LangChain

Welcome to the integration guide for Pinecone and LangChain. This documentation covers the steps to integrate Pinecone, a high-performance vector database, with LangChain, a framework for building applications powered by large language models (LLMs).

Pinecone enables developers to build scalable, real-time recommendation and search systems based on vector similarity search. LangChain, on the other hand, provides modules for managing and optimizing the use of language models in applications. Its core philosophy is to facilitate data-aware applications where the language model interacts with other data sources and its environment.

By integrating Pinecone with LangChain, you can develop sophisticated applications that leverage both the platforms’ strengths. Allowing us to add “long-term memory” to LLMs, greatly enhancing the capabilities of autonomous agents, chatbots, and question answering systems, among others.

There are naturally many ways to use these two tools together. We have covered the process in detail across our many examples and learning material, including:

• LangChain AI Handbook
• Retrieval Augmentation for LLMs
• Retrieval Augmented Conversational Agent

The remainder of this guide will walk you through a simple retrieval augmentation example using Pinecone and LangChain.

Retrieval Augmentation in LangChain

LLMs have a data freshness problem. The most powerful LLMs in the world, like GPT-4, have no idea about recent world events.

The world of LLMs is frozen in time. Their world exists as a static snapshot of the world as it was within their training data.

A solution to this problem is retrieval augmentation. The idea behind this is that we retrieve relevant information from an external knowledge base and give that information to our LLM. In this notebook we will learn how to do that.

To begin, we must install the prerequisite libraries that we will be using in this notebook.

Python

!pip install -qU langchain==0.0.162 openai tiktoken "pinecone-client[grpc]" datasets apache_beam mwparserfromhell

---

🚨 Note: the above pip install is formatted for Jupyter notebooks. If running elsewhere you may need to drop the !.

---

Building the Knowledge Base

Python

from datasets import load_dataset

data = load_dataset("wikipedia", "20220301.simple", split='train[:10000]')
data

Dataset({
    features: ['id', 'url', 'title', 'text'],
    num_rows: 10000
})

Python

data[6]

