Spaces:
Runtime error
Runtime error
File size: 2,937 Bytes
e997a79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
import tiktoken
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Qdrant
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from operator import itemgetter
# from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
def tiktoken_len(text):
tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
text,
)
return len(tokens)
def chunk_documents(docs, tiktoken_len):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 200,
chunk_overlap = 0,
length_function = tiktoken_len,
)
split_chunks = text_splitter.split_documents(docs)
print('len(split_chunks) =', len(split_chunks))
return split_chunks
def create_raqa_chain_from_docs():
# Load the documents from a PDF file using PyMuPDFLoader
# docs = PyMuPDFLoader("data/c7318154-f6ae-4866-89fa-f0c589f2ee3d.pdf").load()
docs = PyMuPDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001326801/c7318154-f6ae-4866-89fa-f0c589f2ee3d.pdf").load()
# Print the number of loaded documents
print("Loaded", len(docs), "documents")
# Print the first document
print(docs[0])
# Split the documents into chunks based on their length
split_chunks = chunk_documents(docs, tiktoken_len)
# Create an instance of the OpenAIEmbeddings model for text embeddings
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
# Create a Qdrant vector store from the split chunks
qdrant_vectorstore = Qdrant.from_documents(
split_chunks,
embedding_model,
location=":memory:",
collection_name="Meta 10-k Filings",
)
# Create a retriever from the Qdrant vector store
qdrant_retriever = qdrant_vectorstore.as_retriever()
# Define the RAG prompt template
RAG_PROMPT = """
CONTEXT:
{context}
QUERY:
{question}
Use the provided context to answer the provided user query. Only use the provided context to answer the query. If you do not know the answer, respond with "I don't know".
"""
# Create a ChatPromptTemplate instance from the RAG prompt template
rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
# Create an instance of the ChatOpenAI model
openai_chat_model = ChatOpenAI(model="gpt-3.5-turbo")
# Define the retrieval augmented QA chain
retrieval_augmented_qa_chain = (
{"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
| RunnablePassthrough.assign(context=itemgetter("context"))
| {"response": rag_prompt | openai_chat_model, "context": itemgetter("context")}
)
return retrieval_augmented_qa_chain
|