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Arcnem Vision

LangChain Go

How github.com/tmc/langchaingo (v0.1.14) fits into the arcnem-vision Go services — focused on text embeddings, agents, chains, tools, and the MCP bridge.

Requires Go 1.25+.

Where It Fits

Section titled “Where It Fits”
ServiceWhat langchaingo does there
models/agentsInngest jobs use text embedders + chains for the processing pipeline
models/mcpMCP tool handlers share the same underlying logic as agent tools
Future agent serviceAutonomous agent with tools for embed, search, describe, ask

What langchaingo does NOT cover: Image embeddings. The Embedder interface is text-only (EmbedDocuments([]string) / EmbedQuery(string)). For embedding images directly (CLIP, Gemini multimodal, Cohere v4), you need direct API calls or a multimodal SDK — see the Embeddings guide.

Installation

Section titled “Installation”
Terminal window
go get github.com/tmc/langchaingo@v0.1.14

Key imports:

import (
    "github.com/tmc/langchaingo/llms"
    "github.com/tmc/langchaingo/llms/openai"
    "github.com/tmc/langchaingo/llms/anthropic"
    "github.com/tmc/langchaingo/llms/googleai"
    "github.com/tmc/langchaingo/llms/ollama"
    "github.com/tmc/langchaingo/agents"
    "github.com/tmc/langchaingo/chains"
    "github.com/tmc/langchaingo/tools"
    "github.com/tmc/langchaingo/embeddings"
    "github.com/tmc/langchaingo/vectorstores"
    "github.com/tmc/langchaingo/schema"
    "github.com/tmc/langchaingo/prompts"
    "github.com/tmc/langchaingo/memory"
)

LLM Providers

Section titled “LLM Providers”

Every provider implements llms.Model. Pick based on use case.

// OpenAI (OPENAI_API_KEY env var)
llm, err := openai.New(openai.WithModel("gpt-4.1-mini"))


// Anthropic (ANTHROPIC_API_KEY env var)
llm, err := anthropic.New(anthropic.WithModel("claude-sonnet-4-5-20250929"))


// Google AI (GOOGLE_API_KEY env var)
llm, err := googleai.New(ctx, googleai.WithDefaultModel("gemini-2.0-flash"))


// Ollama (local, no key)
llm, err := ollama.New(ollama.WithModel("llama3"))

For agents that need tool calling, use OpenAI or Anthropic — they support native function calling. Ollama and Google AI work with prompt-based agents (OneShotAgent).

Text Embeddings

Section titled “Text Embeddings”

LangChainGo handles text embedding generation. For implementation details and schema constraints, see the Embeddings guide.

Interface

Section titled “Interface”
type Embedder interface {
    EmbedDocuments(ctx context.Context, texts []string) ([][]float32, error)
    EmbedQuery(ctx context.Context, text string) ([]float32, error)
}

Creating an Embedder

Section titled “Creating an Embedder”
// OpenAI (default: text-embedding-ada-002)
llm, _ := openai.New()
embedder, _ := embeddings.NewEmbedder(llm, embeddings.WithBatchSize(100))


// Google AI (default: text-embedding-005 as of v0.1.14)
llm, _ := googleai.New(ctx)
embedder, _ := embeddings.NewEmbedder(llm)


// Ollama (local)
llm, _ := ollama.New(ollama.WithModel("nomic-embed-text"))
embedder, _ := embeddings.NewEmbedder(llm)

Storing in Our Custom Schema

Section titled “Storing in Our Custom Schema”

The langchaingo vectorstores/pgvector package creates its own tables. We have custom tables with foreign keys to documents and models, so use langchaingo for generation only and write your own SQL:

// Generate
vec, err := embedder.EmbedQuery(ctx, captionText)


// Store (GORM)
db.Exec(`
    INSERT INTO document_description_embeddings
      (document_description_id, model_id, dim, embedding)
    VALUES ($1, $2, $3, $4)
`, descriptionID, modelID, len(vec), pgvector.NewVector(vec))


// Search
db.Raw(`
    SELECT de.document_id, de.embedding <=> $1::vector AS distance
    FROM document_embeddings de
    ORDER BY distance LIMIT $2
`, pgvector.NewVector(queryVec), limit).Scan(&results)

Tools

Section titled “Tools”

The Interface

Section titled “The Interface”

Every tool a langchaingo agent can use must implement:

type Tool interface {
    Name() string
    Description() string
    Call(ctx context.Context, input string) (string, error)
}

Custom Tool Example: Find Similar Documents

Section titled “Custom Tool Example: Find Similar Documents”
type FindSimilarDocsTool struct {
    embedder embeddings.Embedder
    db       *gorm.DB
}


func (t *FindSimilarDocsTool) Name() string {
    return "find_similar_documents"
}


func (t *FindSimilarDocsTool) Description() string {
    return "Find documents similar to a text query. Input: JSON with 'query' (string) and 'limit' (int) fields."
}


func (t *FindSimilarDocsTool) Call(ctx context.Context, input string) (string, error) {
    var req struct {
        Query string `json:"query"`
        Limit int    `json:"limit"`
    }
    if err := json.Unmarshal([]byte(input), &req); err != nil {
        return "", err
    }
    if req.Limit == 0 {
        req.Limit = 5
    }


