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vin00d commited on
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report generator

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Files changed (8) hide show
  1. Dockerfile.dockerignore +45 -0
  2. REPORT.md +4 -4
  3. app.py +1 -1
  4. fine_tune_emb_and_eval.ipynb +0 -0
  5. pyproject.toml +1 -0
  6. saul/graph.py +88 -0
  7. saul/tools.py +53 -8
  8. uv.lock +566 -0
Dockerfile.dockerignore ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Virtual environment
2
+ .venv/
3
+ venv/
4
+
5
+ # Environment variables
6
+ .env
7
+ */*.env
8
+
9
+ # Python
10
+ __pycache__/
11
+ *.py[cod]
12
+ *$py.class
13
+ *.so
14
+ .Python
15
+ build/
16
+ develop-eggs/
17
+ dist/
18
+ downloads/
19
+ eggs/
20
+ .eggs/
21
+ lib/
22
+ lib64/
23
+ parts/
24
+ sdist/
25
+ var/
26
+ wheels/
27
+ *.egg-info/
28
+ .installed.cfg
29
+ *.egg
30
+
31
+ # IDE
32
+ .idea/
33
+ .vscode/
34
+ *.swp
35
+ *.swo
36
+
37
+ # Logs
38
+ *.log
39
+ logs/
40
+ .chainlit/*
41
+
42
+ **/.DS_Store
43
+ .DS_Store
44
+
45
+ finetune_data/*
REPORT.md CHANGED
@@ -47,7 +47,7 @@
47
  - App deployed in Hugging Face Spaces - https://huggingface.co/spaces/vin00d/agentic-saul
48
 
49
  # 4. Creating a Golden Test Data Set
50
- **Eval Metrics for Base Model**
51
  - Faithfulness and Answer Relevancy stand out as particularly problematic
52
  - Legal terms are indeed unique with their use of Latin in legal contexts which are not in regular use and therefore unseen by AI models in their training data.
53
 
@@ -61,11 +61,11 @@
61
  | Noise Sensitivity Relevant | 0.0354 |
62
 
63
  # 5. Fine-Tuning Open-Source Embeddings
64
- - Finetuned model - https://huggingface.co/vin00d/snowflake-arctic-legal-ft-1
65
 
66
  # 6. Assessing Performance
67
- **Eval Metrics for Fine-Tuned Model**
68
- - The fine-tuned model shows a significant improvement across all metrics, with the most notable improvements in Context Recall, Faithfulness and Answer Relevancy.
69
 
70
  | Metric | Value |
71
  |-----------------------------|---------|
 
47
  - App deployed in Hugging Face Spaces - https://huggingface.co/spaces/vin00d/agentic-saul
48
 
49
  # 4. Creating a Golden Test Data Set
50
+ **Eval Metrics for Base Embedding Model - `snowflake-arctic-embed-l`**
51
  - Faithfulness and Answer Relevancy stand out as particularly problematic
52
  - Legal terms are indeed unique with their use of Latin in legal contexts which are not in regular use and therefore unseen by AI models in their training data.
53
 
 
61
  | Noise Sensitivity Relevant | 0.0354 |
62
 
63
  # 5. Fine-Tuning Open-Source Embeddings
64
+ - Finetuned embedding model - https://huggingface.co/vin00d/snowflake-arctic-legal-ft-1
65
 
66
  # 6. Assessing Performance
67
+ **Eval Metrics for Fine-Tuned Embedding Model**
68
+ - The fine-tuned embedding model shows a significant improvement across all metrics, with the most notable improvements in Context Recall, Faithfulness and Answer Relevancy.
69
 
70
  | Metric | Value |
71
  |-----------------------------|---------|
app.py CHANGED
@@ -90,7 +90,7 @@ async def start():
90
 
91
  await cl.Message(
92
  content="""### Better Call Agentic-Saul
93
- 🫵 Yeah! I'm Saul, your legal research assistant.
94
  I'm here to help you with your legal research needs.
95
 
96
  I will find information from:
 
90
 
91
  await cl.Message(
92
  content="""### Better Call Agentic-Saul
93
+ 🫵 Hey! I'm Saul, your legal research assistant.
94
  I'm here to help you with your legal research needs.
95
 
96
  I will find information from:
fine_tune_emb_and_eval.ipynb CHANGED
The diff for this file is too large to render. See raw diff
 
pyproject.toml CHANGED
@@ -12,6 +12,7 @@ dependencies = [
12
  "langchain-community~=0.3.16",
13
  "langchain-openai~=0.3.2",
14
  "langchain-qdrant~=0.2.0",
 
