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Merge pull request #14 from lpm0073/next
add unit tests for command line examples
2 parents 9bff947 + c945753 commit 3abb0fb

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‎CHANGELOG.md‎

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## [1.1.1](https://github.com/lpm0073/netec-llm/compare/v1.1.0...v1.1.1) (2023-12-01)
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10-
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### Bug Fixes
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* had to switch to bm25_encoder so that vector store is searchable ([bad6994](https://github.com/lpm0073/netec-llm/commit/bad699481d217dde81877d85124395529652dabe))
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- had to switch to bm25_encoder so that vector store is searchable ([bad6994](https://github.com/lpm0073/netec-llm/commit/bad699481d217dde81877d85124395529652dabe))
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# [1.1.0](https://github.com/lpm0073/netec-llm/compare/v1.0.0...v1.1.0) (2023-12-01)
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‎Makefile‎

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@@ -3,7 +3,7 @@ SHELL := /bin/bash
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ifneq ("$(wildcard .env)","")
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include .env
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else
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$(shell echo -e "OPENAI_API_ORGANIZATION=PLEASE-ADD-ME\nOPENAI_API_KEY=PLEASE-ADD-ME\nPINECONE_API_KEY=PLEASE-ADD-ME\nPINECONE_ENVIRONMENT=gcp-starter\nPINECONE_INDEX_NAME=netec-ssm\nDEBUG_MODE=True\n" >> .env)
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$(shell echo -e "OPENAI_API_ORGANIZATION=PLEASE-ADD-ME\nOPENAI_API_KEY=PLEASE-ADD-ME\nPINECONE_API_KEY=PLEASE-ADD-ME\nPINECONE_ENVIRONMENT=gcp-starter\nPINECONE_INDEX_NAME=hsr\nOPENAI_CHAT_MODEL_NAME=gpt-3.5-turbo\nOPENAI_PROMPT_MODEL_NAME=text-davinci-003\nOPENAI_CHAT_TEMPERATURE=0.0\nOPENAI_CHAT_MAX_RETRIES=3\nDEBUG_MODE=True\n" >> .env)
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endif
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.PHONY: analyze init activate test lint clean

‎models/const.py‎

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@@ -1,6 +1,6 @@
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# -*- coding: utf-8 -*-
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# pylint: disable=too-few-public-methods
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"""Sales Support Model (SSM) for the LangChain project."""
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"""Sales Support Model (hsr) for the LangChain project."""
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import os
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@@ -15,11 +15,26 @@
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OPENAI_API_ORGANIZATION = os.environ["OPENAI_API_ORGANIZATION"]
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PINECONE_API_KEY = os.environ["PINECONE_API_KEY"]
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PINECONE_ENVIRONMENT = os.environ["PINECONE_ENVIRONMENT"]
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PINECONE_INDEX_NAME = os.environ["PINECONE_INDEX_NAME"]
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PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX_NAME", "hsr")
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OPENAI_CHAT_MODEL_NAME = os.environ.get("OPENAI_CHAT_MODEL_NAME", "gpt-3.5-turbo")
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OPENAI_PROMPT_MODEL_NAME = os.environ.get("OPENAI_PROMPT_MODEL_NAME", "text-davinci-003")
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OPENAI_CHAT_TEMPERATURE = float(os.environ.get("OPENAI_CHAT_TEMPERATURE", 0.0))
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OPENAI_CHAT_MAX_RETRIES = int(os.environ.get("OPENAI_CHAT_MAX_RETRIES", 3))
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OPENAI_CHAT_CACHE = bool(os.environ.get("OPENAI_CHAT_CACHE", True))
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else:
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raise FileNotFoundError("No .env file found in root directory of repository")
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class Config:
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"""Configuration parameters."""
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OPENAI_CHAT_MODEL_NAME: str = OPENAI_CHAT_MODEL_NAME
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OPENAI_PROMPT_MODEL_NAME: str = OPENAI_PROMPT_MODEL_NAME
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OPENAI_CHAT_TEMPERATURE: float = OPENAI_CHAT_TEMPERATURE
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OPENAI_CHAT_MAX_RETRIES: int = OPENAI_CHAT_MAX_RETRIES
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OPENAI_CHAT_CACHE: bool = OPENAI_CHAT_CACHE
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class Credentials:
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"""Credentials."""
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‎models/examples/load.py‎

