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langchain-ai / langchain

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https://python.langchain.com
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pinecone.init is no longer a top-level attribute of the pinecone package #16513

Open dregules opened 8 months ago

dregules commented 8 months ago

Checked other resources

Example Code

Attempting to initialize PineconeConnected

import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.schema import Document

class PineconeConnected():
    def __init__(self, index_name: str, pinecone_api_key: str, pinecone_env: str, openai_key: str):
        embeddings = OpenAIEmbeddings(openai_api_key=openai_key)
        pinecone.init(api_key=pinecone_api_key)
        self.vector_db = Pinecone.from_existing_index(index_name, embeddings) # VectorStore object with the reference + Pinecone index loaded

    def query(query:str, book_title=None)

Description

when initializing PineconeConnected class, I get this error: *Please note that this was previously working. pinecone-client updated its library a few days ago hence why this integration with 'init' no longer works.

AttributeError: init is no longer a top-level attribute of the pinecone package.

Please create an instance of the Pinecone class instead.

Example:

    import os
    from pinecone import Pinecone, ServerlessSpec

    pc = Pinecone(
        api_key=os.environ.get("PINECONE_API_KEY")
    )

    # Now do stuff
    if 'my_index' not in pc.list_indexes().names():
        pc.create_index(
            name='my_index', 
            dimension=1536, 
            metric='euclidean',
            spec=ServerlessSpec(
                cloud='aws',
                region='us-west-2'
            )
        )

System Info

fastapi-poe==0.0.24 pydantic>=2 openai==0.28.1 langchain==0.0.348 pinecone-client==3.0.1 tiktoken

Related Components

dosubot[bot] commented 8 months ago

🤖

Based on the error message you provided, it seems that the pinecone.init method is no longer available in the latest version of the pinecone-client library. Instead, you should create an instance of the Pinecone class directly.

Here's how you can modify your PineconeConnected class to work with the updated pinecone-client library:

import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.schema import Document

class PineconeConnected():
    def __init__(self, index_name: str, pinecone_api_key: str, pinecone_env: str, openai_key: str):
        embeddings = OpenAIEmbeddings(openai_api_key=openai_key)
        self.pinecone = pinecone.Pinecone(api_key=pinecone_api_key)
        self.vector_db = Pinecone.from_existing_index(index_name, embeddings) # VectorStore object with the reference + Pinecone index loaded

    def query(query:str, book_title=None):
        pass

In this updated code, I replaced the pinecone.init(api_key=pinecone_api_key) line with self.pinecone = pinecone.Pinecone(api_key=pinecone_api_key). This creates an instance of the Pinecone class with your API key, which you can then use to interact with the Pinecone service.

Please note that you might need to adjust other parts of your code that rely on the pinecone.init method, as this method is no longer available in the latest version of the pinecone-client library.

I hope this helps! If you have any other questions or run into any issues, please let me know.

Sources

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

dregules commented 8 months ago

I would encounter another error: 


AttributeError: list_indexes is no longer a top-level attribute of the pinecone package.

To use list_indexes, please create a client instance and call the method there instead.

Example:

    from pinecone import Pinecone

    pc = Pinecone(api_key='YOUR_API_KEY')

    index_name = "quickstart" # or your index name

    if index_name not in pc.list_indexes().names():
        # do something
lampeight commented 8 months ago

I am also having issues since Pinecone updated their API. The following migration guide has helped with the basic initialisation, but now some Langchain Pinecone library functions require an update. Specifically, when using Pinecone.from_documents, I encounter: PineconeConfigurationError: You haven't specified an Api-Key.. This was previously working exactly as-is and I'm not sure how/if I can modify the code to work correctly from this.

rbuck-som commented 8 months ago

I am running into this too. This broke my production environment.

The fix for me was to change my application config.cfg:

pinecone-client==2.2.4
sunaarun commented 8 months ago

🤖

Based on the error message you provided, it seems that the pinecone.init method is no longer available in the latest version of the pinecone-client library. Instead, you should create an instance of the Pinecone class directly.

