pinecone[patch], docs: PineconeVectorStore, release 0.0.3 (#17896)

cookbook/rag_fusion.ipynb

@@ -19,7 +19,9 @@
    "source": [
     "## Setup\n",
     "\n",
-    "For this example, we will use Pinecone and some fake data"
+    "For this example, we will use Pinecone and some fake data. To configure Pinecone, set the following environment variable:\n",
+    "\n",
+    "- `PINECONE_API_KEY`: Your Pinecone API key"
    ]
   },
   {
@@ -29,11 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pinecone\n",
-    "from langchain_community.vectorstores import Pinecone\n",
     "from langchain_openai import OpenAIEmbeddings\n",
-    "\n",
-    "pinecone.init(api_key=\"...\", environment=\"...\")"
+    "from langchain_pinecone import PineconeVectorStore"
    ]
   },
   {
@@ -64,7 +63,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorstore = Pinecone.from_texts(\n",
+    "vectorstore = PineconeVectorStore.from_texts(\n",
     "    list(all_documents.values()), OpenAIEmbeddings(), index_name=\"rag-fusion\"\n",
     ")"
    ]
@@ -162,7 +161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorstore = Pinecone.from_existing_index(\"rag-fusion\", OpenAIEmbeddings())\n",
+    "vectorstore = PineconeVectorStore.from_existing_index(\"rag-fusion\", OpenAIEmbeddings())\n",
     "retriever = vectorstore.as_retriever()"
    ]
   },

docs/docs/integrations/providers/pinecone.mdx

@@ -18,7 +18,7 @@ There exists a wrapper around Pinecone indexes, allowing you to use it as a vect
 whether for semantic search or example selection.
 
 ```python
-from langchain_community.vectorstores import Pinecone
+from langchain_pinecone import PineconeVectorStore
 ```
 
 For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone)

docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb

@@ -119,13 +119,8 @@
     "import pinecone\n",
     "\n",
     "api_key = os.getenv(\"PINECONE_API_KEY\") or \"PINECONE_API_KEY\"\n",
-    "# find environment next to your API key in the Pinecone console\n",
-    "env = os.getenv(\"PINECONE_ENVIRONMENT\") or \"PINECONE_ENVIRONMENT\"\n",
     "\n",
-    "index_name = \"langchain-pinecone-hybrid-search\"\n",
-    "\n",
-    "pinecone.init(api_key=api_key, environment=env)\n",
-    "pinecone.whoami()"
+    "index_name = \"langchain-pinecone-hybrid-search\""
    ]
   },
   {

docs/docs/integrations/retrievers/self_query/pinecone.ipynb

@@ -78,8 +78,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.vectorstores import Pinecone\n",
     "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_pinecone import PineconeVectorStore\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
     "# create new index\n",
@@ -124,7 +124,7 @@
     "        },\n",
     "    ),\n",
     "]\n",
-    "vectorstore = Pinecone.from_documents(\n",
+    "vectorstore = PineconeVectorStore.from_documents(\n",
     "    docs, embeddings, index_name=\"langchain-self-retriever-demo\"\n",
     ")"
    ]

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -71,7 +71,7 @@
    "source": [
     "Now let's assume you have your Pinecone index set up with `dimension=1536`.\n",
     "\n",
-    "We can connect to our Pinecone index and insert those chunked docs as contents with `Pinecone.from_documents`."
+    "We can connect to our Pinecone index and insert those chunked docs as contents with `PineconeVectorStore.from_documents`."
    ]
   },
   {
@@ -81,11 +81,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_pinecone import Pinecone\n",
+    "from langchain_pinecone import PineconeVectorStore\n",
     "\n",
     "index_name = \"langchain-test-index\"\n",
     "\n",
-    "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)"
+    "docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)"
    ]
   },
   {
@@ -143,7 +143,7 @@
     }
    ],
    "source": [
-    "vectorstore = Pinecone(index_name=index_name, embedding=embeddings)\n",
+    "vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)\n",
     "\n",
     "vectorstore.add_texts([\"More text!\"])"
    ]

