Update neo4j cypher templates to the function callback (#20515)

Update Neo4j Cypher templates to use function callback to pass context instead of passing it in user prompt. Co-authored-by: Erick Friis <erick@langchain.dev>
4 weeks ago · e4b38e2822
parent 3d9b26fc28
commit e4b38e2822
9 changed files with 699 additions and 88 deletions
--- a/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py
+++ b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py
@ -1,13 +1,21 @@
-from typing import List, Optional
+from typing import List, Optional, Union

 from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
-from langchain.chains.openai_functions import create_structured_output_chain
-from langchain_community.chat_models import ChatOpenAI
 from langchain_community.graphs import Neo4jGraph
+from langchain_core.messages import (
+    AIMessage,
+    SystemMessage,
+    ToolMessage,
+)
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI

 # Connection to Neo4j
 graph = Neo4jGraph()
@ -66,7 +74,7 @@ def map_to_database(entities: Entities) -> Optional[str]:
    return result


-entity_chain = create_structured_output_chain(Entities, qa_llm, prompt)
+entity_chain = prompt | qa_llm.with_structured_output(Entities)

 # Generate Cypher statement based on natural language input
 cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
@ -89,7 +97,7 @@ cypher_prompt = ChatPromptTemplate.from_messages(
 cypher_response = (
    RunnablePassthrough.assign(names=entity_chain)
    | RunnablePassthrough.assign(
-        entities_list=lambda x: map_to_database(x["names"]["function"]),
+        entities_list=lambda x: map_to_database(x["names"]),
        schema=lambda _: graph.get_schema,
    )
    | cypher_prompt
@ -98,26 +106,51 @@ cypher_response = (
 )

 # Generate natural language response based on database results
-response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
-Question: {question}
-Cypher query: {query}
-Cypher Response: {response}"""  # noqa: E501
+response_system = """You are an assistant that helps to form nice and human 
+understandable answers based on the provided information from tools.
+Do not add any other information that wasn't present in the tools, and use 
+very concise style in interpreting results!
+"""

 response_prompt = ChatPromptTemplate.from_messages(
    [
-        (
-            "system",
-            "Given an input question and Cypher response, convert it to a natural"
-            " language answer. No pre-amble.",
-        ),
-        ("human", response_template),
+        SystemMessage(content=response_system),
+        HumanMessagePromptTemplate.from_template("{question}"),
+        MessagesPlaceholder(variable_name="function_response"),
    ]
 )

+
+def get_function_response(
+    query: str, question: str
+) -> List[Union[AIMessage, ToolMessage]]:
+    context = graph.query(cypher_validation(query))
+    TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
+    messages = [
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "tool_calls": [
+                    {
+                        "id": TOOL_ID,
+                        "function": {
+                            "arguments": '{"question":"' + question + '"}',
+                            "name": "GetInformation",
+                        },
+                        "type": "function",
+                    }
+                ]
+            },
+        ),
+        ToolMessage(content=str(context), tool_call_id=TOOL_ID),
+    ]
+    return messages
+
+
 chain = (
    RunnablePassthrough.assign(query=cypher_response)
    | RunnablePassthrough.assign(
-        response=lambda x: graph.query(cypher_validation(x["query"])),
+        function_response=lambda x: get_function_response(x["query"], x["question"])
    )
    | response_prompt
    | qa_llm
--- a/templates/neo4j-cypher-ft/poetry.lock
+++ b/templates/neo4j-cypher-ft/poetry.lock
@ -722,19 +722,19 @@ uvicorn = ">=0.23.2,<0.24.0"

 [[package]]
 name = "langchain-community"
-version = "0.0.28"
+version = "0.0.33"
 description = "Community contributed LangChain integrations."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.28-py3-none-any.whl", hash = "sha256:bdb015ac455ae68432ea104628717583dce041e1abdfcefe86e39f034f5e90b8"},
-    {file = "langchain_community-0.0.28.tar.gz", hash = "sha256:8664d243a90550fc5ddc137b712034e02c8d43afc8d4cc832ba5842b44c864ce"},
+    {file = "langchain_community-0.0.33-py3-none-any.whl", hash = "sha256:830f0d5f4ff9638b99ca01820c26abfa4b65fa705ef89b5ce55ac9aa3a7d83af"},
+    {file = "langchain_community-0.0.33.tar.gz", hash = "sha256:bb56dbc1ef11ca09f258468e11368781adda9219e144073e30cda69496d342b2"},
 ]

