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embeddings: nomic embed vision (#22482)

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Thank you for contributing to LangChain!

**Description:** Adds Langchain support for Nomic Embed Vision
**Twitter handle:** nomic_ai,zach_nussbaum


- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **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/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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497
cookbook/nomic_multimodal_rag.ipynb Normal file
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@ -0,0 +1,497 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "9fc3897d-176f-4729-8fd1-cfb4add53abd",
"metadata": {},
"source": [
"## Nomic multi-modal RAG\n",
"\n",
"Many documents contain a mixture of content types, including text and images. \n",
"\n",
"Yet, information captured in images is lost in most RAG applications.\n",
"\n",
"With the emergence of multimodal LLMs, like [GPT-4V](https://openai.com/research/gpt-4v-system-card), it is worth considering how to utilize images in RAG:\n",
"\n",
"In this demo we\n",
"\n",
"* Use multimodal embeddings from Nomic Embed [Vision](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) and [Text](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) to embed images and text\n",
"* Retrieve both using similarity search\n",
"* Pass raw images and text chunks to a multimodal LLM for answer synthesis \n",
"\n",
"## Signup\n",
"\n",
"Get your API token, then run:\n",
"```\n",
"! nomic login\n",
"```\n",
"\n",
"Then run with your generated API token \n",
"```\n",
"! nomic login < token > \n",
"```\n",
"\n",
"## Packages\n",
"\n",
"For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54926b9b-75c2-4cd4-8f14-b3882a0d370b",
"metadata": {},
"outputs": [],
"source": [
"! nomic login token"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"! pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain # (newest versions required for multi-modal)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acbdc603-39e2-4a5f-836c-2bbaecd46b0b",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# lock to 0.10.19 due to a persistent bug in more recent versions\n",
"! pip install \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml pillow matplotlib tiktoken"
]
},
{
"cell_type": "markdown",
"id": "1e94b3fb-8e3e-4736-be0a-ad881626c7bd",
"metadata": {},
"source": [
"## Data Loading\n",
"\n",
"### Partition PDF text and images\n",
" \n",
"Let's look at an example pdfs containing interesting images.\n",
"\n",
"1/ Art from the J Paul Getty museum:\n",
"\n",
" * Here is a [zip file](https://drive.google.com/file/d/18kRKbq2dqAhhJ3DfZRnYcTBEUfYxe1YR/view?usp=sharing) with the PDF and the already extracted images. \n",
"* https://www.getty.edu/publications/resources/virtuallibrary/0892360224.pdf\n",
"\n",
"2/ Famous photographs from library of congress:\n",
"\n",
"* https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\n",
"* We'll use this as an example below\n",
"\n",
"We can use `partition_pdf` below from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images.\n",
"\n",
"To supply this to extract the images:\n",
"```\n",
"extract_images_in_pdf=True\n",
"```\n",
"\n",
"\n",
"\n",
"If using this zip file, then you can simply process the text only with:\n",
"```\n",
"extract_images_in_pdf=False\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9646b524-71a7-4b2a-bdc8-0b81f77e968f",
"metadata": {},
"outputs": [],
"source": [
"# Folder with pdf and extracted images\n",
"from pathlib import Path\n",
"\n",
"# replace with actual path to images\n",
"path = Path(\"../art\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77f096ab-a933-41d0-8f4e-1efc83998fc3",
"metadata": {},
"outputs": [],
"source": [
"path.resolve()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc4839c0-8773-4a07-ba59-5364501269b2",
"metadata": {},
"outputs": [],
"source": [
"# Extract images, tables, and chunk text\n",
"from unstructured.partition.pdf import partition_pdf\n",
"\n",
"raw_pdf_elements = partition_pdf(\n",
" filename=str(path.resolve()) + \"/getty.pdf\",\n",
" extract_images_in_pdf=False,\n",
" infer_table_structure=True,\n",
" chunking_strategy=\"by_title\",\n",
" max_characters=4000,\n",
" new_after_n_chars=3800,\n",
" combine_text_under_n_chars=2000,\n",