Here's how you can modify your PineconeConnected class to work with the updated pinecone-client library:

import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.schema import Document

class PineconeConnected():
    def __init__(self, index_name: str, pinecone_api_key: str, pinecone_env: str, openai_key: str):
        embeddings = OpenAIEmbeddings(openai_api_key=openai_key)
        self.pinecone = pinecone.Pinecone(api_key=pinecone_api_key)
        self.vector_db = Pinecone.from_existing_index(index_name, embeddings) # VectorStore object with the reference + Pinecone index loaded

    def query(query:str, book_title=None):
        pass

In this updated code, I replaced the pinecone.init(api_key=pinecone_api_key) line with self.pinecone = pinecone.Pinecone(api_key=pinecone_api_key). This creates an instance of the Pinecone class with your API key, which you can then use to interact with the Pinecone service.

Please note that you might need to adjust other parts of your code that rely on the pinecone.init method, as this method is no longer available in the latest version of the pinecone-client library.

I hope this helps! If you have any other questions or run into any issues, please let me know.

Sources

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

What if I am not using OpenAI embeddings = OpenAIEmbeddings(openai_api_key=openai_key) shall I delete this part or what should I do ? please

VpkPrasanna commented 8 months ago

Follow this docs to integrate or use pinecone with langchain : docs

sali316 commented 7 months ago

I used the snippet given by @sunaarun (see below) however now I am getting list_indexes is not a top-level attribute error. I need to connect to an existing index and save the vectorstore object.

Please advise

class PineconeConnected(): def init(self, index_name: str, pinecone_api_key: str, pinecone_env: str, openai_key: str): embeddings = OpenAIEmbeddings(openai_api_key=openai_key) self.pinecone = pinecone.Pinecone(api_key=pinecone_api_key) self.vector_db = Pinecone.from_existing_index(index_name, embeddings)

naveenfaclon commented 7 months ago

i used this

import pinecone

pinecone.Pinecone( api_key = PINECONE_API_KEY, environment = PINECONE_API_ENV )

index_name = "langchainvector"

and this docsearch = Pinecone.from_texts(chunks, embeddings, index_name=index_name)

i am getting this error

PineconeConfigurationError                Traceback (most recent call last)
Cell In[13], [line 1](vscode-notebook-cell:?execution_count=13&line=1)
----> [1](vscode-notebook-cell:?execution_count=13&line=1) docsearch = Pinecone.from_texts(chunks, embeddings, index_name=index_name)

File [~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:431](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:431), in Pinecone.from_texts(cls, texts, embedding, metadatas, ids, batch_size, text_key, namespace, index_name, upsert_kwargs, pool_threads, embeddings_chunk_size, **kwargs)
    [389](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:389) @classmethod
    [390](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:390) def from_texts(
    [391](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:391)     cls,
   (...)
    [403](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:403)     **kwargs: Any,
    [404](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:404) ) -> Pinecone:
    [405](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:405)     """Construct Pinecone wrapper from raw documents.
    [406](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:406) 
    [407](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:407)     This is a user friendly interface that:
   (...)
    [429](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:429)             )
    [430](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:430)     """
--> [431](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:431)     pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
    [432](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:432)     pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs)
    [434](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:434)     pinecone.add_texts(
    [435](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:435)         texts,
    [436](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:436)         metadatas=metadatas,
   (...)
    [441](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/langchain_community/vectorstores/pinecone.py:441)         **(upsert_kwargs or {}),
...
---> [45](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/pinecone/config/config.py:45)     raise PineconeConfigurationError("You haven't specified an Api-Key.")
     [46](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/pinecone/config/config.py:46) if not host:
     [47](https://vscode-remote+ssh-002dremote-002b10-002e128-002e0-002e15.vscode-resource.vscode-cdn.net/home/ubuntu/Mistral/~/Mistral/new/lib/python3.8/site-packages/pinecone/config/config.py:47)     raise PineconeConfigurationError("You haven't specified a host.")

