Groq Llama 3

cookbook/knowledge/arxiv_kb.py

@@ -2,15 +2,15 @@
 from phi.knowledge.arxiv import ArxivKnowledgeBase
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 # Create a knowledge base with the ArXiv documents
 knowledge_base = ArxivKnowledgeBase(
     queries=["Generative AI", "Machine Learning"],
     # Table name: ai.arxiv_documents
     vector_db=PgVector2(
         collection="arxiv_documents",
-        db_url=vector_db.get_db_connection_local(),
+        db_url=db_url,
     ),
 )
 # Load the knowledge base

cookbook/knowledge/custom_references.py

@@ -13,11 +13,11 @@
 from phi.knowledge.pdf import PDFUrlKnowledgeBase
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
     urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
-    vector_db=PgVector2(collection="recipes", db_url=vector_db.get_db_connection_local()),
+    vector_db=PgVector2(collection="recipes", db_url=db_url),
 )
 # Comment out after first run
 # knowledge_base.load(recreate=False)

cookbook/knowledge/json.py

@@ -4,15 +4,15 @@
 from phi.knowledge.json import JSONKnowledgeBase
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 
 # Initialize the JSONKnowledgeBase
 knowledge_base = JSONKnowledgeBase(
     path=Path("data/docs"),  # Table name: ai.json_documents
     vector_db=PgVector2(
         collection="json_documents",
-        db_url=vector_db.get_db_connection_local(),
+        db_url=db_url,
     ),
     num_documents=5,  # Number of documents to return on search
 )

cookbook/knowledge/pdf.py

@@ -2,15 +2,15 @@
 from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 # Create a knowledge base with the PDFs from the data/pdfs directory
 knowledge_base = PDFKnowledgeBase(
     path="data/pdfs",
     vector_db=PgVector2(
         collection="pdf_documents",
         # Can inspect database via psql e.g. "psql -h localhost -p 5432 -U ai -d ai"
-        db_url=vector_db.get_db_connection_local(),
+        db_url=db_url,
     ),
     reader=PDFReader(chunk=True),
 )

cookbook/knowledge/pdf_url.py

@@ -2,11 +2,11 @@
 from phi.knowledge.pdf import PDFUrlKnowledgeBase
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
     urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
-    vector_db=PgVector2(collection="recipes", db_url=vector_db.get_db_connection_local()),
+    vector_db=PgVector2(collection="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run
 

cookbook/knowledge/text.py

@@ -4,15 +4,15 @@
 from phi.knowledge.text import TextKnowledgeBase
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 
 # Initialize the TextKnowledgeBase
 knowledge_base = TextKnowledgeBase(
     path=Path("data/docs"),  # Table name: ai.text_documents
     vector_db=PgVector2(
         collection="text_documents",
-        db_url=vector_db.get_db_connection_local(),
+        db_url=db_url,
     ),
     num_documents=5,  # Number of documents to return on search
 )

cookbook/knowledge/website.py

@@ -2,7 +2,7 @@
 from phi.vectordb.pgvector import PgVector2
 from phi.assistant import Assistant
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 # Create a knowledge base with the seed URLs
 knowledge_base = WebsiteKnowledgeBase(
@@ -12,7 +12,7 @@
     # Table name: ai.website_documents
     vector_db=PgVector2(
         collection="website_documents",
-        db_url=vector_db.get_db_connection_local(),
+        db_url=db_url,
     ),
 )
 # Load the knowledge base

cookbook/knowledge/wikipedia_kb.py

@@ -2,15 +2,15 @@
 from phi.knowledge.wikipedia import WikipediaKnowledgeBase
 from phi.vectordb.pgvector import PgVector2
 
-from resources import vector_db  # type: ignore
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 # Create a knowledge base with the PDFs from the data/pdfs directory
 knowledge_base = WikipediaKnowledgeBase(
     topics=["Manchester United", "Real Madrid"],
     # Table name: ai.wikipedia_documents
     vector_db=PgVector2(
         collection="wikipedia_documents",
-        db_url=vector_db.get_db_connection_local(),
+        db_url=db_url,
     ),
 )
 # Load the knowledge base

cookbook/llms/bedrock/README.md

@@ -0,0 +1,37 @@
+# AWS Bedrock
+
+> Note: Fork and clone this repository if needed
+
+### 1. Create and activate a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+### 2. Export your AWS Credentials
+
+```shell
+export AWS_ACCESS_KEY_ID=***
+export AWS_SECRET_ACCESS_KEY=***
+```
+
+### 3. Install libraries
+
+```shell
+pip install -U boto3 phidata
+```
+
+### 4. Run Assistant
+
+- stream on
+
+```shell
+python cookbook/llms/bedrock/basic.py
+```
+
+- stream off
+
+```shell
+python cookbook/llms/bedrock/basic_stream_off.py
+```