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 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.31,<0.2.0"
+langchain-core = ">=0.1.43,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@ -744,32 +744,46 @@ tenacity = ">=8.1.0,<9.0.0"

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 [[package]]
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-version = "0.1.31"
+version = "0.1.43"
 description = "Building applications with LLMs through composability"
 optional = false
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 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]

+[[package]]
+name = "langchain-openai"
+version = "0.1.3"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
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+]
+
+[package.dependencies]
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+openai = ">=1.10.0,<2.0.0"
+tiktoken = ">=0.5.2,<1"
+
 [[package]]
 name = "langchain-text-splitters"
 version = "0.0.1"
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+    {file = "tiktoken-0.6.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:17cc8a4a3245ab7d935c83a2db6bb71619099d7284b884f4b2aea4c74f2f83e3"},
+    {file = "tiktoken-0.6.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:284aebcccffe1bba0d6571651317df6a5b376ff6cfed5aeb800c55df44c78177"},
+    {file = "tiktoken-0.6.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0c1a3a5d33846f8cd9dd3b7897c1d45722f48625a587f8e6f3d3e85080559be8"},
+    {file = "tiktoken-0.6.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6318b2bb2337f38ee954fd5efa82632c6e5ced1d52a671370fa4b2eff1355e91"},
+    {file = "tiktoken-0.6.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1f5f0f2ed67ba16373f9a6013b68da298096b27cd4e1cf276d2d3868b5c7efd1"},
+    {file = "tiktoken-0.6.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:75af4c0b16609c2ad02581f3cdcd1fb698c7565091370bf6c0cf8624ffaba6dc"},
+    {file = "tiktoken-0.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:45577faf9a9d383b8fd683e313cf6df88b6076c034f0a16da243bb1c139340c3"},
+    {file = "tiktoken-0.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:7c1492ab90c21ca4d11cef3a236ee31a3e279bb21b3fc5b0e2210588c4209e68"},
+    {file = "tiktoken-0.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e2b380c5b7751272015400b26144a2bab4066ebb8daae9c3cd2a92c3b508fe5a"},
+    {file = "tiktoken-0.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9f497598b9f58c99cbc0eb764b4a92272c14d5203fc713dd650b896a03a50ad"},
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+    {file = "tiktoken-0.6.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5f1495450a54e564d236769d25bfefbf77727e232d7a8a378f97acddee08c1ae"},
+    {file = "tiktoken-0.6.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6c4e4857d99f6fb4670e928250835b21b68c59250520a1941618b5b4194e20c3"},
+    {file = "tiktoken-0.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:168d718f07a39b013032741867e789971346df8e89983fe3c0ef3fbd5a0b1cb9"},
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+    {file = "tiktoken-0.6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:fb7d2ccbf1a7784810aff6b80b4012fb42c6fc37eaa68cb3b553801a5cc2d1fc"},
+    {file = "tiktoken-0.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ccb7a111ee76af5d876a729a347f8747d5ad548e1487eeea90eaf58894b3138"},
+    {file = "tiktoken-0.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2048e1086b48e3c8c6e2ceeac866561374cd57a84622fa49a6b245ffecb7744"},
+    {file = "tiktoken-0.6.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:07f229a5eb250b6403a61200199cecf0aac4aa23c3ecc1c11c1ca002cbb8f159"},
+    {file = "tiktoken-0.6.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:432aa3be8436177b0db5a2b3e7cc28fd6c693f783b2f8722539ba16a867d0c6a"},
+    {file = "tiktoken-0.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:8bfe8a19c8b5c40d121ee7938cd9c6a278e5b97dc035fd61714b4f0399d2f7a1"},
+    {file = "tiktoken-0.6.0.tar.gz", hash = "sha256:ace62a4ede83c75b0374a2ddfa4b76903cf483e9cb06247f566be3bf14e6beed"},
+]
+
+[package.dependencies]
+regex = ">=2022.1.18"
+requests = ">=2.26.0"
+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
 name = "tomlkit"
 version = "0.12.4"
@ -1768,4 +1935,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "835db3f73c8fc07e54293bb8c8e56e1d5c9be403844eac8f8025793c8e6c242b"
+content-hash = "09bce7f4cc85481c418acf3d43764152ba55a29aec1172ecca1408591fec0ab6"
--- a/templates/neo4j-cypher-ft/pyproject.toml
+++ b/templates/neo4j-cypher-ft/pyproject.toml
@ -12,6 +12,8 @@ python = ">=3.8.1,<4.0"
 langchain = "^0.1"
 neo4j = ">5.12"
 openai = "<2"
+langchain-community = "^0.0.33"
+langchain-openai = "^0.1.3"