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@@ -1,15 +1,15 @@
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# -*- coding: utf-8 -*-
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"""Sales Support Model (SSM) Retrieval Augmented Generation (RAG)"""
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"""Sales Support Model (hsr) Retrieval Augmented Generation (RAG)"""
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import argparse
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from models.hybrid_search_retreiver import HybridSearchRetriever
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ssm = HybridSearchRetriever()
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hsr = HybridSearchRetriever()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="RAG example")
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parser.add_argument("filepath", type=str, help="Location of PDF documents")
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args = parser.parse_args()
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ssm.load(filepath=args.filepath)
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hsr.load(filepath=args.filepath)

‎models/examples/prompt.py‎

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@@ -1,18 +1,18 @@
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# -*- coding: utf-8 -*-
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"""Sales Support Model (SSM)"""
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"""Sales Support Model (hsr)"""
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import argparse
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from models.hybrid_search_retreiver import HybridSearchRetriever
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8-
ssm = HybridSearchRetriever()
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hsr = HybridSearchRetriever()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="SSM examples")
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parser = argparse.ArgumentParser(description="hsr examples")
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parser.add_argument("system_prompt", type=str, help="A system prompt to send to the model.")
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parser.add_argument("human_prompt", type=str, help="A human prompt to send to the model.")
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args = parser.parse_args()
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17-
result = ssm.cached_chat_request(args.system_prompt, args.human_prompt)
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result = hsr.cached_chat_request(args.system_prompt, args.human_prompt)
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print(result)

‎models/examples/rag.py‎

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@@ -1,16 +1,16 @@
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# -*- coding: utf-8 -*-
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"""Sales Support Model (SSM) Retrieval Augmented Generation (RAG)"""
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"""Sales Support Model (hsr) Retrieval Augmented Generation (RAG)"""
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import argparse
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from models.hybrid_search_retreiver import HybridSearchRetriever
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ssm = HybridSearchRetriever()
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hsr = HybridSearchRetriever()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="RAG example")
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parser.add_argument("prompt", type=str, help="A question about the PDF contents")
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args = parser.parse_args()
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result = ssm.rag(prompt=args.prompt)
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result = hsr.rag(prompt=args.prompt)
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print(result)

‎models/examples/training_services.py‎

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@@ -1,19 +1,19 @@
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# -*- coding: utf-8 -*-
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"""Sales Support Model (SSM) for the LangChain project."""
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"""Sales Support Model (hsr) for the LangChain project."""
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import argparse
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from models.hybrid_search_retreiver import HybridSearchRetriever
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from models.prompt_templates import NetecPromptTemplates
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ssm = HybridSearchRetriever()
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hsr = HybridSearchRetriever()
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templates = NetecPromptTemplates()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="SSM examples")
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parser = argparse.ArgumentParser(description="hsr examples")
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parser.add_argument("concept", type=str, help="A kind of training that Netec provides.")
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args = parser.parse_args()
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1717
prompt = templates.training_services
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result = ssm.prompt_with_template(prompt=prompt, concept=args.concept)
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result = hsr.prompt_with_template(prompt=prompt, concept=args.concept)
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print(result)

