5b. ollama_embed.py

ollama_embed.py

OllamaEmbeddings Class Breakdown

Custom embedding class for LangChain, using Ollama’s /api/embeddings endpoint to generate vector embeddings for documents and queries.

Provides a drop-in replacement for LangChain-compatible embedding models, allowing use of local Ollama models for efficient, private embedding.

def __init__(self, model: str = "nomic-embed-text", endpoint: str = None)

Parameter	Description
model	Ollama model name (default: "nomic-embed-text")
endpoint	Custom embedding endpoint (defaults to OLLAMA_EMBEDDING_URL from .env or http://localhost:11434/api/embeddings)

def embed_documents(self, texts: List[str]) -> List[List[float]]

Purpose	Embeds a list of text chunks for use in vector search
Input	List of strings (text chunks)
Output	List of vector embeddings (each embedding is a list of floats)
Logging	Logs the total number of documents being embedded

def embed_query(self, text: str) -> List[float]

Purpose	Embeds a single query string
Input	Single string
Output	A single embedding vector (list of floats)

def _embed(self, text: str) -> List[float]

Purpose	Core API call to Ollama’s embedding endpoint
Request	Sends a POST request with
Response	Expects JSON response with "embedding" field
Error Handling	Logs error and returns empty list if the request fails

Variable	Purpose
OLLAMA_EMBEDDING_URL	Used to override the default embedding endpoint (http://localhost:11434/api/embeddings)

Method	Role
init()	Configure model and endpoint
embed_documents()	Embed a batch of text chunks
embed_query()	Embed a single query string
_embed()	Perform actual API request to Ollama