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import os
from typing import List
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores.chroma import Chroma
from langchain_core.runnables.base import RunnableSequence
from langchain_core.vectorstores import VectorStoreRetriever
from dotenv import load_dotenv
load_dotenv()
HF_API_KEY = os.environ["HF_API_KEY"]
class MistralOutputParser(StrOutputParser):
"""OutputParser that parser llm result from Mistral API"""
def parse(self, text: str) -> str:
"""
Returns the input text with no changes.
Args:
text (str): text to parse
Returns:
str: parsed text
"""
return text.split("[/INST]")[-1].strip()
def load_pdf(
document_path: str,
mode: str = "single",
strategy: str = "fast",
chunk_size: int = 500,
chunk_overlap: int = 0,
) -> List[str]:
"""
Load a pdf document and split it into chunks of text.
Args:
document_path (Path): path to the pdf document
mode (str, optional): mode of the loader. Defaults to "single".
strategy (str, optional): strategy of the loader. Defaults to "fast".
chunk_size (int, optional): size of the chunks. Defaults to 500.
chunk_overlap (int, optional): overlap of the chunks. Defaults to 0.
Returns:
List[str]: list of chunks of text
"""
# Load the document
loader = UnstructuredPDFLoader(
document_path,
mode=mode,
strategy=strategy,
)
docs = loader.load()
# Split the document into chunks of text
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
all_splits = text_splitter.split_documents(docs)
return all_splits
def store_vector(all_splits: List[str]) -> VectorStoreRetriever:
"""
Store vector of each chunk of text.
Args:
all_splits (List[str]): list of chunks of text
Returns:
VectorStoreRetriever: retriever that can be used to retrieve the vector of a chunk of text
"""
# Use the HuggingFace distilbert-base-uncased model to embed the text
embeddings_model_url = (
# "https://api-inference.huggingface.co/models/distilbert-base-uncased"
"https://api-inference.huggingface.co/models/Salesforce/SFR-Embedding-Mistral"
)
embeddings = HuggingFaceInferenceAPIEmbeddings(
endpoint_url=embeddings_model_url,
api_key=HF_API_KEY,
)
# Store the embeddings of each chunk of text into ChromaDB
vector_store = Chroma.from_documents(all_splits, embeddings)
retriever = vector_store.as_retriever()
return retriever
def generate_mistral_rag_prompt() -> ChatPromptTemplate:
"""
Generate a prompt for Mistral API wiht RAG.
Returns:
ChatPromptTemplate: prompt for Mistral API
"""
template = "<s>[INST] {context} {prompt} [/INST]"
prompt_template = ChatPromptTemplate.from_template(template)
return prompt_template
def generate_mistral_simple_prompt() -> ChatPromptTemplate:
"""
Generate a simple prompt for Mistral without RAG.
Returns:
ChatPromptTemplate: prompt for Mistral API
"""
template = "[INST] {prompt} [/INST]"
prompt_template = ChatPromptTemplate.from_template(template)
return prompt_template
def generate_rag_chain(retriever: VectorStoreRetriever = None) -> RunnableSequence:
"""
Generate a RAG chain with Mistral API and ChromaDB.
Args:
Retriever (VectorStoreRetriever): retriever that can be used to retrieve the vector of a chunk of text
Returns:
RunnableSequence: RAG chain
"""
# Use the Mistral Free prototype API
mistral_url = (
# "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
"https://api-inference.huggingface.co/models/mistralai/Mixtral-8x22B-Instruct-v0.1"
)
model_endpoint = HuggingFaceEndpoint(
endpoint_url=mistral_url,
huggingfacehub_api_token=HF_API_KEY,
task="text2text-generation",
max_new_tokens=1024
)
# Use a custom output parser
output_parser = MistralOutputParser()
# If no retriever is provided, use a simple prompt
if retriever is None:
entry = {"prompt": RunnablePassthrough()}
return entry | generate_mistral_simple_prompt() | model_endpoint | output_parser
# If a retriever is provided, use a RAG prompt
retrieval = {"context": retriever, "prompt": RunnablePassthrough()}
return retrieval | generate_mistral_rag_prompt() | model_endpoint | output_parser
def load_multiple_pdf(document_paths: List[str]) -> List[str]:
"""
Load multiple pdf documents and split them into chunks of text.
Args:
document_paths (List[str]): list of paths to the pdf documents
Returns:
List[str]: list of chunks of text
"""
docs = []
for document_path in document_paths:
loader = UnstructuredPDFLoader(
document_path,
mode="single",
strategy="fast",
)
docs.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=25)
all_splits = text_splitter.split_documents(docs)
return all_splits
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