second commit

app.py

@@ -1,115 +1,21 @@
-import chainlit as cl
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.embeddings import CacheBackedEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain.chat_models import ChatOpenAI
-from langchain.storage import LocalFileStore
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-import chainlit as cl
 
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+import chainlet as cl
+import sys
 
-# Please respond as if you were Ken from the movie Barbie. Ken is a well-meaning but naive character who loves to Beach. He talks like a typical Californian Beach Bro, but he doesn't use the word "Dude" so much.
-# If you don't know the answer, just say that you don't know, don't try to make up an answer.
-# You can make inferences based on the context as long as it still faithfully represents the feedback.
+sys.path.append(".")
 
-system_template = """
-Use the following pieces of context to answer the user's question.
-Please respond as if you are "RoaringKitty" a Youtuber known for detailed posts and videos on social media platforms like Reddit (particularly the WallStreetBets subreddit) and YouTube, where he shared his investment strategies and analysis . 
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-You can make inferences based on the context as long as it still faithfully represents the feedback.
-Example of your response should be:
-
-```
-The answer is foo
-```
-
-Begin!
-----------------
-{context}"""
-
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}"),
-]
-prompt = ChatPromptTemplate(messages=messages)
-chain_type_kwargs = {"prompt": prompt}
-
-@cl.author_rename
-def rename(orig_author: str):
-    diamond_char = u'\U0001F537'
-    phrase = diamond_char + " Diamond Hands " + diamond_char
-    rename_dict = {"RetrievalQA": phrase}
-    return rename_dict.get(orig_author, orig_author)
+from earnings_app import extract_information
 
 @cl.on_chat_start
-async def init():
-    msg = cl.Message(content=f"Building Index...")
-    await msg.send()
-
-    # build FAISS index from csv
-    loader = CSVLoader(file_path="./data/roaringkitty.csv", source_column="Link")
-    data = loader.load()
-    documents = text_splitter.transform_documents(data)
-    store = LocalFileStore("./cache/")
-    core_embeddings_model = OpenAIEmbeddings()
-    embedder = CacheBackedEmbeddings.from_bytes_store(
-        core_embeddings_model, store, namespace=core_embeddings_model.model
-    )
-    # make async docsearch
-    docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
-
-    chain = RetrievalQA.from_chain_type(
-        ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
-        chain_type="stuff",
-        return_source_documents=True,
-        retriever=docsearch.as_retriever(),
-        chain_type_kwargs = {"prompt": prompt}
-    )
-
-    msg.content = f"Index built!"
-    await msg.send()
-
-    cl.user_session.set("chain", chain)
-
+async def start():
+    cl.user_session.set("chain", extract_information())
+    await cl.Message(content="Welcome to Earnings chat!").send()
 
 @cl.on_message
-async def main(message):
+async def main(message: cl.Message):
     chain = cl.user_session.get("chain")
-    cb = cl.AsyncLangchainCallbackHandler(
-        stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
-    )
-    cb.answer_reached = True
-    res = await chain.acall(message, callbacks=[cb], )
-
-    answer = res["result"]
-    source_elements = []
-    visited_sources = set()
-
-    # Get the documents from the user session
-    docs = res["source_documents"]
-    metadatas = [doc.metadata for doc in docs]
-    all_sources = [m["source"] for m in metadatas]
-
-    for source in all_sources:
-        if source in visited_sources:
-            continue
-        visited_sources.add(source)
-        # Create the text element referenced in the message
-        source_elements.append(
-            cl.Text(content="https://www.youtube.com/watch?" + source, name="Link to Video")
-        )
-
-    if source_elements:
-        answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
-    else:
-        answer += "\nNo sources found"
-
-    await cl.Message(content=answer, elements=source_elements).send()
+    res = chain.chat(message)
+    # res = await chain.aiinvoke({"input": message})
+    # res = res["text"]
+    out = ''.join(str(res))
+    await cl.Message(content=out).send()

