Explain the following like I am a layperson: "" 
pip install -U langchain umap-learn scikit-learn langchain_community tiktoken langchain-openai langchainhub chromadb langchain-anthropic
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
The RAPTOR paper presents an interesting approaching for indexing and retrieval of documents:

The leafs are a set of starting documents
Leafs are embedded and clustered
Clusters are then summarized into higher level (more abstract) consolidations of information across similar documents
This process is done recursivly, resulting in a "tree" going from raw docs (leafs) to more abstract summaries.

We can applying this at varying scales; leafs can be:

Text chunks from a single doc (as shown in the paper)
Full docs (as we show below)
With longer context LLMs, it's possible to perform this over full documents.

Screenshot 2024-03-04 at 12.45.25 PM.png

Docs
Let's apply this to LangChain's LCEL documentation.

In this case, each doc is a unique web page of the LCEL docs.

The context varies from < 2k tokens on up to > 10k tokens.

import matplotlib.pyplot as plt
import tiktoken
from bs4 import BeautifulSoup as Soup
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader


def num_tokens_from_string(string: str, encoding_name: str) -> int:
    """Returns the number of tokens in a text string."""
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens


# LCEL docs
url = "https://python.langchain.com/docs/expression_language/"
loader = RecursiveUrlLoader(
    url=url, max_depth=20, extractor=lambda x: Soup(x, "html.parser").text
)
docs = loader.load()

# LCEL w/ PydanticOutputParser (outside the primary LCEL docs)
url = "https://python.langchain.com/docs/modules/model_io/output_parsers/quick_start"
loader = RecursiveUrlLoader(
    url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text
)
docs_pydantic = loader.load()

# LCEL w/ Self Query (outside the primary LCEL docs)
url = "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"
loader = RecursiveUrlLoader(
    url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text
)
docs_sq = loader.load()

# Doc texts
docs.extend([*docs_pydantic, *docs_sq])
docs_texts = [d.page_content for d in docs]

# Calculate the number of tokens for each document
counts = [num_tokens_from_string(d, "cl100k_base") for d in docs_texts]

# Plotting the histogram of token counts
plt.figure(figsize=(10, 6))
plt.hist(counts, bins=30, color="blue", edgecolor="black", alpha=0.7)
plt.title("Histogram of Token Counts")
plt.xlabel("Token Count")
plt.ylabel("Frequency")
plt.grid(axis="y", alpha=0.75)

# Display the histogram
plt.show
<function matplotlib.pyplot.show(close=None, block=None)>

# Doc texts concat
d_sorted = sorted(docs, key=lambda x: x.metadata["source"])
d_reversed = list(reversed(d_sorted))
concatenated_content = "\n\n\n --- \n\n\n".join(
    [doc.page_content for doc in d_reversed]
)
print(
    "Num tokens in all context: %s"
    % num_tokens_from_string(concatenated_content, "cl100k_base")
)
Num tokens in all context: 68705
# Doc texts split
from langchain_text_splitters import RecursiveCharacterTextSplitter

chunk_size_tok = 2000
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    chunk_size=chunk_size_tok, chunk_overlap=0
)
texts_split = text_splitter.split_text(concatenated_content)
Models
We can test various models, including the new Claude3 family.

Be sure to set the relevant API keys:

ANTHROPIC_API_KEY
OPENAI_API_KEY
from langchain_openai import OpenAIEmbeddings

embd = OpenAIEmbeddings()

# from langchain_openai import ChatOpenAI

# model = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(temperature=0, model="claude-3-opus-20240229")
Tree Constrution
The clustering approach in tree construction includes a few interesting ideas.

GMM (Gaussian Mixture Model)

Model the distribution of data points across different clusters
Optimal number of clusters by evaluating the model's Bayesian Information Criterion (BIC)
UMAP (Uniform Manifold Approximation and Projection)

Supports clustering
Reduces the dimensionality of high-dimensional data
UMAP helps to highlight the natural grouping of data points based on their similarities
Local and Global Clustering

Used to analyze data at different scales
Both fine-grained and broader patterns within the data are captured effectively
Thresholding

Apply in the context of GMM to determine cluster membership
Based on the probability distribution (assignment of data points to ≥ 1 cluster)
Code for GMM and thresholding is from Sarthi et al, as noted in the below two sources:

Origional repo
Minor tweaks
Full credit to both authors.

from typing import Dict, List, Optional, Tuple

import numpy as np
import pandas as pd
import umap
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from sklearn.mixture import GaussianMixture

RANDOM_SEED = 224  # Fixed seed for reproducibility

### --- Code from citations referenced above (added comments and docstrings) --- ###


def global_cluster_embeddings(
    embeddings: np.ndarray,
    dim: int,
    n_neighbors: Optional[int] = None,
    metric: str = "cosine",
) -> np.ndarray:
    """
    Perform global dimensionality reduction on the embeddings using UMAP.

