langchain Query on existing index

How to query from an existing index?

I filled up an index in Pinecode using:

docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name)

Now, I'm creating a separate .py file, where I need to use the existing index to query. As I understand I need to use the following function:

    @classmethod
    def from_existing_index(
        cls,
        index_name: str,
        embedding: Embeddings,
        text_key: str = "text",
        namespace: Optional[str] = None,
    ) -> Pinecone:
        """Load pinecone vectorstore from index name."""
        try:
            import pinecone
        except ImportError:
            raise ValueError(
                "Could not import pinecone python package. "
                "Please install it with `pip install pinecone-client`."
            )

        return cls(
            pinecone.Index(index_name), embedding.embed_query, text_key, namespace
        )

but what are the arguments there? I know only my index_name . What are the rest arguments? Embeddings are embeddings from OpenAI, right? e.g.:

embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

What is the text_key? And what name_space ?

Mar 19 '23 20:03 mzhadigerov

Okay, seems like sending index_name and embedding is enough.

Mar 19 '23 21:03 mzhadigerov

text_key is the key in the metadata where the text associated with the embeddings is stored. namespace is optional, depends if you are using it or not

Mar 20 '23 03:03 hwchase17

Could we have an example for this please?

Mar 23 '23 07:03 gbhall

Hi there. I am also looking for an example for querying an existing index without populating more records. Anybody happen to know how this is done? Thanks in advance!

Mar 28 '23 01:03 cycloner2020

I actually figured it out if anyone needs the code.

docsearch = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings)

Mar 28 '23 02:03 cycloner2020

Bravo....Mr. cycloner2020

Apr 12 '23 17:04 siddhantdante

How are you re-associating the documents themselves?

Apr 13 '23 00:04 RobAdkerson

Bless you. Where did you find the docs to help you riddle that one out?

Apr 13 '23 20:04 jamesbuchanan27

Should anyone need help, you can create an "ingest" file where you create your embeddings, etc. and then create a query file. In that query file, re-init Pinecone and then use something like:

embeddings = OpenAIEmbeddings()
docsearch = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings, namespace="your_namespace_name")

Apr 14 '23 00:04 ogmios2

Thanks, these wrappers have the worst documentation I've ever seen... half the parameters aren't explained at all. The examples are all over the place.

Apr 16 '23 04:04 tandemloop

I ended up with a variation of this to go a level deeper into namespaces, which I use to cache my document in slices of different sizes:

index = pinecone.Index(index_name) extant_namespaces = index.describe_index_stats()['namespaces']

if not name_space in extant_namespaces: ### Build a new namespace ...

Apr 16 '23 13:04 jamesbuchanan27

isn't pageContent the property with the text? is this needed if we add just texts to Pinecone instead of a Document?

I can't find any information on the difference between text and Document when saving to Pinecone, it's confusing.

Apr 27 '23 19:04 michaelnagy

honestly though, you'd expect a team with some of the most powerful AI tools ever known to the common man might be able to drum something up... with AI for instance. The docs are written in parable form. I deeply respect the work and i'm very impressed by the diversity, reach, and output of the teams working on this project, but these notes need a lot of work. often the tutorials are incomplete, broken from an update, or otherwise not working. My embeddings were made elsewhere but fully functional with openai in that purpose - why wouldn't they be usable here?

May 02 '23 06:05 amuhareb

Thanks for this, was driving me nuts. You would think this would be in a 101 tutorial somewhere.

May 04 '23 10:05 monkeydust

Was searching for hours, Thanks!