earnings_app.py

@@ -0,0 +1,186 @@
+# -*- coding: utf-8 -*-
+# Imports
+import asyncio
+import os
+import openai
+
+from typing import List, Optional
+from pydantic import BaseModel, Field
+
+# from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+# from langchain.utils.openai_functions import convert_pydantic_to_openai_function
+from llama_index.tools import FunctionTool
+from llama_index.vector_stores.types import (
+    VectorStoreInfo,
+    MetadataInfo,
+    ExactMatchFilter,
+    MetadataFilters,
+)
+from llama_index.agent import OpenAIAgent
+from llama_index.retrievers import VectorIndexRetriever
+from llama_index.query_engine import RetrieverQueryEngine
+
+from typing import List, Tuple, Any
+from pydantic import BaseModel, Field
+from llama_index import load_index_from_storage
+from llama_index import set_global_handler
+import llama_index
+from llama_index.embeddings import OpenAIEmbedding
+from llama_index import ServiceContext
+from llama_index.llms import OpenAI
+from llama_index.ingestion import IngestionPipeline
+from llama_index.node_parser import TokenTextSplitter
+
+set_global_handler("wandb", run_args={"project": "final-project-v1"})
+wandb_callback = llama_index.global_handler
+
+from dotenv import load_dotenv
+load_dotenv()
+
+openai.api_key = os.environ['OPENAI_API_KEY']
+
+top_k = 3
+
+vector_store_info = VectorStoreInfo(
+    content_info="transcripts of earnings calls",
+    metadata_info=[MetadataInfo(
+        name="title",
+        type="str",
+        description="Title of the earnings call",
+    ),
+    MetadataInfo(
+        name="period",
+        type="str",
+        description="Period of the earnings call"
+    ),
+    MetadataInfo(
+        name="ticker",
+        type="str",
+        description="Ticker of the company"
+    ),
+    MetadataInfo(
+        name="year",
+        type="str",
+        description="Year of the earnings call"
+    ),
+    MetadataInfo(
+        name="quarter",
+        type="str",
+        description="Quarter of the earnings call"
+    ),
+    MetadataInfo(
+        name="path",
+        type="str",
+        description="Path to the earnings call"
+    ),
+])
+
+class AutoRetrieveModel(BaseModel):
+    query: str = Field(..., description="natural language query string")
+    filter_key_list: List[str] = Field(
+        ..., description="List of metadata filter field names"
+    )
+    filter_value_list: List[str] = Field(
+        ...,
+        description=(
+            "List of metadata filter field values (corresponding to names specified in filter_key_list)"
+        )
+    )
+
+embed_model = OpenAIEmbedding()
+chunk_size = 500
+
+llm = OpenAI(
+    temperature=0,
+    model="gpt-4-1106-preview" ### YOUR CODE HERE
+)
+
+service_context = ServiceContext.from_defaults(
+    llm=llm,
+    chunk_size=chunk_size,
+    embed_model=embed_model,
+)
+
+text_splitter = TokenTextSplitter(
+    chunk_size=chunk_size
+)
+
+node_parser_pipeline = IngestionPipeline(
+    transformations=[text_splitter]
+)
+
+storage_context = wandb_callback.load_storage_context(
+    artifact_url="llmop/final-project-v1/earnings-index:v0"
+)
+
+index = load_index_from_storage(storage_context, service_context=service_context)
+
+def auto_retrieve_fn(
+    query: str, filter_key_list: List[str], filter_value_list: List[str]
+):
+    """Auto retrieval function.
+
+    Performs auto-retrieval from a vector database, and then applies a set of filters.
+
+    """
+    query = query or "Query"
+
+    exact_match_filters = [
+        ExactMatchFilter(key=k, value=v)
+        for k, v in zip(filter_key_list, filter_value_list)
+    ]
+    retriever = VectorIndexRetriever(
+        index, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k
+    )
+    query_engine = RetrieverQueryEngine.from_args(retriever, service_context=service_context)
+
+    response = query_engine.query(query)
+    return str(response)
+
+# App
+
+# Pydantic is an easy way to define a schema
+class AutoRetrieveModel(BaseModel):
+    query: str = Field(..., description="natural language query string")
+    filter_key_list: List[str] = Field(
+        ..., description="List of metadata filter field names"
+    )
+    filter_value_list: List[str] = Field(
+        ...,
+        description=(
+            "List of metadata filter field values (corresponding to names specified in filter_key_list)"
+        )
+    )
+
+# Main function to extract information
+def extract_information():
+    # Make sure to use a recent model that supports tools
+    
+    auto_retrieve_tool = FunctionTool.from_defaults(
+        fn=auto_retrieve_fn,
+        name="earnings-transcripts",
+        description="Earnings Bot",
+        fn_schema=AutoRetrieveModel
+    )
+
+    agent = OpenAIAgent.from_tools(
+        tools=[auto_retrieve_tool],
+    )
+
+    return agent
+
+
+if __name__ == "__main__":
+    text = "Who is the CEO of MSFT."
+    chain = extract_information()
+    print(str(chain.chat(text)))
+
+    async def extract_information_async(message: str):
+        return str(chain.chat(text))
+
+    async def main():
+        res = await extract_information_async(text)
+        print(res)
+
+    asyncio.run(main())