    Parameters:
    - embeddings: The input embeddings as a numpy array.
    - dim: The target dimensionality for the reduced space.
    - n_neighbors: Optional; the number of neighbors to consider for each point.
                   If not provided, it defaults to the square root of the number of embeddings.
    - metric: The distance metric to use for UMAP.

    Returns:
    - A numpy array of the embeddings reduced to the specified dimensionality.
    """
    if n_neighbors is None:
        n_neighbors = int((len(embeddings) - 1) ** 0.5)
    return umap.UMAP(
        n_neighbors=n_neighbors, n_components=dim, metric=metric
    ).fit_transform(embeddings)


def local_cluster_embeddings(
    embeddings: np.ndarray, dim: int, num_neighbors: int = 10, metric: str = "cosine"
) -> np.ndarray:
    """
    Perform local dimensionality reduction on the embeddings using UMAP, typically after global clustering.

    Parameters:
    - embeddings: The input embeddings as a numpy array.
    - dim: The target dimensionality for the reduced space.
    - num_neighbors: The number of neighbors to consider for each point.
    - metric: The distance metric to use for UMAP.

    Returns:
    - A numpy array of the embeddings reduced to the specified dimensionality.
    """
    return umap.UMAP(
        n_neighbors=num_neighbors, n_components=dim, metric=metric
    ).fit_transform(embeddings)


def get_optimal_clusters(
    embeddings: np.ndarray, max_clusters: int = 50, random_state: int = RANDOM_SEED
) -> int:
    """
    Determine the optimal number of clusters using the Bayesian Information Criterion (BIC) with a Gaussian Mixture Model.

    Parameters:
    - embeddings: The input embeddings as a numpy array.
    - max_clusters: The maximum number of clusters to consider.
    - random_state: Seed for reproducibility.

    Returns:
    - An integer representing the optimal number of clusters found.
    """
    max_clusters = min(max_clusters, len(embeddings))
    n_clusters = np.arange(1, max_clusters)
    bics = []
    for n in n_clusters:
        gm = GaussianMixture(n_components=n, random_state=random_state)
        gm.fit(embeddings)
        bics.append(gm.bic(embeddings))
    return n_clusters[np.argmin(bics)]


def GMM_cluster(embeddings: np.ndarray, threshold: float, random_state: int = 0):
    """
    Cluster embeddings using a Gaussian Mixture Model (GMM) based on a probability threshold.

    Parameters:
    - embeddings: The input embeddings as a numpy array.
    - threshold: The probability threshold for assigning an embedding to a cluster.
    - random_state: Seed for reproducibility.

    Returns:
    - A tuple containing the cluster labels and the number of clusters determined.
    """
    n_clusters = get_optimal_clusters(embeddings)
    gm = GaussianMixture(n_components=n_clusters, random_state=random_state)
    gm.fit(embeddings)
    probs = gm.predict_proba(embeddings)
    labels = [np.where(prob > threshold)[0] for prob in probs]
    return labels, n_clusters


def perform_clustering(
    embeddings: np.ndarray,
    dim: int,
    threshold: float,
) -> List[np.ndarray]:
    """
    Perform clustering on the embeddings by first reducing their dimensionality globally, then clustering
    using a Gaussian Mixture Model, and finally performing local clustering within each global cluster.

    Parameters:
    - embeddings: The input embeddings as a numpy array.
    - dim: The target dimensionality for UMAP reduction.
    - threshold: The probability threshold for assigning an embedding to a cluster in GMM.

    Returns:
    - A list of numpy arrays, where each array contains the cluster IDs for each embedding.
    """
    if len(embeddings) <= dim + 1:
        # Avoid clustering when there's insufficient data
        return [np.array([0]) for _ in range(len(embeddings))]

    # Global dimensionality reduction
    reduced_embeddings_global = global_cluster_embeddings(embeddings, dim)
    # Global clustering
    global_clusters, n_global_clusters = GMM_cluster(
        reduced_embeddings_global, threshold
    )

    all_local_clusters = [np.array([]) for _ in range(len(embeddings))]
    total_clusters = 0

