MongoDB Atlas
MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.
This notebook shows how to use MongoDB Atlas Vector
Search
to store your embeddings in MongoDB documents, create a vector search
index, and perform KNN search with an approximate nearest neighbor
algorithm (Hierarchical Navigable Small Worlds
). It uses the
\$vectorSearch MQL
Stage.
To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. To get started head over to Atlas here: quick start.
Note:
- This feature is Generally Available and ready for production deployments.
- The langchain version 0.0.305 (release notes) introduces the support for \$vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to \<=0.0.304
In the notebook we will demonstrate how to perform
Retrieval Augmented Generation
(RAG) using MongoDB Atlas, OpenAI and
Langchain. We will be performing Similarity Search, Similarity Search
with Metadata Pre-Filtering, and Question Answering over the PDF
document for GPT 4 technical
report that came out in March
2023 and hence is not part of the OpenAI’s Large Language Model(LLM)’s
parametric memory, which had a knowledge cutoff of September 2021.
We want to use OpenAIEmbeddings
so we need to set up our OpenAI API
Key.
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
Now we will setup the environment variables for the MongoDB Atlas cluster
!pip install langchain pypdf pymongo openai tiktoken
import getpass
MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
from pymongo import MongoClient
# initialize MongoDB python client
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
DB_NAME = "langchain_db"
COLLECTION_NAME = "test"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
Create Vector Search Index
Now, let’s create a vector search index on your cluster. In the below
example, embedding
is the name of the field that contains the
embedding vector. Please refer to the
documentation
to get more details on how to define an Atlas Vector Search index. You
can name the index {ATLAS_VECTOR_SEARCH_INDEX_NAME}
and create the
index on the namespace {DB_NAME}.{COLLECTION_NAME}
. Finally, write the
following definition in the JSON editor on MongoDB Atlas:
{
"name": "index_name",
"type": "vectorSearch",
"fields":[
{
"type": "vector",
"path": "embedding",
"numDimensions": 1536,
"similarity": "cosine"
}
]
}
Insert Data
from langchain.document_loaders import PyPDFLoader
# Load the PDF
loader = PyPDFLoader("https://arxiv.org/pdf/2303.08774.pdf")
data = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
docs = text_splitter.split_documents(data)
print(docs[0])
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import MongoDBAtlasVectorSearch
# insert the documents in MongoDB Atlas with their embedding
vector_search = MongoDBAtlasVectorSearch.from_documents(
documents=docs,
embedding=OpenAIEmbeddings(disallowed_special=()),
collection=MONGODB_COLLECTION,
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)
# Perform a similarity search between the embedding of the query and the embeddings of the documents
query = "What were the compute requirements for training GPT 4"
results = vector_search.similarity_search(query)
print(results[0].page_content)
Querying data
We can also instantiate the vector store directly and execute a query as follows:
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import MongoDBAtlasVectorSearch
vector_search = MongoDBAtlasVectorSearch.from_connection_string(
MONGODB_ATLAS_CLUSTER_URI,
DB_NAME + "." + COLLECTION_NAME,
OpenAIEmbeddings(disallowed_special=()),
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)
Pre-filtering with Similarity Search
Atlas Vector Search supports pre-filtering using MQL Operators for filtering. Below is an example index and query on the same data loaded above that allows you do metadata filtering on the “page” field. You can update your existing index with the filter defined and do pre-filtering with vector search.
{
"name": "index_name",
"type": "vectorSearch",
"fields":[
{
"type": "vector",
"path": "embedding",
"numDimensions": 1536,
"similarity": "cosine"
},
{
"type": "filter",
"path": "page"
}
]
}
query = "What were the compute requirements for training GPT 4"
results = vector_search.similarity_search_with_score(
query=query, k=5, pre_filter={"page": {"$eq": 1}}
)
# Display results
for result in results:
print(result)
Similarity Search with Score
query = "What were the compute requirements for training GPT 4"
results = vector_search.similarity_search_with_score(
query=query,
k=5,
)
# Display results
for result in results:
print(result)
Question Answering
qa_retriever = vector_search.as_retriever(
search_type="similarity",
search_kwargs={"k": 25},
)
from langchain.prompts import PromptTemplate
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=qa_retriever,
return_source_documents=True,
chain_type_kwargs={"prompt": PROMPT},
)
docs = qa({"query": "gpt-4 compute requirements"})
print(docs["result"])
print(docs["source_documents"])
GPT-4 requires significantly more compute than earlier GPT models. On a dataset derived from OpenAI’s internal codebase, GPT-4 requires 100p (petaflops) of compute to reach the lowest loss, while the smaller models require 1-10n (nanoflops).