{'id': '13',
 'url': 'https://simple.wikipedia.org/wiki/Alan%20Turing',
 'title': 'Alan Turing',
 'text': 'Alan Mathison Turing OBE FRS (London, 23 June 1912 – Wilmslow, Cheshire, 7 June 1954) was an English mathematician and computer scientist. He was born in Maida Vale, London.\n\nEarly life and family \nAlan Turing was born in Maida Vale, London on 23 June 1912. His father was part of a family of merchants from Scotland. His mother, Ethel Sara, was the daughter of an engineer.\n\nEducation \nTuring went to St. Michael\'s, a school at 20 Charles Road, St Leonards-on-sea, when he was five years old.\n"This is only a foretaste of what is to come, and only the shadow of what is going to be.” – Alan Turing.\n\nThe Stoney family were once prominent landlords, here in North Tipperary. His mother Ethel Sara Stoney (1881–1976) was daughter of Edward Waller Stoney (Borrisokane, North Tipperary) and Sarah Crawford (Cartron Abbey, Co. Longford); Protestant Anglo-Irish gentry.\n\nEducated in Dublin at Alexandra School and College; on October 1st 1907 she married Julius Mathison Turing, latter son of Reverend John Robert Turing and Fanny Boyd, in Dublin. Born on June 23rd 1912, Alan Turing would go on to be regarded as one of the greatest figures of the twentieth century.\n\nA brilliant mathematician and cryptographer Alan was to become the founder of modern-day computer science and artificial intelligence; designing a machine at Bletchley Park to break secret Enigma encrypted messages used by the Nazi German war machine to protect sensitive commercial, diplomatic and military communications during World War 2. Thus, Turing made the single biggest contribution to the Allied victory in the war against Nazi Germany, possibly saving the lives of an estimated 2 million people, through his effort in shortening World War II.\n\nIn 2013, almost 60 years later, Turing received a posthumous Royal Pardon from Queen Elizabeth II. Today, the “Turing law” grants an automatic pardon to men who died before the law came into force, making it possible for living convicted gay men to seek pardons for offences now no longer on the statute book.\n\nAlas, Turing accidentally or otherwise lost his life in 1954, having been subjected by a British court to chemical castration, thus avoiding a custodial sentence. He is known to have ended his life at the age of 41 years, by eating an apple laced with cyanide.\n\nCareer \nTuring was one of the people who worked on the first computers. He created the theoretical  Turing machine in 1936. The machine was imaginary, but it included the idea of a computer program.\n\nTuring was interested in artificial intelligence. He proposed the Turing test, to say when a machine could be called "intelligent". A computer could be said to "think" if a human talking with it could not tell it was a machine.\n\nDuring World War II, Turing worked with others to break German ciphers (secret messages). He  worked for the Government Code and Cypher School (GC&CS) at Bletchley Park, Britain\'s codebreaking centre that produced Ultra intelligence.\nUsing cryptanalysis, he helped to break the codes of the Enigma machine. After that, he worked on other German codes.\n\nFrom 1945 to 1947, Turing worked on the design of the ACE (Automatic Computing Engine) at the National Physical Laboratory. He presented a paper on 19 February 1946. That paper was "the first detailed design of a stored-program computer". Although it was possible to build ACE, there were delays in starting the project. In late 1947 he returned to Cambridge for a sabbatical year. While he was at Cambridge, the Pilot ACE was built without him. It ran its first program on 10\xa0May 1950.\n\nPrivate life \nTuring was a homosexual man. In 1952, he admitted having had sex with a man in England. At that time, homosexual acts were illegal. Turing was convicted. He had to choose between going to jail and taking hormones to lower his sex drive. He decided to take the hormones. After his punishment, he became impotent. He also grew breasts.\n\nIn May 2012, a private member\'s bill was put before the House of Lords to grant Turing a statutory pardon. In July 2013, the government supported it. A royal pardon was granted on 24 December 2013.\n\nDeath \nIn 1954, Turing died from cyanide poisoning. The cyanide came from either an apple which was poisoned with cyanide, or from water that had cyanide in it. The reason for the confusion is that the police never tested the apple for cyanide. It is also suspected that he committed suicide.\n\nThe treatment forced on him is now believed to be very wrong. It is against medical ethics and international laws of human rights. In August 2009, a petition asking the British Government to apologise to Turing for punishing him for being a homosexual was started. The petition received thousands of signatures. Prime Minister Gordon Brown acknowledged the petition. He called Turing\'s treatment "appalling".\n\nReferences\n\nOther websites \nJack Copeland 2012. Alan Turing: The codebreaker who saved \'millions of lives\'. BBC News / Technology \n\nEnglish computer scientists\nEnglish LGBT people\nEnglish mathematicians\nGay men\nLGBT scientists\nScientists from London\nSuicides by poison\nSuicides in the United Kingdom\n1912 births\n1954 deaths\nOfficers of the Order of the British Empire'}

Every record contains a lot of text. Our first task is therefore to identify a good preprocessing methodology for chunking these articles into more “concise” chunks to later be embedding and stored in our Pinecone vector database.

For this we use LangChain’s RecursiveCharacterTextSplitter to split our text into chunks of a specified max length.

Python

import tiktoken

tiktoken.encoding_for_model('gpt-3.5-turbo')

<Encoding 'cl100k_base'>

Python

import tiktoken

tokenizer = tiktoken.get_encoding('cl100k_base')

# create the length function
def tiktoken_len(text):
    tokens = tokenizer.encode(
        text,
        disallowed_special=()
    )
    return len(tokens)

tiktoken_len("hello I am a chunk of text and using the tiktoken_len function "
             "we can find the length of this chunk of text in tokens")

26

Python

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=400,
    chunk_overlap=20,
    length_function=tiktoken_len,
    separators=["\n\n", "\n", " ", ""]
)

