### Install Flashrank - Lightweight Pairwise Rerankers

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Installs the core flashrank package for lightweight pairwise reranking. This is the default installation and does not require additional dependencies like Torch or Transformers.

```bash
pip install flashrank
```

--------------------------------

### Install Flashrank - LLM Based Listwise Rerankers

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Installs flashrank with support for LLM-based listwise rerankers. This installation includes additional dependencies required for running larger language models.

```bash
pip install flashrank[listwise]
```

--------------------------------

### Initialize FlashRank Ranker

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Demonstrates how to instantiate the Ranker class with different model configurations, including specifying model names and cache directories. These models vary in size, performance, and language support.

```python
from flashrank import Ranker

# Initialize with specific model and cache directory
ranker = Ranker(model_name="rank-T5-flan", cache_dir="/opt")
ranker = Ranker(model_name="ms-marco-MultiBERT-L-12", cache_dir="/opt")
ranker = Ranker(model_name="rank_zephyr_7b_v1_full", max_length=1024)
```

--------------------------------

### Initialize Ranker class

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Demonstrates how to instantiate the Ranker class with various pre-trained models, ranging from nano-sized cross-encoders to large-scale LLM-based listwise models.

```python
from flashrank import Ranker

# Default: Nano model (~4MB)
ranker = Ranker(max_length=128)

# Small model (~34MB)
ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2", cache_dir="/opt")

# Medium model (~110MB)
ranker = Ranker(model_name="rank-T5-flan", cache_dir="/opt")

# Multi-lingual model (~150MB)
ranker = Ranker(model_name="ms-marco-MultiBERT-L-12", cache_dir="/opt")

# LLM-based listwise reranker (~4GB)
ranker = Ranker(model_name="rank_zephyr_7b_v1_full", max_length=1024)

# Arabic-specific reranker
ranker = Ranker(model_name="miniReranker_arabic_v1", cache_dir="/opt")
```

--------------------------------

### Initialize Ranker with Cache Directory in Python

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Demonstrates how to initialize the Ranker class from the flashrank library, specifying a custom cache directory. This is particularly useful in serverless environments like AWS Lambda where the filesystem might be read-only.

```python
ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2", cache_dir="/opt")
```

--------------------------------

### Initialize Flashrank Ranker with Specific Model and Cache Directory

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Initializes the Ranker object with a specific model, 'ms-marco-MiniLM-L-12-v2' (~34MB), known for its slightly slower speed but best performance in ranking precision. It also specifies a custom cache directory for downloaded models.

```python
from flashrank import Ranker

# Small (~34MB), slightly slower & best performance (ranking precision).
ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2", cache_dir="/opt")
```

--------------------------------

### Configure Listwise LLM Reranker

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Demonstrates the initialization of the listwise reranker using the rank_zephyr_7b model. This approach supports larger context windows and holistic passage comparison.

```python
from flashrank import Ranker, RerankRequest

# Initialize listwise LLM reranker
ranker = Ranker(
    model_name="rank_zephyr_7b_v1_full",
    max_length=1024
)
```

--------------------------------

### Initialize Flashrank Ranker with Default Model

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Initializes the Ranker object using the default lightweight model (~4MB). This model offers a balance of speed and competitive ranking precision. The max_length parameter should be set to accommodate the longest passage plus query tokens.

```python
from flashrank import Ranker

# Nano (~4MB), blazing fast model & competitive performance (ranking precision).
ranker = Ranker(max_length=128)
```

