53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
from typing import Tuple
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import chromadb
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from langchain_chroma import Chroma
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from uuid import uuid4
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# from chromadb.utils.embedding_functions.ollama_embedding_function import (
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# OllamaEmbeddingFunction,
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# )
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from langchain_ollama import OllamaEmbeddings
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from chromadb.api.types import (Metadata,Document,OneOrMany)
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# Define a custom embedding function for ChromaDB using Ollama
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class ChromaDBEmbeddingFunction:
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"""
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Custom embedding function for ChromaDB using embeddings from Ollama.
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"""
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def __init__(self, langchain_embeddings):
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self.langchain_embeddings = langchain_embeddings
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def __call__(self, input):
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# Ensure the input is in a list format for processing
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if isinstance(input, str):
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input = [input]
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return self.langchain_embeddings.embed_documents(input)
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# Initialize the embedding function with Ollama embeddings
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embedding = ChromaDBEmbeddingFunction(
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OllamaEmbeddings(
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model="nomic-embed-text",
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base_url="http://localhost:11434" # Adjust the base URL as per your Ollama server configuration
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)
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)
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persistent_client = chromadb.PersistentClient()
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collection = persistent_client.get_or_create_collection(
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name="collection_name",
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metadata={"description": "A collection for RAG with Ollama - Demo1"},
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embedding_function=embedding # Use the custom embedding function)
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)
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def add_documents(documents: Tuple[OneOrMany[Document], OneOrMany[Metadata]]):
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docs, metas = documents
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uuids = [str(uuid4()) for _ in range(len(docs))]
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collection.add(documents=docs, ids=uuids, metadatas=metas)
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def retrieve(query_text, n_results=1):
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# return vector_store.similarity_search(query, k=3)
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results = collection.query(
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query_texts=[query_text],
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n_results=n_results
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)
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return results["documents"], results["metadatas"] |