 [tool.poetry.group.dev.dependencies]
 langchain-cli = ">=0.0.21"
--- a/templates/neo4j-cypher-memory/neo4j_cypher_memory/chain.py
+++ b/templates/neo4j-cypher-memory/neo4j_cypher_memory/chain.py
@ -1,13 +1,22 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union

 from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
 from langchain.memory import ChatMessageHistory
-from langchain_community.chat_models import ChatOpenAI
 from langchain_community.graphs import Neo4jGraph
+from langchain_core.messages import (
+    AIMessage,
+    SystemMessage,
+    ToolMessage,
+)
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI

 # Connection to Neo4j
 graph = Neo4jGraph()
@ -56,7 +65,9 @@ def get_history(input: Dict[str, Any]) -> ChatMessageHistory:


 def save_history(input):
-    input.pop("response")
+    print(input)
+    if input.get("function_response"):
+        input.pop("function_response")
    # store history to database
    graph.query(
        """MERGE (u:User {id: $user_id})
@ -107,26 +118,51 @@ cypher_response = (
 )

 # Generate natural language response based on database results
-response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
-Question: {question}
-Cypher query: {query}
-Cypher Response: {response}"""  # noqa: E501
+response_system = """You are an assistant that helps to form nice and human 
+understandable answers based on the provided information from tools.
+Do not add any other information that wasn't present in the tools, and use 
+very concise style in interpreting results!
+"""

 response_prompt = ChatPromptTemplate.from_messages(
    [
-        (
-            "system",
-            "Given an input question and Cypher response, convert it to a "
-            "natural language answer. No pre-amble.",
-        ),
-        ("human", response_template),
+        SystemMessage(content=response_system),
+        HumanMessagePromptTemplate.from_template("{question}"),
+        MessagesPlaceholder(variable_name="function_response"),
    ]
 )

+
+def get_function_response(
+    query: str, question: str
+) -> List[Union[AIMessage, ToolMessage]]:
+    context = graph.query(cypher_validation(query))
+    TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
+    messages = [
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "tool_calls": [
+                    {
+                        "id": TOOL_ID,
+                        "function": {
+                            "arguments": '{"question":"' + question + '"}',
+                            "name": "GetInformation",
+                        },
+                        "type": "function",
+                    }
+                ]
+            },
+        ),
+        ToolMessage(content=str(context), tool_call_id=TOOL_ID),
+    ]
+    return messages
+
+
 chain = (
    RunnablePassthrough.assign(query=cypher_response)
    | RunnablePassthrough.assign(
-        response=lambda x: graph.query(cypher_validation(x["query"])),
+        function_response=lambda x: get_function_response(x["query"], x["question"]),
    )
    | RunnablePassthrough.assign(
        output=response_prompt | qa_llm | StrOutputParser(),
--- a/templates/neo4j-cypher-memory/poetry.lock
+++ b/templates/neo4j-cypher-memory/poetry.lock
@ -722,19 +722,19 @@ uvicorn = ">=0.23.2,<0.24.0"

 [[package]]
 name = "langchain-community"
-version = "0.0.28"
+version = "0.0.33"
 description = "Community contributed LangChain integrations."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.28-py3-none-any.whl", hash = "sha256:bdb015ac455ae68432ea104628717583dce041e1abdfcefe86e39f034f5e90b8"},
-    {file = "langchain_community-0.0.28.tar.gz", hash = "sha256:8664d243a90550fc5ddc137b712034e02c8d43afc8d4cc832ba5842b44c864ce"},
+    {file = "langchain_community-0.0.33-py3-none-any.whl", hash = "sha256:830f0d5f4ff9638b99ca01820c26abfa4b65fa705ef89b5ce55ac9aa3a7d83af"},
+    {file = "langchain_community-0.0.33.tar.gz", hash = "sha256:bb56dbc1ef11ca09f258468e11368781adda9219e144073e30cda69496d342b2"},
 ]

