metadata_value']
In this example,
index is your
VectorStoreIndex,
"your_metadata_key" should be replaced with the key of the metadata you're interested in, and
"your_metadata_value" should be replaced with the value of the metadata you're interested in. The
get_all_nodes() method will return all nodes in the index, and the list comprehension will filter these nodes based on the specified metadata.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai what does ToolMetadata do?
Let me check the knowledge base for you. One moment! :books:
----------------------
ToolMetadata is a data class in LlamaIndex that holds metadata about a tool. It contains the following attributes:
description: A string that describes what the tool does.name: An optional string that specifies the name of the tool.fn_schema: An optional type hint for the function schema of the tool. If not provided, it defaults to DefaultToolFnSchema.return_direct: A boolean that indicates whether the tool should return direct results. It defaults to False.
Here's an example of how
ToolMetadata is used:
from llama_index.core.tools import ToolMetadata
metadata = ToolMetadata(
name="query_plan_tool",
description="This is a query plan tool that takes in a list of tools and executes a query plan over these tools to answer a query. The query plan is a DAG of query nodes. Given a list of tool names and the query plan schema, you can choose to generate a query plan to answer a question. The tool names and descriptions will be given alongside the query.",
fn_schema=<class 'llama_index.core.tools.query_plan.QueryPlan'>,
return_direct=False
)In this example, a
ToolMetadata object is created for a tool named "query_plan_tool". The
description provides information about what the tool does, and the
fn_schema specifies the function schema of the tool.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai is there a way to call all nodes associated with a metadata name
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can retrieve all nodes associated with a specific metadata name using the
get_nodes() method of the
QdrantVectorStore class in LlamaIndex. Here's an example:
from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
# Define your metadata filter
filters = MetadataFilters(
filters=[MetadataFilter(key="your_metadata_key", value="your_metadata_value")]
)
# Get nodes from the index
nodes = index.get_nodes(filters=filters)In this example,
index is your
QdrantVectorStore,
"your_metadata_key" should be replaced with the key of the metadata you're interested in, and
"your_metadata_value" should be replaced with the value of the metadata you're interested in. The
get_nodes() method will return all nodes associated with the specified metadata.
Please note that the code above is a general example and might need adjustments based on your specific use case and setup.
For more information, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai get nodes is not a method for vectorstoreindex
Let me check the knowledge base for you. One moment! :books:
----------------------
I apologize for the confusion. You're correct, the get_nodes() method is not available in the VectorStoreIndex class in LlamaIndex.
To retrieve all nodes associated with a specific metadata name, you would typically use a retriever with a metadata filter. However, the retriever methods in LlamaIndex are designed to return a subset of nodes that are most relevant to a given query, and they don't provide a built-in way to retrieve all nodes without a query.
If you have direct access to the underlying data structure or database where the nodes are stored, you could potentially retrieve all nodes associated with a specific metadata name directly from there. However, this would depend on the specifics of your setup and may require knowledge of the underlying data structure or database.
For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