" image_output_dir_path=path,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "969545ad",
"metadata": {},
"outputs": [],
"source": [
"# Categorize text elements by type\n",
"tables = []\n",
"texts = []\n",
"for element in raw_pdf_elements:\n",
" if \"unstructured.documents.elements.Table\" in str(type(element)):\n",
" tables.append(str(element))\n",
" elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
" texts.append(str(element))"
]
},
{
"cell_type": "markdown",
"id": "5d8e6349-1547-4cbf-9c6f-491d8610ec10",
"metadata": {},
"source": [
"## Multi-modal embeddings with our document\n",
"\n",
"We will use [nomic-embed-vision-v1.5](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) embeddings. This model is aligned \n",
"to [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) allowing for multimodal semantic search and Multimodal RAG!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bc15842-cb95-4f84-9eb5-656b0282a800",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import uuid\n",
"\n",
"import chromadb\n",
"import numpy as np\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_nomic import NomicEmbeddings\n",
"from PIL import Image as _PILImage\n",
"\n",
"# Create chroma\n",
"text_vectorstore = Chroma(\n",
" collection_name=\"mm_rag_clip_photos_text\",\n",
" embedding_function=NomicEmbeddings(\n",
" vision_model=\"nomic-embed-vision-v1.5\", model=\"nomic-embed-text-v1.5\"\n",
" ),\n",
")\n",
"image_vectorstore = Chroma(\n",
" collection_name=\"mm_rag_clip_photos_image\",\n",
" embedding_function=NomicEmbeddings(\n",
" vision_model=\"nomic-embed-vision-v1.5\", model=\"nomic-embed-text-v1.5\"\n",
" ),\n",
")\n",
"\n",
"# Get image URIs with .jpg extension only\n",
"image_uris = sorted(\n",
" [\n",
" os.path.join(path, image_name)\n",
" for image_name in os.listdir(path)\n",
" if image_name.endswith(\".jpg\")\n",
" ]\n",
")\n",
"\n",
"# Add images\n",
"image_vectorstore.add_images(uris=image_uris)\n",
"\n",
"# Add documents\n",
"text_vectorstore.add_texts(texts=texts)\n",
"\n",
"# Make retriever\n",
"image_retriever = image_vectorstore.as_retriever()\n",
"text_retriever = text_vectorstore.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "02a186d0-27e0-4820-8092-63b5349dd25d",
"metadata": {},
"source": [
"## RAG\n",
"\n",
"`vectorstore.add_images` will store / retrieve images as base64 encoded strings.\n",
"\n",
"These can be passed to [GPT-4V](https://platform.openai.com/docs/guides/vision)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "344f56a8-0dc3-433e-851c-3f7600c7a72b",
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"import io\n",
"from io import BytesIO\n",
"\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"\n",
"def resize_base64_image(base64_string, size=(128, 128)):\n",
" \"\"\"\n",
" Resize an image encoded as a Base64 string.\n",
"\n",
" Args:\n",
" base64_string (str): Base64 string of the original image.\n",
" size (tuple): Desired size of the image as (width, height).\n",
"\n",
" Returns:\n",
" str: Base64 string of the resized image.\n",
" \"\"\"\n",
" # Decode the Base64 string\n",
" img_data = base64.b64decode(base64_string)\n",
" img = Image.open(io.BytesIO(img_data))\n",
"\n",
" # Resize the image\n",
" resized_img = img.resize(size, Image.LANCZOS)\n",
"\n",
" # Save the resized image to a bytes buffer\n",
" buffered = io.BytesIO()\n",
" resized_img.save(buffered, format=img.format)\n",
"\n",
" # Encode the resized image to Base64\n",
" return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n",
"\n",
"\n",
"def is_base64(s):\n",
" \"\"\"Check if a string is Base64 encoded\"\"\"\n",
" try:\n",
" return base64.b64encode(base64.b64decode(s)) == s.encode()\n",
" except Exception:\n",
" return False\n",
"\n",
"\n",
"def split_image_text_types(docs):\n",
" \"\"\"Split numpy array images and texts\"\"\"\n",
" images = []\n",
" text = []\n",
" for doc in docs:\n",
" doc = doc.page_content # Extract Document contents\n",
" if is_base64(doc):\n",
" # Resize image to avoid OAI server error\n",
" images.append(\n",
" resize_base64_image(doc, size=(250, 250))\n",
" ) # base64 encoded str\n",
" else:\n",
" text.append(doc)\n",
" return {\"images\": images, \"texts\": text}"
]
},
{
"cell_type": "markdown",
"id": "23a2c1d8-fea6-4152-b184-3172dd46c735",
"metadata": {},
"source": [
"Currently, we format the inputs using a `RunnableLambda` while we add image support to `ChatPromptTemplates`.\n",