‎models/examples/training_services_oracle.py‎

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@@ -1,19 +1,19 @@
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# -*- coding: utf-8 -*-
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"""Sales Support Model (SSM) for the LangChain project."""
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"""Sales Support Model (hsr) for the LangChain project."""
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import argparse
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from models.hybrid_search_retreiver import HybridSearchRetriever
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from models.prompt_templates import NetecPromptTemplates
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ssm = HybridSearchRetriever()
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hsr = HybridSearchRetriever()
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templates = NetecPromptTemplates()
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1212
if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="SSM Oracle examples")
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parser = argparse.ArgumentParser(description="hsr Oracle examples")
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parser.add_argument("concept", type=str, help="An Oracle certification exam prep")
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args = parser.parse_args()
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1717
prompt = templates.oracle_training_services
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result = ssm.prompt_with_template(prompt=prompt, concept=args.concept)
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result = hsr.prompt_with_template(prompt=prompt, concept=args.concept)
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print(result)

‎models/hybrid_search_retreiver.py‎

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@@ -20,7 +20,6 @@
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import glob
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import os
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import textwrap
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from typing import List
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# pinecone integration
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import pinecone
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4443
from pinecone_text.sparse import BM25Encoder
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# this project
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from models.const import Credentials
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from models.const import Config, Credentials
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###############################################################################
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# initializations
5251
###############################################################################
53-
DEFAULT_MODEL_NAME = "text-davinci-003"
52+
DEFAULT_MODEL_NAME = Config.OPENAI_PROMPT_MODEL_NAME
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pinecone.init(api_key=Credentials.PINECONE_API_KEY, environment=Credentials.PINECONE_ENVIRONMENT)
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set_llm_cache(InMemoryCache())
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class TextSplitter:
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"""
60-
Custom text splitter that add metadata to the Document object
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Custom text splitter that adds metadata to the Document object
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which is required by PineconeHybridSearchRetriever.
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"""
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64-
# ...
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def create_documents(self, texts):
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"""Create documents"""
6865
documents = []
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7673
class HybridSearchRetriever:
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"""Sales Support Model (SSM)."""
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"""Hybrid Search Retriever (OpenAI + Pinecone)"""
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# prompting wrapper
8077
chat = ChatOpenAI(
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api_key=Credentials.OPENAI_API_KEY,
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organization=Credentials.OPENAI_API_ORGANIZATION,
83-
cache=True,
84-
max_retries=3,
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model="gpt-3.5-turbo",
86-
temperature=0.0,
80+
cache=Config.OPENAI_CHAT_CACHE,
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max_retries=Config.OPENAI_CHAT_MAX_RETRIES,
82+
model=Config.OPENAI_CHAT_MODEL_NAME,
83+
temperature=Config.OPENAI_CHAT_TEMPERATURE,
8784
)
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8986
# embeddings
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112109
retval = llm(prompt.format(concept=concept))
113110
return retval
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115-
def fit_tf_idf_values(self, corpus: List[str]):
116-
"""Fit TF-IDF values.
117-
1. Fit the BM25 encoder on the corpus
118-
2. Encode the corpus
119-
3. Store the encoded corpus in Pinecone
120-
"""
121-
corpus = ["foo", "bar", "world", "hello"]
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123-
# fit tf-idf values on your corpus
124-
self.bm25_encoder.fit(corpus)
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# persist the values to a json file
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self.bm25_encoder.dump("bm25_values.json")
128-
self.bm25_encoder = BM25Encoder().load("bm25_values.json")
129-
self.bm25_encoder.fit(corpus)
130-
131112
def load(self, filepath: str):
132113
"""
133114
Embed PDF.
@@ -201,9 +182,9 @@ def rag(self, prompt: str):
201182
document_texts = [doc.page_content for doc in documents]
202183
leader = textwrap.dedent(
203184
"""\
204-
You can assume that the following is true,
205-
and you should attempt to incorporate these facts
206-
in your response:
185+
\n\nYou can assume that the following is true.
186+
You should attempt to incorporate these facts
187+
into your response:\n\n
207188
"""
208189
)
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‎models/prompt_templates.py‎

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# -*- coding: utf-8 -*-
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# pylint: disable=too-few-public-methods
3-
"""Sales Support Model (SSM) prompt templates"""
3+
"""Sales Support Model (hsr) prompt templates"""
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from langchain.prompts import PromptTemplate
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