    # Iterate through each global cluster to perform local clustering
    for i in range(n_global_clusters):
        # Extract embeddings belonging to the current global cluster
        global_cluster_embeddings_ = embeddings[
            np.array([i in gc for gc in global_clusters])
        ]

        if len(global_cluster_embeddings_) == 0:
            continue
        if len(global_cluster_embeddings_) <= dim + 1:
            # Handle small clusters with direct assignment
            local_clusters = [np.array([0]) for _ in global_cluster_embeddings_]
            n_local_clusters = 1
        else:
            # Local dimensionality reduction and clustering
            reduced_embeddings_local = local_cluster_embeddings(
                global_cluster_embeddings_, dim
            )
            local_clusters, n_local_clusters = GMM_cluster(
                reduced_embeddings_local, threshold
            )

        # Assign local cluster IDs, adjusting for total clusters already processed
        for j in range(n_local_clusters):
            local_cluster_embeddings_ = global_cluster_embeddings_[
                np.array([j in lc for lc in local_clusters])
            ]
            indices = np.where(
                (embeddings == local_cluster_embeddings_[:, None]).all(-1)
            )[1]
            for idx in indices:
                all_local_clusters[idx] = np.append(
                    all_local_clusters[idx], j + total_clusters
                )

        total_clusters += n_local_clusters

    return all_local_clusters


### --- Our code below --- ###


def embed(texts):
    """
    Generate embeddings for a list of text documents.

    This function assumes the existence of an `embd` object with a method `embed_documents`
    that takes a list of texts and returns their embeddings.

    Parameters:
    - texts: List[str], a list of text documents to be embedded.

    Returns:
    - numpy.ndarray: An array of embeddings for the given text documents.
    """
    text_embeddings = embd.embed_documents(texts)
    text_embeddings_np = np.array(text_embeddings)
    return text_embeddings_np


def embed_cluster_texts(texts):
    """
    Embeds a list of texts and clusters them, returning a DataFrame with texts, their embeddings, and cluster labels.

    This function combines embedding generation and clustering into a single step. It assumes the existence
    of a previously defined `perform_clustering` function that performs clustering on the embeddings.

    Parameters:
    - texts: List[str], a list of text documents to be processed.

    Returns:
    - pandas.DataFrame: A DataFrame containing the original texts, their embeddings, and the assigned cluster labels.
    """
    text_embeddings_np = embed(texts)  # Generate embeddings
    cluster_labels = perform_clustering(
        text_embeddings_np, 10, 0.1
    )  # Perform clustering on the embeddings
    df = pd.DataFrame()  # Initialize a DataFrame to store the results
    df["text"] = texts  # Store original texts
    df["embd"] = list(text_embeddings_np)  # Store embeddings as a list in the DataFrame
    df["cluster"] = cluster_labels  # Store cluster labels
    return df


def fmt_txt(df: pd.DataFrame) -> str:
    """
    Formats the text documents in a DataFrame into a single string.

    Parameters:
    - df: DataFrame containing the 'text' column with text documents to format.

    Returns:
    - A single string where all text documents are joined by a specific delimiter.
    """
    unique_txt = df["text"].tolist()
    return "--- --- \n --- --- ".join(unique_txt)


def embed_cluster_summarize_texts(
    texts: List[str], level: int
) -> Tuple[pd.DataFrame, pd.DataFrame]:
    """
    Embeds, clusters, and summarizes a list of texts. This function first generates embeddings for the texts,
    clusters them based on similarity, expands the cluster assignments for easier processing, and then summarizes
    the content within each cluster.

    Parameters:
    - texts: A list of text documents to be processed.
    - level: An integer parameter that could define the depth or detail of processing.

    Returns:
    - Tuple containing two DataFrames:
      1. The first DataFrame (`df_clusters`) includes the original texts, their embeddings, and cluster assignments.
      2. The second DataFrame (`df_summary`) contains summaries for each cluster, the specified level of detail,
         and the cluster identifiers.
    """

    # Embed and cluster the texts, resulting in a DataFrame with 'text', 'embd', and 'cluster' columns
    df_clusters = embed_cluster_texts(texts)

    # Prepare to expand the DataFrame for easier manipulation of clusters
    expanded_list = []

    # Expand DataFrame entries to document-cluster pairings for straightforward processing
    for index, row in df_clusters.iterrows():
        for cluster in row["cluster"]:
            expanded_list.append(
                {"text": row["text"], "embd": row["embd"], "cluster": cluster}
            )

    # Create a new DataFrame from the expanded list
    expanded_df = pd.DataFrame(expanded_list)

    # Retrieve unique cluster identifiers for processing
    all_clusters = expanded_df["cluster"].unique()

    print(f"--Generated {len(all_clusters)} clusters--")

    # Summarization
    template = """Here is a sub-set of LangChain Expression Langauge doc. 
    
    LangChain Expression Langauge provides a way to compose chain in LangChain.
    