"\n",
"Our runnable follows the classic RAG flow - \n",
"\n",
"* We first compute the context (both \"texts\" and \"images\" in this case) and the question (just a RunnablePassthrough here) \n",
"* Then we pass this into our prompt template, which is a custom function that formats the message for the gpt-4-vision-preview model. \n",
"* And finally we parse the output as a string."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d8919dc-c238-4746-86ba-45d940a7d260",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c93fab3-74c4-4f1d-958a-0bc4cdd0797e",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain_core.messages import HumanMessage, SystemMessage\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"\n",
"def prompt_func(data_dict):\n",
" # Joining the context texts into a single string\n",
" formatted_texts = \"\\n\".join(data_dict[\"text_context\"][\"texts\"])\n",
" messages = []\n",
"\n",
" # Adding image(s) to the messages if present\n",
" if data_dict[\"image_context\"][\"images\"]:\n",
" image_message = {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": f\"data:image/jpeg;base64,{data_dict['image_context']['images'][0]}\"\n",
" },\n",
" }\n",
" messages.append(image_message)\n",
"\n",
" # Adding the text message for analysis\n",
" text_message = {\n",
" \"type\": \"text\",\n",
" \"text\": (\n",
" \"As an expert art critic and historian, your task is to analyze and interpret images, \"\n",
" \"considering their historical and cultural significance. Alongside the images, you will be \"\n",
" \"provided with related text to offer context. Both will be retrieved from a vectorstore based \"\n",
" \"on user-input keywords. Please use your extensive knowledge and analytical skills to provide a \"\n",
" \"comprehensive summary that includes:\\n\"\n",
" \"- A detailed description of the visual elements in the image.\\n\"\n",
" \"- The historical and cultural context of the image.\\n\"\n",
" \"- An interpretation of the image's symbolism and meaning.\\n\"\n",
" \"- Connections between the image and the related text.\\n\\n\"\n",
" f\"User-provided keywords: {data_dict['question']}\\n\\n\"\n",
" \"Text and / or tables:\\n\"\n",
" f\"{formatted_texts}\"\n",
" ),\n",
" }\n",
" messages.append(text_message)\n",
"\n",
" return [HumanMessage(content=messages)]\n",
"\n",
"\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
"\n",
"# RAG pipeline\n",
"chain = (\n",
" {\n",
" \"text_context\": text_retriever | RunnableLambda(split_image_text_types),\n",
" \"image_context\": image_retriever | RunnableLambda(split_image_text_types),\n",
" \"question\": RunnablePassthrough(),\n",
" }\n",
" | RunnableLambda(prompt_func)\n",
" | model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1566096d-97c2-4ddc-ba4a-6ef88c525e4e",
"metadata": {},
"source": [
"## Test retrieval and run RAG"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90121e56-674b-473b-871d-6e4753fd0c45",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import HTML, display\n",
"\n",
"\n",
"def plt_img_base64(img_base64):\n",
" # Create an HTML img tag with the base64 string as the source\n",
" image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
"\n",
" # Display the image by rendering the HTML\n",
" display(HTML(image_html))\n",
"\n",
"\n",
"docs = text_retriever.invoke(\"Women with children\", k=5)\n",
"for doc in docs:\n",
" if is_base64(doc.page_content):\n",
" plt_img_base64(doc.page_content)\n",
" else:\n",
" print(doc.page_content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44eaa532-f035-4c04-b578-02339d42554c",
"metadata": {},
"outputs": [],
"source": [
"docs = image_retriever.invoke(\"Women with children\", k=5)\n",
"for doc in docs:\n",
" if is_base64(doc.page_content):\n",
" plt_img_base64(doc.page_content)\n",
" else:\n",
" print(doc.page_content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69fb15fd-76fc-49b4-806d-c4db2990027d",
"metadata": {},
"outputs": [],
"source": [
"chain.invoke(\"Women with children\")"
]
},
{
"cell_type": "markdown",
"id": "227f08b8-e732-4089-b65c-6eb6f9e48f15",
"metadata": {},
"source": [
"We can see the images retrieved in the LangSmith trace:\n",
"\n",
"LangSmith [trace](https://smith.langchain.com/public/69c558a5-49dc-4c60-a49b-3adbb70f74c5/r/e872c2c8-528c-468f-aefd-8b5cd730a673)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