15
  "wikipedia~=1.4.0",
16
  "praw~=7.8.1",
17
  "semanticscholar~=0.9.0",
 
12
  "langchain-community~=0.3.16",
13
  "langchain-openai~=0.3.2",
14
  "langchain-qdrant~=0.2.0",
15
+ "langchain_huggingface~=0.1.2",
16
  "wikipedia~=1.4.0",
17
  "praw~=7.8.1",
18
  "semanticscholar~=0.9.0",
saul/graph.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Annotated, Dict, TypedDict
2
+ from langgraph.graph import START, END, StateGraph
3
+ from langgraph.graph.message import add_messages
4
+ from langgraph.prebuilt import ToolNode
5
+ from langchain_openai import ChatOpenAI
6
+ from tools import tools
7
+ from loguru import logger
8
+
9
+
10
+ ##### Research Subgraph #####
11
+ # State for the research agent
12
+ class ResearchState(TypedDict):
13
+ """State for the research agent."""
14
+ messages: Annotated[list, add_messages]
15
+
16
+ # Initialize the LLM
17
+ research_model = ChatOpenAI(model="gpt-4o-mini", temperature=0, streaming=True)
18
+
19
+ # bind tools to the llm
20
+ research_model = research_model.bind_tools(tools)
21
+
22
+
23
+ # Agent node implementation
24
+ async def pick_research_tool(state: ResearchState) -> Dict:
25
+ """Agent node that decides which tool to use."""
26
+ logger.success(f"Calling research_model from research_agent with state: {state}")
27
+ # print(f"State:: {state}\n\n")
28
+ response = research_model.invoke(state["messages"])
29
+ return {"messages": [response]}
30
+
31
+
32
+ execute_tool = ToolNode(tools)
33
+
34
+ # Create the Research Sub Graph
35
+ research_sg_builder = StateGraph(ResearchState)
36
+
37
+ # Add nodes
38
+ research_sg_builder.add_node("research_agent", pick_research_tool)
39
+ research_sg_builder.add_node("research_action", execute_tool)
40
+
41
+
42
+ # conditional edge function
43
+ def should_continue(state: ResearchState) -> str:
44
+ last_message = state["messages"][-1]
45
+
46
+ if last_message.get("tool_calls"):
47
+ return "research_action"
48
+
49
+ return END
50
+
51
+
52
+ # Add edges
53
+ research_sg_builder.add_edge(START, "research_agent")
54
+ research_sg_builder.add_conditional_edges("research_agent", should_continue)
55
+ research_sg_builder.add_edge("research_action", "research_agent")
56
+
57
+
58
+ # system_prompt = """You are a helpful legal research assistant.
59
+ # Only answer questions that are related to legal research, else politely decline to answer.
60
+ # Only answer the last question.
61
+ # """
62
+
63
+ ## Report Graph
64
+ # State for the report agent
65
+ class ReportState(TypedDict):
66
+ """State for the report agent."""
67
+ messages: Annotated[list, add_messages]
68
+
69
+ # Initialize the LLM
70
+ report_model = ChatOpenAI(model="gpt-4o-mini", temperature=0, streaming=True)
71
+ # bind tools to the llm
72
+ report_model = report_model.bind_tools(tools)
73
+ # Agent node implementation
74
+ async def pick_report_tool(state: ReportState) -> Dict:
75
+ """Agent node that decides which tool to use."""
76
+ logger.success(f"Calling report_model from report_agent with state: {state}")
77
+ # print(f"State:: {state}\n\n")
78
+ response = report_model.invoke(state["messages"])
79
+ return {"messages": [response]}
80
+
81
+ execute_tool = ToolNode(tools)
82
+
83
+
84
+ ## Create full graph
85
+ builder = StateGraph(ReportState)
86
+ # Add nodes
87
+ builder.add_node("conduct_research", research_sg_builder.compile())
88
+ builder.add_node("report_agent", pick_report_tool)
saul/tools.py CHANGED
@@ -12,8 +12,8 @@ from langchain_community.utilities import WikipediaAPIWrapper
12
  from serpapi import GoogleSearch
13
 
14
  ## imports for vectore store retriever
15
- from langchain_openai import OpenAIEmbeddings
16
- # from langchain_huggingface import HuggingFaceEmbeddings
17
  from langchain_qdrant import QdrantVectorStore
18
  from qdrant_client import QdrantClient
19
  from qdrant_client.http.models import Distance, VectorParams
@@ -22,7 +22,18 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
22
 
23
  from loguru import logger
24
 
25
- load_dotenv()
 
 
 
 
 
 
 
 
 
 
 
26
 
27
  # Add the parent directory to the system path
28
  parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@@ -69,16 +80,21 @@ def google_scholar_tool(query: str, top_k: int = 10) -> str:
69
 