cookbook/llms/bedrock/basic.py

@@ -0,0 +1,9 @@
+from phi.assistant import Assistant
+from phi.llm.aws.claude import Claude
+
+assistant = Assistant(
+    llm=Claude(model="anthropic.claude-v2"),
+    description="You help people with their health and fitness goals.",
+    debug_mode=True,
+)
+assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True, stream=False)

cookbook/llms/bedrock/basic_stream_off.py

@@ -0,0 +1,8 @@
+from phi.assistant import Assistant
+from phi.llm.anthropic import Claude
+
+assistant = Assistant(
+    llm=Claude(model="claude-3-haiku-20240307"),
+    description="You help people with their health and fitness goals.",
+)
+assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True, stream=False)

cookbook/llms/claude/README.md

@@ -1,4 +1,4 @@
-# Claude function calling
+# Anthropic Claude
 
 [Models overview](https://docs.anthropic.com/claude/docs/models-overview)
 
@@ -11,7 +11,7 @@ python3 -m venv ~/.venvs/aienv
 source ~/.venvs/aienv/bin/activate
 ```
 
-### 2. Export your Anthropic API Key
+### 2. Set your `ANTHROPIC_API_KEY`
 
 ```shell
 export ANTHROPIC_API_KEY=xxx

cookbook/llms/groq/README.md

@@ -2,70 +2,35 @@
 
 > Note: Fork and clone this repository if needed
 
-## RAG AI App with Groq & PgVector
-
-1. Create and activate a virtual environment
+### 1. Create a virtual environment
 
 ```shell
 python3 -m venv ~/.venvs/aienv
 source ~/.venvs/aienv/bin/activate
 ```
 
-2. Export your Groq & OpenAI API Key
-
-> Need to use OpenAI for embeddings as Groq doesn't support embeddings yet.
-
-```shell
-export GROQ_API_KEY=***
-export OPENAI_API_KEY=sk-***
-```
-
-3. Install libraries
-
-```shell
-pip install -r cookbook/llms/groq/requirements.txt
-```
-
-4. Start pgvector
-
-> Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) first.
-
-- Run using a helper script
+### 2. Install libraries
 
 ```shell
-./cookbook/run_pgvector.sh
+pip install -U groq phidata
 ```
 
-- OR run using the docker run command
-
-```shell
-docker run -d \
-  -e POSTGRES_DB=ai \
-  -e POSTGRES_USER=ai \
-  -e POSTGRES_PASSWORD=ai \
-  -e PGDATA=/var/lib/postgresql/data/pgdata \
-  -v pgvolume:/var/lib/postgresql/data \
-  -p 5532:5432 \
-  --name pgvector \
-  phidata/pgvector:16
-```
+### 3. Run Assistants
 
-5. Run RAG App
+- basic
 
 ```shell
-streamlit run cookbook/llms/groq/app.py
+python cookbook/llms/groq/basic.py
 ```
 
-## Build AI Assistants with Groq
-
-1. Install libraries
+- web search
 
 ```shell
-pip install -U groq phidata
+python cookbook/llms/groq/assistant.py
 ```
 
-2. Run Assistant
+- structured output
 
 ```shell
-python cookbook/llms/groq/assistant.py
+python cookbook/llms/groq/structured_output.py
 ```

cookbook/llms/groq/basic.py

@@ -2,7 +2,7 @@
 from phi.llm.groq import Groq
 
 assistant = Assistant(
-    llm=Groq(model="mixtral-8x7b-32768"),
+    llm=Groq(model="llama3-70b-8192"),
     description="You help people with their health and fitness goals.",
 )
 assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True)

cookbook/llms/groq/rag/README.md

@@ -0,0 +1,61 @@
+# RAG with Groq & PgVector
+
+> Note: Fork and clone this repository if needed
+
+### 1. Create a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+### 2. Export your Groq & OpenAI API Key
+
+> Need to use OpenAI for embeddings as Groq doesn't support embeddings yet.
+
+```shell
+export GROQ_API_KEY=***
+export OPENAI_API_KEY=sk-***
+```
+
+### 3. Install libraries
+
+```shell
+pip install -r cookbook/llms/groq/rag/requirements.txt
+```
+
+### 4. Run PgVector
+
+> Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) first.
+
+- Run using a helper script
+
+```shell
+./cookbook/run_pgvector.sh
+```
+
+- OR run using the docker run command
+
+```shell
+docker run -d \
+  -e POSTGRES_DB=ai \
+  -e POSTGRES_USER=ai \[app.py](..%2Fapp.py)
+  -e POSTGRES_PASSWORD=ai \
+  -e PGDATA=/var/lib/postgresql/data/pgdata \
+  -v pgvolume:/var/lib/postgresql/data \
+  -p 5532:5432 \
+  --name pgvector \
+  phidata/pgvector:16
+```
+
+### 5. Run RAG App
+
+```shell
+streamlit run cookbook/llms/groq/rag/app.py
+```
+
+- Open [localhost:8501](http://localhost:8501) to view your RAG app.
+
+### 6. Message on [discord](https://discord.gg/4MtYHHrgA8) if you have any questions
+
+### 7. Star ⭐️ the project if you like it.