12
docs/scripts/arxiv_references.py
View File

@ -515,7 +515,8 @@ def log_results(arxiv_id2type2key2urls):
def generate_arxiv_references_page(file_name: Path, papers: list[ArxivPaper]) -> None: def generate_arxiv_references_page(file_name: Path, papers: list[ArxivPaper]) -> None:
with open(file_name, "w") as f: with open(file_name, "w") as f:
# Write the table headers # Write the table headers
f.write("""# arXiv f.write(
"""# arXiv
LangChain implements the latest research in the field of Natural Language Processing. LangChain implements the latest research in the field of Natural Language Processing.
This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference, This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference,
@ -525,7 +526,8 @@ This page contains `arXiv` papers referenced in the LangChain Documentation, API
| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation| | arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
|------------------|---------|-------------------|------------------------| |------------------|---------|-------------------|------------------------|
""") """
)
for paper in papers: for paper in papers:
refs = [] refs = []
if paper.referencing_doc2url: if paper.referencing_doc2url:
@ -595,7 +597,8 @@ This page contains `arXiv` papers referenced in the LangChain Documentation, API
if el if el
] ]
) )
f.write(f""" f.write(
f"""
## {paper.title} ## {paper.title}
- **arXiv id:** {paper.arxiv_id} - **arXiv id:** {paper.arxiv_id}
@ -608,7 +611,8 @@ This page contains `arXiv` papers referenced in the LangChain Documentation, API
{refs} {refs}
**Abstract:** {paper.abstract} **Abstract:** {paper.abstract}
""") """
)
logger.warning(f"Created the {file_name} file with {len(papers)} arXiv references.") logger.warning(f"Created the {file_name} file with {len(papers)} arXiv references.")

4
libs/partners/nomic/langchain_nomic/__init__.py
View File

@ -1,5 +1,3 @@
from langchain_nomic.embeddings import NomicEmbeddings from langchain_nomic.embeddings import NomicEmbeddings
__all__ = [ __all__ = ["NomicEmbeddings"]
"NomicEmbeddings",
]