PineconeConfigurationError: You haven't specified an Api-Key.
Dephinate commented 6 months ago

Hi, here is how I fixed it.

pinecone version = 3.0.3 langchain_pinecone version = 0.0.3

My task was to create vector embeddings for a list of text chunks and upload them to a newly created index. To do so, I initialized my index externally and passed that to langchain's PineconeVectorStore object as the internal initialization was throwing NotFoundException: (404)

step 1) I double-checked if there was no problem connecting to my DB, and I was able to find my index I initialized it.

from pinecone import Pinecone pc = Pinecone( api_key = "PINECONE_API_KEY", ) pc.list_indexes().names() # to check if my index exsist index_name = "my-index-name" index = pc.Index(index_name) index.describe_index_stats()

This returned: {'dimension': 384, 'index_fullness': 0.0, 'namespaces': {}, 'total_vector_count': 0}

step 2) Now, you can see there is nothing there and I had to create embeddings and upsert them to my index.

from langchain_pinecone import PineconeVectorStore docs_chunks =[t.page_content for t in chunks]

vectorstore = PineconeVectorStore( index=index, pinecone_api_key = 'PINECONE_API_KEY'', embedding=embeddings, namespace="medicalChatBot", index_name='medical-chatbot' )

vectorstore.add_texts(texts=[t.page_content for t in chunks])

eliterajkumar commented 6 months ago

i am using this code #Initializing the Pinecone pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_API_ENV)

index_name="aidevelopmentplatform"

Creating Embeddings for Each of The Text Chunks & storing

docsearch=Pinecone.from_texts([t.page_content for t in text_chunks], embeddings, index_name=index_name) and i am facing this issue please tell me how it fix AttributeError Traceback (most recent call last) Cell In[19], line 2 1 #Initializing the Pinecone ----> 2 pinecone.init(api_key=PINECONE_API_KEY, 3 environment=PINECONE_API_ENV) 5 index_name="aidevelopmentplatform" 7 #Creating Embeddings for Each of The Text Chunks & storing

File ~/Desktop/End-to-end-Medical-Chatbot-using-Llama2/env/lib/python3.11/site-packages/pinecone/deprecation_warnings.py:38, in init(*args, **kwargs) 11 example = """ 12 import os 13 from pinecone import Pinecone, ServerlessSpec (...) 29 ) 30 """ 31 msg = f"""init is no longer a top-level attribute of the pinecone package. 32 33 Please create an instance of the Pinecone class instead. (...) 36 {example} 37 """ ---> 38 raise AttributeError(msg)

AttributeError: init is no longer a top-level attribute of the pinecone package. ... region='us-west-2' ) )

TheArmanAnsari commented 4 months ago

thankyou Dephinate it really worked after me searching it for 2 hours!

henrykohl commented 4 months ago 
!pip install langchain_community --quiet
!pip install -U langchain-openai --quiet

from langchain_openai import OpenAIEmbeddings
from langchain_openai import OpenAI

OPENAI_API_KEY = your openai api key

embedding = OpenAIEmbeddings(openai_api_key = OPENAI_API_KEY)

os.environ['PINECONE_API_KEY'] = your pinecone api key

Then, Pinecone.from_existing_index() may work.

HusnainIzhar commented 2 months ago

No need to worry I was facing the same problem the issue was that the latest versions have updated their initializing process. you can follow these steps to solve the problem, as you have already installed pinecone-client, also install pip install langchain_pinecone now initialize as below 

from pinecone import Pinecone as PP
from langchain_pinecone import PineconeVectorStore

pc = PP(api_key=PINECONE_API_KEY)
index_name="chat"
docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)

remember this is by using the latest version of all.

it will work!

UsamaArif406 commented 2 months ago

still not working

Mutiu123 commented 1 month ago

This is what I did, and it worked:

import os pip install langchain_pinecone or add langchain_pinecone to requirements.txt, and install it using pip install -r requirements.txt from langchain_pinecone import PineconeVectorStore from pinecone import Pinecone as PP

PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY','your_api_key') PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV','gcp-starter') os.environ['PINECONE_API_ENV'] = PINECONE_API_ENV os.environ['PINECONE_API_KEY'] = PINECONE_API_KEY

pc = PP(api_key=PINECONE_API_KEY) index_name="medical-chatbot" docsearch = PineconeVectorStore.from_texts([t.page_content for t in text_chunks], embeddings, index_name=index_name)