    Give a detailed summary of the documentation provided.
    
    Documentation:
    {context}
    """
    prompt = ChatPromptTemplate.from_template(template)
    chain = prompt | model | StrOutputParser()

    # Format text within each cluster for summarization
    summaries = []
    for i in all_clusters:
        df_cluster = expanded_df[expanded_df["cluster"] == i]
        formatted_txt = fmt_txt(df_cluster)
        summaries.append(chain.invoke({"context": formatted_txt}))

    # Create a DataFrame to store summaries with their corresponding cluster and level
    df_summary = pd.DataFrame(
        {
            "summaries": summaries,
            "level": [level] * len(summaries),
            "cluster": list(all_clusters),
        }
    )

    return df_clusters, df_summary


def recursive_embed_cluster_summarize(
    texts: List[str], level: int = 1, n_levels: int = 3
) -> Dict[int, Tuple[pd.DataFrame, pd.DataFrame]]:
    """
    Recursively embeds, clusters, and summarizes texts up to a specified level or until
    the number of unique clusters becomes 1, storing the results at each level.

    Parameters:
    - texts: List[str], texts to be processed.
    - level: int, current recursion level (starts at 1).
    - n_levels: int, maximum depth of recursion.

    Returns:
    - Dict[int, Tuple[pd.DataFrame, pd.DataFrame]], a dictionary where keys are the recursion
      levels and values are tuples containing the clusters DataFrame and summaries DataFrame at that level.
    """
    results = {}  # Dictionary to store results at each level

    # Perform embedding, clustering, and summarization for the current level
    df_clusters, df_summary = embed_cluster_summarize_texts(texts, level)

    # Store the results of the current level
    results[level] = (df_clusters, df_summary)

    # Determine if further recursion is possible and meaningful
    unique_clusters = df_summary["cluster"].nunique()
    if level < n_levels and unique_clusters > 1:
        # Use summaries as the input texts for the next level of recursion
        new_texts = df_summary["summaries"].tolist()
        next_level_results = recursive_embed_cluster_summarize(
            new_texts, level + 1, n_levels
        )

        # Merge the results from the next level into the current results dictionary
        results.update(next_level_results)

    return results
# Build tree
leaf_texts = docs_texts
results = recursive_embed_cluster_summarize(leaf_texts, level=1, n_levels=3)
--Generated 7 clusters--
--Generated 1 clusters--
The paper reports best performance from collapsed tree retrieval.

This involves flattening the tree structure into a single layer and then applying a k-nearest neighbors (kNN) search across all nodes simultaneously.

We do simply do this below.

from langchain_community.vectorstores import Chroma

# Initialize all_texts with leaf_texts
all_texts = leaf_texts.copy()

# Iterate through the results to extract summaries from each level and add them to all_texts
for level in sorted(results.keys()):
    # Extract summaries from the current level's DataFrame
    summaries = results[level][1]["summaries"].tolist()
    # Extend all_texts with the summaries from the current level
    all_texts.extend(summaries)

# Now, use all_texts to build the vectorstore with Chroma
vectorstore = Chroma.from_texts(texts=all_texts, embedding=embd)
retriever = vectorstore.as_retriever()
Now we can using our flattened, indexed tree in a RAG chain.

from langchain import hub
from langchain_core.runnables import RunnablePassthrough

# Prompt
prompt = hub.pull("rlm/rag-prompt")


# Post-processing
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


# Chain
rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

# Question
rag_chain.invoke("How to define a RAG chain? Give me a specific code example.")
'Here is a code example of how to define a RAG (Retrieval Augmented Generation) chain in LangChain:\n\n```python\nfrom langchain.vectorstores import FAISS\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.prompts import ChatPromptTemplate\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.output_parsers import StrOutputParser\n\n# Load documents into vector store\nvectorstore = FAISS.from_texts(\n    ["harrison worked at kensho"], embedding=OpenAIEmbeddings()\n)\nretriever = vectorstore.as_retriever()\n\n# Define prompt template\ntemplate = """Answer the question based only on the following context:\n{context}\nQuestion: {question}"""\nprompt = ChatPromptTemplate.from_template(template)\n\n# Define model and output parser\nmodel = ChatOpenAI()\noutput_parser = StrOutputParser()\n\n# Define RAG chain\nchain = (\n    {"context": retriever, "question": RunnablePassthrough()}\n    | prompt\n    | mo""