<code># Import necessary modules and classes from langchain_community and langchain_core
from langchain_community.vectorstores import Chroma
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_openai import OpenAIEmbeddings ## paid
from langchain.embeddings import HuggingFaceEmbeddings #free
## setup vector db
# Create an instance of Chroma vector store
vectorstore = Chroma()
# Delete any existing collection in the vector store
# vectorstore.delete_collection()
# Create a SemanticSimilarityExampleSelector instance using examples, OpenAI embeddings, and the vector store
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples, # List of example queries and inputs
HuggingFaceEmbeddings(), #HuggingFaceEmbeddings() OpenAIEmbeddings() # OpenAI Embeddings for generating vector representations
vectorstore, # Chroma vector store for storing and querying vector representations
k=2, # Number of similar examples to retrieve
input_keys=["input"], # Define the input keys to consider for semantic similarity
)
</code>
<code># Import necessary modules and classes from langchain_community and langchain_core
from langchain_community.vectorstores import Chroma
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_openai import OpenAIEmbeddings ## paid
from langchain.embeddings import HuggingFaceEmbeddings #free
## setup vector db
# Create an instance of Chroma vector store
vectorstore = Chroma()
# Delete any existing collection in the vector store
# vectorstore.delete_collection()
# Create a SemanticSimilarityExampleSelector instance using examples, OpenAI embeddings, and the vector store
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples, # List of example queries and inputs
HuggingFaceEmbeddings(), #HuggingFaceEmbeddings() OpenAIEmbeddings() # OpenAI Embeddings for generating vector representations
vectorstore, # Chroma vector store for storing and querying vector representations
k=2, # Number of similar examples to retrieve
input_keys=["input"], # Define the input keys to consider for semantic similarity
)
</code>
# Import necessary modules and classes from langchain_community and langchain_core
from langchain_community.vectorstores import Chroma
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_openai import OpenAIEmbeddings ## paid
from langchain.embeddings import HuggingFaceEmbeddings #free
## setup vector db
# Create an instance of Chroma vector store
vectorstore = Chroma()
# Delete any existing collection in the vector store
# vectorstore.delete_collection()
# Create a SemanticSimilarityExampleSelector instance using examples, OpenAI embeddings, and the vector store
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples, # List of example queries and inputs
HuggingFaceEmbeddings(), #HuggingFaceEmbeddings() OpenAIEmbeddings() # OpenAI Embeddings for generating vector representations
vectorstore, # Chroma vector store for storing and querying vector representations
k=2, # Number of similar examples to retrieve
input_keys=["input"], # Define the input keys to consider for semantic similarity
)
This is my code, its showing an error “name ‘examples’ is not defined”. How to solve it ?
I tried to debug, but i failed. Could anyone hepl me out.
Does examples get defined? Here’s an example of SemanticSimilarityExampleSelector.from_examples
<code>example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}nOutput: {output}",
)
# Examples of a pretend task of creating antonyms.
examples = [
{"input": "happy", "output": "sad"},
{"input": "tall", "output": "short"},
{"input": "energetic", "output": "lethargic"},
{"input": "sunny", "output": "gloomy"},
{"input": "windy", "output": "calm"},
]
example_selector = SemanticSimilarityExampleSelector.from_examples(
# The list of examples available to select from.
examples,
# The embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# The VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,
# The number of examples to produce.
k=1,
)
similar_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}nOutput:",
input_variables=["adjective"],
)
</code>
<code>example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}nOutput: {output}",
)
# Examples of a pretend task of creating antonyms.
examples = [
{"input": "happy", "output": "sad"},
{"input": "tall", "output": "short"},
{"input": "energetic", "output": "lethargic"},
{"input": "sunny", "output": "gloomy"},
{"input": "windy", "output": "calm"},
]
example_selector = SemanticSimilarityExampleSelector.from_examples(
# The list of examples available to select from.
examples,
# The embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# The VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,
# The number of examples to produce.
k=1,
)
similar_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}nOutput:",
input_variables=["adjective"],
)
</code>
example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}nOutput: {output}",
)
# Examples of a pretend task of creating antonyms.
examples = [
{"input": "happy", "output": "sad"},
{"input": "tall", "output": "short"},
{"input": "energetic", "output": "lethargic"},
{"input": "sunny", "output": "gloomy"},
{"input": "windy", "output": "calm"},
]
example_selector = SemanticSimilarityExampleSelector.from_examples(
# The list of examples available to select from.
examples,
# The embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# The VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,
# The number of examples to produce.
k=1,
)
similar_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}nOutput:",
input_variables=["adjective"],
)
Take a look at the reference > https://python.langchain.com/v0.2/docs/how_to/example_selectors_mmr/