add q&a auto eval cookbook

Author: jalexanderII

parea/cookbook/evals_and_experiments/RAG_experiment_with_auto_evals.py

@@ -0,0 +1,121 @@
+from typing import Any
+
+import json
+import os
+import re
+from functools import lru_cache
+
+import markdownify
+import requests
+from dotenv import load_dotenv
+from langchain.text_splitter import TokenTextSplitter
+from langchain_community.vectorstores.faiss import FAISS
+from langchain_core.documents import Document
+from langchain_openai import OpenAIEmbeddings
+from openai import OpenAI
+
+from parea import Parea, trace, trace_insert
+from parea.evals.general import answer_matches_target_llm_grader_factory
+from parea.evals.rag import (
+    answer_context_faithfulness_binary_factory,
+    answer_context_faithfulness_statement_level_factory,
+    context_query_relevancy_factory,
+    percent_target_supported_by_context_factory,
+)
+
+load_dotenv()
+
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+p = Parea(api_key=os.getenv("PAREA_API_KEY"))
+p.wrap_openai_client(client)
+
+CHUNK_SIZE = 2000
+CHUNK_OVERLAP = 200
+text_splitter = TokenTextSplitter(model_name="gpt-3.5-turbo", chunk_size=CHUNK_SIZE, chunk_overlap=200)
+embeddings = OpenAIEmbeddings()
+
+MODEL = "gpt-4o"
+TOPK = 4
+NUM_SECTIONS = 20
+CODA_QA_FILE_LOC = "https://gist.githubusercontent.com/wong-codaio/b8ea0e087f800971ca5ec9eef617273e/raw/39f8bd2ebdecee485021e20f2c1d40fd649a4c77/articles.json"
+CODA_QA_PAIRS_LOC = "https://gist.githubusercontent.com/nelsonauner/2ef4d38948b78a9ec2cff4aa265cff3f/raw/c47306b4469c68e8e495f4dc050f05aff9f997e1/qa_pairs_coda_data.jsonl"
+
+
+@lru_cache()
+def get_coda_qa_content(CODA_QA_FILE_LOC) -> list[Document]:
+    coda_qa_content_data = requests.get(CODA_QA_FILE_LOC).json()
+    return [
+        Document(page_content=section.strip(), metadata={"doc_id": row["id"], "markdown": section.strip()})
+        for row in coda_qa_content_data
+        for section in re.split(r"(.*\n=+\n)", markdownify.markdownify(row["body"]))
+        if section.strip() and not re.match(r".*\n=+\n", section)
+    ]
+
+
+@lru_cache()
+def get_coda_qa_pairs_raw(CODA_QA_PAIRS_LOC):
+    coda_qa_pairs = requests.get(CODA_QA_PAIRS_LOC)
+    qa_pairs = [json.loads(line) for line in coda_qa_pairs.text.split("\n") if line]
+    return [{"question": qa_pair["input"], "doc_metadata": qa_pair["metadata"], "target": qa_pair["expected"]} for qa_pair in qa_pairs]
+
+
+class DocumentRetriever:
+    def __init__(self):
+        coda_qa_content_data = get_coda_qa_content(CODA_QA_FILE_LOC)
+        documents = text_splitter.split_documents(coda_qa_content_data)
+        vectorstore = FAISS.from_documents(documents, embeddings)
+        self.retriever = vectorstore.as_retriever(search_kwargs={"k": TOPK})
+
+    @trace
+    def retrieve_top_k(self, question: str) -> list[Document]:
+        trace_insert({"metadata": {"source_file": CODA_QA_FILE_LOC}})
+        return self.retriever.invoke(question)
+
+
+@trace(
+    eval_funcs=[
+        # Evals that do not need a target
+        answer_context_faithfulness_binary_factory(),
+        answer_context_faithfulness_statement_level_factory(),
+        context_query_relevancy_factory(context_fields=["context"]),
+        # Eval that need a target
+        answer_matches_target_llm_grader_factory(model="gpt-4o"),
+        percent_target_supported_by_context_factory(context_fields=["context"]),
+    ]
+)
+def generate_answer_from_docs(question: str, context: str) -> str:
+    return (
+        client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"""Use the following pieces of context to answer the question.
+                    Do not make up an answer if no context is provided to help answer it.
+                    \n\nContext:\n---------\n{context}\n\n---------\nQuestion: {question}\n---------\n\nAnswer:
+                    """,
+                }
+            ],
+        )
+        .choices[0]
+        .message.content
+    )
+
+
+@trace
+def main(question: str, doc_metadata: dict[str, Any]) -> str:
+    relevant_sections = DocumentRetriever().retrieve_top_k(question)
+    context = "\n\n".join(doc.page_content for doc in relevant_sections)
+    trace_insert({"metadata": doc_metadata})
+    return generate_answer_from_docs(question, context)
+
+
+if __name__ == "__main__":
+    metadata = dict(model=MODEL, topk=str(TOPK), num_sections=str(NUM_SECTIONS), chunk_size=str(CHUNK_SIZE), chunk_overlap=str(CHUNK_OVERLAP))
+    qa_pairs = get_coda_qa_pairs_raw(CODA_QA_PAIRS_LOC)
+    p.experiment(
+        name="Coda_RAG",
+        data=qa_pairs[:NUM_SECTIONS],
+        func=main,
+        metadata=metadata,
+    ).run()

parea/cookbook/parea_llm_proxy/tracing_with_agent.py

@@ -15,7 +15,7 @@
 p = Parea(api_key=os.getenv("PAREA_API_KEY"))
 
 # Parea SDK makes it easy to use different LLMs with the same apis structure and standardized request/response schemas.
-LLM_OPTIONS = [("gpt-3.5-turbo-0125", "openai"), ("gpt-4-0125-preview", "openai"), ("claude-instant-1", "anthropic"), ("claude-2.1", "anthropic")]
+LLM_OPTIONS = [("gpt-3.5-turbo-0125", "openai"), ("gpt-4o", "openai"), ("claude-3-haiku-20240307", "anthropic"), ("claude-3-opus-20240229", "anthropic")]
 LIMIT = 1
 
 

poetry.lock

@@ -80,6 +80,7 @@ boto3 = "^1.34.6"
 langchain-openai = "^0.0.5"
 pinecone-client = "^3.1.0"
 guidance = "^0.1.13"
+markdownify = "^0.12.1"
 
 [tool.black]
 # https://github.com/psf/black