70
 
71
  ######## Vector Store Retriever ########
72
- embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
73
- # finuned model embeddings - model name "vin00d/snowflake-arctic-legal-ft-1"
74
- # embeddings = HuggingFaceEmbeddings(model_name="vin00d/snowflake-arctic-legal-ft-1")
 
 
 
 
 
75
 
76
  # Initialize Qdrant client
77
  client = QdrantClient(":memory:")
78
 
79
  client.create_collection(
80
  collection_name="legal_mumbo_jumbo",
81
- vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
82
  )
83
 
84
  vector_store = QdrantVectorStore(
@@ -119,7 +135,35 @@ def rag_tool(query: str) -> str:
119
 
120
  return results
121
 
122
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
123
 
124
  # Initialize tools
125
  tools = [
@@ -127,4 +171,5 @@ tools = [
127
  reddit_tool,
128
  google_scholar_tool,
129
  rag_tool,
 
130
  ]
 
12
  from serpapi import GoogleSearch
13
 
14
  ## imports for vectore store retriever
15
+ from langchain_openai import OpenAIEmbeddings, ChatOpenAI
16
+ from langchain_huggingface import HuggingFaceEndpointEmbeddings
17
  from langchain_qdrant import QdrantVectorStore
18
  from qdrant_client import QdrantClient
19
  from qdrant_client.http.models import Distance, VectorParams
 
22
 
23
  from loguru import logger
24
 
25
+ EMB_MODEL = os.getenv("HF_FT_EMBED_MODEL_URL")
26
+ if EMB_MODEL is None or EMB_MODEL == "":
27
+ EMB_MODEL = "text-embedding-3-small"
28
+ EMB_DIM = 1536
29
+ logger.warning("No embedding model specified. Defaulting to 'text-embedding-3-small', emb_dim=1536")
30
+ else:
31
+ HF_TOKEN = os.getenv("HF_TOKEN")
32
+ if HF_TOKEN is None or HF_TOKEN == "":
33
+ raise ValueError("HF_TOKEN not found in .env. Please set it to use the embedding model.")
34
+ EMB_DIM = 1024 # 1024 for vin00d/snowflake-arctic-legal-ft-1
35
+ logger.info(f"Using embedding model: {EMB_MODEL}, emb_dim={EMB_DIM}")
36
+
37
 
38
  # Add the parent directory to the system path
39
  parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
80
 
81
 
82
  ######## Vector Store Retriever ########
83
+ if EMB_MODEL == "text-embedding-3-small":
84
+ embeddings = OpenAIEmbeddings(model=EMB_MODEL)
85
+ else:
86
+ embeddings = HuggingFaceEndpointEmbeddings(
87
+ model=EMB_MODEL,
88
+ task="feature-extraction",
89
+ huggingfacehub_api_token=HF_TOKEN,
90
+ )
91
 
92
  # Initialize Qdrant client
93
  client = QdrantClient(":memory:")
94
 
95
  client.create_collection(
96
  collection_name="legal_mumbo_jumbo",
97
+ vectors_config=VectorParams(size=EMB_DIM, distance=Distance.COSINE),
98
  )
99
 
100
  vector_store = QdrantVectorStore(
 
135
 
136
  return results
137
 
138
+ @tool
139
+ def report_writer_tool(content: str) -> str:
140
+ """A tool to write a report based on the given content."""
141
+ logger.info(f"Invoking report writer tool")
142
+
143
+ llm = ChatOpenAI(model="gpt-4o-mini", temperature=0, streaming=True)
144
+
145
+ system_prompt = """
146
+ You are a helpful legal research assistant and an expert report writer.
147
+ Organize the given information into a well-structured report (300 words or less) with the following sections:
148
+ - Introduction
149
+ -- Abstract
150
+ -- Background
151
+ - Legal Analysis
152
+ -- Identify and discuss relevant laws, statutes, regulations, and case law.
153
+ -- Analyze the legal principles and precedents.
154
+ -- Explain the reasoning behind legal arguments.
155
+ - Sources
156
+ -- Primary Sources: Cite relevant statutes, regulations, and case law.
157
+ -- Secondary Sources: Cite legal textbooks, articles, and other relevant literature.
158
+ - Conclusion
159
+ """
160
+ logger.trace(f"Content to write report: {content}")
161
+ response = llm.invoke(
162
+ [{"role": "system", "content": system_prompt},
163
+ {"role": "user", "content": content}]
164
+ )
165
+ logger.trace(f"Report writer tool response: {response}")
166
+ return response.content
167
 
168
  # Initialize tools
169
  tools = [
 
171
  reddit_tool,
172
  google_scholar_tool,
173
  rag_tool,
174
+ report_writer_tool,
175
  ]
uv.lock CHANGED
@@ -16,6 +16,7 @@ dependencies = [
16
  { name = "ipywidgets" },
17
  { name = "langchain" },
18
  { name = "langchain-community" },
 
