OllamaDocumentEmbedder
This component computes the embeddings of a list of documents using embedding models compatible with the Ollama Library.
| Most common position in a pipeline | Before a DocumentWriter in an indexing pipeline |
| Mandatory run variables | “documents”: A list of documents to be embedded |
| Output variables | “documents”: A list of documents (enriched with embeddings) “meta”: A dictionary of metadata strings |
| API reference | Ollama |
| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/ollama |
OllamaDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses embedding models compatible with the Ollama Library.
The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector that represents the query is compared with those of the documents to find the most similar or relevant documents.
Overview
OllamaDocumentEmbedder should be used to embed a list of documents. For embedding a string only, use the OllamaTextEmbedder.
The component uses http://localhost:11434 as the default URL as most available setups (Mac, Linux, Docker) default to port 11434.
Compatible Models
Unless specified otherwise while initializing this component, the default embedding model is "nomic-embed-text". See other possible pre-built models in Ollama's library. To load your own custom model, follow the instructions from Ollama.
Installation
To start using this integration with Haystack, install the package with:
pip install ollama-haystack
Make sure that you have a running Ollama model (either through a docker container, or locally hosted). No other configuration is necessary as Ollama has the embedding API built in.
Embedding Metadata
Most embedded metadata contains information about the model name and type. You can pass optional arguments, such as temperature, top_p, and others, to the Ollama generation endpoint.
The name of the model used will be automatically appended as part of the document metadata. An example payload using the nomic-embed-text model will look like this:
{'meta': {'model': 'nomic-embed-text'}}
Usage
On its own
from haystack import Document
from haystack_integrations.components.embedders.ollama import OllamaDocumentEmbedder
doc = Document(content="What do llamas say once you have thanked them? No probllama!")
document_embedder = OllamaDocumentEmbedder()
result = document_embedder.run([doc])
print(result['documents'][0].embedding)
#Calculating embeddings: 100%|██████████| 1/1 [00:02<00:00, 2.82s/it]
#[-0.16412407159805298, -3.8359334468841553, ... ]
In a pipeline
from haystack import Pipeline
from haystack_integrations.components.embedders.ollama import OllamaDocumentEmbedder
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.converters import PyPDFToDocument
from haystack.components.writers import DocumentWriter
from haystack.document_stores.types import DuplicatePolicy
from haystack.document_stores.in_memory import InMemoryDocumentStore
document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
embedder = OllamaDocumentEmbedder(model="nomic-embed-text", url="http://localhost:11434") # This is the default model and URL
cleaner = DocumentCleaner()
splitter = DocumentSplitter()
file_converter = PyPDFToDocument()
writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)
indexing_pipeline = Pipeline()
# Add components to pipeline
indexing_pipeline.add_component("embedder", embedder)
indexing_pipeline.add_component("converter", file_converter)
indexing_pipeline.add_component("cleaner", cleaner)
indexing_pipeline.add_component("splitter", splitter)
indexing_pipeline.add_component("writer", writer)
# Connect components in pipeline
indexing_pipeline.connect("converter", "cleaner")
indexing_pipeline.connect("cleaner", "splitter")
indexing_pipeline.connect("splitter", "embedder")
indexing_pipeline.connect("embedder", "writer")
# Run Pipeline
indexing_pipeline.run({"converter": {"sources": ["files/test_pdf_data.pdf"]}})
# Calculating embeddings: 100%|██████████| 115/115
# {'embedder': {'meta': {'model': 'nomic-embed-text'}}, 'writer': {'documents_written': 115}}
Updated 4 months ago