--------------------------------

### FlashRank Supported Models Reference

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Provides a reference for the various models supported by FlashRank, detailing their size, type (pairwise/listwise), and characteristics. It shows how to initialize the Ranker with a specific model name.

```python
from flashrank import Ranker

# Available models with their characteristics:
models = {
    # Default - Ultra lightweight, blazing fast
    "ms-marco-TinyBERT-L-2-v2": {"size": "~4MB", "type": "pairwise"},

    # Best cross-encoder accuracy
    "ms-marco-MiniLM-L-12-v2": {"size": "~34MB", "type": "pairwise"},

    # Best zero-shot performance on out-of-domain data
    "rank-T5-flan": {"size": "~110MB", "type": "pairwise"},

    # Multi-lingual support (100+ languages, not for English)
    "ms-marco-MultiBERT-L-12": {"size": "~150MB", "type": "pairwise"},

    # Fine-tuned on Amazon ESCI e-commerce dataset
    "ce-esci-MiniLM-L12-v2": {"size": "~34MB", "type": "pairwise"},

    # LLM-based listwise reranker with large context
    "rank_zephyr_7b_v1_full": {"size": "~4GB", "type": "listwise"},

    # Dedicated Arabic language reranker
    "miniReranker_arabic_v1": {"size": "~34MB", "type": "pairwise"},
}

# Initialize any model by name
for model_name in ["ms-marco-TinyBERT-L-2-v2", "ms-marco-MiniLM-L-12-v2"]:
    ranker = Ranker(model_name=model_name)
    print(f"Loaded: {model_name}")
```

--------------------------------

### Deploy FlashRank to AWS Lambda

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Provides a template for deploying FlashRank in a serverless environment. It highlights the use of the /tmp directory for model caching and efficient cold-start configurations.

```python
from flashrank import Ranker, RerankRequest

def lambda_handler(event, context):
    # Use /tmp for model caching in Lambda
    ranker = Ranker(
        model_name="ms-marco-TinyBERT-L-2-v2",
        cache_dir="/tmp",
        max_length=128
    )

    query = event.get("query", "")
    passages = event.get("passages", [])

    if not query or not passages:
        return {"statusCode": 400, "body": "Missing query or passages"}

    request = RerankRequest(query=query, passages=passages)
    results = ranker.rerank(request)

    return {
        "statusCode": 200,
        "body": {
            "query": query,
            "results": results
        }
    }
```

--------------------------------

### Rerank Lexical Search Results with FlashRank

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Demonstrates how to integrate FlashRank with traditional full-text search systems. It initializes a Ranker, processes search results, and returns the top-k most relevant passages based on a user query.

```python
from flashrank import Ranker, RerankRequest

# Initialize ranker
ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2")

# Simulated results from a lexical search
lexical_search_results = [
    {"id": "doc_001", "text": "Python is a programming language with dynamic typing and automatic memory management."},
    {"id": "doc_002", "text": "Python snakes are non-venomous constrictors found in Africa, Asia, and Australia."},
    {"id": "doc_003", "text": "Learn Python programming fundamentals including variables, loops, and functions."},
    {"id": "doc_004", "text": "The Python package index hosts thousands of third-party modules for Python programming."},
]

# Rerank based on user query
query = "Python programming tutorial for beginners"
request = RerankRequest(query=query, passages=lexical_search_results)
reranked = ranker.rerank(request)

# Return top-k most relevant results
top_k = 3
for passage in reranked[:top_k]:
    print(f"[{passage['id']}] Score: {passage['score']:.4f} - {passage['text'][:80]}...")
```

--------------------------------

### Create RerankRequest object

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Shows how to structure the query and passages into a RerankRequest object, including optional metadata for tracking.

```python
from flashrank import RerankRequest

query = "How to speedup LLMs?"
passages = [
    {"id": 1, "text": "Introduce lookahead decoding...", "meta": {"source": "paper1"}},
    {"id": 2, "text": "LLM inference efficiency...", "meta": {"source": "blog1"}},
    {"id": 3, "text": "There are many ways to increase...", "meta": {"source": "tutorial1"}}
]

rerank_request = RerankRequest(query=query, passages=passages)
```

--------------------------------

### Optimizing FlashRank Performance with max_length

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Illustrates how to configure the `max_length` parameter in the Ranker for different passage lengths to optimize performance. Shorter `max_length` values lead to faster processing, suitable for short texts like titles or snippets.

```python
from flashrank import Ranker, RerankRequest

# For short passages (e.g., titles, snippets < 100 tokens)
ranker_fast = Ranker(max_length=128)

# For medium passages (e.g., paragraphs, 100-300 tokens)
ranker_medium = Ranker(max_length=256)

# For long passages (e.g., full documents, 300-500 tokens)
ranker_full = Ranker(max_length=512)

# Example: Short snippet reranking
short_passages = [
    {"id": 1, "text": "Quick Python tutorial"},
    {"id": 2, "text": "Python programming basics"},
    {"id": 3, "text": "Learn Python in 10 minutes"},
]

request = RerankRequest(query="beginner Python guide", passages=short_passages)
results = ranker_fast.rerank(request)  # Fastest for short text

print("Reranking complete for short passages.")
```