docs/docs/use_cases/question_answering/per_user.ipynb

@@ -35,28 +35,22 @@
     "\n",
     "## Code Example\n",
     "\n",
-    "Let's see a concrete example of what this looks like in code. We will use Pinecone for this example."
+    "Let's see a concrete example of what this looks like in code. We will use Pinecone for this example.\n",
+    "\n",
+    "To configure Pinecone, set the following environment variable:\n",
+    "\n",
+    "- `PINECONE_API_KEY`: Your Pinecone API key"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "75823b2d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import pinecone\n",
-    "from langchain_community.vectorstores import Pinecone\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_pinecone import PineconeVectorStore"
    ]
   },
   {
@@ -77,12 +71,8 @@
     }
    ],
    "source": [
-    "# The environment should be the one specified next to the API key\n",
-    "# in your Pinecone console\n",
-    "pinecone.init(api_key=\"...\", environment=\"...\")\n",
-    "index = pinecone.Index(\"test-example\")\n",
     "embeddings = OpenAIEmbeddings()\n",
-    "vectorstore = Pinecone(index, embeddings, \"text\")\n",
+    "vectorstore = PineconeVectorStore(index_name=\"test-example\", embedding=embeddings)\n",
     "\n",
     "vectorstore.add_texts([\"i worked at kensho\"], namespace=\"harrison\")\n",
     "vectorstore.add_texts([\"i worked at facebook\"], namespace=\"ankush\")"
@@ -301,15 +291,16 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "For more vectorstore implementations for multi-user, please refer to specific pages, such as [Milvus](/docs/integrations/vectorstores/milvus)."
-   ],
+   "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%% md\n"
     }
-   }
+   },
+   "source": [
+    "For more vectorstore implementations for multi-user, please refer to specific pages, such as [Milvus](/docs/integrations/vectorstores/milvus)."
+   ]
   }
  ],
  "metadata": {
@@ -333,4 +324,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

libs/community/langchain_community/vectorstores/pinecone.py

@@ -402,7 +402,9 @@ def from_texts(
         embeddings_chunk_size: int = 1000,
         **kwargs: Any,
     ) -> Pinecone:
-        """Construct Pinecone wrapper from raw documents.
+        """
+        DEPRECATED: use langchain_pinecone.PineconeVectorStore.from_texts instead:
+        Construct Pinecone wrapper from raw documents.
 
         This is a user friendly interface that:
             1. Embeds documents.
@@ -411,21 +413,20 @@ def from_texts(
         This is intended to be a quick way to get started.
 
         The `pool_threads` affects the speed of the upsert operations.
+
         Example:
             .. code-block:: python
 
-                from langchain_community.vectorstores import Pinecone
-                from langchain_community.embeddings import OpenAIEmbeddings
-                import pinecone
+                from langchain_pinecone import PineconeVectorStore
+                from langchain_openai import OpenAIEmbeddings
 
-                # The environment should be the one specified next to the API key
-                # in your Pinecone console
-                pinecone.init(api_key="***", environment="...")
                 embeddings = OpenAIEmbeddings()
-                pinecone = Pinecone.from_texts(
-                    texts,
-                    embeddings,
-                    index_name="langchain-demo"
+                index_name = "my-index"
+                namespace = "my-namespace"
+                vectorstore = Pinecone(
+                    index_name=index_name,
+                    embedding=embedding,
+                    namespace=namespace,
                 )
         """
         pinecone_index = cls.get_pinecone_index(index_name, pool_threads)

libs/partners/pinecone/Makefile

@@ -5,13 +5,9 @@ all: help
 
 # Define a variable for the test file path.
 TEST_FILE ?= tests/unit_tests/
+integration_test integration_tests: TEST_FILE = tests/integration_tests/
 
