Replies: 2 comments 5 replies
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To use the
This example demonstrates how to integrate |
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@dosu.... since I am using pandas dataframe, I am getting this error. Can you help me how to resolve this please? |
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The error you're encountering is because the
By using the |
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@dosu. I got this error |
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Yes, you need to specify an Here is how you can modify your code to include an import pandas as pd
from llama_index.core.schema import BaseNode
from llama_index.vector_stores.txtai import TxtaiVectorStore
import txtai
# Assuming you have a pandas DataFrame `df` with your data
df = pd.DataFrame({
'index_id': [1, 2, 3], # Add a unique index_id for each row
'text': ['text1', 'text2', 'text3'],
'embedding': [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]
})
# Convert DataFrame to list of BaseNode objects
nodes = [BaseNode(index_id=row['index_id'], embedding=row['embedding']) for _, row in df.iterrows()]
# Initialize TxtaiVectorStore
txtai_index = txtai.ann.ANNFactory.create({"backend": "numpy"}) # Initialize your txtai index here
vector_store = TxtaiVectorStore(txtai_index=txtai_index)
# Add nodes to the vector store
vector_store.add(nodes)
# Persist the vector store to disk
vector_store.persist(persist_path='path/to/save/vector_store')Make sure to replace |
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@dosu... Please help. I am getting this error : ValueError: No existing llama_index.vector_stores.txtai.base found at c:/parquet/storage\default__vector_store.json. |
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To resolve the error "ValueError: No existing llama_index.vector_stores.txtai.base found at c:/parquet/storage\default__vector_store.json," ensure that the file Here is an example of how you can load the index from storage: from llama_index.vector_stores.txtai.base import TxtaiVectorStore
# Specify the path to your vector store JSON file
vector_store_path = 'c:/parquet/storage/default__vector_store.json'
# Load the TxtaiVectorStore from the specified path
try:
vector_store = TxtaiVectorStore.from_persist_path(vector_store_path)
print("Vector store loaded successfully.")
except ValueError as e:
print(f"Error loading vector store: {e}")Ensure that the file If you encounter issues with the file format, note that |
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Currently, I am creating a vector_index using
VectorStoreIndex(df_nodes)and retrieving viavector_retriever = vector_index.as_retriever(similarity_top_k=1)before using it in the query engine.However, if I use the txtai vector store, would I get better performance in RAG? If so, could you provide an example of how to use txtai vector store with my pandas DataFrame? The examples provided are for documents. I would appreciate a similar example for a pandas DataFrame or df_nodes. Thank you.
The below code for df_nodes:
Txtai suggested code for documents is below. I like to get a example how I taxai do the same for pandas dataframe:
https://docs.llamaindex.ai/en/stable/examples/vector_stores/TxtaiIndexDemo/
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