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pinecone: Add embedding Inference Support (#24515)

Browse Source 
**Description**

Add support for Pinecone hosted embedding models as
`PineconeEmbeddings`. Replacement for #22890

**Dependencies**
Add `aiohttp` to support async embeddings call against REST directly

- [x] **Add tests and docs**: If you're adding a new integration, please
include

Added `docs/docs/integrations/text_embedding/pinecone.ipynb`


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Twitter: `gdjdg17`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Gareth 2024-07-23 18:50:28 -04:00 committed by GitHub
parent aaf788b7cb
commit ac41c97d21
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GPG Key ID: B5690EEEBB952194
9 changed files with 947 additions and 71 deletions
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155
docs/docs/integrations/text_embedding/pinecone.ipynb Normal file
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@ -0,0 +1,155 @@
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Pinecone Embeddings\n",
"\n",
"Pinecone's inference API can be accessed via `PineconeEmbeddings`. Providing text embeddings via the Pinecone service. We start by installing prerequisite libraries:"
],
"metadata": {
"collapsed": false
},
"id": "f4b5d823fee826c2"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"!pip install -qU \"langchain-pinecone>=0.2.0\" "
],
"metadata": {
"collapsed": false
},
"id": "3bc5d3a5ed7f5ce3",
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"Next, we [sign up / log in to Pinecone](https://app.pinecone.io) to get our API key:"
],
"metadata": {
"collapsed": false
},
"id": "62a77d25c3fd8bd5"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n",
" \"Enter your Pinecone API key: \"\n",
")"
],
"metadata": {
"collapsed": false
},
"id": "8162dbcbcf7d3d55",
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"Check the document for available [models](https://docs.pinecone.io/models/overview). Now we initialize our embedding model like so:"
],
"metadata": {
"collapsed": false
},
"id": "98d860a0a2d8b907"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"from langchain_pinecone import PineconeEmbeddings\n",
"\n",
"embeddings = PineconeEmbeddings(model=\"multilingual-e5-large\")"
],
"metadata": {
"collapsed": false
},
"id": "2b3adb72786a5275",
"execution_count": null
},
{
"cell_type": "markdown",
"source": [
"From here we can create embeddings either sync or async, let's start with sync! We embed a single text as a query embedding (ie what we search with in RAG) using `embed_query`:"
],
"metadata": {
"collapsed": false
},
"id": "11e24da855517230"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"docs = [\n",
" \"Apple is a popular fruit known for its sweetness and crisp texture.\",\n",
" \"The tech company Apple is known for its innovative products like the iPhone.\",\n",
" \"Many people enjoy eating apples as a healthy snack.\",\n",
" \"Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.\",\n",
" \"An apple a day keeps the doctor away, as the saying goes.\",\n",
"]"
],
"metadata": {
"collapsed": false
},
"id": "2da515e2a61ef7e9",
"execution_count": null
},
{
"cell_type": "code",
"outputs": [],
"source": [
"doc_embeds = embeddings.embed_documents(docs)\n",
"doc_embeds"
],
"metadata": {
"collapsed": false
},
"id": "2897e0d570c90b2f",
"execution_count": null
},
{
"cell_type": "code",
"outputs": [],
"source": [
"query = \"Tell me about the tech company known as Apple\"\n",
"query_embed = embeddings.embed_query(query)\n",
"query_embed"
],
"metadata": {
"collapsed": false
},
"id": "510784963c0e17a",
"execution_count": null
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
libs/partners/pinecone/langchain_pinecone/__init__.py
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@ -1,6 +1,8 @@
from langchain_pinecone.embeddings import PineconeEmbeddings
from langchain_pinecone.vectorstores import Pinecone, PineconeVectorStore from langchain_pinecone.vectorstores import Pinecone, PineconeVectorStore
__all__ = [ __all__ = [
"PineconeEmbeddings",
"PineconeVectorStore", "PineconeVectorStore",
"Pinecone", "Pinecone",
] ]

