Langchain Output Parsing

Download Data

%pip install llama-index-llms-openai

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

Will not apply HSTS. The HSTS database must be a regular and non-world-writable file.
ERROR: could not open HSTS store at '/home/loganm/.wget-hsts'. HSTS will be disabled.
--2023-12-11 10:24:04--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.04s

2023-12-11 10:24:04 (1.74 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

Load documents, build the VectorStoreIndex

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from IPython.display import Markdown, display

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

index = VectorStoreIndex.from_documents(documents, chunk_size=512)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

Define Query + Langchain Output Parser

from llama_index.core.output_parsers import LangchainOutputParser
from langchain.output_parsers import StructuredOutputParser, ResponseSchema

Define custom QA and Refine Prompts

response_schemas = [
    ResponseSchema(
        name="Education",
        description=(
            "Describes the author's educational experience/background."
        ),
    ),
    ResponseSchema(
        name="Work",
        description="Describes the author's work experience/background.",
    ),
]

lc_output_parser = StructuredOutputParser.from_response_schemas(
    response_schemas
)
output_parser = LangchainOutputParser(lc_output_parser)

from llama_index.core.prompts.default_prompts import (
    DEFAULT_TEXT_QA_PROMPT_TMPL,
)

# take a look at the new QA template!
fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL)
print(fmt_qa_tmpl)

Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {query_str}
Answer:

The output should be a markdown code snippet formatted in the following schema, including the leading and trailing "```json" and "```":

```json
{{
  "Education": string  // Describes the author's educational experience/background.
  "Work": string  // Describes the author's work experience/background.
}}
```

Query Index

from llama_index.llms.openai import OpenAI

llm = OpenAI(output_parser=output_parser)

query_engine = index.as_query_engine(
    llm=llm,
)
response = query_engine.query(
    "What are a few things the author did growing up?",
)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"


INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"

print(response)

{'Education': 'The author did not plan to study programming in college, but initially planned to study philosophy.', 'Work': 'Growing up, the author worked on writing short stories and programming. They wrote simple games, a program to predict rocket heights, and a word processor.'}