!pip install -U langchain umap-learn scikit-learn langchain_community tiktoken langchain-openai langchainhub chromadb
!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install -qU llama-cpp-python

import locale
def getpreferredencoding(do_setlocale = True):
  return "UTF-8"
locale.getpreferredencoding = getpreferredencoding

!wget "<https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q4_K_M.gguf>"

from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
from langchain_core.prompts import PromptTemplate
#
n_gpu_layers = -1  # The number of layers to put on the GPU. The rest will be on the CPU. If you don't know how many layers there are, you can use -1 to move all to GPU.
n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
#
# Callbacks support token-wise streaming
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
#
# Make sure the model path is correct for your system!
model = LlamaCpp(
    model_path="/content/zephyr-7b-beta.Q4_K_M.gguf",
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    temperature=0.75,
    max_tokens=1000,
    top_p=1,
    n_ctx=35000,
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)

from langchain.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy
#
EMBEDDING_MODEL_NAME = "thenlper/gte-small"


embd = HuggingFaceEmbeddings(
    model_name=EMBEDDING_MODEL_NAME,
    multi_process=True,
    model_kwargs={"device": "cuda"},
    encode_kwargs={"normalize_embeddings": True},  # set True for cosine similarity
)

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


## Helper Fuction to count the number of Tokensin each text
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]

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()

# 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")
)
#
# Response
Num tokens in all context: 69108

# Doc texts split
from langchain_text_splitters import RecursiveCharacterTextSplitter


chunk_size_tok = 1000
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    chunk_size=chunk_size_tok, chunk_overlap=0
)
texts_split = text_splitter.split_text(concatenated_content)
#
print(f"Number of text splits generated: {len(texts_split)}")
#
# Response
Number of text splits generated: 142

global_embeddings = [embd.embed_query(txt) for txt in texts_split]
print(len(global_embeddings[0])
###########
384

import matplotlib.pyplot as plt
from typing import Optional
import numpy as np
import umap


def reduce_cluster_embeddings(
    embeddings: np.ndarray,
    dim: int,
    n_neighbors: Optional[int] = None,
    metric: str = "cosine",
) -> np.ndarray:
    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)


dim = 2
global_embeddings_reduced = reduce_cluster_embeddings(global_embeddings, dim)
print(global_embeddings_reduced[0])
#
plt.figure(figsize=(10, 8))
plt.scatter(global_embeddings_reduced[:, 0], global_embeddings_reduced[:, 1], alpha=0.5)
plt.title("Global Embeddings")
plt.xlabel("Dimension 1")
plt.ylabel("Dimension 2")
plt.show()

import matplotlib.pyplot as plt
import numpy as np
from sklearn.mixture import GaussianMixture


def get_optimal_clusters(embeddings: np.ndarray, max_clusters: int = 50, random_state: int = 1234):
    max_clusters = min(max_clusters, len(embeddings))
    bics = [GaussianMixture(n_components=n, random_state=random_state).fit(embeddings).bic(embeddings)
            for n in range(1, max_clusters)]
    return np.argmin(bics) + 1


def gmm_clustering(embeddings: np.ndarray, threshold: float, random_state: int = 0):
    n_clusters = get_optimal_clusters(embeddings)
    gm = GaussianMixture(n_components=n_clusters, random_state=random_state).fit(embeddings)
    probs = gm.predict_proba(embeddings)
    labels = [np.where(prob > threshold)[0] for prob in probs]
    return labels, n_clusters
#
labels, _ = gmm_clustering(global_embeddings_reduced, threshold=0.5)


plot_labels = np.array([label[0] if len(label) > 0 else -1 for label in labels])
plt.figure(figsize=(10, 8))


unique_labels = np.unique(plot_labels)
colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_labels)))


for label, color in zip(unique_labels, colors):
    mask = plot_labels == label
    plt.scatter(global_embeddings_reduced[mask, 0], global_embeddings_reduced[mask, 1], color=color, label=f'Cluster {label}', alpha=0.5)


plt.title("Cluster Visualization of Global Embeddings")
plt.xlabel("Dimension 1")
plt.ylabel("Dimension 2")
plt.legend()
plt.show()

