retrieval_with_namespace.py

from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone

# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(
    api_key="...", environment="..."
)
index = pinecone.Index("test123")
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone(index, embeddings.embed_query, "text")

vectorstore.add_texts(["i worked at kensho"], namespace="harrison")
vectorstore.add_texts(["i worked at facebook"], namespace="ankush")

# The pinecone kwarg for namespace can be used to separate documents
vectorstore.as_retriever(search_kwargs={"namespace": None}).get_relevant_documents(
    "where did i work?"
)
vectorstore.as_retriever(search_kwargs={"namespace": "harrison"}).get_relevant_documents(
    "where did i work?"
)

from operator import itemgetter

from typing import Optional, Mapping, Any
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough, RunnableBinding
from langchain_core.runnables import ConfigurableField

template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)

model = ChatOpenAI()

retriever = vectorstore.as_retriever()

# Here we mark the retriever as configurable
# All vectorstore retrievers have `search_kwargs` as a field
# This is just a dictionary, with vectorstore specific fields
configurable_retriever = retriever.configurable_fields(
    search_kwargs=ConfigurableField(
        id="search_kwargs",
        name="Search Kwargs",
        description="The search kwargs to use",
    )
)

chain = (
    {"context": configurable_retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

chain.invoke(
  "where did the user work?", 
  # We can now invoke the chain with configurable options
  # search_kwargs is the id of the configurable field
  # The value is the search kwargs to use for Pinecone
  config={"configurable": {"search_kwargs": {"namespace": "harrison"}}}
)

A comment for line 21 saying that this'll respond with "Kensho"

Useless itemgetter import