Python

chunks = text_splitter.split_text(data[6]['text'])[:3]
chunks

['Alan Mathison Turing OBE FRS (London, 23 June 1912 – Wilmslow, Cheshire, 7 June 1954) was an English mathematician and computer scientist. He was born in Maida Vale, London.\n\nEarly life and family \nAlan Turing was born in Maida Vale, London on 23 June 1912. His father was part of a family of merchants from Scotland. His mother, Ethel Sara, was the daughter of an engineer.\n\nEducation \nTuring went to St. Michael\'s, a school at 20 Charles Road, St Leonards-on-sea, when he was five years old.\n"This is only a foretaste of what is to come, and only the shadow of what is going to be.” – Alan Turing.\n\nThe Stoney family were once prominent landlords, here in North Tipperary. His mother Ethel Sara Stoney (1881–1976) was daughter of Edward Waller Stoney (Borrisokane, North Tipperary) and Sarah Crawford (Cartron Abbey, Co. Longford); Protestant Anglo-Irish gentry.\n\nEducated in Dublin at Alexandra School and College; on October 1st 1907 she married Julius Mathison Turing, latter son of Reverend John Robert Turing and Fanny Boyd, in Dublin. Born on June 23rd 1912, Alan Turing would go on to be regarded as one of the greatest figures of the twentieth century.',
 'A brilliant mathematician and cryptographer Alan was to become the founder of modern-day computer science and artificial intelligence; designing a machine at Bletchley Park to break secret Enigma encrypted messages used by the Nazi German war machine to protect sensitive commercial, diplomatic and military communications during World War 2. Thus, Turing made the single biggest contribution to the Allied victory in the war against Nazi Germany, possibly saving the lives of an estimated 2 million people, through his effort in shortening World War II.\n\nIn 2013, almost 60 years later, Turing received a posthumous Royal Pardon from Queen Elizabeth II. Today, the “Turing law” grants an automatic pardon to men who died before the law came into force, making it possible for living convicted gay men to seek pardons for offences now no longer on the statute book.\n\nAlas, Turing accidentally or otherwise lost his life in 1954, having been subjected by a British court to chemical castration, thus avoiding a custodial sentence. He is known to have ended his life at the age of 41 years, by eating an apple laced with cyanide.\n\nCareer \nTuring was one of the people who worked on the first computers. He created the theoretical  Turing machine in 1936. The machine was imaginary, but it included the idea of a computer program.\n\nTuring was interested in artificial intelligence. He proposed the Turing test, to say when a machine could be called "intelligent". A computer could be said to "think" if a human talking with it could not tell it was a machine.',
 'During World War II, Turing worked with others to break German ciphers (secret messages). He  worked for the Government Code and Cypher School (GC&CS) at Bletchley Park, Britain\'s codebreaking centre that produced Ultra intelligence.\nUsing cryptanalysis, he helped to break the codes of the Enigma machine. After that, he worked on other German codes.\n\nFrom 1945 to 1947, Turing worked on the design of the ACE (Automatic Computing Engine) at the National Physical Laboratory. He presented a paper on 19 February 1946. That paper was "the first detailed design of a stored-program computer". Although it was possible to build ACE, there were delays in starting the project. In late 1947 he returned to Cambridge for a sabbatical year. While he was at Cambridge, the Pilot ACE was built without him. It ran its first program on 10\xa0May 1950.\n\nPrivate life \nTuring was a homosexual man. In 1952, he admitted having had sex with a man in England. At that time, homosexual acts were illegal. Turing was convicted. He had to choose between going to jail and taking hormones to lower his sex drive. He decided to take the hormones. After his punishment, he became impotent. He also grew breasts.\n\nIn May 2012, a private member\'s bill was put before the House of Lords to grant Turing a statutory pardon. In July 2013, the government supported it. A royal pardon was granted on 24 December 2013.\n\nDeath \nIn 1954, Turing died from cyanide poisoning. The cyanide came from either an apple which was poisoned with cyanide, or from water that had cyanide in it. The reason for the confusion is that the police never tested the apple for cyanide. It is also suspected that he committed suicide.']