19
  { name = "langchain-openai" },
20
  { name = "langchain-qdrant" },
21
  { name = "langgraph" },
@@ -44,6 +45,7 @@ requires-dist = [
44
  { name = "ipywidgets", specifier = "~=8.1.5" },
45
  { name = "langchain", specifier = "~=0.3.15" },
46
  { name = "langchain-community", specifier = "~=0.3.16" },
 
47
  { name = "langchain-openai", specifier = "~=0.3.2" },
48
  { name = "langchain-qdrant", specifier = "~=0.2.0" },
49
  { name = "langgraph", specifier = "~=0.2.67" },
@@ -554,6 +556,15 @@ wheels = [
554
  { url = "https://files.pythonhosted.org/packages/8f/7d/2d6ce181d7a5f51dedb8c06206cbf0ec026a99bf145edd309f9e17c3282f/fastapi-0.115.8-py3-none-any.whl", hash = "sha256:753a96dd7e036b34eeef8babdfcfe3f28ff79648f86551eb36bfc1b0bf4a8cbf", size = 94814 },
555
  ]
556
 
 
 
 
 
 
 
 
 
 
557
  [[package]]
558
  name = "filetype"
559
  version = "1.2.0"
@@ -617,6 +628,15 @@ wheels = [
617
  { url = "https://files.pythonhosted.org/packages/c6/c8/a5be5b7550c10858fcf9b0ea054baccab474da77d37f1e828ce043a3a5d4/frozenlist-1.5.0-py3-none-any.whl", hash = "sha256:d994863bba198a4a518b467bb971c56e1db3f180a25c6cf7bb1949c267f748c3", size = 11901 },
618
  ]
619
 
 
 
 
 
 
 
 
 
 
620
  [[package]]
621
  name = "google-search-results"
622
  version = "2.4.2"
@@ -822,6 +842,24 @@ wheels = [
822
  { url = "https://files.pythonhosted.org/packages/e1/9b/a181f281f65d776426002f330c31849b86b31fc9d848db62e16f03ff739f/httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f", size = 7819 },
823
  ]
824
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
825
  [[package]]
826
  name = "hyperframe"
827
  version = "6.1.0"
@@ -956,6 +994,18 @@ wheels = [
956
  { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278 },
957
  ]
958
 
 
 
 
 
 
 
 
 
 
 
 
 
959
  [[package]]
960
  name = "jiter"
961
  version = "0.8.2"
@@ -1000,6 +1050,15 @@ wheels = [
1000
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1001
  ]
1002
 
 
 
 
 
 
 
 
 
 
1003
  [[package]]
1004
  name = "jsonpatch"
1005
  version = "1.33"
@@ -1124,6 +1183,22 @@ wheels = [
1124
  { url = "https://files.pythonhosted.org/packages/6a/f5/9ce2a94bc49b64c0bf53b17524d5fc5c926070e911b11d489979d47d5491/langchain_core-0.3.37-py3-none-any.whl", hash = "sha256:8202fd6506ce139a3a1b1c4c3006216b1c7fffa40bdd1779f7d2c67f75eb5f79", size = 413717 },
1125
  ]
1126
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1127
  [[package]]
1128
  name = "langchain-openai"
1129
  version = "0.3.6"
@@ -1255,6 +1330,44 @@ wheels = [
1255
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1256
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1257
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1258
  [[package]]
1259
  name = "marshmallow"
1260
  version = "3.26.1"
@@ -1279,6 +1392,15 @@ wheels = [
1279
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1280
  ]
1281
 
 
 
 
 
 
 
 
 
 
1282
  [[package]]
1283
  name = "msgpack"
1284
  version = "1.1.0"
@@ -1424,6 +1546,30 @@ wheels = [
1424
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1425
  ]
1426
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1427
  [[package]]
1428
  name = "numpy"
1429
  version = "1.26.4"
@@ -1459,6 +1605,124 @@ wheels = [
1459
  { url = "https://files.pythonhosted.org/packages/f4/5f/fafd8c51235f60d49f7a88e2275e13971e90555b67da52dd6416caec32fe/numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0", size = 15709730 },
1460
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1461
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1462
  [[package]]
1463
  name = "openai"
1464
  version = "1.64.0"
@@ -1692,6 +1956,54 @@ wheels = [
1692
  { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772 },
1693
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1694
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1695
  [[package]]
1696
  name = "platformdirs"
1697
  version = "4.3.6"
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2272
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2275
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2276
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2289
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2465
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3036
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3039
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3060
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3061
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3068
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3069
+
3070
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3071
  name = "typing-extensions"
3072
  version = "4.12.2"