--------------------------------

### Listwise Reranking with FlashRank

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Demonstrates how to perform listwise reranking using the Ranker and RerankRequest objects. It takes a query and a list of passages, returning the passages in a ranked order based on relevance to the query. This method does not explicitly return scores.

```python
from flashrank import Ranker, RerankRequest

passages = [
    {"id": 1, "text": "Climate change is causing rising sea levels and extreme weather events worldwide."},
    {"id": 2, "text": "The stock market experienced significant volatility during the economic downturn."},
    {"id": 3, "text": "Renewable energy sources like solar and wind are becoming more cost-effective."},
    {"id": 4, "text": "Global warming affects biodiversity and threatens many species with extinction."},
]

query = "Environmental impact of climate change"
request = RerankRequest(query=query, passages=passages)

# Initialize Ranker (default model)
ranker = Ranker()

# Listwise reranking - returns passages in ranked order (no explicit scores)
results = ranker.rerank(request)

for i, passage in enumerate(results, 1):
    print(f"Rank {i}: [{passage['id']}] {passage['text'][:60]}...")
```

--------------------------------

### Perform Reranking Request

Source: https://github.com/prithivirajdamodaran/flashrank/blob/main/README.md

Shows how to structure a reranking request by defining a query and a list of passages with associated metadata. The Ranker object then processes these inputs to return ranked results.

```python
from flashrank import RerankRequest

query = "How to speedup LLMs?"
passages = [
   {"id": 1, "text": "Introduce lookahead decoding...", "meta": {"additional": "info1"}},
   {"id": 2, "text": "LLM inference efficiency...", "meta": {"additional": "info2"}}
]

rerankrequest = RerankRequest(query=query, passages=passages)
results = ranker.rerank(rerankrequest)
print(results)
```

--------------------------------

### Execute Rerank method

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Performs the reranking process on a request and iterates through the sorted results, which are ordered by relevance score.

```python
from flashrank import Ranker, RerankRequest

ranker = Ranker(max_length=128)
# ... (passages defined as above)
rerank_request = RerankRequest(query=query, passages=passages)
results = ranker.rerank(rerank_request)

for result in results:
    print(f"ID: {result['id']}, Score: {result['score']:.6f}")
    print(f"Text: {result['text'][:100]}...")
```

--------------------------------

### Rerank Vector Search Results for RAG

Source: https://context7.com/prithivirajdamodaran/flashrank/llms.txt

Shows how to enhance RAG pipelines by reranking semantically similar results from vector databases. This improves the quality of context provided to the LLM.

```python
from flashrank import Ranker, RerankRequest

# Initialize ranker with appropriate max_length for your passage sizes
ranker = Ranker(max_length=256)

# Simulated vector search results
vector_search_results = [
    {"id": "chunk_1", "text": "Machine learning models require large amounts of training data to achieve good performance.", "meta": {"source": "ml_guide.pdf", "page": 12}},
    {"id": "chunk_2", "text": "Deep learning is a subset of machine learning using neural networks with multiple layers.", "meta": {"source": "dl_intro.pdf", "page": 3}},
    {"id": "chunk_3", "text": "Transfer learning allows models to leverage knowledge from pre-trained models.", "meta": {"source": "transfer_learning.pdf", "page": 1}},
    {"id": "chunk_4", "text": "Fine-tuning involves training a pre-trained model on a specific downstream task.", "meta": {"source": "fine_tuning.pdf", "page": 5}},
]

# User query for RAG
query = "How can I train a model with limited data?"

# Rerank to get most relevant chunks for LLM context
request = RerankRequest(query=query, passages=vector_search_results)
reranked = ranker.rerank(request)

# Use top results as context for LLM
context_for_llm = "\n\n".join([p["text"] for p in reranked[:2]])
print(f"Context for LLM:\n{context_for_llm}")
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.