requirements.txt

@@ -3,3 +3,7 @@ langchain==0.0.265
 tiktoken==0.4.0
 openai==0.27.8
 faiss-cpu==1.7.4
+llama-index
+cohere
+wandb
+pydantic==1.10.11

talking_app.py

@@ -0,0 +1,115 @@
+import chainlit as cl
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.storage import LocalFileStore
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+import chainlit as cl
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+
+# Please respond as if you were Ken from the movie Barbie. Ken is a well-meaning but naive character who loves to Beach. He talks like a typical Californian Beach Bro, but he doesn't use the word "Dude" so much.
+# If you don't know the answer, just say that you don't know, don't try to make up an answer.
+# You can make inferences based on the context as long as it still faithfully represents the feedback.
+
+system_template = """
+Use the following pieces of context to answer the user's question.
+Please respond as if you are "RoaringKitty" a Youtuber known for detailed posts and videos on social media platforms like Reddit (particularly the WallStreetBets subreddit) and YouTube, where he shared his investment strategies and analysis . 
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+You can make inferences based on the context as long as it still faithfully represents the feedback.
+Example of your response should be:
+
+```
+The answer is foo
+```
+
+Begin!
+----------------
+{context}"""
+
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}"),
+]
+prompt = ChatPromptTemplate(messages=messages)
+chain_type_kwargs = {"prompt": prompt}
+
+@cl.author_rename
+def rename(orig_author: str):
+    diamond_char = u'\U0001F537'
+    phrase = diamond_char + " Diamond Hands " + diamond_char
+    rename_dict = {"RetrievalQA": phrase}
+    return rename_dict.get(orig_author, orig_author)
+
+@cl.on_chat_start
+async def init():
+    msg = cl.Message(content=f"Building Index...")
+    await msg.send()
+
+    # build FAISS index from csv
+    loader = CSVLoader(file_path="./data/roaringkitty.csv", source_column="Link")
+    data = loader.load()
+    documents = text_splitter.transform_documents(data)
+    store = LocalFileStore("./cache/")
+    core_embeddings_model = OpenAIEmbeddings()
+    embedder = CacheBackedEmbeddings.from_bytes_store(
+        core_embeddings_model, store, namespace=core_embeddings_model.model
+    )
+    # make async docsearch
+    docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
+
+    chain = RetrievalQA.from_chain_type(
+        ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
+        chain_type="stuff",
+        return_source_documents=True,
+        retriever=docsearch.as_retriever(),
+        chain_type_kwargs = {"prompt": prompt}
+    )
+
+    msg.content = f"Index built!"
+    await msg.send()
+
+    cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message):
+    chain = cl.user_session.get("chain")
+    cb = cl.AsyncLangchainCallbackHandler(
+        stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
+    )
+    cb.answer_reached = True
+    res = await chain.acall(message, callbacks=[cb], )
+
+    answer = res["result"]
+    source_elements = []
+    visited_sources = set()
+
+    # Get the documents from the user session
+    docs = res["source_documents"]
+    metadatas = [doc.metadata for doc in docs]
+    all_sources = [m["source"] for m in metadatas]
+
+    for source in all_sources:
+        if source in visited_sources:
+            continue
+        visited_sources.add(source)
+        # Create the text element referenced in the message
+        source_elements.append(
+            cl.Text(content="https://www.youtube.com/watch?" + source, name="Link to Video")
+        )
+
+    if source_elements:
+        answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
+    else:
+        answer += "\nNo sources found"
+
+    await cl.Message(content=answer, elements=source_elements).send()