Python

tiktoken_len(chunks[0]), tiktoken_len(chunks[1]), tiktoken_len(chunks[2])

(299, 323, 382)

Using the text_splitter we get much better sized chunks of text. We’ll use this functionality during the indexing process later. Now let’s take a look at embedding.

Creating Embeddings

Building embeddings using LangChain’s OpenAI embedding support is fairly straightforward. We first need to add our OpenAI api key by running the next cell:

Python

from getpass import getpass

OPENAI_API_KEY = getpass("OpenAI API Key: ")  # platform.openai.com

(Note that OpenAI is a paid service and so running the remainder of this notebook may incur some small cost)

After initializing the API key we can initialize our text-embedding-ada-002 embedding model like so:

Python

from langchain.embeddings.openai import OpenAIEmbeddings

model_name = 'text-embedding-ada-002'

embed = OpenAIEmbeddings(
    model=model_name,
    openai_api_key=OPENAI_API_KEY
)

Now we embed some text like so:

Python

texts = [
    'this is the first chunk of text',
    'then another second chunk of text is here'
]

res = embed.embed_documents(texts)
len(res), len(res[0])

(2, 1536)

From this we get two (aligning to our two chunks of text) 1536-dimensional embeddings.

Now we move on to initializing our Pinecone vector database.

Vector Database

To create our vector database we first need a free API key from Pinecone. Then we initialize like so:

Python

import pinecone

# find API key in console at app.pinecone.io
YOUR_API_KEY = getpass("Pinecone API Key: ")
# find ENV (cloud region) next to API key in console
YOUR_ENV = input("Pinecone environment: ")

index_name = 'langchain-retrieval-augmentation'
pinecone.init(
    api_key=YOUR_API_KEY,
    environment=YOUR_ENV
)

if index_name not in pinecone.list_indexes():
    # we create a new index
    pinecone.create_index(
        name=index_name,
        metric='cosine',
        dimension=len(res[0])  # 1536 dim of text-embedding-ada-002
    )

Then we connect to the new index:

Python

index = pinecone.GRPCIndex(index_name)

index.describe_index_stats()

{'dimension': 1536,
 'index_fullness': 0.1,
 'namespaces': {'': {'vector_count': 27437}},
 'total_vector_count': 27437}

We should see that the new Pinecone index has a total_vector_count of 0, as we haven’t added any vectors yet.

Indexing

We can perform the indexing task using the LangChain vector store object. But for now it is much faster to do it via the Pinecone client directly. We will do this in batches of 100 or more.

Python

from tqdm.auto import tqdm
from uuid import uuid4

batch_limit = 100

texts = []
metadatas = []

for i, record in enumerate(tqdm(data)):
    # first get metadata fields for this record
    metadata = {
        'wiki-id': str(record['id']),
        'source': record['url'],
        'title': record['title']
    }
    # now we create chunks from the record text
    record_texts = text_splitter.split_text(record['text'])
    # create individual metadata dicts for each chunk
    record_metadatas = [{
        "chunk": j, "text": text, **metadata
    } for j, text in enumerate(record_texts)]
    # append these to current batches
    texts.extend(record_texts)
    metadatas.extend(record_metadatas)
    # if we have reached the batch_limit we can add texts
    if len(texts) >= batch_limit:
        ids = [str(uuid4()) for _ in range(len(texts))]
        embeds = embed.embed_documents(texts)
        index.upsert(vectors=zip(ids, embeds, metadatas))
        texts = []
        metadatas = []

if len(texts) > 0:
    ids = [str(uuid4()) for _ in range(len(texts))]
    embeds = embed.embed_documents(texts)
    index.upsert(vectors=zip(ids, embeds, metadatas))

  0%|          | 0/10000 [00:00<?, ?it/s]

We’ve now indexed everything. We can check the number of vectors in our index like so:

Python

index.describe_index_stats()

{'dimension': 1536,
 'index_fullness': 0.1,
 'namespaces': {'': {'vector_count': 27437}},
 'total_vector_count': 27437}