181
libs/partners/pinecone/langchain_pinecone/embeddings.py Normal file
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@ -0,0 +1,181 @@
import logging
import os
from typing import Dict, Iterable, List, Optional
import aiohttp
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
BaseModel,
Extra,
Field,
SecretStr,
root_validator,
)
from langchain_core.utils import convert_to_secret_str
from pinecone import Pinecone as PineconeClient # type: ignore
logger = logging.getLogger(__name__)
DEFAULT_BATCH_SIZE = 64
class PineconeEmbeddings(BaseModel, Embeddings):
"""PineconeEmbeddings embedding model.
Example:
.. code-block:: python
from langchain_pinecone import PineconeEmbeddings
model = PineconeEmbeddings(model="multilingual-e5-large")
"""
# Clients
_client: PineconeClient = Field(default=None, exclude=True)
_async_client: aiohttp.ClientSession = Field(default=None, exclude=True)
model: str
"""Model to use for example 'multilingual-e5-large'."""
# Config
batch_size: Optional[int] = None
"""Batch size for embedding documents."""
query_params: Dict = Field(default_factory=dict)
"""Parameters for embedding query."""
document_params: Dict = Field(default_factory=dict)
"""Parameters for embedding document"""
#
dimension: Optional[int] = None
#
show_progress_bar: bool = False
pinecone_api_key: Optional[SecretStr] = None
class Config:
extra = Extra.forbid
@root_validator(pre=True)
def set_default_config(cls, values: dict) -> dict:
"""Set default configuration based on model."""
default_config_map = {
"multilingual-e5-large": {
"batch_size": 96,
"query_params": {"input_type": "query", "truncation": "END"},
"document_params": {"input_type": "passage", "truncation": "END"},
"dimension": 1024,
}
}
model = values.get("model")
if model in default_config_map:
config = default_config_map[model]
for key, value in config.items():
if key not in values:
values[key] = value
return values
@root_validator()
def validate_environment(cls, values: dict) -> dict:
"""Validate that Pinecone version and credentials exist in environment."""
pinecone_api_key = values.get("pinecone_api_key") or os.getenv(
"PINECONE_API_KEY", None
)
if pinecone_api_key:
api_key_secretstr = convert_to_secret_str(pinecone_api_key)
values["pinecone_api_key"] = api_key_secretstr
api_key_str = api_key_secretstr.get_secret_value()
else:
api_key_str = None
if api_key_str is None:
raise ValueError(
"Pinecone API key not found. Please set the PINECONE_API_KEY "
"environment variable or pass it via `pinecone_api_key`."
)
client = PineconeClient(api_key=api_key_str, source_tag="langchain")
values["_client"] = client
# initialize async client
if not values.get("_async_client"):
values["_async_client"] = aiohttp.ClientSession(
headers={
"Api-Key": api_key_str,
"Content-Type": "application/json",
"X-Pinecone-API-Version": "2024-07",
}
)
return values
def _get_batch_iterator(self, texts: List[str]) -> Iterable:
if self.batch_size is None:
batch_size = DEFAULT_BATCH_SIZE
else:
batch_size = self.batch_size
if self.show_progress_bar:
try:
from tqdm.auto import tqdm # type: ignore
except ImportError as e:
raise ImportError(
"Must have tqdm installed if `show_progress_bar` is set to True. "
"Please install with `pip install tqdm`."
) from e
_iter = tqdm(range(0, len(texts), batch_size))
else:
_iter = range(0, len(texts), batch_size)
return _iter
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
response = self._client.inference.embed(
model=self.model,
parameters=self.document_params,
inputs=texts[i : i + self.batch_size],
)
embeddings.extend([r["values"] for r in response])
return embeddings
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
response = await self._aembed_texts(
model=self.model,
parameters=self.document_params,
texts=texts[i : i + self.batch_size],
)
embeddings.extend([r["values"] for r in response["data"]])
return embeddings
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
return self._client.inference.embed(
model=self.model, parameters=self.query_params, inputs=[text]
)[0]["values"]
async def aembed_query(self, text: str) -> List[float]:
"""Asynchronously embed query text."""
response = await self._aembed_texts(
model=self.model,
parameters=self.document_params,
texts=[text],
)
return response["data"][0]["values"]
async def _aembed_texts(
self, texts: List[str], model: str, parameters: dict
) -> Dict:
data = {
"model": model,
"inputs": [{"text": text} for text in texts],
"parameters": parameters,
}
async with self._async_client.post(
"https://api.pinecone.io/embed", json=data
) as response:
response_data = await response.json(content_type=None)
return response_data

13
libs/partners/pinecone/langchain_pinecone/vectorstores.py
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@ -40,13 +40,12 @@ class PineconeVectorStore(VectorStore):
Example: Example:
.. code-block:: python .. code-block:: python
from langchain_pinecone import PineconeVectorStore from langchain_pinecone import PineconeVectorStore, PineconeEmbeddings
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings() embeddings = PineconeEmbeddings(model="multilingual-e5-large")
index_name = "my-index" index_name = "my-index"
namespace = "my-namespace" namespace = "my-namespace"
vectorstore = Pinecone( vectorstore = PineconeVectorStore(
index_name=index_name, index_name=index_name,
embedding=embedding, embedding=embedding,
namespace=namespace, namespace=namespace,
@ -439,10 +438,10 @@ class PineconeVectorStore(VectorStore):
Example: Example:
.. code-block:: python .. code-block:: python
from langchain_pinecone import PineconeVectorStore from langchain_pinecone import PineconeVectorStore, PineconeEmbeddings
from langchain_openai import OpenAIEmbeddings
embeddings = PineconeEmbeddings(model="multilingual-e5-large")
embeddings = OpenAIEmbeddings()
index_name = "my-index" index_name = "my-index"
vectorstore = PineconeVectorStore.from_texts( vectorstore = PineconeVectorStore.from_texts(
texts, texts,