May 04 '23 13:05 sohaybmelgendy

lol glad to see that I'm not the only one who was searching all over the docs for this simple piece of code

Jun 01 '23 10:06 arsentievalex

I did this, but the RetrievalQA returns text other than what's indexed, which is driving me nuts:

# imports

from dotenv import load_dotenv
from IPython.display import display
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.vectorstores import Pinecone
import ipywidgets as widgets
import os
import pinecone

load_dotenv()

pinecone.init(api_key=os.environ["PINECONE_API_KEY"], environment=os.environ["PINECONE_ENV"])

index_name = "langchain-demo"

embeddings = OpenAIEmbeddings()

docsearch = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings)

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(), 
    chain_type="stuff", 
    retriever=docsearch.as_retriever(),
    return_source_documents=True
)

status_text = widgets.Output()

output_text = widgets.Output(
    style={'description_width': 'initial', 'font_size': '20px'}
)

def demo(query):
    with status_text:
        print("thinking...")

    result = qa(
        {
            "query": query['new']
        }
    )

    with output_text:
        if result["result"]:
            print(result["result"])
        else:
            print("I'm sorry I don't have any idea about this ask. Try a different question?")

    status_text.clear_output(wait=False)


input_text = widgets.Text(
    continuous_update=False, 
    layout=widgets.Layout(width='62%'), placeholder='What do you want to know?',
    style={'description_width': 'initial', 'font_size': '24px'}
)

# Display widget
display(
    input_text, 
    status_text,
    widgets.Label(value="Summary", style={'font_size': '24px'}), 
    output_text
    )

input_text.observe(demo, names='value')

Any help?

Jun 01 '23 22:06 tigerinus

Just to highlight for future readers, it seems that Pinecone indexes all metadata by default, so make sure you enable selective metadata indexing.

Jun 02 '23 21:06 dmakwana

For JS/Node/Nextjs guys,

//intialise pineconeClient const pineconeIndex = client.Index(process.env.PINECONE_INDEX_NAME); const vectorStore = await PineconeStore.fromExistingIndex( new OpenAIEmbeddings(),//embeddings { pineconeIndex } );

Jun 21 '23 01:06 iPanchalShubham

you are a life saver

Jul 11 '23 17:07 sssssmike

Whenever I do

docsearch = Pinecone.from_existing_index(index_name=index_name, embedding=embeddings, namespace=namespace)
query = "My query blah blah?"

output = vectorstore.similarity_search(query, k=6)

I get an error saying

ApiAttributeError                         Traceback (most recent call last)
[/var/folders/2k/8pygwk417nx88ph1cch271kr0000gn/T/ipykernel_17491/1263193631.py](https://file+.vscode-resource.vscode-cdn.net/var/folders/2k/8pygwk417nx88ph1cch271kr0000gn/T/ipykernel_17491/1263193631.py) in 
     27 query = "What did Balaji say about fiat currency?"
     28 
---> 29 output = vectorstore.similarity_search(query, k=6)
     30 
     31 # retrieval_chain.run(input_documents=docs, question=query)

[~/opt/anaconda3/envs/generative/lib/python3.9/site-packages/langchain/vectorstores/pinecone.py](https://file+.vscode-resource.vscode-cdn.net/Users/yj/Developer/Dreamteam/embeddings/notebooks/~/opt/anaconda3/envs/generative/lib/python3.9/site-packages/langchain/vectorstores/pinecone.py) in similarity_search(self, query, k, filter, namespace, **kwargs)
    160             List of Documents most similar to the query and score for each
    161         """
--> 162         docs_and_scores = self.similarity_search_with_score(
    163             query, k=k, filter=filter, namespace=namespace, **kwargs
    164         )

[~/opt/anaconda3/envs/generative/lib/python3.9/site-packages/langchain/vectorstores/pinecone.py](https://file+.vscode-resource.vscode-cdn.net/Users/yj/Developer/Dreamteam/embeddings/notebooks/~/opt/anaconda3/envs/generative/lib/python3.9/site-packages/langchain/vectorstores/pinecone.py) in similarity_search_with_score(self, query, k, filter, namespace)
    130         )
    131         for res in results["matches"]:
--> 132             metadata = res["metadata"]
    133             if self._text_key in metadata:
    134                 text = metadata.pop(self._text_key)