11
libs/partners/nomic/langchain_nomic/embeddings.py
View File

@ -22,6 +22,7 @@ class NomicEmbeddings(Embeddings):
self, self,
*, *,
model: str, model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ..., dimensionality: Optional[int] = ...,
inference_mode: Literal["remote"] = ..., inference_mode: Literal["remote"] = ...,
): ):
@ -32,6 +33,7 @@ class NomicEmbeddings(Embeddings):
self, self,
*, *,
model: str, model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ..., dimensionality: Optional[int] = ...,
inference_mode: Literal["local", "dynamic"], inference_mode: Literal["local", "dynamic"],
device: Optional[str] = ..., device: Optional[str] = ...,
@ -43,6 +45,7 @@ class NomicEmbeddings(Embeddings):
self, self,
*, *,
model: str, model: str,
nomic_api_key: Optional[str] = ...,
dimensionality: Optional[int] = ..., dimensionality: Optional[int] = ...,
inference_mode: str, inference_mode: str,
device: Optional[str] = ..., device: Optional[str] = ...,
@ -57,6 +60,7 @@ class NomicEmbeddings(Embeddings):
dimensionality: Optional[int] = None, dimensionality: Optional[int] = None,
inference_mode: str = "remote", inference_mode: str = "remote",
device: Optional[str] = None, device: Optional[str] = None,
vision_model: Optional[str] = None,
): ):
"""Initialize NomicEmbeddings model. """Initialize NomicEmbeddings model.
@ -80,6 +84,7 @@ class NomicEmbeddings(Embeddings):
self.dimensionality = dimensionality self.dimensionality = dimensionality
self.inference_mode = inference_mode self.inference_mode = inference_mode
self.device = device self.device = device
self.vision_model = vision_model
def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]: def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]:
"""Embed texts. """Embed texts.
@ -121,3 +126,9 @@ class NomicEmbeddings(Embeddings):
texts=[text], texts=[text],
task_type="search_query", task_type="search_query",
)[0] )[0]
def embed_image(self, uris: List[str]) -> List[List[float]]:
return embed.image(
images=uris,
model=self.vision_model,
)["embeddings"]

14
libs/partners/nomic/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]] [[package]]
name = "annotated-types" name = "annotated-types"
@ -276,7 +276,7 @@ files = [
[[package]] [[package]]
name = "langchain-core" name = "langchain-core"
version = "0.2.0rc1" version = "0.2.3"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
optional = false optional = false
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
@ -285,7 +285,7 @@ develop = true
[package.dependencies] [package.dependencies]
jsonpatch = "^1.33" jsonpatch = "^1.33"
langsmith = "^0.1.0" langsmith = "^0.1.65"
packaging = "^23.2" packaging = "^23.2"
pydantic = ">=1,<3" pydantic = ">=1,<3"
PyYAML = ">=5.3" PyYAML = ">=5.3"
@ -300,13 +300,13 @@ url = "../../core"
[[package]] [[package]]
name = "langsmith" name = "langsmith"
version = "0.1.58" version = "0.1.65"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false optional = false
python-versions = "<4.0,>=3.8.1" python-versions = "<4.0,>=3.8.1"
files = [ files = [
{file = "langsmith-0.1.58-py3-none-any.whl", hash = "sha256:1148cc836ec99d1b2f37cd2fa3014fcac213bb6bad798a2b21bb9111c18c9768"}, {file = "langsmith-0.1.65-py3-none-any.whl", hash = "sha256:ab4487029240e69cca30da1065f1e9138e5a7ca2bbe8c697f0bd7d5839f71cf7"},
{file = "langsmith-0.1.58.tar.gz", hash = "sha256:a5060933c1fb3006b498ec849677993329d7e6138bdc2ec044068ab806e09c39"}, {file = "langsmith-0.1.65.tar.gz", hash = "sha256:d3c2eb2391478bd79989f02652cf66e29a7959d677614b6993a47cef43f7f43b"},
] ]
[package.dependencies] [package.dependencies]
@ -1309,4 +1309,4 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
content-hash = "369d2f7218d797a01a533380de9cce01037963f628dce10bc9927eac014edeeb" content-hash = "bf51336a3b4035385ddd68946aa5bbe699f4b805dd0503ba1dd1454a69248616"

1
libs/partners/nomic/pyproject.toml
View File

@ -14,6 +14,7 @@ license = "MIT"
python = ">=3.8.1,<4.0" python = ">=3.8.1,<4.0"
langchain-core = ">=0.1.46,<0.3" langchain-core = ">=0.1.46,<0.3"
nomic = "^3.0.29" nomic = "^3.0.29"
pillow = "^10.3.0"
[tool.poetry.group.test] [tool.poetry.group.test]
optional = true optional = true