 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.31,<0.2.0"
+langchain-core = ">=0.1.43,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@ -744,32 +744,46 @@ tenacity = ">=8.1.0,<9.0.0"

 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]

 [[package]]
 name = "langchain-core"
-version = "0.1.31"
+version = "0.1.43"
 description = "Building applications with LLMs through composability"
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.1.31-py3-none-any.whl", hash = "sha256:ff028f00db8ff03565b542cea81be27426022a72c6545b54d8de66fa00948ab3"},
-    {file = "langchain_core-0.1.31.tar.gz", hash = "sha256:d660cf209bb6ce61cb1c853107b091aaa809015a55dce9e0ce19b51d4c8f2a70"},
+    {file = "langchain_core-0.1.43-py3-none-any.whl", hash = "sha256:9b601916602c17cb7588e8089302e30872cbd049b583a27debf5566018af6405"},
+    {file = "langchain_core-0.1.43.tar.gz", hash = "sha256:499133fadc28efcf7d24306236521518080bb10fd8bf6f7426de4a2bbf2aebb5"},
 ]

 [package.dependencies]
-anyio = ">=3,<5"
 jsonpatch = ">=1.33,<2.0"
 langsmith = ">=0.1.0,<0.2.0"
 packaging = ">=23.2,<24.0"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
-requests = ">=2,<3"
 tenacity = ">=8.1.0,<9.0.0"

 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]

+[[package]]
+name = "langchain-openai"
+version = "0.1.3"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_openai-0.1.3-py3-none-any.whl", hash = "sha256:fa1f27815649291447e5370cb08e2f5a84e5c7c6121d0c055a6e296bd16d1e47"},
+    {file = "langchain_openai-0.1.3.tar.gz", hash = "sha256:7f6e377d6bf88d6c2b1969fe5eecc1326271757512739e2f17c855cd7af53345"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.42,<0.2.0"
+openai = ">=1.10.0,<2.0.0"
+tiktoken = ">=0.5.2,<1"
+
 [[package]]
 name = "langchain-text-splitters"
 version = "0.0.1"
@ -1301,7 +1315,6 @@ files = [
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+    {file = "regex-2023.12.25.tar.gz", hash = "sha256:29171aa128da69afdf4bde412d5bedc335f2ca8fcfe4489038577d05f16181e5"},
+]
+
 [[package]]
 name = "requests"
 version = "2.31.0"
@ -1545,6 +1660,58 @@ files = [
 [package.extras]
 doc = ["reno", "sphinx", "tornado (>=4.5)"]

+[[package]]
+name = "tiktoken"
+version = "0.6.0"
+description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "tiktoken-0.6.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:432aa3be8436177b0db5a2b3e7cc28fd6c693f783b2f8722539ba16a867d0c6a"},
+    {file = "tiktoken-0.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:8bfe8a19c8b5c40d121ee7938cd9c6a278e5b97dc035fd61714b4f0399d2f7a1"},
+    {file = "tiktoken-0.6.0.tar.gz", hash = "sha256:ace62a4ede83c75b0374a2ddfa4b76903cf483e9cb06247f566be3bf14e6beed"},
+]
+
+[package.dependencies]
+regex = ">=2022.1.18"
+requests = ">=2.26.0"
+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
 name = "tomlkit"
 version = "0.12.4"
@ -1768,4 +1935,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "835db3f73c8fc07e54293bb8c8e56e1d5c9be403844eac8f8025793c8e6c242b"
+content-hash = "09bce7f4cc85481c418acf3d43764152ba55a29aec1172ecca1408591fec0ab6"
--- a/templates/neo4j-cypher-memory/pyproject.toml
+++ b/templates/neo4j-cypher-memory/pyproject.toml
@ -12,6 +12,8 @@ python = ">=3.8.1,<4.0"
 langchain = "^0.1"
 neo4j = ">5.12"
 openai = "<2"
+langchain-community = "^0.0.33"
+langchain-openai = "^0.1.3"

 [tool.poetry.group.dev.dependencies]
 langchain-cli = ">=0.0.21"
--- a/templates/neo4j-cypher/neo4j_cypher/chain.py
+++ b/templates/neo4j-cypher/neo4j_cypher/chain.py
@ -1,10 +1,21 @@
+from typing import List, Union
+
 from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
-from langchain_community.chat_models import ChatOpenAI
 from langchain_community.graphs import Neo4jGraph
+from langchain_core.messages import (
+    AIMessage,
+    SystemMessage,
+    ToolMessage,
+)
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI

 # Connection to Neo4j
 graph = Neo4jGraph()
@ -46,27 +57,51 @@ cypher_response = (
    | StrOutputParser()
 )

-# Generate natural language response based on database results
-response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
-Question: {question}
-Cypher query: {query}
-Cypher Response: {response}"""  # noqa: E501
+response_system = """You are an assistant that helps to form nice and human 
+understandable answers based on the provided information from tools.
+Do not add any other information that wasn't present in the tools, and use 
+very concise style in interpreting results!
+"""

 response_prompt = ChatPromptTemplate.from_messages(
    [
-        (
-            "system",
-            "Given an input question and Cypher response, convert it to a "
-            "natural language answer. No pre-amble.",
-        ),
-        ("human", response_template),
+        SystemMessage(content=response_system),
+        HumanMessagePromptTemplate.from_template("{question}"),
+        MessagesPlaceholder(variable_name="function_response"),
    ]
 )

+
+def get_function_response(
+    query: str, question: str
+) -> List[Union[AIMessage, ToolMessage]]:
+    context = graph.query(cypher_validation(query))
+    TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
+    messages = [
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "tool_calls": [
+                    {
+                        "id": TOOL_ID,
+                        "function": {
+                            "arguments": '{"question":"' + question + '"}',
+                            "name": "GetInformation",
+                        },
+                        "type": "function",
+                    }
+                ]
+            },
+        ),
+        ToolMessage(content=str(context), tool_call_id=TOOL_ID),
+    ]
+    return messages
+
+
 chain = (
    RunnablePassthrough.assign(query=cypher_response)
    | RunnablePassthrough.assign(
-        response=lambda x: graph.query(cypher_validation(x["query"])),
+        function_response=lambda x: get_function_response(x["query"], x["question"])
    )
    | response_prompt
    | qa_llm
--- a/templates/neo4j-cypher/poetry.lock
+++ b/templates/neo4j-cypher/poetry.lock
@ -722,19 +722,19 @@ uvicorn = ">=0.23.2,<0.24.0"

 [[package]]
 name = "langchain-community"
-version = "0.0.28"
+version = "0.0.33"
 description = "Community contributed LangChain integrations."
 optional = false
-python-versions = ">=3.8.1,<4.0"
+python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.28-py3-none-any.whl", hash = "sha256:bdb015ac455ae68432ea104628717583dce041e1abdfcefe86e39f034f5e90b8"},
-    {file = "langchain_community-0.0.28.tar.gz", hash = "sha256:8664d243a90550fc5ddc137b712034e02c8d43afc8d4cc832ba5842b44c864ce"},
+    {file = "langchain_community-0.0.33-py3-none-any.whl", hash = "sha256:830f0d5f4ff9638b99ca01820c26abfa4b65fa705ef89b5ce55ac9aa3a7d83af"},
+    {file = "langchain_community-0.0.33.tar.gz", hash = "sha256:bb56dbc1ef11ca09f258468e11368781adda9219e144073e30cda69496d342b2"},
 ]

 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.31,<0.2.0"
+langchain-core = ">=0.1.43,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@ -744,32 +744,46 @@ tenacity = ">=8.1.0,<9.0.0"

 [package.extras]
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 doc = ["reno", "sphinx", "tornado (>=4.5)"]

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+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
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 version = "0.12.4"
@ -1768,4 +1935,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "835db3f73c8fc07e54293bb8c8e56e1d5c9be403844eac8f8025793c8e6c242b"
+content-hash = "09bce7f4cc85481c418acf3d43764152ba55a29aec1172ecca1408591fec0ab6"
--- a/templates/neo4j-cypher/pyproject.toml
+++ b/templates/neo4j-cypher/pyproject.toml
@ -12,6 +12,8 @@ python = ">=3.8.1,<4.0"
 langchain = "^0.1"
 neo4j = ">5.12"
 openai = "<2"
+langchain-openai = "^0.1.3"
+langchain-community = "^0.0.33"

 [tool.poetry.group.dev.dependencies]
 langchain-cli = ">=0.0.21"