[~/opt/anaconda3/envs/generative/lib/python3.9/site-packages/pinecone/core/client/model_utils.py](https://file+.vscode-resource.vscode-cdn.net/Users/yj/Developer/Dreamteam/embeddings/notebooks/~/opt/anaconda3/envs/generative/lib/python3.9/site-packages/pinecone/core/client/model_utils.py) in __getitem__(self, name)
    500             return self.get(name)
...
--> 502         raise ApiAttributeError(
    503             "{0} has no attribute '{1}'".format(
    504                 type(self).__name__, name),

ApiAttributeError: ScoredVector has no attribute 'metadata' at ['['received_data', 'matches', 0]']['metadata']

No idea what that's supposed to mean, but it only occurs when I add the namespace argument...

Jul 19 '23 04:07 pmespresso

i have saved the documents in one namespace.Now I dont know how do i extract the vectors of my documents. I need it because my usecase is to find the difference between two documents and summarize it with LLM help. Seeking your support here.

Aug 05 '23 19:08 nishasharma149

In my situation, I encountered an issue due to the metadata associated with the vectors, specifically the need to include a key labeled "text" (e.g., "text": text). I found the solution to this problem in the comment at this https://github.com/langchain-ai/langchain/issues/3460#issuecomment-1583471622, which proved to be extremely helpful for me.

Aug 07 '23 19:08 AMRedichkina

@dmakwana What is it used for? if the embedding associated with the text_key is a closest match does it return the text_key's value from the metadata?

Aug 11 '23 20:08 yrraadi-io

Hi, @mzhadigerov! I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.

From what I understand, the issue was about querying from an existing index in Pinecone. The maintainers provided clarification on the arguments required for the from_existing_index function and shared examples. Other users also shared their experiences and provided additional code snippets. There were discussions about the text_key and namespace parameters, as well as some confusion about the documentation.

Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself or it will be automatically closed in 7 days.

Thank you for your contribution to the LangChain repository!

Nov 10 '23 16:11 dosubot[bot]

anyone faced this error

Error: type object 'Pinecone' has no attribute 'Index'

Mar 12 '24 14:03 naveenfaclon

docsearch=Pinecone.from_existing_index(index_name, embeddings)

query = "What are Allergies"

docs=docsearch.similarity_search(query, k=3)

print("Result", docs)

NameError Traceback (most recent call last) Cell In[132], line 1 ----> 1 docsearch=Pinecone.from_existing_index(index_name, embeddings) 3 query = "What are Allergies" 5 docs=docsearch.similarity_search(query, k=3)

NameError: name 'Pinecone' is not defined

Mar 13 '24 17:03 dnyanugarule

try from pinecone import Pinecone

Mar 17 '24 09:03 naveenfaclon

I tried a variation of this for RAG

and this is my code:

searcher = PineconeVectorStore(index_name, embeddings)

from langchain.chains import RetrievalQA
from langchain.callbacks import StdOutCallbackHandler

retriever = searcher.as_retriever()
retriever.search_kwargs['fetch_k'] = 25
retriever.search_kwargs['maximal_marginal_relevance'] = True
retriever.search_kwargs['k'] = 15

chain = RetrievalQA.from_chain_type(
    llm=llm, 
    retriever=retriever,
    verbose=True
)

handler = StdOutCallbackHandler()

print(chain.run(
    'Sample Question',
    callbacks=[handler]
))

i am getting the following error:

how can I resolve this error?

Mar 22 '24 12:03 adveatkarnik1

help me find the solution docsearch=Pinecone.from_existing_index(index_name, embeddings)

query = "What are Allergies"

docs=docsearch.similarity_search(query, k=3)

print("Result", docs)

AttributeError Traceback (most recent call last) Cell In[159], line 1 ----> 1 docsearch=Pinecone.from_existing_index(index_name, embeddings) 3 query = "What are Allergies" 5 docs=docsearch.similarity_search(query, k=3)

AttributeError: type object 'PineconeGRPC' has no attribute 'from_existing_index'

Jul 05 '24 18:07 KhushiGilhotra

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Query on existing index

print("Result", docs)

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