    queryVec, err := t.embedder.EmbedQuery(ctx, req.Query)
    if err != nil {
        return "", err
    }


    var results []struct {
        DocumentID string  `json:"document_id"`
        Distance   float64 `json:"distance"`
    }
    t.db.Raw(`
        SELECT de.document_id, de.embedding <=> $1::vector AS distance
        FROM document_embeddings de
        ORDER BY distance
        LIMIT $2
    `, pgvector.NewVector(queryVec), req.Limit).Scan(&results)


    out, _ := json.Marshal(results)
    return string(out), nil
}

Built-in Tools

Section titled “Built-in Tools”
PackageToolNotes
tools/calculatorCalculatorArithmetic evaluation
tools/serpapiSerpAPIWeb search (needs SERPAPI_API_KEY)
tools/duckduckgoDuckDuckGoWeb search, no API key needed
tools/wikipediaWikipediaWikipedia search
tools/scraperScraperWeb page scraping
tools/sqldatabaseSQL DatabaseQuery a SQL database

Agents

Section titled “Agents”

Agents decide which tools to call based on the user’s input. They loop: reason -> pick tool -> call tool -> reason again -> answer.

Agent Types

Section titled “Agent Types”
TypeConstructorWhen to use
OpenAIFunctionsAgentagents.NewOpenAIFunctionsAgent(llm, tools)Models with native tool calling (OpenAI, Anthropic). Most reliable.
OneShotAgentagents.NewOneShotAgent(llm, tools)Any LLM. ReAct-style prompting.
ConversationalAgentagents.NewConversationalAgent(llm, tools)Any LLM. ReAct-style with chat history support.

Creating and Running an Agent

Section titled “Creating and Running an Agent”
agentTools := []tools.Tool{
    &FindSimilarDocsTool{embedder: embedder, db: db},
    &CreateEmbeddingTool{embedder: embedder, db: db},
}


agent := agents.NewOpenAIFunctionsAgent(llm, agentTools)
executor := agents.NewExecutor(agent)


result, err := chains.Run(ctx, executor,
    "Find documents similar to 'sunset over the ocean' and return the top 3")

Chains

Section titled “Chains”

Chains compose LLM calls into reusable pipelines. Unlike agents, chains follow a fixed path.

RetrievalQA (RAG)

Section titled “RetrievalQA (RAG)”
retriever := vectorstores.ToRetriever(store, 5,
    vectorstores.WithScoreThreshold(0.7),
)


qaChain := chains.NewRetrievalQAFromLLM(llm, retriever)
answer, err := chains.Run(ctx, qaChain, "What images contain circuit boards?")

When to Use Chains vs Agents

Section titled “When to Use Chains vs Agents”
Use chains when…Use agents when…
The pipeline is fixed and predictableThe LLM needs to decide what to do
Performance matters (fewer LLM calls)The task is open-ended
You don’t need tool selection logicMultiple tools, LLM picks which ones

Bridging MCP Tools and LangChain Agents

Section titled “Bridging MCP Tools and LangChain Agents”

The MCP server uses github.com/modelcontextprotocol/go-sdk. LangChain agents use the tools.Tool interface. Three ways to bridge them:

Section titled “Option 1: Shared Logic (Recommended)”

Write core logic as standalone functions. Both MCP handlers and langchaingo tools call the same code:

models/
  shared/
    embedding/
      create.go        // GenerateEmbedding(ctx, text) -> []float32
      search.go        // FindSimilar(ctx, query, limit) -> []Result
  mcp/
    tools/             // MCP handler -> calls shared/embedding
  agents/
    tools/             // tools.Tool -> calls shared/embedding

No network hop. No dependency conflicts. Both surfaces get the same behavior.

Option 2: Write Your Own Adapter

Section titled “Option 2: Write Your Own Adapter”

Wrap the official SDK client to satisfy tools.Tool:

type MCPToolBridge struct {
    name        string
    description string
    callFn      func(ctx context.Context, args map[string]any) (string, error)
}


func (t *MCPToolBridge) Name() string        { return t.name }
func (t *MCPToolBridge) Description() string { return t.description }
func (t *MCPToolBridge) Call(ctx context.Context, input string) (string, error) {
    var args map[string]any
    json.Unmarshal([]byte(input), &args)
    return t.callFn(ctx, args)
}

Gotchas

Section titled “Gotchas”

  1. Dimension mismatch: Embeddings are stored as variable-length vectors with an embedding_dim check. Ensure generated embedding length matches embedding_dim, and query/filter against the same dimension (currently indexed for 768 and 1536).

  2. langchaingo pgvector store vs custom schema: The vectorstores/pgvector package creates its own tables. We have custom tables with foreign keys. Use langchaingo only for embedding generation; write your own SQL.

  3. MCP SDK mismatch: The community adapter uses mark3labs/mcp-go. We use the official modelcontextprotocol/go-sdk. Prefer the shared-logic approach.

  4. Separate go.mod per service: models/agents, models/mcp, models/db each have their own module. Share code via models/shared.

  5. Anthropic has no embeddings: Claude can’t generate embeddings. Use OpenAI, Google, Ollama, or others for that.

  6. Image embeddings need direct API calls: LangChainGo’s Embedder only handles text. For embedding raw images, use direct API calls to CLIP, Gemini multimodal, Cohere v4, etc.

Section titled “Related Docs”