import pandas as pd


simple_labels = [label[0] if len(label) > 0 else -1 for label in labels]


df = pd.DataFrame({
    'Text': texts_split,
    'Embedding': list(global_embeddings_reduced),
    'Cluster': simple_labels
})
print(df.head(3))

def format_cluster_texts(df):
    clustered_texts = {}
    for cluster in df['Cluster'].unique():
        cluster_texts = df[df['Cluster'] == cluster]['Text'].tolist()
        clustered_texts[cluster] = " --- ".join(cluster_texts)
    return clustered_texts
#
clustered_texts = format_cluster_texts(df)
#
clustered_texts
####################################################################
# Response

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

leaf_texts = docs_texts
results = recursive_embed_cluster_summarize(leaf_texts, level=1, n_levels=3)

# 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)
#Final Summaries extracted
print(all_texts)

# Now, use all_texts to build the vectorstore with Chroma
vectorstore = Chroma.from_texts(texts=all_texts, embedding=embd)
retriever = vectorstore.as_retriever()

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()
)

print(prompt)
####################### Response #####################
ChatPromptTemplate(input_variables=['context', 'question'], metadata={'lc_hub_owner': 'rlm', 'lc_hub_repo': 'rag-prompt', 'lc_hub_commit_hash': '50442af133e61576e74536c6556cefe1fac147cad032f4377b60c436e6cdcb6e'}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template="You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: {question} \\nContext: {context} \\nAnswer:"))])

print(prompt.messages[0].prompt.template)


####################### Response ############################
You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
Answer:

# Question
response =rag_chain.invoke("What is LCEL?")
print(str(response))


############# Response ######################################
 LangChain Expression Language (LCEL) is a declarative way to easily compose chains together in LangChain. It was designed from day 1 to support putting prototypes in production with no code changes, from the simplest "prompt + LLM" chain to complex chains with hundreds of steps. Some reasons why one might want to use LCEL include streaming support (allowing for the best possible time-to-first-token), async support (enabling use in both synchronous and asynchronous APIs), optimized parallel execution (automatically executing parallel steps with the smallest possible latency), retries and fallbacks (a great way to make chains more reliable at scale), access to intermediate results (useful for letting end-users know something is happening or debugging), input and output schemas (providing Pydantic and JSONSchema schemas inferred from chain structure for validation), seamless LangSmith tracing integration (maximum observability and debuggability), and seamless LangServe deployment integration (easy chain deployment).

# Question
response =rag_chain.invoke("How to define a RAG chain? Give me a specific code example.")


################### Response ########################
To define a RAG chain in LangChain Expression Language (LCEL), you can follow these steps:
1. Create a function decorated with `@chain`. This function becomes a runnable.
2. Inside this function, create chains by composing Runnable components using the `|` operator. For example, `prompt | model | parser`.
3. Save the chain as a variable and return it.
4. Call this function using `invoke()`.


Here's an example:


```python
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import chain
from langchain_openai import ChatOpenAI


prompt1 = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
prompt2 = ChatPromptTemplate.from_template("What is the subject of this joke: {joke}")


@chain
def custom_chain(text):
    prompt_val1 = prompt1.invoke({"topic": text})
    output1 = ChatOpenAI().invoke(prompt_val1)
    parsed_output1 = StrOutputParser().invoke(output1)
    chain2 = prompt2 | ChatOpenAI() | StrOutputParser()
    return chain2.invoke({"joke": parsed_output1})


custom_chain("bears")


In this example, custom_chain() is a runnable function that takes text as input and returns a chain. The chain starts with prompt1, which is passed text as the value for topic. The output of prompt1 is passed through ChatOpenAI() and StrOutputParser() to extract the joke. Then, prompt2 is executed with parsed_output1 as the value for joke. The output of prompt2 is also passed through ChatOpenAI() and StrOutputParser(). The resulting output is returned from custom_chain().
You can also use @chain with other Runnable components, such as Retriever, Memory, Fallbacks, Tools, etc., to create more complex chains for various tasks.