---
title: Recency Filtering
 | Developer Documentation
---

Showcase capabilities of recency-weighted node postprocessor

```
import os


os.environ["OPENAI_API_KEY"] = "sk-..."
```

```
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import (
    FixedRecencyPostprocessor,
    EmbeddingRecencyPostprocessor,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.response.notebook_utils import display_response
```

### Parse Documents into Nodes, add to Docstore

In this example, there are 3 different versions of PG’s essay. They are largely identical **except** for one specific section, which details the amount of funding they raised for Viaweb.

V1: 50k, V2: 30k, V3: 10K

V1: 2020-01-01, V2: 2020-02-03, V3: 2022-04-12

The idea is to encourage index to fetch the most recent info (which is V3)

```
# load documents
from llama_index.core import StorageContext




def get_file_metadata(file_name: str):
    """Get file metadata."""
    if "v1" in file_name:
        return {"date": "2020-01-01"}
    elif "v2" in file_name:
        return {"date": "2020-02-03"}
    elif "v3" in file_name:
        return {"date": "2022-04-12"}
    else:
        raise ValueError("invalid file")




documents = SimpleDirectoryReader(
    input_files=[
        "test_versioned_data/paul_graham_essay_v1.txt",
        "test_versioned_data/paul_graham_essay_v2.txt",
        "test_versioned_data/paul_graham_essay_v3.txt",
    ],
    file_metadata=get_file_metadata,
).load_data()


# define settings
from llama_index.core import Settings


Settings.text_splitter = SentenceSplitter(chunk_size=512)


# use node parser to parse into nodes
nodes = Settings.text_splitter.get_nodes_from_documents(documents)


# add to docstore
docstore = SimpleDocumentStore()
docstore.add_documents(nodes)


storage_context = StorageContext.from_defaults(docstore=docstore)
```

```
print(documents[2].get_text())
```

### Build Index

```
# build index
index = VectorStoreIndex(nodes, storage_context=storage_context)
```

```
INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens
INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 84471 tokens
```

### Define Recency Postprocessors

```
node_postprocessor = FixedRecencyPostprocessor()
```

```
node_postprocessor_emb = EmbeddingRecencyPostprocessor()
```

### Query Index

```
# naive query


query_engine = index.as_query_engine(
    similarity_top_k=3,
)
response = query_engine.query(
    "How much did the author raise in seed funding from Idelle's husband"
    " (Julian) for Viaweb?",
)
```

```
INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 1813 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens
```

```
# query using fixed recency node postprocessor


query_engine = index.as_query_engine(
    similarity_top_k=3, node_postprocessors=[node_postprocessor]
)
response = query_engine.query(
    "How much did the author raise in seed funding from Idelle's husband"
    " (Julian) for Viaweb?",
)
```

```
# query using embedding-based node postprocessor


query_engine = index.as_query_engine(
    similarity_top_k=3, node_postprocessors=[node_postprocessor_emb]
)
response = query_engine.query(
    "How much did the author raise in seed funding from Idelle's husband"
    " (Julian) for Viaweb?",
)
```

```
INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 541 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens
```

### Query Index (Lower-Level Usage)

In this example we first get the full set of nodes from a query call, and then send to node postprocessor, and then finally synthesize response through a summary index.

```
from llama_index.core import SummaryIndex
```

```
query_str = (
    "How much did the author raise in seed funding from Idelle's husband"
    " (Julian) for Viaweb?"
)
```

```
query_engine = index.as_query_engine(
    similarity_top_k=3, response_mode="no_text"
)
init_response = query_engine.query(
    query_str,
)
resp_nodes = [n.node for n in init_response.source_nodes]
```

```
INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 0 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens
```

```
summary_index = SummaryIndex(resp_nodes)
query_engine = summary_index.as_query_engine(
    node_postprocessors=[node_postprocessor]
)
response = query_engine.query(query_str)
```

```
INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens
INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 541 tokens
INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens
```