21
templates/rag-multi-modal-local/README.md
View File

@ -7,11 +7,11 @@ With the release of open source, multi-modal LLMs it's possible to build this ki
This template demonstrates how to perform private visual search and question-answering over a collection of your photos. This template demonstrates how to perform private visual search and question-answering over a collection of your photos.
It uses OpenCLIP embeddings to embed all of the photos and stores them in Chroma. It uses [`nomic-embed-vision-v1`](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) multi-modal embeddings to embed the images and `Ollama` for question-answering.
Given a question, relevant photos are retrieved and passed to an open source multi-modal LLM of your choice for answer synthesis. Given a question, relevant photos are retrieved and passed to an open source multi-modal LLM of your choice for answer synthesis.
![Diagram illustrating the visual search process with OpenCLIP embeddings and multi-modal LLM for question-answering, featuring example food pictures and a matcha soft serve answer trace.](https://github.com/langchain-ai/langchain/assets/122662504/da543b21-052c-4c43-939e-d4f882a45d75 "Visual Search Process Diagram") ![Diagram illustrating the visual search process with nomic-embed-vision-v1 embeddings and multi-modal LLM for question-answering, featuring example food pictures and a matcha soft serve answer trace.](https://github.com/langchain-ai/langchain/assets/122662504/da543b21-052c-4c43-939e-d4f882a45d75 "Visual Search Process Diagram")
## Input ## Input
@ -34,22 +34,23 @@ python ingest.py
## Storage ## Storage
This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings to embed the images. This template will use [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) multi-modal embeddings to embed the images.
You can select different embedding model options (see results [here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_results.csv)).
The first time you run the app, it will automatically download the multimodal embedding model. The first time you run the app, it will automatically download the multimodal embedding model.
By default, LangChain will use an embedding model with moderate performance but lower memory requirments, `ViT-H-14`.
You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/ingest.py`: You can choose alternative models in `rag_chroma_multi_modal/ingest.py`, such as `OpenCLIPEmbeddings`.
``` ```
langchain_experimental.open_clip import OpenCLIPEmbeddings
embedding_function=OpenCLIPEmbeddings(
model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
)
vectorstore_mmembd = Chroma( vectorstore_mmembd = Chroma(
collection_name="multi-modal-rag", collection_name="multi-modal-rag",
persist_directory=str(re_vectorstore_path), persist_directory=str(re_vectorstore_path),
embedding_function=OpenCLIPEmbeddings( embedding_function=embedding_function
model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
),
) )
``` ```

6
templates/rag-multi-modal-local/ingest.py
View File

@ -2,7 +2,7 @@ import os
from pathlib import Path from pathlib import Path
from langchain_community.vectorstores import Chroma from langchain_community.vectorstores import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings from langchain_nomic import NomicMultimodalEmbeddings
# Load images # Load images
img_dump_path = Path(__file__).parent / "docs/" img_dump_path = Path(__file__).parent / "docs/"
@ -21,7 +21,9 @@ re_vectorstore_path = vectorstore.relative_to(Path.cwd())
# Load embedding function # Load embedding function
print("Loading embedding function") print("Loading embedding function")
embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k") embedding = NomicMultimodalEmbeddings(
vision_model="nomic-embed-vision-v1", text_model="nomic-embed-text-v1"
)
# Create chroma # Create chroma
vectorstore_mmembd = Chroma( vectorstore_mmembd = Chroma(

6
templates/rag-multi-modal-local/rag_multi_modal_local/chain.py
View File

@ -9,7 +9,7 @@ from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableLambda, RunnablePassthrough from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_experimental.open_clip import OpenCLIPEmbeddings from langchain_nomic import NomicMultimodalEmbeddings
from PIL import Image from PIL import Image
@ -102,8 +102,8 @@ def multi_modal_rag_chain(retriever):
vectorstore_mmembd = Chroma( vectorstore_mmembd = Chroma(
collection_name="multi-modal-rag", collection_name="multi-modal-rag",
persist_directory=str(Path(__file__).parent.parent / "chroma_db_multi_modal"), persist_directory=str(Path(__file__).parent.parent / "chroma_db_multi_modal"),
embedding_function=OpenCLIPEmbeddings( embedding_function=NomicMultimodalEmbeddings(
model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k" vision_model="nomic-embed-vision-v1", text_model="nomic-embed-text-v1"
), ),
) )