-integration_tests: TEST_FILE = tests/integration_tests/
-
-test integration_tests:
-	poetry run pytest $(TEST_FILE)
-
-tests:
+test tests integration_test integration_tests:
 	poetry run pytest $(TEST_FILE)
 
 

libs/partners/pinecone/README.md

@@ -12,14 +12,13 @@ And you should configure credentials by setting the following environment variab
 
 - `PINECONE_API_KEY`
 - `PINECONE_INDEX_NAME`
-- `PINECONE_ENVIRONMENT`
 
 ## Usage
 
 The `Pinecone` class exposes the connection to the Pinecone vector store.
 
 ```python
-from langchain_pinecone import Pinecone
+from langchain_pinecone import PineconeVectorStore
 
 embeddings = ... # use a LangChain Embeddings class
 

libs/partners/pinecone/langchain_pinecone/__init__.py

@@ -1,5 +1,6 @@
-from langchain_pinecone.vectorstores import Pinecone
+from langchain_pinecone.vectorstores import Pinecone, PineconeVectorStore
 
 __all__ = [
+    "PineconeVectorStore",
     "Pinecone",
 ]

libs/partners/pinecone/langchain_pinecone/vectorstores.py

@@ -15,6 +15,7 @@
 )
 
 import numpy as np
+from langchain_core._api.deprecation import deprecated
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils.iter import batch_iterate
@@ -31,13 +32,15 @@
 VST = TypeVar("VST", bound=VectorStore)
 
 
-class Pinecone(VectorStore):
+class PineconeVectorStore(VectorStore):
     """`Pinecone` vector store.
 
+    Setup: set the `PINECONE_API_KEY` environment variable to your Pinecone API key.
+
     Example:
         .. code-block:: python
 
-            from langchain_pinecone import Pinecone
+            from langchain_pinecone import PineconeVectorStore
             from langchain_openai import OpenAIEmbeddings
 
             embeddings = OpenAIEmbeddings()
@@ -401,7 +404,7 @@ def from_texts(
         pool_threads: int = 4,
         embeddings_chunk_size: int = 1000,
         **kwargs: Any,
-    ) -> Pinecone:
+    ) -> PineconeVectorStore:
         """Construct Pinecone wrapper from raw documents.
 
         This is a user friendly interface that:
@@ -411,21 +414,22 @@ def from_texts(
         This is intended to be a quick way to get started.
 
         The `pool_threads` affects the speed of the upsert operations.
+
+        Setup: set the `PINECONE_API_KEY` environment variable to your Pinecone API key.
+
         Example:
             .. code-block:: python
 
-                from langchain_community.vectorstores import Pinecone
-                from langchain_community.embeddings import OpenAIEmbeddings
-                import pinecone
+                from langchain_pinecone import PineconeVectorStore
+                from langchain_openai import OpenAIEmbeddings
 
-                # The environment should be the one specified next to the API key
-                # in your Pinecone console
-                pinecone.init(api_key="***", environment="...")
                 embeddings = OpenAIEmbeddings()
-                pinecone = Pinecone.from_texts(
+                index_name = "my-index"
+                vectorstore = PineconeVectorStore.from_texts(
                     texts,
-                    embeddings,
-                    index_name="langchain-demo"
+                    index_name=index_name,
+                    embedding=embedding,
+                    namespace=namespace,
                 )
         """
         pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
@@ -450,7 +454,7 @@ def from_existing_index(
         text_key: str = "text",
         namespace: Optional[str] = None,
         pool_threads: int = 4,
-    ) -> Pinecone:
+    ) -> PineconeVectorStore:
         """Load pinecone vectorstore from index name."""
         pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
         return cls(pinecone_index, embedding, text_key, namespace)
@@ -485,3 +489,10 @@ def delete(
             raise ValueError("Either ids, delete_all, or filter must be provided.")
 
         return None
+
+
+@deprecated(since="0.0.3", removal="0.2.0", alternative="PineconeVectorStore")
+class Pinecone(PineconeVectorStore):
+    """Deprecated. Use PineconeVectorStore instead."""
+
+    pass