Pgvector template (#13267)

Including pvector template, adapting what is covered in the
[cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/retrieval_in_sql.ipynb).

---------

Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Erick Friis <erick@langchain.dev>

templates/sql-pgvector/sql_pgvector/chain.py

@@ -0,0 +1,118 @@
+import os
+import re
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+from langchain.sql_database import SQLDatabase
+
+from sql_pgvector.prompt_templates import final_template, postgresql_template
+
+"""
+IMPORTANT: For using this template, you will need to 
+follow the setup steps in the readme file
+"""
+
+if os.environ.get("OPENAI_API_KEY", None) is None:
+    raise Exception("Missing `OPENAI_API_KEY` environment variable")
+
+postgres_user = os.environ.get("POSTGRES_USER", "postgres")
+postgres_password = os.environ.get("POSTGRES_PASSWORD", "test")
+postgres_db = os.environ.get("POSTGRES_DB", "vectordb")
+postgres_host = os.environ.get("POSTGRES_HOST", "localhost")
+postgres_port = os.environ.get("POSTGRES_PORT", "5432")
+
+# Connect to DB
+# Replace with your own
+CONNECTION_STRING = (
+    f"postgresql+psycopg2://{postgres_user}:{postgres_password}"
+    f"@{postgres_host}:{postgres_port}/{postgres_db}"
+)
+db = SQLDatabase.from_uri(CONNECTION_STRING)
+
+# Choose LLM and embeddings model
+llm = ChatOpenAI(temperature=0)
+embeddings_model = OpenAIEmbeddings()
+
+
+# # Ingest code - you will need to run this the first time
+# # Insert your query e.g. "SELECT Name FROM Track"
+# column_to_embed = db.run('replace-with-your-own-select-query')
+# column_values = [s[0] for s in eval(column_to_embed)]
+# embeddings = embeddings_model.embed_documents(column_values)
+
+# for i in range(len(embeddings)):
+#     value = column_values[i].replace("'", "''")
+#     embedding = embeddings[i]
+
+#     # Replace with your own SQL command for your column and table.
+#     sql_command = (
+#         f'UPDATE "Track" SET "embeddings" = ARRAY{embedding} WHERE "Name" ='
+#         + f"'{value}'"
+#     )
+#     db.run(sql_command)
+
+
+# -----------------
+# Define functions
+# -----------------
+def get_schema(_):
+    return db.get_table_info()
+
+
+def run_query(query):
+    return db.run(query)
+
+
+def replace_brackets(match):
+    words_inside_brackets = match.group(1).split(", ")
+    embedded_words = [
+        str(embeddings_model.embed_query(word)) for word in words_inside_brackets
+    ]
+    return "', '".join(embedded_words)
+
+
+def get_query(query):
+    sql_query = re.sub(r"\[([\w\s,]+)\]", replace_brackets, query)
+    return sql_query
+
+
+# -----------------------
+# Now we create the chain
+# -----------------------
+
+query_generation_prompt = ChatPromptTemplate.from_messages(
+    [("system", postgresql_template), ("human", "{question}")]
+)
+
+sql_query_chain = (
+    RunnablePassthrough.assign(schema=get_schema)
+    | query_generation_prompt
+    | llm.bind(stop=["\nSQLResult:"])
+    | StrOutputParser()
+)
+
+
+final_prompt = ChatPromptTemplate.from_messages(
+    [("system", final_template), ("human", "{question}")]
+)
+
+full_chain = (
+    RunnablePassthrough.assign(query=sql_query_chain)
+    | RunnablePassthrough.assign(
+        schema=get_schema,
+        response=RunnableLambda(lambda x: db.run(get_query(x["query"]))),
+    )
+    | final_prompt
+    | llm
+)
+
+
+class InputType(BaseModel):
+    question: str
+
+
+chain = full_chain.with_types(input_type=InputType)