Creating a Vector Store and Querying

Now that we’ve build our index we can switch back over to LangChain. We start by initializing a vector store using the same index we just built. We do that like so:

Python

from langchain.vectorstores import Pinecone

text_field = "text"

# switch back to normal index for langchain
index = pinecone.Index(index_name)

vectorstore = Pinecone(
    index, embed.embed_query, text_field
)

Python

query = "who was Benito Mussolini?"

vectorstore.similarity_search(
    query,  # our search query
    k=3  # return 3 most relevant docs
)

[Document(page_content='Benito Amilcare Andrea Mussolini KSMOM GCTE (29 July 1883 – 28 April 1945) was an Italian politician and journalist. He was also the Prime Minister of Italy from 1922 until 1943. He was the leader of the National Fascist Party.\n\nBiography\n\nEarly life\nBenito Mussolini was named after Benito Juarez, a Mexican opponent of the political power of the Roman Catholic Church, by his anticlerical (a person who opposes the political interference of the Roman Catholic Church in secular affairs) father. Mussolini\'s father was a blacksmith. Before being involved in politics, Mussolini was a newspaper editor (where he learned all his propaganda skills) and elementary school teacher.\n\nAt first, Mussolini was a socialist, but when he wanted Italy to join the First World War, he was thrown out of the socialist party. He \'invented\' a new ideology, Fascism, much out of Nationalist\xa0and Conservative views.\n\nRise to power and becoming dictator\nIn 1922, he took power by having a large group of men, "Black Shirts," march on Rome and threaten to take over the government. King Vittorio Emanuele III gave in, allowed him to form a government, and made him prime minister. In the following five years, he gained power, and in 1927 created the OVRA, his personal secret police force. Using the agency to arrest, scare, or murder people against his regime, Mussolini was dictator\xa0of Italy by the end of 1927. Only the King and his own Fascist party could challenge his power.', lookup_str='', metadata={'chunk': 0.0, 'source': 'https://simple.wikipedia.org/wiki/Benito%20Mussolini', 'title': 'Benito Mussolini', 'wiki-id': '6754'}, lookup_index=0),
 Document(page_content='Fascism as practiced by Mussolini\nMussolini\'s form of Fascism, "Italian Fascism"- unlike Nazism, the racist ideology that Adolf Hitler followed- was different and less destructive than Hitler\'s. Although a believer in the superiority of the Italian nation and national unity, Mussolini, unlike Hitler, is quoted "Race? It is a feeling, not a reality. Nothing will ever make me believe that biologically pure races can be shown to exist today".\n\nMussolini wanted Italy to become a new Roman Empire. In 1923, he attacked the island of Corfu, and in 1924, he occupied the city state of Fiume. In 1935, he attacked the African country Abyssinia (now called Ethiopia). His forces occupied it in 1936. Italy was thrown out of the League of Nations because of this aggression. In 1939, he occupied the country Albania. In 1936, Mussolini signed an alliance with Adolf Hitler, the dictator of Germany.\n\nFall from power and death\nIn 1940, he sent Italy into the Second World War on the side of the Axis countries. Mussolini attacked Greece, but he failed to conquer it. In 1943, the Allies landed in Southern Italy. The Fascist party and King Vittorio Emanuel III deposed Mussolini and put him in jail, but he was set free by the Germans, who made him ruler of the Italian Social Republic puppet state which was in a small part of Central Italy. When the war was almost over, Mussolini tried to escape to Switzerland with his mistress, Clara Petacci, but they were both captured and shot by partisans. Mussolini\'s dead body was hanged upside-down, together with his mistress and some of Mussolini\'s helpers, on a pole at a gas station in the village of Millan, which is near the border  between Italy and Switzerland.', lookup_str='', metadata={'chunk': 1.0, 'source': 'https://simple.wikipedia.org/wiki/Benito%20Mussolini', 'title': 'Benito Mussolini', 'wiki-id': '6754'}, lookup_index=0),
 Document(page_content='Fascist Italy \nIn 1922, a new Italian government started. It was ruled by Benito Mussolini, the leader of Fascism in Italy. He became head of government and dictator, calling himself "Il Duce" (which means "leader" in Italian). He became friends with German dictator Adolf Hitler. Germany, Japan, and Italy became the Axis Powers. In 1940, they entered World War II together against France, Great Britain, and later the Soviet Union. During the war, Italy controlled most of the Mediterranean Sea.\n\nOn July 25, 1943, Mussolini was removed by the Great Council of Fascism. On September 8, 1943, Badoglio said that the war as an ally of Germany was ended. Italy started fighting as an ally of France and the UK, but Italian soldiers did not know whom to shoot. In Northern Italy, a movement called Resistenza started to fight against the German invaders. On April 25, 1945, much of Italy became free, while Mussolini tried to make a small Northern Italian fascist state called the Republic of Salò. The fascist state failed and Mussolini tried to flee to Switzerland and escape to Francoist Spain, but he was captured by Italian partisans. On 28 April 1945 Mussolini was executed by a partisan.\n\nAfter World War Two \n\nThe state became a republic on June 2, 1946. For the first time, women were able to vote. Italian people ended the Savoia dynasty and adopted a republic government.\n\nIn February 1947, Italy signed a peace treaty with the Allies. They lost all the colonies and some territorial areas (Istria and parts of Dalmatia).\n\nSince then Italy has joined NATO and the European Community (as a founding member). It is one of the seven biggest industrial economies in the world.\n\nTransportation \n\nThe railway network in Italy totals . It is the 17th longest in the world. High speed trains include ETR-class trains which travel at .', lookup_str='', metadata={'chunk': 5.0, 'source': 'https://simple.wikipedia.org/wiki/Italy', 'title': 'Italy', 'wiki-id': '363'}, lookup_index=0)]