568
libs/partners/pinecone/poetry.lock generated
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@ -1,4 +1,114 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
version = "3.9.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
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[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.8.1,<3.13" python-versions = ">=3.8.1,<3.13"
content-hash = "eafe23ab7fe60b5a8c792f255fb40a0816d66441726c84bf49fe2b49056cc22b" content-hash = "0b573e4c0f16aad93c1ee236a6018c339a23fce0ae2d0e88506737519e5fddb7"

6
libs/partners/pinecone/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry] [tool.poetry]
name = "langchain-pinecone" name = "langchain-pinecone"
version = "0.1.2" version = "0.1.3"
description = "An integration package connecting Pinecone and LangChain" description = "An integration package connecting Pinecone and LangChain"
authors = [] authors = []
readme = "README.md" readme = "README.md"
@ -21,7 +21,9 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.8.1,<3.13" python = ">=3.8.1,<3.13"
langchain-core = ">=0.1.52,<0.3" langchain-core = ">=0.1.52,<0.3"
pinecone-client = ">=3.2.2,<5" pinecone-client = "^5.0.0"
aiohttp = "^3.9.5"
[[tool.poetry.dependencies.numpy]] [[tool.poetry.dependencies.numpy]]
version = "^1" version = "^1"
python = "<3.12" python = "<3.12"

76
libs/partners/pinecone/tests/integration_tests/test_embeddings.py Normal file
View File

@ -0,0 +1,76 @@
import time
import pytest
from langchain_core.documents import Document
from pinecone import Pinecone, ServerlessSpec # type: ignore
from langchain_pinecone import PineconeEmbeddings, PineconeVectorStore
DIMENSION = 1024
INDEX_NAME = "langchain-pinecone-embeddings"
MODEL = "multilingual-e5-large"
@pytest.fixture()
def embd_client() -> PineconeEmbeddings:
return PineconeEmbeddings(model=MODEL)
@pytest.fixture
def pc() -> Pinecone:
return Pinecone()
@pytest.fixture()
def pc_index(pc: Pinecone) -> Pinecone.Index:
if INDEX_NAME not in [index["name"] for index in pc.list_indexes()]:
pc.create_index(
name=INDEX_NAME,
dimension=DIMENSION,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
while not pc.describe_index(INDEX_NAME).status["ready"]:
time.sleep(1)
yield pc.Index(INDEX_NAME)
pc.delete_index(INDEX_NAME)
def test_embed_query(embd_client: PineconeEmbeddings) -> None:
out = embd_client.embed_query("Hello, world!")
assert isinstance(out, list)
assert len(out) == DIMENSION
@pytest.mark.asyncio
async def test_aembed_query(embd_client: PineconeEmbeddings) -> None:
out = await embd_client.aembed_query("Hello, world!")
assert isinstance(out, list)
assert len(out) == DIMENSION
def test_embed_documents(embd_client: PineconeEmbeddings) -> None:
out = embd_client.embed_documents(["Hello, world!", "This is a test."])
assert isinstance(out, list)
assert len(out) == 2
assert len(out[0]) == DIMENSION
@pytest.mark.asyncio
async def test_aembed_documents(embd_client: PineconeEmbeddings) -> None:
out = await embd_client.aembed_documents(["Hello, world!", "This is a test."])
assert isinstance(out, list)
assert len(out) == 2
assert len(out[0]) == DIMENSION
def test_vector_store(
embd_client: PineconeEmbeddings, pc_index: Pinecone.Index
) -> None:
vectorstore = PineconeVectorStore(index_name=INDEX_NAME, embedding=embd_client)
vectorstore.add_documents([Document("Hello, world!"), Document("This is a test.")])
time.sleep(5)
resp = vectorstore.similarity_search(query="hello")
assert len(resp) == 2

16
libs/partners/pinecone/tests/unit_tests/test_embeddings.py Normal file
View File

@ -0,0 +1,16 @@
from langchain_core.utils import convert_to_secret_str
from langchain_pinecone import PineconeEmbeddings
API_KEY = convert_to_secret_str("NOT_A_VALID_KEY")
MODEL_NAME = "multilingual-e5-large"
def test_default_config() -> None:
e = PineconeEmbeddings(pinecone_api_key=API_KEY, model=MODEL_NAME)
assert e.batch_size == 96
def test_custom_config() -> None:
e = PineconeEmbeddings(pinecone_api_key=API_KEY, model=MODEL_NAME, batch_size=128)
assert e.batch_size == 128

1
libs/partners/pinecone/tests/unit_tests/test_imports.py
View File

@ -3,6 +3,7 @@ from langchain_pinecone import __all__
EXPECTED_ALL = [ EXPECTED_ALL = [
"PineconeVectorStore", "PineconeVectorStore",
"Pinecone", "Pinecone",
"PineconeEmbeddings",
] ]