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Introduction

For many practical use cases it may be necessary to give bots information; but, due to limited context windows, there may not be enough room in the prompt. Thus, Vocode allows you to plug into vector databases that contain embeddings.

Each time the bot receives a message, it can query for the most similar embeddings; these embeddings will be shown to the agent to guide its responses.

Currently, we support Pinecone. Under the hood, we use an approach similar to LangChain to store the documents in Pinecone. Each vector in Pinecone must have two pieces of metadata to be compatible with Vocode:

  • text: The text that will be shown to the agent.
  • source: The name of the document where the text comes from. This could be the title of an article, for example.

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Connecting to Pinecone

To add your vector database to your agent, you need to add a vector database config to your agent:

from vocode import AgentUpdateParams, PineconeVectorDatabaseUpdateParams

agent = vocode_client.agents.update_agent(
    id="AGENT_ID",
    request=AgentUpdateParams(
        vector_database=PineconeVectorDatabaseUpdateParams(
            type="vector_database_pinecone",
            index="PINECONE_INDEX",
            api_key="PINECONE_API_KEY",
            api_environment="PINECONE_API_ENVIRONMENT",
        )
    ),
)

Now your agent will query the vector database for each user message.

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Document loading script

You can manaully add documents to Pinecone any way you like as long as you include the required metadata. If you have a folder of PDFs, docx files, text files, etc. that you want to add to pinecone, you can use the below script which uses Unstructured to parse many kinds of files types, extract the text, and add it to pinecone.

The script was tested with these package versions:

python = "^3.10"
langchain = "^0.0.237"
spacy = "^3.6.0"
unstructured = {extras = ["local-inference"], version = "^0.8.1"}
layoutparser = {extras = ["layoutmodels", "tesseract"], version = "^0.3.4"}
pinecone-client = "^2.2.2"
openai = "^0.27.8"
torch = ">=2.0.0, !=2.0.1"
tiktoken = "^0.4.0"

import os
import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import SpacyTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import DirectoryLoader, UnstructuredFileLoader

PINECONE_API_KEY = os.environ["PINECONE_API_KEY"]
PINECONE_ENVIRONMENT = os.environ["PINECONE_ENVIRONMENT"]
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]


loader = DirectoryLoader('./docs', glob="**/*.*", show_progress=True, loader_cls=UnstructuredFileLoader)
print("Loading documents...")
documents = loader.load()
text_splitter = SpacyTextSplitter(chunk_size=1000)
print("Splitting documents...")
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

pinecone.init(
    api_key=PINECONE_API_KEY,
    environment=PINECONE_ENVIRONMENT,
)

index_name = "your_index_name"

print("Creating index...")
docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)