All of these are good, relevant results. But what can we do with this? There are many tasks, one of the most interesting (and well supported by LangChain) is called “Generative Question-Answering” or GQA.

Generative Question-Answering

In GQA we take the query as a question that is to be answered by a LLM, but the LLM must answer the question based on the information it is seeing being returned from the vectorstore.

To do this we initialize a RetrievalQA object like so:

Python

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# completion llm
llm = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY,
    model_name='gpt-3.5-turbo',
    temperature=0.0
)

qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

Python

qa.run(query)

'Benito Mussolini was an Italian politician and journalist who served as the Prime Minister of Italy from 1922 until 1943. He was the leader of the National Fascist Party and invented the ideology of Fascism. Mussolini was a dictator of Italy by the end of 1927, and his form of Fascism, "Italian Fascism," was different and less destructive than Hitler\'s Nazism. Mussolini wanted Italy to become a new Roman Empire and attacked several countries, including Abyssinia (now called Ethiopia) and Greece. He was removed from power in 1943 and was executed by Italian partisans in 1945.'

We can also include the sources of information that the LLM is using to answer our question. We can do this using a slightly different version of RetrievalQA called RetrievalQAWithSourcesChain:

Python

from langchain.chains import RetrievalQAWithSourcesChain

qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

Python

qa_with_sources(query)

{'question': 'who was Benito Mussolini?',
 'answer': 'Benito Mussolini was an Italian politician and journalist who was the Prime Minister of Italy from 1922 until 1943. He was the leader of the National Fascist Party and invented the ideology of Fascism. He became dictator of Italy by the end of 1927 and was friends with German dictator Adolf Hitler. Mussolini attacked Greece and failed to conquer it. He was removed by the Great Council of Fascism in 1943 and was executed by a partisan on April 28, 1945. After the war, several Neo-Fascist movements have had success in Italy, the most important being the Movimento Sociale Italiano. His granddaughter Alessandra Mussolini has outspoken views similar to Fascism. \n',
 'sources': 'https://simple.wikipedia.org/wiki/Benito%20Mussolini, https://simple.wikipedia.org/wiki/Fascism'}

Now we answer the question being asked, and return the source of this information being used by the LLM.